ML19319D149
| ML19319D149 | |
| Person / Time | |
|---|---|
| Site: | Crystal River  |
| Issue date: | 10/31/1974 |
| From: | FLORIDA POWER CORP. |
| To: | |
| Shared Package | |
| ML19319D139 | List: |
| References | |
| NUDOCS 8003130716 | |
| Download: ML19319D149 (200) | |
Text
{{#Wiki_filter:- . .. . . O crystal river power plant Environmental Considerations final report to the Interagency Research Advisory Committee THE ATTACHED FILES ARE OFFICIAL RECORDS OF THE OFFICE OF REGULATION. THEY HAVE BEEN CHARGED TO YOU FOR A LIMITED TIME PERIOD ANS MUST BE RETURNED TO THE CENTRAL RECORDS STATION 008. ANY PAGE(S) REMOVED FOR REPRODUCTION MUST BE RETURNED TO ITS/THEla ORIGINAL ODNR. E DEADLINE RETURN DATE U 5 O ' 3# 2 C g
~
z , Florida 7-/9' o o MARY JINKS, CHIEF CENTRAL RECORDS STATION
,_ Z1 l
8003130 9 / 6
/1
~
CRYSTAL RIVER POWER PLANT ENVIRONMENTAL CONSIDERATIONS VOLUME IV OUT0BER,1974
-K
TABLE OF CONTENTS VOLUME I INTRODUCTION Florida Power Corporation I-3 I-7 PROG 1AM OVERVIEW AND STATISTICAL REVIEW Law Engineering Testing Company POWER PLANTS AND ESTUARIES AT CRYSTAL RIVER, FLURIDA An Energy Evaluation of the System of Power Plants, Estuarine Ecology and Alternatives for Management Howard T. Odum, W.M. Kemp, W.H.B. Smith, H.N. McKellar, D.L. Young, M.E. Lehman, M.L. Homer, L.H. Gunderson, and A.D. Merriam SYSTEMS ECOLOGY GROUP Department of Environmental Engineering Sciences University of Florida I-13 INTRODUCTION AND RECOMMENDATIONS H.T. Odum I-29 ENERGY EVALUATION OF COOLING ALTERNATIVES AND REGIONAL IMPACT OF POWER PLANTS AT CRYSTAL RIVER M. Kemp I-73 , MAIN ECOLOGICAL SUBSYSTEMS OF THE ESTUARY AND THEIR l ADAPTATION TO THE POWER PLANTS I-77 A. SHALLOW INSH0RE ECOSYSTEM 0F BOTTOM COMMUNITIES AND THE EFFECT OF THERMAL PLUME Wade Smith l B. METAB0LISM AND MODELS OF OUTER BAY PLANKTUN ECOSYSTEMS I-IS9 i AFFECTED BY POWER PLANT i H. McKellar I-269 C. 0YSTER REEFS AT CRYSTAL RIVER AND THEIR ADAPTATIUN j TO THERMAL PLUMES l M. Lehman . I-361 D. ECOSYSTEMS UF THE INTAKE AND DISCHARCE CANALS M. Kemp I- 387 E. TIDAL CREEKS AND EFFECTS OF POWER PLANTS M. Homer i
l t TABLE OF CONTENTS i VOLUME II i AN ENERGY EVALUATION OF THE SYSTEM OF POWER PLANTS, ESTAURINE ECOLOGY, AND ' ALTERNATIVES FOR MANAGEMENT (CONTINUED) F. ' SALT MARSH AND THE EFFECT OF THERMAL PLUME II-l D., Young VALUE RATIOSOF HIGHER ANIMALS AT CRYSTAL RIVER ESTIMATED WITH ENERGY II-93QUALITY M. Kemp, H. McKellar, and M. Homer MONITORING BUOY FUTURE TRENDS AND ONSET OF ADDITIONAL PLUMES WITH II-109 A METAB0LISM L. Gunderson and A. Merriam APPENDIX A. PAPERS RECENTLY PUBLISHED IN THERMAL ECOLOGY SYMPOSIUM 11-117 APPENDIX A1. ENERGY COST-EENEFIT MODELS FOR EVALUATING THERMAL H.T. Odum II-118PLUMES APPENDIX A2. STUDIES OF FLORIDA GULF C0AST SALT MARSHES RECEIVINGII-140 THERMAL DISCHARGES - D.L. Young APPENDIX A3. TOTAL METAB0LISM OF THERMALLY AFFECTED COASTALII-159 SYSTEMS ON THE WEST C0AST OF FLORIDA W. Smith, H. McKellar, D. Young, and M. Lehman . APPENDIX B. ENERGY COST-BENEFIT APPRUACH TO EVALUATING POWER PLANT ALTERNATIVES II-175 H.T. Odum APPENDIX C. PRELIMINARY CALCULATIONS OF ENERGY QUALITY RATIOS OR WORK EQUIVALENT FACTORS II-185 M. Kemp and W. Boynton APPENDIX D. MODELS OF THE INTERACTION OF THE CRYSTAL RIVER POWER PLANT II-209 AND THE ADJACENT DUTER BAY ECOSYSTEM: RELATION TO COASTAL FISHERIES H. McKellar APPENDIX E. SALT MARSH MICROARTHROPOD POPULATIONS E. A. McMahan and D.L. Young II-241 11 -
TABLE OF CONTENTS VOLUME II FINAL REPORT TO THE FLORIDA POWER CORPORATION II-255 Dr. Samuel C. Snedaker, Principal Resource Management Systems Program Institute of Food and Agricultural Sciences University of Florida Gainesville, Florida . REPORT A. EVALUATIONS OF INTERACTIONS BETWEEN A POWER GENERATION II-257 FACILITY AND A CONTIGUOUS ESTUARINE ECOSYSTEM
. Samuel C. Snedaker REPORT B. IMPINGEMENT AT THE CRYSTAL RIVER POWER GENERATION FACILITY II-259 A' QUANTITATIVE ANALYSIS l Samuel C. Snedaker REPORT C. SEDIMENT COMPOSITION AND DISTRIBUTION AT CRYSTAL RIVER II-309 POWER PLANT: EROSION VS. DEPOSITION Daniel J. Cottrell REPORT D. COMPARISONS OF THE BENIHIC FLORA IN ESTUARIES ADJACENT TO II-377
- THE CRYSTAL RIVER POWER GENERATION FACILITY Robin F. Van Tine VOLUME III FINAL REPORT TO THE FLORIDA POWER CORPORATION SUBMITTED BY Dr. Samuel C. Snedaker, Principal Investigator (continued)
REPORT E. BENTHIC INVERTEBRATE COMPARISONS IN TWO ESTUARIES ADJACENT TO III-l THE CRYSTAL RIVER POWER GENERATION FACILITY Gary Evink and Barbara Green ) i REPORT F. COMPARISON OF SELECTED VERTEBRATE POPULATIONS IN TWO ESTUARIES ADJACENT TO THE CRYSTAL RIVER POWER GENERATION FACILITY III-l Clayton A. Adams REPORT G. EFFECTS OF IMPINGMENT AND ENIRAPMENT ON THE CRYSTAL RIVER III-107 BLUE CRAB, CALLINECTES SAPIDUS RATHBUN, POPULATION Clayton A. Adams, Michael J. Oesterling and Samuel C. Snedaker APPENDIX A.. PHYLOGENETIC LISTING OF ESTUARINE SPECIES AT CRYSTAL III-147 l RIVER, FLORIDA - l CI Ayton A. Adams, Gary L. Evink, Michael J. Oesterling, William Seaman and Robin Van Tine 111 L
- 4 TABLE OF CONTENTS VOLUME III APPENDIX B1 IMPINGEMENT DATA RECORD III-165 Clayton A. Adams, Charles J. Bilgere and Samuel C. Snedaker APPENDIX B2 IMPINGEMENT DATA SUMMARIES III-315 Clayton A. Adims, Charles J. Bilgere and Samuel C. Snedaker INDEPENDENT ENVIPJNMENTAL STUDY OF THERMnl EFFECTS .
0F POWER PLANT DISCHARGE ' Or. Ker.dal'1 L. Carder Principa' Investigator Department of Marine Science University of South Florida
" NATURAL HEATING 0F SALT MARSH WATERS IN THE AREA 0F THE CRYSTAL RIVER III-379 l POWER PLANT" - TECHNICAL REPORT #3 l Ronald H. Klausewitz, Steven L. Palmer, Bruce A. Rodgers, and i Kendall L. Carder RESULTS ON BATHYMETRY AND BOTTOM TYPE ANALYSIS OF THE CRYSTAL RIVER III-413 POWER PLANT DISCHARGE BASIN - TECHNICAL REPORT #5 Bruce A. Rodgers, Ronald H. Klausewitz, and Thonas J. Keller VOLUME IV ZOOPLANKTON RESEARCH Dr. Frank J. Maturo, Jr.
Principal Investigator University of Florida Marine Laboratory Gainesville, Florida A SUPPLEMENTARY ZOOPLANKTON SURVEY AT THE~ CRYSTAL RIVER PLANT SITE IV-1 Frank J. Maturo, Jr., John W. Caldwell, and William Ingram III EFFECTS OF POWER PLANT ENTRAINMENT ON MAJOR SPECIES OF COPEPODS IV-69 Frank J. Maturo, Jr., Ray Alden and William Ingram III APPENDIX A DATA TABLES IV-103 APPENDIX B BIOLOGICAL PARAMETERS GRAPHS IV-105 : APPENDIX C NET MORTALITY GRAPHS 'IV-151 APPENDIX 0 CONT 0UR GRAPHS IV-205 APPENDIX E FECUNDITY RATE ANALYSIS IV-209 APPENDIX.F GROWTH CURVES IV-215 iv l
TABLE OF CONTENTS VOLUME IV EFFECTS OF POWER PLANT ENTRAINMENT ON MAJOR SPECIES OF COPEPODS IV-235 MEASUREMENT OF ZOOPLANKTON MORTALITY USING ADENOSINE TRI-PHOSPHAT.: AS A VIABLE BIOMASS INDICATOR. Frank J. Maturo, Jr. and Richard D. Drew EFFECT OF POWER PLANT OPERATION ON SHALLOW WATER IV-265 C0ASTAL ZOOPLANKTON Frank J. Maturo, Jr. John W. Caldwell and William In: am III GENERAL OBJECTIVES IV-269 OBJECTIVE 1 SOURCE AND DISCHARGE AREAS OF CRYSTAL RIVER IV-282 POWER PLANT'S COOLING MATER IN RELATION TO ZOOPLANKTON SAMPLING STATIONS Richard Cullen and Ron DuBose OBJECTIVE 2 STANDING CR0P ESTIMATES IV-282 Tom Chaney OBJECTIVE 3 PRODUCTION OF ZOOPLANKTON POPUL TIONS AT CRYSTAL VER IV-287 Ray Alden and Frank Hearne OBJECTIVE 4a CTEN0 PHORE STANDING CROP AND PREDATION IV-299 Eric F. Hallquist OBJECTIVE 4b CHAETOGNATH PREDATION IV-306 Alex Smart OBJECTIVE 4c . DECAPOD PREDATION IV-312 Alex Smart OBJECTIVE 5 A COMPARISON OF POWER PLANT PREDATION , ND NATURA: IV-331 PREDATION Richard Cullen and Ronald DuBose OBJECTIVE 6 STATISTICAL ANALYSIS OF NATURAL AND PU..ER PLANT .UENCES IV-334 ON ZOOPLANKTON COMMUNITIES AT CRYSTAL RIVER. William Ingram OBJECTIVE 7 COMPARISON OF ZOOPLANKTON DIVERSITY OF SEVERAL IV-392 AREAS IN THE EASTERN GULF 0F MEXICO Herbert Hickox and Arthur Wenderoth y
.a.. --
TABLE OF CONTENTS VOLUME IV ~ PHYTOPLANKTON RESEARCH Dr. Thomas L. Hopkins Principal Investigator Department of Marine Science University of South Florida PHYTOPLANKTON ECOLOGY IN THE VICINITY OF THE FLORIDA POWER CORPORATION IV-419 < GENERATING PLANT AT CRYSTAL RIVER, NOVEMBER,1973 - APRIL,19/4. R:bert A. Gibson, J.0. Roger Johansson, Mark E. Gorman and Thomas L. Hopkins APPENDIX I IV-445 i 6 l l l vi l l r-
4 f-r
' A SUPPLEMENTARY ZOOPLANKTON SURVEY AT THE CRYSTAL RIVER PLANT SITE I
FINAL REPORT October 15, 1974 , i UNIVERSITY OF FLORIDA MARINE LABORATORY 3 i Frank J. Maturo, Jr. Principal Investigator John W. Caldwell Marine Biologist William Ingram III Biological Systems Analyst
. IV-1
.h'
-; e , - sa. - ~ , ..-- , . , - - < , , , , . l, l , , . , - . , ,
~ " l g
1 T i 1 s
)
I i i. 1 ACKNOWLEDGMENTS d i The following personnel provided technical assistance. Their efforts are greatly appreciated. Raymond W. Alden
- Kalani Cairns
! James Indianos Buckley Parnell 4 4 i i i 4 I l i. 1 i l 1 9 IV-2
ABSTRACT Research was conducted in the area adjacent to Florida Power Corporation's Crystal River Power Plant. The objectives were to assess the distribution and structure of the zooplankton population of the intake area and to gain some degree of statistical knowledge concerning determination of the appropriate sample size of the extensive sampling program implemented in the following year. The sampling program was conducted at biweekly intervals from July 14, 1972 until July 16, 1973. Tows with a 1/2 meter diameter, 202 mesh net were made at five representative stations. Salinity and temperature were recorded for each tow. Quangitativeestimagionsoftheplanktonicorganismsincluded and biomass /m (mg/m3) for individual categories and numbers /m category totals. Salinity and temperature were found to be posi-tively correlated with several of the categories. There appeared to be three distinct station faunal-assemblage groupings: inshore, intennediate, and offshore. Present power plant operation (Units 1 and 2) entrainment was estimated to be an average of 283 lbs/ day of dry weight zooplankton (202uandabove). Projected power plant operation (Units 1, 2, and 3) would entrain an estimated average of 592 lbs/ day of dry weight zooplankton (202u and above). A sample size range of 9 (.05 confidence 'evel) to 17 (.01 confidence level) was determined for the exte - 've sampling program. The time scale of a season was selected as the most appropriate time period desired to test. IV-3
INTRODUCTION I This project was initiated to: 1) determine the presence of major food chain species and the planktonic forms of comercially important finfish and shellfish in the area adjacent to the Crystal River plant site, 2) qualitatively and quantitatively assess the occurrenes< hese organisms within the intake area of Units 1 and 2 as a means of evaluating the entrainment potential of these organisms,
-3) evaluate differences in seasonal and horizontal distribution of important planktonic groups in the area, and 4) use this study as a presampling survey to determine: a) desired statistical confidence level of data, and b) sample size needed to achieve desired confidence level. These determinations would be used in designing the extensive sampling program implemented in the following year.
The intake canal and its adjacent areas are shown in Figure 1. We selected five stations to monitor. Stations 1,2,3, and 5 were established as a result of a preliminary survey made shortly after project funding in mid-July, 1972. We presumed the water entrained in the intake canal was drawn primarily from the shallow waters imediately south of the southern dike of the canal (Area 1) and from the somewhat deeper waters west of this but in the immediate vicinity of the canal mouth (part of Area 2). Area 1 includes a large shallow area containing numerous oyster bars running roughly parallel to the shore, its western boundary being the last gulf-ward string of these bars. Area'2 includes all of the water from the west boundary of Area 1 to an imaginary line drawn south from the physical end of the north intake spoil bank and bounded on the north by the north bank. Station 4 was added December 19,1972, after information from very preliminary hydrographic studies by Dr. K. Carder (Technical Report #2, January 1973) which indicated that entrained water is drawn mostly from Areas 2 and 3 (the area west of Area 2 and otherwise unbounded into the Gulf of Mexico 1. According to Carder, his data "...suggest that during flood [ tide] the intake water is primarily of gulf shelf origin." He states that water of Area 1 is retarded in its access to the plant intake channel by high (shoal water) friction and oyster bars. Station 1 is located inshore south of the intake canal. The station is within 25 yards of the coastal marsh, the depth being 2 ft. at MLW. The bottom substrates in this area consist of attached Sargassum and sand patches between limestone oct-crops. The salinity is noticeably influenced by the freshwater drainage from the Crystal River and adjacent marsh land. Figure 2 shows the salinity char-acteristics of this and the following stations. Station 2 is also south of the intake canal and is located midway between Station 1 and the canal opening, a distance of 1.2 nautical miles offshore. The substrate in this area is sand and . shell between prominent oyster bars. The depth is 4 feet at MLW. The salinity is consistently higher than at Station 1. IV 4
r V zQV d.
, , M a # S M't, g o* %.,' /
-.i \
j' ___ __4 / p e P - N station a e de
.g. [,,--,
, S tation sg 5.,
p d 9 svarie= a-g ,. [* . , ,, svarese s ,g ,,,,, . , s..q . ; e
*. '. , ' 1 3
, ' .. .y
* .,f ,
W i 3tg
'*E
. Qr '3
; y N ,
. Q(h, Figure 1. Map of Crystal River area showing collecting stations.
IV 5
r . ~ , , .. Figure 2 Season vs Salinity 30 9 25 - 5 E 3 20 t 15 n 3 10 b c
;; 5 a
0 1 Season 1 Season 2 Season 3 Season 4 Season K.eey, Season 1 = spring Season 3 = fall Season 2 = sumer Season 4 = winter a IV-6 i E-
Station 3 is southwest of the intake canal opening in an area which is considered to be part of the source of the entrained water. The depth is 7 ft. at MLW. The substrate is hard sand. The salinity is slightly but consistently higher than Station 2. Station 4, six miles offshore, is located beyond the end of the unbroken northern side of the intake canal. The depth is 9 ft. at MLW. The substrate appears to be hard sand. The data indicate the salinity is equal to or slightly higher than Station 3. Station 5 is located just in front of the intake screens of Unit 1 and 2. The depth is 15 ft. at MLW. The substrate appears to be a fine coal dust sediment. The Salinity is essentially the same as that at Station 3. EXPERIMENTAL TECHNIQUES The sampling program was begun July 24, 1972, and continued , at biweekly intervals until July 16, 1973. Initially, 10-minute l plankton tows were made using 50 cm. dia. nets with 202 micron and 80 micron mesh. Because of the high level of suspended matter the nets clogged quickly and prevented accurate metering of the water column sampled. After several trials, the best tow duration was found to be 1 minute (which samples approximately 12,000 liters of water). Use of the 80 micron mesh net was discontinued because of i the clogging produced by the suspended matter. The sampling regime established for each station consisted of two 1-minute horizontal surface tows at b4 weekly intervals. Samples were preserved in buffered formalin and returned to the laboratory. Our 50 cm. dia, nets were not believed to be collecting fish eggs and larvae in proportion to the numbers thought to be present by fisheries experts. On April 20, 1973, we initiated a program using 1 meter dia. (5:1) 505 micron mesh nets. Samples were taken on a monthly basis, with single tows of ten minute duration made at a station. Station 3, 4, and 5 were sampled with large nets. The inshore stations ( l and 2) were not sampled because of the shallowness of the water in these areas. Temperature and salinity data were recorded for each station sample. The following procedures were employed for examination of each sample. The two 50 cm net samples from each station were pooled prior to splitting. The pooling of samples was considered appropriate in order to get a more " representative" sample because of the inherent patchiness of plankton distribution (a procedure recommended by Reeve, 1970, in his Turkey Point survey). One half of the total sample was separated in a sieve series with standard mesh sizes of Nos.10(2000 micron), 20(850 micron), 30(600 micron), 60(250 micron), and120(125 micron). The contents of each sieve were divided using a Folsom plankton splitter until a " counting size" (usually 1200-1500 animals) was obtained. The aliquot was then placed in a 10 x 10 cm IV-7
i gridded chamber and five 2 x 2 cm aliquots were counted. This aliquot method of counting was first described by Hopkins in 1962. Total counts of fish eggs and larvae were made on both the 1 meter dia, and the .5 meter dia, net samples from Stations 3, 4, and 5. Objective #1 Shortage of time and state-of-the-art limitations prevented us from identifying the major food chain plankton and connercially im-portant animals to the species level. Our classification scheme included these categories in a more general classification system. Objective #2 Qualitative determinations were made by use of the following categories: Copepods: calanoid harpacticoid cyclopoid Mollusc veligers: gastropods _. bivalves, including oyster (Crassostrea) Barnacle larvae Shrimp larvae penaeid others (mysids, etc.) Crab larvae stone crab (Menippe) - like blue crab (Callinectes) - like others Other Crustaceans Polychaete larvae Echinoderm larvae Chaetognaths Tunicates Medusae (includingsiphonophores) Miscellaneous invertebrates Eggs l Fish eggs Fi"sE larvae IV-8
~
Quantitative determinations included: 1) total numbers of each' zooplankton category per unit volume, and 2) approximations of dry weight biomass of each zooplankton category per unit volume. Biomass determinations were made based on the sieve separation scheme. We believe this method provided a more accucate estima-tion than the traditional procedures. This gravimetric procedure, devised by Mr. Clay Adams (Masters Thesis, UF 1972) involved mechanically separating zooplankton using a set of paleontological sieves; making a random sample of the individual fractions; deter-mining the per cent composition of each fraction by recording counts per zooplankton type divided by the total count of all zooplankton in the fraction sample; vacuum filtering each sieve fraction onto a preweighed Whatman No. 5 filter disc; oven-drying loaded discs and weighing each to determine the dry weight of the fraction; and finally. compiling the weights and percentages of the several sieve fractions to determine the dry-weight percentage composition of the zooplankton types. Dry weight approximations, if converted to calories, can be used for future systems analyses by Odum and Snedaker in another FPC contract. The sieve separation facilitated counting procedures because it sorted organisms to size and reduced the number of species per sample. DATA ANALYSIS METHODS All raw data, both numbers and biomass comon volumetric unit (either numbers /m3biomass or, were converted
/m3). The to a data analysis program consisted of two basic types: descriptive statistics and hypothesis testing. -Descriptive Statistics These consist of tables and graphs containing information on biweekly levels of the following statistics:
- 1) total numbers of zooplankters/m3
- 2) total numbers of different kinds of zooplankters/m3
- 3) numbers of zooplankters of a specific size range /m 3
- 4) total zooplankton biomass /m3
- 5) biomass of each animal category /m3 l
- 6) weight in pounds and kilograms of zooplankton that are entrained by the plant at the intake site for present operation, and ,
- 7) predicted entrainment potential for future operation.
These statistics are used in aiding the interpretation of our hypothesis testing and are available from the authors. In addition to the above statistics generated for each sampling period, several of the statistical analyses also generate descriptive statistics that are reported in the form of graphs and tables. IV-9
. .~ , Objective #3 Hypothesis Testing Three basic groups of data were analyzed and tested statistically.
- 1) To investigate the presence of seasonal or areal trends in the zoo-plankton fauga and related ccanunity concepts, standing crop estimates in numbers /m3, biomass, and diversity information index [Pielou,1969 ]) were(utilizing the Shannon-Weaver used in a multivariate ANOVA model (Morrison, 1967). 2) Fisn eggs and fish larvae were analyzed separately to compare the results of different techniques for sampling these organisms. 3) Entrainment of zooplankton biomass was examined.
The data were analyzed on the University of Florida North East Regional Data Center IBM 370/165 computer utilizing two statistical packages, Statistical Analysis System (SAS) (Service, 1972) and Statistical Package for the Social Sciences (SPSS) (Nie, et al. 1970) and several programs written especially for the study by one of the authors (W. Ingram). Analysis of Seasonal and Areal Trends The effect of spatio-seasonal fluctuations on the numbers of the - various zooplankton types was examined via a multivariate analysis of variance (MANOVA) model (Morrison,1967). The MANOVA approach was selected because of theoretical considerations and because actual configuration of the data of an integrated zooplankton community would lead one to suspect that the population sizes of the component species or categories are in some way interrelated. In fact, initial nalysis of correlations between numbers of different categories shows r any significant bivariate correlations. In using the multivariate techniques we are trying to assess the differences of the comunity, as represented by the selected categories, that are statistically significant and Lttributable to the various terms of the model. If significance were found in the more general multivariate model, then univariate ANOVA was applied to the categories in an attempt to determine more specifi-cally where the significance occurred. The dependent variables were the plankton categories mentioned accve. The dates of collecting were lumped into seasons in the following manner: December, January, February = Winter; March. April, May = Spring; June, July and August = Sumer; September, October, November = Fall. The lumping was done to help insure adequate replication for the testing of all main effects and inter-action terms of the MANOVA model. The model was formu!ated as follows: kjk)
- E + Ei jkl + "i + 0 j +T- k + -i j + "lik + 0Jdk 40 Yjjg + c gjk(1) l l
IV-10 l l
where Y, represents the dependent vector of organisms; X i jkl represents the matrix of effects due to the vector Of cctariates; u_ represents the vector of effects due to the mean;
~
a represents the vector of effects due to stations, i = 1,2,....a (areal effect)
,8, represents the vector of effects due to seasons, j = 1,2,...,b; y represents the vector of effects due to size class, k= 1,2,... c a_8,,g 8 ,81 represent the appropriate vectors of effects due to two-way interactions; and a8y represents the vector of effects due to the.
three-way interaction. c is the vector representing the error term of
~1jk(l) the model, 1 = 1,2,...s.
The model is thus a multivariate extension of a three-way factorial fixed effects model wito covariates. Since sampling is occasionally limited by poor weather conditions, the data available resulted in an unbalanced design. This necessitated the use of a general linear hypothesis approach, utilizing dummy variables to accomplish the fixed effect analysis of variance terms (Searle, 1970). A multivariate least-squares estimation computer routine (SAS:REGR) was used to estimate the regression weights and thus assess the signi-
-ficance of the various aspects of the model. To achieve full rank of the hypothesis model a reparameterization of the ANOVA tenns was performed using the method built into the SAS:REGR routine (Service,1970).
The expected mean square table for a univariate model is listed below. Source df EMS Stations 4 ca + sbc Ij(a)4 /(a-1) IV-ll x -
um# l l I
-l Seasons 3 o- +
sac I)(s) /(b-1) Size Class 4 a 2 + sab I (y)k/(c-1) k Station
- Season 12 o z + sc I ij (as)$3/ (a-1)(b-1)
Station
- Size Class 16 a 2 + sb I (ay)ik/(a-1)(c-1) ik Size Class
- Season 12 ?
+ sa I (Sy)jk/ (b-1)(c-1) jk Station *Si.e Class
- Season 48 a2 +s I (aBy)ijk/(a-1)(b-1)(c-1) I ijk '
Error 400 a2 The interpretation of this model is as follows: There is a mean level for a zooplankton categor River with a certain fluctuation about that mean (o2 )y (u)
. Theatvariance Crystalabout the mean is riue to experimental errors of various types, induced by fluctua-tions of unmeasured environmental factors, as well as intrinsic fluctuations in the level of the zooplankters. In addition, various covariatas are examinedfortherelationshipbetweentheirvarianceandthezooplankger's variance. The portion the plankter variance that can be " explained" by covariance with environnental variables is adjusted out of the model prior to analysis of treatment effects or estimation of the error mean square.
The seasonal, areal, and size class terms of the model and their interactions have the effect of predicting an elevation or depression in the mean level of a plankton category, thus, in general, the model is testing the null hypothesis:
- 1) h There is no difference in zooplankter numbers across i
0 ' 1 t seasons that is not attributable to variance in the data. .
- 2) h There is no difference in zooplankter numbers across 0
l 2 stations that is not attributable to variance in the data.
- 3) h There is no diffarence in zooplankter numbers across 0-3 size classes that is not attributable to variance of the data.
I c " explained" means there is a correlation between the fluctuations observed in the variables; it may or may not indicate a causal relationship. IV-12
The interaction terms hav'e a slightly different interpretation. If the interaction is significant, it means that the zooplankter levels for the terms under consideration do not have the same profile (see Morrisson,1967). For example, a significant interaction between station and season would indicate that the levels of zooplankters for at least one station across seasons did not follow the same pattern as that for at least one other station. Thus, in' general, the model predicts a certain mean level of a zooplankter that is defined by the combination of station, size class, and season along with the effect of the interactions. The model also Jdefines a certain fluctuation about this mean level that is accounted for by environmental variation, the rest of the variability being assigned to residual or " error". With this type of statistical model we are proposing cet cain measurable sources of variation and effeci.s that change the level of the mean. Those terms that test out to ti statistically significant are investigated for their biological interr :ation while those that do not are examined to see if the cause of insignificance is lack of information or actual biological insignificance. Those terms that are assessed to possess both statistical'and biological insignificance are dropped from the model in the interest of parsimony. It is this last aspect of the modelling and data analysis process that is most subjective. Because of this subjectivity we try to employ as broad an interdisciplinary approach as possible. This is accomplished by total staff review of our reports and an attempt to assimilate the knowledge gathered by the other projects in progress at Crystal River. The test statistic used for the MANOVA portion of the analysis was Wilk's Lambda (A) where Lambda is equal to the ratio of the determinant of the error term and determinant of the sum of the error and hypothesis terms of the model ( l El/ E + Hj). We actually used a transformation of Lambda that approximates the F-distribution in deciding on the significance of. an hypothesis. This transformation, (MS-28)/(PQ)(1-A l /s)fxl/s, is approximately distributed as an F with P and MS-2B degrees of freedom, where M = (error degrees of freedom) .5 (P-Q+1) Ln (A). 2 S = (P2 Q2 - 4)/(p2 + q - 5) B'= (PQ - 2)/4 P = number of dependent variables Q = rank of hypothesis matrix I IV-13 j 1
This transfomation is discussed by Cooley and Lohnes (1971) and was developed by Rao (1952). It may be interpreted in essentially the same manner as the F-statistic usually associated with univariate ANOVA. Prior to applying the data to the statistical analysis of the model, it was thought necessary to detemine if the data needed to be transformed in any manner. This is desirable since the MANOVA assumes that c follows a multivariate normal distribution and that the effects of thi various tems in the model are additive. The transfomation that appeared to be appropriate was the log transformation, X = log 10 (X + 1). From a theoretical point of view. Cassie (1957) found that zooplankton in oceanic situations had an over-dispersed pattern of the discrete log-normal type (see Pielou,1968). Thus, the data were tested for significant skewness with the following statistic: Skewness gj = *q (Xi-X)3 N This can be tested for significance with a T-test; however, since the degrees of freedom for that test are approximately infinite when N is greater than 100 (Sokal and Rohlf,1969), a Z-test was used. The test was 7, 91-0 Sg 3 where Sgz= / o/n Another property of the lognormal distribution is th'a t the variance is positively correlated with the mean. Since one of the assumptions of any parametric analysis of variance is the independence of the mean and error tems, 'a test for correlation of sample mean and sample variance was performed using the non-parametric test for association outlined in Sokal and Rohlf,1969. The test statistic was Kendall's T at the .05 level. Another justification for making the log transformation is given by Cassie (1960) when he suggests that a log-normal distribution of plankton may arise owing to the relative abundance of plankton species being geometrically related to variations in the physical properties of their environment. Salinity and termperature were two environmental variables that were measured consistently with the samples. They were included in the model and examined for possible significant covariate effects. Both salinity and temperature were untransformed. l iV-14 l l _
In addition to the analysis of transformed zooplankter scores, biomass and diversity of the sample were examined using the same statistical model. Biomass values were untransformed. The Shannon-Weaver index of diversity, H' = EPj logPg , was used in this analysis for two reasons:
- 1) This index is the most widely used of diversity indices and thus results reported here could be compared to results elsewhere.
- 2) As Pielou (1969) pointed out, this measure of diversity satisfies certain desirable conditions:
a) For a given number of categories, diversity is at its maximum when the proportion cf individuals in category jj(P3 ), is 1/s. b) The diversity is unchanged 17 we add extra categories to which no individuals belong. c) If the individuals are classified by two separate classifications A and B which are not mutually exclusive, the diversity of classification A , B, and AB is: H'(AB) = H'(A)+Hj(B)=H'(B)+H(A). B In addition, the Shannon-Weaver index is desirable because it incorporates information about the number of separate types of individuals in the collec-tions as well as the shape of the distribution of relative proportions of the categories. Once the issue of the proper (if any) transformation was decided, each of the covariates, main effects, and interaction tenns of the model were tested for significance. In conjunction with the multivariate approach which attempts to analyze the way a system of related variables reacts to exposure to different " treatment" levels, there are several unique statistics and methods of examining the data that may be employed. These techniques are summarized in the following text (see Cooley and Lohnes, 1971). As previously mentioned we use a transformation of Wilks' A (Rao, 1952) for testing significance of the term in the model. In addition to A, there is another useful statistic, n =2 1-A. This has the same i Rgterpretation in MANOVA as does the multiple correlation coefficient,
, in ANOVA (Wilks, 1932; Baggaley and Campbell, 1967).
In univariate ANOVA, we have sums of squares which tell us when at least one of the means in the term under scrutiny is different from at least one of the other means in that term. Further examination by a simultaneous test procedure, Duncan's Multiple Range, for instance, would tell us which means are significantly different. In other words, for that variable we could find out how well it discriminates between different levels of a particular term in the model. In systems where a multivariate model is appropriate, we would like to do the same thing except that we want to consider not only variation of the variables but also their covariance. IV-15
.w . e:;
Once a term in the model is found to be significant, for instance, o, it would be desirable to suggest where amongst all the variables and their covariances does this significance exist. Separate examination of the variables by ANOVA is useful, but prevents any examination of the correlation structure of the set of variables. If we think of finding where the significance lies as a process of determining which variables or groups of variables are good discriminators between levels of the term in question, then the techniques developed by Hotelling (1936) and Wilks (1932) for canonical discriminant analysis are definitely useful (Cooley and Lohnes, 1971). These techniques derive from the hypothesis sum of squares and cross products matrix, H,, and the error sum of squares, and cross products matrix, E_, separate the levels of the termainset of orthogonal question. functions This is done that maximall{H-AIlV by solving: lE- = 0 with the restriction that V'V = 1. This is to be looked on as a method of producing a model of reduceH Tank, or reduced dimensionality, that optimally displays the differences observed in levels of a particular term in the model. The reduced rank is achieved by applying the discriminant function V to the vector of dependent variables Y to obtain a new variable, w = V7Y. The ' linear combination, V jjYis termed a canonical discriminant function. Thenumberofcanonicaldiscriminantfunctionsthatcanbeobtainedare limited to the number of levels in the term, when the number of levels in the Mrm is less than or equal to the number of dependent variables measured. Since these functions are orthogonal, they may be used as axes for graphical examination of the relationship between different levels of the term under consideration. In addition, the correlation between the original dependent variables and the canonical variables may be obtained. This correlation is useful in deciding which dependent variables or complexes of dependent variables played a major role in the significance of the particular term. Once one discriminant function has been determined, it is possible to test to see if there are any more that are significantly different from zero. This is done as follows: n lHI = A= H 1 lH+E l j l+A) Then i x2 = -(N-P+9 - 1) LnA' 2 n with degrees of freedom ndf = (p-k) (g-k-1) and A ' = H 1 j=k+1 1+A ! j l where n = total sample size p = number of dependent variables l IV-16
g = number of levels in model' term - k = number of discriminant functions already extracted-A H 3 = Jth characteristic root of l E-I - AIlV =0. The proportion of the discriminating power of the model term that is contained.in the jth discriminant function is:
^j g-1 I Aj i=1 The percent of variation of each dependent variable that is accounted for by a discriminant function, called comunality, is found by suming the squares of the correlations between dependent variables and canonical discriminant functions for each dependent variable across the number of discriminant functions extracted. The variance for those variables with low communalities was not accounted for by the term of the model in l question. This indicated they contain little information regarding the significance of the term. Also, the error sum of squares and cross products matrix may be transformed to a correlation matrix and examined for significant correlations that remain after the effects specified in the model have been removed from the dependent vector.
i The analysis of fish egg and fish larvae data proceeded in two stages: 1) a series of paired observation T-tests were performed to discover if any significant differences were present between whole count and aliquot estimates of standing crop; 2) tests were made of meter and
-- half-meter net fishing abilities.
RESULTS Seasonal and Areal Effects Analysis Examination of the untransformed data for five selected categories showed a definite skewness toward larger values, a positive gj statistic (see Table 1), and a marked tendency for variance to increase as means increase. The test for association was significant at the .05 level with concordance of rank orders being perfect,T = 1.00. The test of signi-ficant skewness was highly significant (P <.001) for each of the five categories. Following the log transformation, the correlation between sample variance.and sample means dropped to a T = 0.0, which was obviously not significant at the .05 level. The tests of skewness also improved with only penaeid shrimp (P < .002) and stone crab-like larvae (.01 < P <.05) still having a significant skew to the right. However, even in the cate-gories where the skewness was still significant the amount was drastically reduced. We decided, therefore, to. continue the remainder of the spatio-IV-17
f. t Table 1. G; Statistics for Five Selected Categories * : 'a Category Untransformed Data Transformed Data 2 2 i s gj Sig(a) z(b) i s gj Z Sig. Calanoid 1.03x 104 7.02x100 1.024 5.40 ** 3.880 1.220 -0.328 -1.37 NS
' Bivalve larvae 860 6 3.98x10 3.960 16.50 ** 1.611 1.820 0.125 0.52 705 Penae'id' 5 147 3.04x10 6.360 26.26 ** 0.793 1.098 1.010 4.21 **
Stone . crab 4 64 2.38x10 5.105 21.30 ** 0.863 0.951 0.532
- 2.22 .
Blue Crab 4 119 6.57x10 3.237 13.50 ** 1.068 4 1.163 0.319 1.33 NS 02 L
- o' (a) significance levels are reported generally:
- means p j[ .05 and ** means p fi .01 (b) Sgj = .24 I
t
seasonal analysis on the log transformed data. In the analysis of the data, the appropriate place to begin is with the stap that looks at the broadest interpretation of the data. This is the MANOVA of all categories of zooplanston. The examination of a correlation matrix formed from the dependent variables of the model, the zooplankton categories showed that the assumption of this being a multivariate system is correct. There are numerous significant intercorrelations among the dependent variables. We will now take each term in the model and report the results of the MANOVA. Salinity Wilks' A = .858 Significance .0001 Since salinity is one of the two covariate continuous variables, there is only one possible canonical discriminant function, CDF. The zooplankton categories with the strongest positive relationships with salinity are: bivalve larvae, other crab larvae, harpacticoid copepods and other shrimp. Only the category associated with fish eggs showed even a moderately strong negative association with salinity. All of the above categories are the ones demonstrating variance patterns that correlate most strongly witr. Variance patterns of salinity. A MANOVA was run on salinity and temperature with season and station as independent variables. This analysis shawed that temperature and salinity were strongly correlated in a positive nature. In addition, there were strong season differences for both variables, and station differences for salinity. The interaction between season and station ~ was nonsignificant. Inspection of the mean shows both temperature and salinity are at their highest level in the spring and summer. Temperature Wilks' A = .702 Significance .0001 As with salinity, temperature is a continuous variable and as such has only one canonical correlation. Those variables having the highest correlations with temperature are other crab larvae, blue crab-like larvae, stone crab-like larvae, gastropod larvae, penaeid shrimp, and other shrimp. Temperature generally shows positive correla-tion with the zooplankton community on the intake side. This is possibly linked with seasonal increases that correspond to temperature increases. Thus, the pa} tern for temperature - related fluctuations is an increase in numbers /m as temperature increases. Stations Wilks' A = .63601 Significance .0001 IV-19
I The station term is essentially measuring the overall geographical, or areal, effect on zooplankton. The chi-square test for the number of significant discriminant functions that may be extracted showed the first two functions were the only significant ones (see following table). Characteristic Percent Variation function Root accounted for Chi-square df sig. I .2478 50.57 108.920 57 .0001 II .1321 26.96 50.728 36 .0551 III .0655 13.37 20.722 17 .2459 IV .0446 9.10 - - - The variables' contributions to the discrimination ability were weighted by examination of the correlations between the canonical discriminant functions and the dependent variables. Those combinations and total communalities of the dependent variables are shown in Table 3. The communalities show that chaetognaths, other crab larvae, other shrimp larvae, and blue crab-like larvae have a greater portion of their variance associated with the functions that maximally separate stations than do any of the other variables, although many of the categories show a strong areal trend 'to their distribution. Station Mean Value for Each Canonical Discriminant Function and Euclidean Distances Between Station Station CDF I CDF II 1 2 3 4 -5 1 .0172 .0606 0 6.25 7.65 7.52 7.12 2 .0366 .0914 0 3.87 5.26 4.04 3 .0315 .1107 0 4.28 4.96 4 -.0207 .1025 0 5.23 l 5 - 0487 .0663 0 From :his display of the station relationships to each other, certain clasterings of stations become apparent. In general, Station 1 l is least like any of the other station; there seems to be a clustering ! of Station 2,3, and 4. Station 5, the station nearest the intake scree,s, is remarkably like Station 1 with regards to CDF II, and slightly higher than"the cluster of Stations 2, 3, and 4 along the axis defined by CDF I. Using the nearest neighbors of the stations (as defined by the Euclidean distance matrix) as a measure of similarity allows the assess- { ment of which stations are most alike overall. One sees that Stations 3 and 4 IV-20 1
~
Table 2. Correlations between Canonical Discriminant Functions and the Dependent Variables Stations (#'s/m3) CDFI CDFII Communalities 1 2 3 4 5 Calanoids .0474 .3388 .327 13463. 7203.0 12780.0 9282.3 8389.6 Harpacticoids .2482 .2361 .266 461.5 151.0 570.0 668.2 206.5 Cyclopoids .1682 .1308 .195 81.0 260.5 1095.0 837.9 436.8 Gastropod larvae .2330 .3407 .189 466.0 1713.0 1781.0 1604.1 1592.5 Bivalve larvae .0964 .2856 .112 774.5 690.0 1105.4 1517.9 -959.0 Barnacle larvae .3286 .0144 .125 41.0 178.8 215.9 234.6 285.7 Penaeid shrimp .1930 .2228 .294 47.0 389.8 159.8 5.'4 80.5 Other shrimp .0578 .4827 .423 10.0 71.6 188.7 206.3 54.94 Stone Crab larvae .0408' .3341 .148 47.5 118.2 75.0 39.2 32.25
-- Blue Crab larvae .0755 .4573 .368 35.0 166.2 215.0 137.4 54.33-1 Other Crab larvae .0358 .5004 .359 22.5 33.5 69.1 105.3 26.5 D! Other crustaceans .1275 .2410 .211 65.0 24.0 23.6 4.7 16.25 Polychaetes .1830 .3338 .200 53.0 86.0 100.5 303.4 96.7 Echinoderms .1268 .1260 .313 1.0 3.8 4.4 0.0 0.0 Chaetognaths .5910 .3540 .688 24.0 128.0 217 0 196.3 226.8 .
Tunicates .0310 .2690 .152 73.0 206.0 70.0 2.3 70.5 Medusae .0043 .3323 .272 4.5 8.0 7.75 1.66 .213 Miscellaneous .0891 '.0094 .061 79.5 82.2 96.8 146.4 178.0 Fish eggs .3412 .0044 .370 .1 6.8 1.3 .7275 3.43. Fish larvae .1769 .0703 .066 0.0 3.0 .7 1.26 1.93 Totals 15523.6 11523.4 18776.95 15294.947 1272.443. Salinity (0/oo)_ 16.31 20.43 22.12 22.080 21.280 - Temperature ( C) 22.51 22.88 22.68 20.830 23.03: . Diversity .5857 .8092 .8215 .8352 .6900
4 are most similar. While Station 2 is most similar to Station 3 and 4, Stations 1 and 5 are most similar to Station 2. Thus, Station 2 is somewhat intermediate to the offshore and inshore stations. In general, CDF I serves to separate Station 1 from the other offshore stations, while CDF II emphasizes the differences t.etween Stations 2, 3, 4 and Station 5. Upon examination of the zooplankton category means for the different stations, it appears that the low score of Stations 1 and 5 for CDF II is due,at least in part, to lower mean numbers /m3 of shrimp and crab larvae at those station. The different mean values in stations along CDF I is due to different categories being present in different numbers, e.g., chaetognaths are low in Station 1 and highest in Station 5; harpacticoid copepods are highest in Station 1 and lowest in Station 5 and barnacle larvae are lowest in Station 2 and highest in Station 5. This analysis displays the overall station similarities with respect to plankton category composition and numbers. In a preliminary analysis limited to the most common category, calanoid copepods, and the ones considered economically important (Penaeid shrimp larvae, blue crab-like larvae, stone crab-like larvae, fish eggs and fish larvae),we found that these categories display a certain relationship between stations. If one recalls that the categories that separated Station 5 from Station 1 were not included in the initial preliminary analysis, then the apparent inconsistency of oJr preliminary analysis with our investi-gations concerning all zooplankton categories is resolved. Upon examination of the correlations between the dependent variables and the CDF's,an interpretation emerges. On the primary discriminant axis a contrast is formed between the standing crop of harpacticoid copepods and the standing crops of the remaining categories. Particular emphasis is given to cyclopoid copepods, gastropod veligers, barnacle larvae, penaeid shrimp larvac, other crustaceans, polychaetes, chaetognaths, fish eggs, and fish larvae. This axis serves to separate the shallow inshore Station 1 from the rest of the stations. Station 1, with an average depth of approximately 3 feet and a predominately sea grass and sand substrate, is separated from the rest of the stations by a large series of oyster bars. Although Station 3 and 4 also demonstrate levels of harpacticoids comparable to Station 1, these two stations show high levels of barnacle larvae, chaetognaths, and gastropod veligers, which have a negative weighing on CDF I. The high numbers of categories receiving both negative and positive weighting places Stations 3 and 4 at an inter-med' ate position on CDF I. Stations 2 and 5 have the lowest standing cro,e of harpacticoid copepods and high levels in at least oneof the categories given strong negative weighting. This axis seems to i establish a faunal assemblage gradient. The gradient runs from stations rich in benthic holoplankters and poor in the more free swimming mero- and holoplankton to those stations with low levels
, - of epibenthic. plankton and moderate to high levels of the free .
l IV-22 l
i swimming p1'ankters. ~ This faunal gradient' axis describes approximately 50% of the variation observed that is related to difference in stations. The second CDF axis serves to form three groups. Stations 3 and 4 have the highest scores, Station 2 is intermediate and Stations 1 and 5 have the lowest scores for CDFII. On this axis the weighting is generally positive and only barnacle larvae, miscellaneous plankters, fish eggs, and fish larvae can be thought to have no real influence on this axis. However, certain categories may be considered more important than others. The categories most important in determining the level of a station on CDF II are: calanoid copepods, gastropod veligers, the shrimp and crab larvae, chaetognaths, and medusae. Some of these categories are meroplankton, the larvae of benthic or infaunal animals . Contained within these categories are most of the economically important categories. Following examination of the means of the categories at each station, an interesting facet of the CDF II axis appears. Although calanoid copepods have a relatively high weighting, in order to receive a high score on this axis a station must also have a good representation in the shrimp and crab-larvae categories. While Station 1 and 5 have high levels of calaniods they are relatively depauperate in other categories important to CDF II. This is true of Station 1 to a greater extent than Station 5. Station 5 is separated from the offshore Stations 2,3, and 4 primarily by a much lower average of shr. imp larvae, crab larvae and nedusae. Considering these two axes in conjunction with each other, approxi-mately 77% of the variation between stations is retained. Utilizing the information about overall station relationships contained in these two axis , a close resemblence.between the spatially adjacent offshore Station 2, 3, and 4 is present. The inshore S6ation 1 is shown to be quite unlike the cluster of offshore stations. This dis-similarity is probably due to different fauna beino associated with the physical characteristics of environment (e.g. Icwer average salinity, possible higher afternoon thermal loading). These faunal differences are expressed primarily in CDF axis I. Station 5 is distinctly separated from the offshore cluster even though hydrographic studies indicate that most of the water passing through Station 5 is of offshore origin (Klausewitz and Carde'r,1973). This separation occurs along CDF axis II. At Station 5 the standing crop of plankton predators (chaetognaths) is M ahest. Chaetognaths are known to be.able to consume decapod larvae and fish larvae up to 15 mm in length and also are to some extent capable of selectively choosing their prey (Reeve, 1966). Thus, the differences observed between Station 5 and the offshore stations may be the result of the concentration of zooplankton predators within the intake canal and their resultant l predation on crab and shrimp larvae. Although Station 1 also scores low on CDF axis II, the same interpretation does not necessarily apply. The lower numbers of crab and shrimp larvae could be a result of plankton predation by larval fish, planktonic predators, benthic connunity predators or negative reactions to changes in environmental l conditions. As the crab and shrimp larvae enter the last stages of l their planktonic existance they are capable of behaviorally selecting the type of water mass in which they are found (A. Kuris, personal communication). l The station differences are best described by axes representing differences in faunal assemblages rather than in axes describing total IV-23
levels of zooplankton standing crop. Station 3 has the highest standing crop. Stations 1 and 4 have intermediate levels and Stations 2 and 5 have the lowest standing crop. Although diversities are generally low, equitability coefficients are less than .5, the lowest diversities being observed at Stations 1 and 5. Diversity in this context may be an indication of the reaction of narrow niche organisms to stressful situations, but it is not an indication of lower standing crop. Seasons Wilks' A = .4282 Significance .0001 Characteristic Percent Variation Function Root Accounted For Chi square df Sig. I .6368 61.72 167.68 38.0001 II .2823 27.36 49.73 18.0001 III .1126 89.08 --- --- In the case of seasonal differences, there are three significant axes that can be used to discriminate between seasons. The correlations and communalities for the dependent variables are listed in Table 4. There are several categories with an appreciable amount of their variance act 3unted for by differences between the seasonal means, but penaeid shrimp, chaetognaths, and other shrimp have the highest seasonally related communalities. The means of the seasons for the three canonical discriminant functions and the distances between the means are listed below. Mean Values of the Seasons for the Canonical Discriminant Functions and Season Distances CDF I CDF II CDF III SPRING SUMMER FALL WINTER Spring .0050 .1091 .0619 0 2.94 10.35 8.94 Summer .0108 .0810 .0551 0 9.88 6.16 Fall .1070 .1016 .0458 0 9.25 Winter .0458 .0323 .0413 0 This table shows that CDF I serves to separate fall and to a-lesser extent winter, spring, and summer, while CDF II separates winter from spring, summer, and fall. These two axes account for 89% of the between-seasons variation. The third axis, which adds an additional 11% variation, further emphasizes the differences of spring and summer with fall and a,- .:winte r. ' An examination of the variable CCF correlations in Table 3 points IV-24
~
-Table 3. Correlations between Canonical Variables
'and Dependent Variables CDF I CDF II CDF III Communality Calanoid .1821 .2492 .2014 .1359 Harpacticoids .1236 .4545 .0370 .2233
. Cyclopoid .3149 .1809 .0911 .1402 Gastropod larvae .1112 .3799 .3117 .2320 Bivalve larvae .2114 .2852 .0236 .1266 Barnacle larvae .0354 .2641 .2829 .1509 Penaeid shrimp .5397 .1741 .2334 .3761 Other shrimp .3500 .4231 .1062 .3128 Stone crab .0161 .0650 .0248 .0050 Blue crab .2251 .1724 .0112 .0805 Other crabs .1919 .1985 .3006 .1364 Other crustaceans .4003 .0437 .1299 .1730 Polychaetes .1067 .0868 .4212 .1963 Echinoderms .1563 .1403 .4040 .0641 Chaetognaths .0074 .2423 .4863 .2953 Tunicates .4143 .0941 .0704 .1854 Medusae' .1971 .0149 .0847 .0462 Miscellaneous .1998 .0543 .0768 .0486 Fish eggs .1021 .3543 .2383 .1927 Fish larvae .0496 .2398 .1295 .0768 IV-25 l
out some of the important seasenal differences in the faunal compositions of the overall zooplankton comunities. These correlations,when used in conjunction with the mean values of the variables for each season , (Table 4) and the position of the mean for each season on the vector space created by the CDF's, allow a more precise estimate of these 1 i seasonal differences. ! Thus, in examination of these categories important to CDF I, fall is lowest in cyclopoids and other shrimp, the most important positively correlated variables. Converse?y, fall is highest or among the highest in the categories with strong negative correlation, penaeid shrimp, other crustaceans, and tunicates. In the case of spring, another season separated out by CDF I, the reverse trend is generally true. Summer is moderately high in other crustaceans and highest in penaeid shrimp larvae, but this trend is more than counter-balanced by the high numbers of cyclopoid copepods. Winter exhibits low levels of almost all categories except for cyclopoid copepods which are 25% of the non-calanoid copepod winter zooplankton standing crop. In the winter, 90% of the zooplankton standing crop is contained within the calanoid compartment. Thus, the CDF I axis is seen to be a contrast between seasonal comunity compositions. In particular, the seasonal differences in the zooplankton comunity expressed by CDF I concern differences in the non-calanoid portion of the comunity. The second axis seems to be primarily separating winter from the rest of the seasons. Interpretation of this axis brings out two differences between comunities present during spring, sumer, and fall and the comunity found during the winter. The absence of appreciably large negative correlations between the zooplankton categories and CDF II suggeststhat the axis is indicating the level of standing crop. This can be verified on a general level, the winter tandng cropis between 42% and 68% of that for the other seasons. Examination of the categories given high weighting brings out a further interpretation of this axis. Many of the categories emphasized, i.e. Other shrimp larvae, chaetognaths, fish eggs, fish larvae, represent planktonic predators. It would necessarily be advantageous for them to be more numerous when their prey are more abundant. The third CDF axis emphasizes the higher levels of calanoid copepods, gastropod larvae, polychaetes , and chaetognaths found in the spring and summer seasons. When the three axes are considered together, spring and summer are shown to be the most similar seasons. This similarity seems to be the result of an increase in the standing crop of holoplankters IV-26
'~ Table ' 4.~ Mean Values iaf the Dependent Vari bles for Ea'ch Season Spring Summer Fall Winter Calanoids 11623.645 11607.615 10119.465 7800.170 Harpacticoids 823.130 127.430 226.220 347.150 Cyclopoids 349.500 1420.030 3'5.745 204.105 Gastropod larvae 1574.715 3507.920 443.170 20.880 Bivalve larvae 1189.960 1836.880 329.630 3.3835 Barnacle larvae 280.720 203.880 243.460 38.010 Penaeid shrimp 6.515 308.285 295.150 .1575 Other shrimp 140.810 211.575 22.960 3.4845 Stone crab larvae 46.445 158.415 48.250 .6815 Blue Crab larvae 99.245 312.120 59.235 .0025 Other crab larvae 40.230 100.575 44.695 .1025 Other crustaceans 2.994 21.115 44.025 .2745 Polychaetes 79.890 234.990 89.450 39.845 , i Echinoderms 6.045 .9785 0 .571 Chaetognaths 124.560 315.060 180.350 11.855 t Tunicates 2.921 16.275 389.855 .0020 Medusae 2.141 5.660 108.100 .8385 Miscellaneous 215.740 68.120 7.775 152.685 Fish eggs 8.095 1.307 .815 0 Fish larvae- 2.773 1.4085 1.773 .0215 Totals (#'s/m3) 16620.254 20459.638 12690.123 8624.2195 Diversity .856 . 9 31 .783 .397 Biomass (mg/m3 ) 60.600 52.700 52.350 28.600 Salinity (0/oo) 17.840 23.810 23.590 16.260 i Temperature (OC ) 21.290 29.630 25.410 14.070 IV-27
as well as an increase in the standing crop of many of the larval forms. The increases in standing crop show some interesting patterns. Calanoid copepods generally increase in the spring, remain at that level though the summer, and decline in the fall, where they reach their lowest abundance. Heinle (1966) found that population structures of Acartia tonsa were rich in juvenile individuals in the spring and sunener and consisted mostly of adults in the winter. This rise and decline may signal the beginning and the end of a period of major reproductive activity in the calanoid ceopepod portion of the zoo-plankton community. The harpacticoids also shows a unimodal seasonal distribution curve which begins peaking earlier than the calanoid copepods. Harpacticoids start to show a higher standing crop in the winter season with a peak in the spring. The major decline occurs in the summer and then the population levels start to climb in the fall. The cyclopoid copepods show the same basic pattern as the other copepod categories, however, their peak is in the summer. These three categories contain the majority of the holoplankton organisms. In general, they show a peaking in standing crop during the time of the year most likely to have high levels of food available, less fluctuations in salinity from fresh water input, and warmer termpera-ture. In addition, they seem to show some evidence of temporal parti-tioning of available resources by having their peaks occur at different seasons of the year. The other categories that are distinctly holoplank-tonic, medusae and chaetongnaths, are also plankton predators. They show fall and summer peaks, respectively, and might be demonstrating a time lag which times their maximum abundance with the abundance of their prey. The remaining categories are almost exclusively meroplankton and peak in the summer months. Many of these, especially the crab, fish and shrimp larvae, are predators. Their abundance is probably timed by some type of environmental cue to coincide with a maximum abundance of their prey. Only three categories peak in the fall, medusae, other crustaceans, and tunicates. In his review article on ascidians, P.. H. Milar (1971)
. points out that temperature appears to be most important in determining spawning time. The fall marks the beginning of a period of declining water temperatures which might serve as a cue for initiating reproduction.
Although the majority of the tunicates are larvaceans and their biology is little known, infor. nation availatle on ascidsans biology may give insight into this pulse of tunicates. This late pulse in the standing crop of three relatively low level categories helps to distinguish fall from the highly prc
- tive season of spring and summer. On a seasonal basis diversity is positively correlated with standing crop.
This indicates an increase in diversity that is related with the turning on of reproduction and the possible adaptations for temporal resource partitioning of the various zooplankton categories. Size Classas ., Wilks' Ly .0172 ' Significance '"
.0001 IV-28
' Pe' rcent Variation Discriminant Characteristic Accounted Function Root for Chi square df sio.
I 8.5802 72.47 848.70 57 .0001 II 2.6979 22.71 233.63 36 .0001 III 0.4004 3.38 74.12 17 .0001 IV 0.1711 1.44 ----- -- ---- The high value of X' demonstrates that a great deal of variation in zooplankton category numbers is " explained" by the difference in size class. Although all four discriminant axes are significant statistically, the first two describe relationships that account for 95% of the variance that is due to size class difference. Table 5 gives insight into the faunal components that comprose the size classes discriminant axes. Examination of the distance matrix (Table 6) shows that the similarities of the size classes go in a linear order. The plankton composition of the 2000p size class is most like that of the 850u size class which is equally distant from, or equally similar to, the 2000u and 600p size classes. The 600p micron class is closest in zooplankton standing crop resemblence to the 850p size class and generally shows a closer affinity to the larger size classes. The two smaller size classes, while most similar to their closest size classes, seem to be more distinctive. In general, the size class variation shows a loose cluster of size classes 2000p , 850p , and 600p, with the 125p and 250p size classes being separated from e:ch other and the loose cluster. A weak link between the clusters is seen being formed bf the 600p size class. These distance relationships express graphically the similarity of the size classes with respect to types of zooplankton and the frequency distribution of the zooplankton categories. The interpretation of the biological meaning of the axes is reasonably straight forward. The first axis, which explains about 72% of the between size classes variation, is weighted most mearly on the holoplankton and small meroplankton categories, the copepods, the gastropod larvae, bivalve veligers, barnacle larvae, polychaete larvae and miscellaneous. This axis tends to separate the 125p and E50p classes from an intermediate 600u size class and more distinctly from the size classes, the 850p and 2000u. The second CDF axis, which adds an additional 23% " explained" variance, isolates the largest and smallest size classes from the rest of the size classes. This axis places an emphasis on the categories of moderate size, calanoid copepods, which contain the largest of the copepods occurring at Crystal River, the crab and shrimp larvae, and chaetagnaths. Those size classes tnat are either too large or too small to contain large numbers of these size classes are separated from the intermediate size classes. IV-29
- 3. g.ec . m--u- ,, _
Table 5. Correlations Between Size Class CDF Axes, and Dependent Variables CDFI CDFII CDFIII CDFIV COMMUNALITY { Calanoid .686 .609 .276 .016 .918 Harpacticoid .344 .034 .171 .321 .252 Cyclopoid .348 .183 .118 .011 .168 Gastropod .520 .189 .045 .363 .439 Bivalve .311 .210 .352 .024 .265 Barnacle .359 .359 .012 .160 .170 Penaeid .008 .214 .078 .330 .161 Other shrimp .004 .316 .040 .071 .106 Stone crab .041 .293 .256 .206 .195 Blue crab .052 .355 .228 .027 .181 Other crab .108 .236 .311 .259 .231 Other crust. .098 .015 .026 .179 .043 Polychaetes .270 .020 .202 .200 .155 Echinoderms .038 .010 .014 .025 .002 Chaetognaths .103 .468 .418 .366 .539 Tunicates .098 .045 .048 .012 .014 Medusae .053 .047 .070 .200 .050 Misc. .265 .291 .462 .286 .450 Fish eggs .020 .120 .015 .257 .081 Fish larvae .030 .062 .069 .507 .266 i l . . .; 1 y . . , IV-30 !
~ . , . . -
' Table 6.' Mean Values for Subclasses on each.CDF Axis and Euclidean Distances between Centroids.
' Size CDFI CDFII CDFIII CDFIV 2000 850 600 250 125 2000p .0010 .0201 .0203 '.0108 0.0 11.54 20.19 30.91 35.06
'850p .0197 .1060 .0857 .0456 0.0 12.34 25.85 33.69 600p' .0916 .1895 .0163 .0088 0.0 17.43 30.35 250p .2529 .1955 .0215 .0276 0.0 20.37 125p .3447 .0234 .0784 .0140 0.0 1
a l e 4 IV-31
, e
The additional two axes, while statistically significant, only contribute a total of about 5% of the variance. These axes tend to emphasize more subtle differences between the size classes. The first two axes seem to contain the general pattern of relationships among the size classes. Once egsin the concept of faunal association appears. The 125p size class is comprised primarily of those categories that tend to be filter feeders of one type or another. The smaller calanoids, harpacti-coid and cyclopoid copepods, the gastropod, bivalve, t,arnacle, and polychaete larvae total 97% of the samples that fell into the 125u 249p range. These are most likely detriivores and herbivores. The categories that contain mostly predator-types, e.g. crab larvae, shrimp larvae, and chaetognaths are most et won in the 850p and 600p size classes with substantial representation in the 202p size class. The calanoid copepods which are relatively common in all but the largest size class contain both small particle filter feeders and zooplankton predators. The largest size class is poorly represented but contains 54% of obvious plankton predators, chaetognaths, crab larvae and shrimp larvae; an additional 40% are probable zooplankton predators, the large calanoid copepods. Thus, the size classes serve to some extent as trophic level indicators. The small size zooplankton are equivalent to primary consumers and have high standing crops. The intermediate size plankers represent omnivores and first level predators. This level is not far removed on the food chain from the source of energy and in addition, has a wide range of food types available and is also quite plentiful in the water sampled. The largest size class would contain predators further removed from the source of energy and thus would necessarily be fewer in number. This size class partitioning of zooplankton fauna is suggestive of an adaptive response to a limited resource. As we have already noted, there appears to be a temporal partitioning of resources primarily by a sequential blooming of different components of the zooplankton community. The results of the size class analyses point to a resource partitioning by size class selection, _ possibly mediated by particle size selection of the feeding apparatus of the individual organisms. In an environment that is necessarily patchy and inhabited by organisms with low capability for inter-patch locomotion,-the two most likely sources for adaptation to resource partitioning are size partitioning and temporal partitioning. Additionally the estuarine environment of Crystal River shows definite seasonality on a time scale that is greater than the generation length of many of the zoo-plankton constituents. It is basically a temperate environment with a spring-summer growing season that is optimally suited for population growth.
.. . ~ . .
IV-32
l This interpretat' ion of the results of the main e'ffects analysis is open to the same criticism as all zooplankton surveys in that: sample collection, laboratory enumeration techniques, and sophistication of our plankton classification scheme might bias our results. However, the results present strong statistical evidence as well as high biological plausibility. These factors cause us to present the above explanation as one underlying the size class distribution of zooplankton in the Crystal River area. The remaining terms of the statistical model represent interactions between the main effects of the model. All of the two-way interactions proved to be statistically significant but the three way interaction was nonsignificant. The significance of an interaction term often makes the analysis and interpretation of ;ain effects' significance meaningless. Sokal and Rohlf (1970) point ; that in biological investigationsit is often desirable to interpret .se results of the main effects' test of significance. Since the significance levels on the tests of the main effects tere so high and the ensuing analyses gave interpretation with a high degt(e of biological relevance, we decided to include an analysis of the ma,n effects' terms. Station By Season Interaction Wilks' A = 0.394 Significance .0001 This interaction (see Table 7) was proposed to examine the possibility that the pattern of zooplankton category standing crop across the seasons was different for different stations. This suggests the possibility of geographic variation, albeit on a small scale, in the temporal pattern of zoopit.nkton fluctuations. The significant of the statistice.1 test supports this hypothesis. Interestingly, the test to determine how many significant CDF axes were present showed that after one axis was accounted for the rest were not statistically significant. The first CDF only accounts for 29% of the total variation that is a result of the interaction between stations which results in a large portion of this interaction's variance not being organizable into orthogonal axes that are statistically significant. Since there is only one significant axis, the mean response of the station on a seasonal basis can be illustrated in the same way one would graphically show the results of a univariate ANOVA interaction. The advantage is that the variable found by the CDF I contains much more information about the total zooplankton community ra-sponse to the season-station interaction than would any simple plankton category we could choose as a dependent variable. Figure 3 shows three basic patterns: (1) The CDF I monotonically rises sharply to a fall peak and declines sharply to a winter low. T his is demonstrated by Stations 2 and 3 and probably 4, although fall values are not shown for the latter. (2) The CDF I rises monotonously. to a sumer peak, which is lower than that observed in the fall in the first l l IV-33 1
~--
Table 7. Canonical Discriminant Function Statistics for Station by Season Interaction Term. Characteristic Percent Variation Function Root accounted for Significance I 0.2887 28.78 .001 II 0.1602 15.97 NS III 0.1534 15.29 NS IV 0.1138 11.35 NS V. 0.0812 8.09 NS VI 0.0736 7.34 NS VII '0.0612 6.10 'NS VIII 0.0333 3.32 NS IX 0.0258 2.58 NS X 0.0088 0.88 NS XI 0.0030 0.30 NS Variables C0F I correlation 1 -0.3607 2 -0.1788 3 -0.1558 4 -0.3412 5 -0.3758 6 -0.2520 7 -0.4332 8 -0.1092 9 -0.1865 10 -0.1424 11 -0.3125 12 -0.2020
.13 -0.1292 14 -0.0040 15 -0.1298 16 -0.4165 17 -0.2583 18 0.1496 19 0.0325 20 -0.0716
_ q y a "q) s fa \* * *
'l
- IV-34
-,-.y
~ ' .
Figure 3 i STATIONS X SEASONS INTERACTION CDF I
-21 .
-18 .
-15 .
-12 . g __.
-9 .
-6 .
1 2 4 3
-3 . . . . .s Spring Sununer Fall Winter SEASONS IV-35
w -4 -- curve. The curve then declines slowly in an almost linear fashion to a winter minimum. This type of curve is exhibited at Station 1. (3) The curve for CDF I seen at Station 5 is bimodal with a spring peak and a higher one in the fall. The interaction seems to be ex-pressed by the different seasonal patterns of Station 1 and 5. (See Table 8 for average season by station standing crop ). An examination of the CDF-dependent variable correlations show the following zooplankters to have strong negative correlations: calanoid copepods, gastropod larvae, bivalve larvae, barnacle larvae, penaeid shrimp, other crab larvae, other crustaceans, tunicates and medusae. Two of the categories have moderate to weak negative correlations: harpacticoids copepods and stone crab larvae. Only four categories show positive correlations and of these only two categories have correlations with magnitudes great enough to allow them to be considered as having an effect on the CDF: cyclopoid copepods and miscellaneous. This interaction shows a spatial separation of the temporal cycling of the zooplankton abundance at Crystal River. In the spring Station 1 shows the highest standing crop for that season. It is composed of 91.5% calanoid copepods. In addition, l Station 1 has much lower values for mollusc and barnacle larvae than ' are found at the other stations. The levels for the other categories that are important to the station by season interaction CDF are about the same as at the remaining station. Considering the Stations 2, 3, and 4 as being arranged in a linear fashion along an inshore-offshore axis, a spring-time trend in the zooplankton standing crop emerges. As one moves progressively offshore the standing crop of mollusc, barnacle, shrimp and crab larvae increases. At th.is period in the yearly cycle it may be that the conditions for larval survival are most favorable in the more offshore waters. An environmental difference observed in the spring is that the salinity decreases as one moves shoreward. This is probably a result of increased fresh water run off during the rainy season of late winter and early spring. The salinity gradient may be indicative of other environmental gradients not measured. In the summer, Station 1 shows a bloom of meroplankton. The most noticeable increases are in penaeid shrimp larvae and gastropod larvae. In addition, the high levels of calanoids have dropped off and the numbers of cyclopoid copepods are significantly lower. The offshore Stations 2, 3, and 4 show essentially the same response. The level of calanoid and cyclopoid copepods increases,where-as, that of harpacticoids decreases. The meroplankton, in general, increase in numbers /m3 Gastropod larvae increase markedly while barnacle larvae stay at a steady level or increase only slightly. The crab and shrimp larvae show what appear to be significant increases over the spring season levels. Station 5 is different from the rest of the stations. The difference is seen in the decrease in the calanoid copepod portien of the zooplankton
, . A e., . e t,t - .& .,..... , ,' s s, , ,
1 IV-36 L >
Table 8. Average Season x Station Standing Crop (Spring) Station 1 Station 2 Station 3 Station 4 Station 5 Calanoid 18375.4000 7195.9800 8051.12 10469.7100 13045.8580 Harpacticoid 340.6200 32.1000 1663.205 1576.0020 647.4740 Cyclopoid 182.4400 330.7950 492.955 240.5150 488.3850 Gastropod larvae 344.2600 2371.6000 1671.335 1966.7200 1676.2330 Bivalve larvae 328.8850 1297.6850 1471.135 2603.6550 766.4130 Barnacle larvae 58.2850 84.9150 295.2195 538.6200 514.9550 Penaeid shrimp 4.8625 .3230 7.4050 6.9610 13.3250 Other shrimp 14.9650 149.1700 147.2950 287.1850 155.3180 Stone crab 27.5750 20.9785 62.9900 69.7750 61.4500 Blue crab 14.8350 90.4750 186.1350 142.6150 91.9090 Other crab 29.5800 24.0600 38.2450 59.0850 56.1330 2 Other Crustaceans 0.0 1.3105 2.2820 15.3600 .0215 2, Polychaete 95.5600 45.4750 66.6150 159.3250 56.7560
'd Echinoderm .4185 14.1750 15.1450 0.0 0.0 Chaetognath 31.2450 98.1095 143.0440 258.9750 139.3100 Tunicate 0.0 0.0 13.8240 0.0 1.6240 Medusae 1.5165 .0635 9.4050 .1235 .1365 Miscellaneous 238.5500 162.9335 220.2950 42.3700 357.5370 Fish eggs .2680 23.1040 3.3810 1.8885 8.9940 Fish larvae .4400 6.9400 2.0315 .7305 2.9180 Total (#'s/m3 ) 20089.7040 10950.1900 14563.2760 18439.8140 18084.7820 Salinity ( /oo) 14.9000 17.5000 19.9700 20.3500 17.3600 Temperature (OC ) 21.6000 20.9000 21.1000 20.9500 21.7200 Diversity .751 .842 1.013 .945 .784
u.- a_ Table 8. Average Season x Station Standing Crop (Continue - Sumer) Station 1 Station 2 Station 3 Station 4 Station 5
, Calanoid 1192.244 9027.999 15747.24 11625.12 8418.943 i Harpacticoid 4.926 32.195 290.158 303.48 2.096.
Cyclopoid 9.n79 444.845 3460.72 2193.154 936.494
'. Gastropod larvae 1564 's9 3407.668 4411.866 3217.141 4516.353 Bivalve larvae 809.63 777.995 2349.97 2322.63 3014.88 Barnacle larvae 75.045 202.068 267.848 183.87 262.4295 {
-Penaeid Shrimp 156.525 817.89 285.04 9.54 147.557 Other shrimp 22.485 97.915 550.97 377.04 33.1195 Stone crab 128.285 340.295 171.499 53.9025 58.243 Blue crab 108.34 442.345 553.685 304.14 115.464 Other crab 75.475 60.444 136.566 282.754 19.1855
*2 Other Crustaceans 11.625 26.628 32.7575 0.0 25.%
2, ' Polychaete 67.175 93.755 196.342
' 682.205 256.578 Echinodenn 3.1475 0.0 1.7815 0.0 0.0 Chaetognath 40.28 201.7135 410.263 366.903 527.629 Tunicate ' 57.365 13.4025 6.959 7.615 0.0
. Medusae 14.485 9.6565 3.578 .2285 .0150 Miscellaneous 31.905 61.734 82.287 86.135 78.5265
~
Fish eggs 0.0 .482 .3565 .476 4.726 Fish larvae 1.227 .0845 1.1695 3.235 1.9055 Total (#'s/m3 ) 4375.2335 16059.113 28961.011 22019.528 18420.102 Salinity ( /00) 17.56 22.9 26.1 28.0 24.78
^
Temperature ( C) 29.32 29.65 29.7 29.38 29.95 Diversity .799 901 1.020 1.11 .865
Table 8. Average Season x Station Standing Crop (Continued - Fall) Station 1 Station 2 Station 3 Station 4* Station 5 Calanoid 7084.995 7286.709 17232.965 0.0 7681.072 Harpacticoid 124.59 169.408 491.679 0.0 109.75 Cyclopoid 10.2 14.552 20.327 0.0 94.378 a Gastropod larvae 65.265 586.67 1012.175 0.0 132.5 , Bivalve Ic ' 3.14 542.895 756.995 0.0 51.074 Barnacle larvae 25.88 413.679 251.465 0.0 311.2025 Penaeid shrimp 37.87 733.015 321.485 0.0 161.215 Other shrimp 1.9835 11.2285 45.938 0.0 30.752 Stone crab 39.16 86.356 64.3945 0.0 9.4545 Blue crab 24.775 92.235 115.482 0.0 9.951
. Other crab 5.585 46.234 95.3 0.0 30.727 1: Other Crustaceans 13.1 72.835 0.0 0.0 39.1665 h! Polychaete 20.145 195.508 281.46 0.0 64.535 Echinoderm 0,0 0.0 251.34 0.0 0.0 Chaetognath 23.86 193.903 18.18 0.0 224.44 Tunicate 220.585 890.43 4.648 0.0 280.506 Medusae 2.8315 23.685 1.7345 0.0 .6915 Miscellaneous 5.185 4.2695 0.0 0.0 16.4215 Fish eggs 0.0 1.668 0.0 0.0 0.0
- Fish larvae 0.0 4.679 0.0 0.0 2.8985 Total (#'s/m3 ) 7709.15 11369.958 20965.527 0.0 9250.735 Salinity ( /00) 20.0 24.8 24.8 0.0 24.9 Temperature ( C) 24.9 25.3 25.5 0.0 25.9 Diversity .566 1.052 .896 0.0 .664
- Station 4 was not sampled.
(
-i Table 8. Average Season x Station Standing Crop (Continued - Winter)
Station 1 Station 2 Station 3 Station 4 Station 5 Calanoid 14272.6 4936.69 9298.484 6458.18 1427.63 Harpacticoid 1416.47 70.367 61.209 233.87 1416.472 7 Cyclopoid 21.408 201.254 311.704 j 231.82 21.408 - Gastropod larvae 4 4.4195 16.315 11.704 23.64 4.4195 Bivalve larvae .832 1.931 4.413 32.095 .832 Barnacle larvae 4.6475 28.497 62.279 .8515 4.6474 Penaeid shrimp 0.0 0.0 0.0 5.232 0.0 Other shrimp 1.033 7.441 - 3.689 3.033 1.033 Stone crab 0.0 0.0 .539 0.0 0.0 Blue crab 0.0 .0145 0.0 .499 0.0 Other crab .014 .01 .026 0.0 .0142
- Other Crustaceans 1.3475 .135 0.0 115.683 1.3472 1, Polychaete 26.7625 15.925 38.338 c'
s 0.0 26.7628 Echinoderm 0.0 0.0 2.569 9.844 - 0.0 Chaetognath .1535 9.605 21.126 0.0 .1536 Tunicate .011 0.0 0.0 4.0565 .011 -
,. Medusae 0.0 .316 .12 277.858 0.0 Miscellaneous 26.05 87.995 100.616 0.0 26.0522
- Fish eggs 0.0 0.0 0.0 .1088 0.0 Fish larvae 0.0085 0.0 0.0 _ 0.0 .0084 3
Total (#'s/m ) 15775.754 5376.495 9916.816 7396.771 2930.791 Salinity ( /00) 12.0 16.0 16.83 18.7 17.42 Temperature ( C) 13.7 14.3 14.08 13.9 14.33 Diversity .197 .416 .390 .527 .447 9
c'ommunity'an'd an increase in the cyclopoid copepod population. At the same time we see a rise in the zooplankton predator population, e.g. chaetognaths increase by 3.8 times their spring values. Thus, in the summer we see a decline .in the copepod populations of the in-shore region with an increase in the larvae component of the zooplankton comunity. This decline of inshore copepod standing crop results in an overall standing crop decline in this region. Station 1 standing crop declines to 22% of the spring value. At the offshore stations larval componer.ts as well as copepod components are increasing slightly or remaining at a steady state. At the intake canal station (Station 5) the decline of zooplankton types is possibly linked to an increase in predation. In the fall the stations fall into two groups: (1) offshore Stations 2 and 3, and (2) the inshore Station 1 and the intake canal Station 5. The offshore stations show a decline in cyclopoid copepods and the aforementioned bloom in tunicate, presumably larvacean, larvae. The crab, shrimp, barnacle and molluscan larvae show a decline over the summer months. At Station 1 the larvae meroplankton population is back to an extremely low level. The calanoid copepods comprise 92% of the total fall sample. At Station 5, the pattern is similar to the Station 1, except that there are relatively high numbers of tunicates, barnacle larvae, and chaetognaths. In the winter all stations with the exception of Station 1, drop to their lowest levels. In Station 1 the levels are higher than the fall or summer but lower than the spring. This is primarily the result of a doubling of the fall level of calanoid copepods and a substantial increase in harpacticoid copepods. At Station 1 calanoid copepods make up 90% of the total and harpacticoid copepods make up an additional 9%. Overall there seems to be several basic patterns that emerge. At a station level, there is a strong inshore pattern exhibited by Station 1. This is characterized by low diversity highest standing crops in the winter and spring, low levels of meroplankton and low standing crops of secondary or tertiary trophic level consumers. The low diversity may be indicative of a stressful environment that is exploited by the possibly wider niche calanoid copepods. Low diversity is not necessarily indicative of standing crop potentials on productivity. As Steel (1974) pointed out, low diversity by itself is not predictive of an unstable system. The wide fluctuations in standing crop at Station 1 may be l population level clues that the environment is unpredictable. The only ' station that shows greater fluctuations over the year is Station 5 I and it appears to be under a stress caused by the plant intake pumps. IV-41
The pattern observed at Station 5 is also unique. During the spring, Station 5 is most like the offshore Stations 2 and 3. During the sumer when all the other stations are relatively similar Station 5 is unique possibly for the reasons mentioned for Station 1. During the fall and winter it is most like Station 1. The offshore Stations 2 , 3 and 4 are similar and generally follow the same pattern across the year. They show a period of breeding, signaled by the appearance of gastropod, bivalve and barnacle larvae in the spring, which continues into the sumer. This is followed by a pulse of shrimp and crab larvae in the sumer and the fall. Station 1 shows a similar pattern except the period of peak larval forms appearing in the plankton is shortened on both ends of the breeding season, presumably by unfavorable environ-mental conditions. Thus the standing crop trends show a seasonal and spatial inter-action that is to some degree explainable by environmental fluctuation and heterogeneity and temporal partitioning of the resource by the zoo-plankton comunity. Station x Size Wilks' A = .3489 Significance .0001 The significance of this interaction suggests that the size class distribution of the different zooplankton categories is not the same for each station. The chi-square test for the number of significant axes showsthat only one axis is significant. Characteristic Roots, Percent Variation and results of x2 Test for Station x Size Intera: tion Characteristic Percent Chi Degrees of Root Variation Square Freedom Significance
.3647 31.7 324.49 437 .99 l .2037 17.7 256.91 396 .99
.1400 12.2 196.46 357 .99
.1125 9.8 147.26 320 .99
.0915 8.0 106.87 285 .99 ,
.0668 5.8 77.02 252 .99 I
.0529 4.6 53.22 221 .99 3
.0422 3.7 34.14 192 .99 '
.0267 2.3 21.97 165 .99 ;
.0154 1.3 14.89 140 .99 i
.0142 1.2 8.38 117 .99
.0098 .9 3.88 96 .99
.0043 .4 1,91 77 .99 I
.0023 .2 .83 60 .99 I
.0014 .1 .18 45 .99
.0004 0 -- --- --
IV-42 i i
' ~
This means that the information about variation between stations of the size class distribution for the zooplankton categories can be best expressed in a single new variable which is a linear combination of the original dependent variables (Table 9 ). Figure 4 shows the overall similarity of size class distribution for the different stations. This is especially true for Stations 3, 4, and 5 which have almost identi-cal CDF I - size class curves. Station 2 is similar to the offshore stations in most of the size classes with the notable exception of the 600 u size class. Station 1 again seems to be quite different with respect to the size class distribution of its zooplankton constituents. The canonical discriminant function's correlation with the dependent variables shows strong negative association with calanoid copepods and other crab larvae and moderate to weak negative associations with chaetognaths and medusae. The highest correlation is a positive one with barnacle larvae. Other positive correlations are found with cyclopoid copepods, gastropod larvae, polychaetes, miscellaneous and other crustaceans. An examination of the 600u size class means for each station reveals some differences at Station 2 (see Table 10). The calanoid copepod category is much lower than at the other stations, the other crab larvae category is higher and penaeid shrimp larvae are more numerous at this location than at other locatSns. Although this interaction does not immediately point out any o 'ous biological principle it does provide some important information. Station 2 contains a strikingly different plankton composition with respect to the 600 u size class. Size By Season Wilks' A = .0968 Significance .0001 This is a very important interaction and contains four significant CDF axes. Characteristic Percent Chi Degrees of Significance CDF Root Variation Square Freedom level I 1.110 37.7 737.32 342 .0001 II 0.562 19.1 530.43 306 .0001 III 0.425 14.4 366.16 272 .0001 IV 0.256 8.7 260.24 240 .1760 V 0.177 6.0 184.76 210 .8947 VI 0.133 4.5 126.82 182 .9993 VII 0.108 3.7 79.34 156 .9999 VIII 0.058 2.0 53.30 132 .9999
-IX 0.042 1.4 33.97 110 .9999 X 0.037 1.3 16.90 90 .9999 XI 0.026 0.9 5.13 72 .9999 XII 0.011 0.4 --- --- ----
IV-43
= a a -- , , .
Table 9. Correlations Between the Station Size Class CDF I and the Dependent Variables Calanoid .342 Harpacticoid .054 Cyclopoid .298 Gastropod .310 Bivalve .042 Barnacle .579 Penaeid .082 Other shrimp .061 Stone crab .035 Blue crab .029 Other crab .354 Other crustaceans .115 Polychaetes .199 Echinoderms .020 Chaetognaths .200 Tunicates .068 Medusae .119 Miscellaneous .198 Fish eggs .058 Fish larvae .050
. ,,. ... . , , . ...... .s s - , . . . . . .v .
IV-44
Figure 4 STATION X SIZE INTERACTION CDF I 15 12 9 6 e
-3 . 2
-6 . !
L
-9 !
[
-12 . . . . .
2000 850 600 250 125 Size Class (Microns)
.IV-45
w# Table 10. Mean Values for Station x Size Class Interaction Station 1 2000p 850p 6009 250p 125u Total Calanoids 0.15 35.00 856.18 9118.15 3453.52 13463.00 Harpacticoids 0.00 0.19 2.55 347.60 111.18 461.52 Cyclopoids 0.00 0.00 0.41 12.38 68.06 80.85 Gastropod larvae 0.00 0.14 0.00 57.92 407.80 465.87 Bivalve larvae 0.00 0.00 0.62 43.65 230.47 274.75 Barnacle larvae 0.00 0.00 1.52 4.29 35.25 41.07 Penaeid shrimp 0.18 3.76 3.87 36.31 3.11 47.23 Other shrimp 0.30 1.84 1.92 5.55 0.35 9.97 Stone crab larvae 1.02 2.56 8.72 35.06 0.00 47.36 Blue crab larvae 0.93 1.33 7.05 23.60 2.51 35.43 Other crab larvae 0.49 3.96 2.03 16.17 0.00 R2.65 Other crustaceans 0.01 0.62 2.06 2.04 1.79 6.52 Polychaetes 0.01 0.01 1.20 22.80 28.89 52.91 Echinoderms 0.00 0.00 0.11 0.00 0.72 0.83 Chaetognaths 0.28 6.16 9.02 8.05 24.22 0.72 Tunicates 0.00 0.00 1.02 10.29 61.89 73.20 Medusae 0.00 0.18 0.03 2.17 2.08 4.48 Miscellaneous 0.00 0.19 1.04 1.02 77.39 79.65 Fish eggs 0.00 0.00 0.07 0.00 0.07 Fish larvae 0.00 0.10 0.22 0.08 0.00 0.00 0.40 Total 3.48 56.19 899.51 9747.07 4485.73 15191.97 Station 2 2000p 850p 600p 250 125p Total Calanoids 1.66 66.46 454.76 4246.38 7202.76 Harpacticoids 2433.51 0.02 0.11 1.05 48.87 100.77 150.82 Cyclopoids 0.00 0.00 0.42 9.98 Gastropod larvae 250.18 260.59 0.00 1.06 2.46 199.05 1510.63 1713.21 Bivalve larvae 0.03 4.76 1.26 19.73 664.14 689.92 Barnacle larvae 0.01 0.79 1.79 57.81 118.36 178.76 Penaeid shrimp 0.33 14.65 54.17 317.01 389.74 Other shrimp 3.59 0.17 2.69 18.77 48.94 1.06 71.63 Stone crab larvae 0.00 1.10 11.97 91.37 13.71 118.16 Blue crab larvae 0.06 0.78 19.21 139.24 6.98 166.28 Other crab larvae 1.03 4.42 7.25 20.85 0.00 33.56 Other crustaceans 0.30 1.55 0.63 8.97 12.75 24.20 Polychactes 0.00 0.70 3.79 34.81 46.72 86.03 Echinoderms 0.00 0.00 0.00 3.87 0.00 3.87 Chaetognaths 1.82 27.02 40.98 51.43 128.02 6.77 Tunicates 0.00 0.35 3.41 35.07 167.19 206.03 Medusae 0.04 1.10 1.06 3.56 2.34 8.11 Hiscellaneous 0.00 0.00 0.38 1.03 80.83 82.24 Fish eggs 0.00 0.00 0.94 3.36 2.50 6.81 Fish larvae 0.03 0.60 0.17 2.19 0.00 2.98 Total 5.52 128.15 624.48 5343.54 5422.02 11523.70
,, , ,. .s. ,,, ,,. . v .o " .
- IV-46
Table 10. Mean Values for Station x Size Class Interaction (Continued) Station 3 2000u 850p 600u 250u 125u Total Calanoids 11.43 274.14 1410.25 7076.09 4007.56 12779.47
.Harpacticoids 0.00 2.14 9.29 175.85 383.35 570.62 Cyclopoids 0.00 0.00 2.10 96.08 998.40 1096.58 Gastropod larvae 0.08 2.39 1.65 165.12 1612.12 1781.35 Bivalve larvae 0.30 0.97 2.98 97.02 1004.11 1105.38 Barnacle larvae 0.00 0.00 0.32 45.55 170.03 215.90 Penaeid shrimp 1.54 17.80 25.23 112.98 2.27 159.83 Other shrimp 0.45 12.96 42.25 132.56 0.48 188.70 Stone crab larvae 0.10 5.52 5.85 63.91 0.00 75.37 Blue crab larvae 0.23 5.17 17.48 191.52 0.63 215.03 Other crab larvae 3.14 44.10 13.24 8.65 0.00 69.12 Other crustaceans 0.33 5.28 1.31 4.95 13.73 23.60 Polychaetes 0.00 0.01 1.00 44.42 55.14 100.56 Echinoderms 0.00 0.00 0.67 0.00 3.76 4.43 Chaetognaths 5.76 70.07 66.20 70.80 4.23 217.06 Tunicates 0.02 1.05 8.50 39.09 21.73 70.39 Medusae 0.04 0.13 0.11 5.48 2.00 7.75 Miscellaneous 0.00 0.00 1.21 0.54 95.06 96.82 Fish eggs 0.00 0.05 0.78 0.45 0.00 1.28 Fish larvae 0.03 0.63 0.09 0.00 0.00 0.75 Total 23.44 440.42 1610.49 8331.05 8374.57 18779.97 Station 4 2000u 850p 600u E30p 125p Total Calanoids 0.76 124.48 599.80 5774.30 2782.97 9282.32 Harpacticoids 0.00 0.05 0.00 217.04 451.16 668.25 Cyclopoids 0.00 0.00 1.53 108.69 727.77 837.99 Gastropod larvae 0.00 0.64 1.18 370.60 1231-.71 1604.13 Bivalve larvae 0.00 11.38 1.63 45.73 1459.25 1517.99 Barnacle larvae 0.00 0.10 1.31 80.29 152.95 234.65 Penaeid shrimp 0.09 2.32 0.65 2.34 0.00 5.41 Other shrimp 0.23 25.06 62.58 107.86 10.66 206.39 Stone crab larvae 0.02 1.33 13.60 24.17 0.11 39.22 Blue crab larvae 0.03 2,06 26.62 108.76 0.00 137.46 Other crab larvae 8.40 58.42 28.89 9.66 0.00 105.37 Other crustaceans 0.00 0.00 0.00 4.72 0.00 4.73 Polychaetes 0.01 0.05 0.49 84.40 218.48 303.43 Echinoderms 0.00 0.00 0.00 0.00 0.00 0.00 Chaetognaths 1.70 65.14 37.27 92.26 0.00 196.36 funicates 0.00 0.00 0.00 2.34 0.00 2.34 Medusae 0.03 0.08 0.05 0.00 1.50 1.67 Miscellaneous 0.00 0.00 0.00 2.99 143.41 146.41 Fish eggs 0.00 0.00 0.72 0.00 0.00 0.73 Fish larvae 0.04 0.12 0.21 0.89 0.00 1.26 Total 11.32 291.25 776.54 7037.05 7179.96 15296.10 IV-47
Table 10. Mean Values for Station x Size Class Interaction (Centinued) Station 5 2000u 850p' 600p 250p 125p Total Calanoids 6.04 285.12 645.56 5081.18 2371.74 8389.64 Harpacticoids 0.05 0.79 0.89 74.42 130.44 206.60 Cyclopoids 0.00 0.00 1.76 38.18 396.88 436.82 Gastropod larvae 0.00 1,81 1.72 100.07 1488.99 1592.59 8ivalve larvae 0.05 4.55 4.06 7.88 942.52 959.06 Barnacle. larvae 0.00 0.18 0.06 28.41 257.10 285.75 Penaeid shrimp '0.12 11.31 16.04 51.11 1.94 80.52 Other shrimp 0.10 4.59 12.04 36.74 1.47 54.94
' Stone crab larvae 0.05 2.06 7.61 22.55 0.00 32.29 Blue crab' larvae 0.00 1.85 10.59 38.31 3.58 54.33 Other crab larvae 0.21 11.61 7.S8 7.12 0.00 26.51 Other crustaceans 0.01 0.12 0.06 4.73 11.37 16.29 Polychaetes 0.00- 0.00 1.21 51.54 43.96 96.72 Echinoderms 0.00 0.00 0.00 0.00 0.00 0.00 Chaetognaths 1.38 54.20 68.13 93.44 9.66 226.82
.Tunicates 0.00 .0.16 2.54 10.31 57.52 70.53 Medusae 0.04 0.18 0.00 0.00 0.00 0.21 Miscellaneous 0.00 0.00 0.65 1.31 176.14 178.09 Fish eggs 0.00 0.00 1.0A 2.39 0.00 3.43 Fish larvae 0.01 1.45 0.00 0.47 0.00 1.93 Total 8.05 379.99 781.54 5650.19 5893.30 12713.06 I
1
..m. . . , . . . . . . . .. .. .
n r.. ...- IV-48
\
The interpretation of this interaction is very difficult as can be sea in toe confusing pattern of important correlation observed between the four significant discriminant axes. Most of the zoo-plankton categories have a sizable effect on one or more of the CDF axes. Therefore, we will attack the results of this term in two approaches: first, a general description of the size-class-season relationships, and, secondly, a season by season account of certain category size class fluctuations (see Tables 11 to 13 for standing crop averages, CDF correlations and Euclidean distances). In general the size classes are most like the next largest and smallest size classes, as was seen in the main effects analysis. The distribution of organisms in the largest size classes, 2000p and 850p , show the least variation across the seasons. The smaller size classes show more seasonal changes in their zooplankton composition and each appears to have a unique pattern of variation. In the 125p class, spring marks the appearance of gastropod larvae, bivalve contains larvae and the mostly unidentifiable miscellaneous larvae category ( Alden, personal (which probably ). communication The calanoid copepods are quite plentiful and the harpacticoid copepods are at their yearly high. In the summer, calanoid copepods increase somewhat and cyclopoids peak as harpacticoidsbegin to fade. In the meroplankton categories, larvae of gastropods, bivalves, and polychaetes reach their peak. The summer season 125u size class has the largest average standing crop for any of the size classes across all the seasons. The summer and spring 125p size class contains the majority of the bivalve, barnacle, gastro-pod and polychaetes larvae. In the fall, most of the categories start to decline and during the winter season most of the zooplankton categories are at a low level. This size class contains primarily detritivors and phytoplanktivores. Their seasonal fluctuation seems to coincide with the flushing of nutrients into the estuary from the marsh system during the late winter and early spring. This flushing during the rainy season results in high levels of detritus in the spring and detritus and phytoplankton in the summer. 250p The 250u size class seems to be composed of three types of fauna that are following different yearly or seasonal patterns. The first type i consists of large individuals of the faunal group that was found in the 1 125u size class. Thus the larvae of gastropod, bivalves, barnacles, and polychaetes show about the same pattern as those in the 125p size class. These categcries may appear in the 250u and larger size classes as a result of sieving techniques or actually may be larger than normal individuals. The second type of fauna found in this size class consists of those categories that are ecologically and trophically quite diverse, e. g. the calanoid copepods and ther other copepod categories. These show IV-49
s. h fi Table 11. Average Size x Season Standing Crop (Spring) 2000p 850p 600p 250p 125p Calanoid .26 69.599 -344.40 7517.129 3692.25 Harpacticold 0.0 .190 .0855 207.181 '614.907
- Cyclopold~ 0.0 0.0 0.0 34.125 315.377-
? Gastropod larvae 0.0 1.029 1.11648- 219.189 1353.335
.'- Bivalve larvae 0.0 7.013 1.6716 18.495 1162.783 Barnacle larvae ' O.0 .112 .2568 61.006 219.348
, Penaeid shrimp .074 3.294 1.0018 1.267 .8787 Other shrimp .148 16.272 47.483 74.834 2.0757
. Stone crab .016 .195 7.5489 38.688 0.0
, Blue crab .046 1.250 22.638 73.463 2.0267
~ 0ther crab .057 7.821 12.549 19.8019 0.0 Other crustaceans
.007 0.0 0.0 2.6959 .29125 1: Polychaete 0.0 .026 .2377 36.697 42.932 ig . ; Echinoderm 0.0 0.0 .093 3.1505 2.805 Chaetognaths 1.371 54.648 36.211 27.636 4.693 Tunicates 0.0 0.0 0.0 0.0 . 2.921 Medusae 0.062 .02 .01348 1.543 .4983 Miscellaneous 0.001 .003 0.0 .1691 215.571 Fish eggs 0.0 .048 1.9689 4.042 2.0389 Fish larvae 0.05 1.285 .2712 1.1651 0.0
[ Total 2.092 162.805 477.5464 8342.27 7634.731 f 4
, 'T t
=
Table 11. Average Size x Season Standing Crop (Continued - Summer) 2000p 850p 600p 250p 125p Calanoid .54185 53.935 608.759 6834.83 4109.5479 Harpacticold .0201 .2545 .2409 33.32 93.5937 Cyclopoid 0.0 0.0 .0784 93.984 1325.9687 Gastropod larvae 0.0 0.0 1.3303 356.292 3150.297 Bivalve larvae 0.0 5.2781 3.216 122.314 1706.0725 Barnacle larvae 0.0 3.0 1.2426 35.238 167.4 Penaeid shrimp .4546 8.1779 27.251 267.425 4.977 Other shrimp .1022 7.663 40.5638 157.625 5.624 ' Stone crab .8245 4.181 20.630 122.228 10.5536 Blue crab .8426 3.522 26.487 273.749 7.5184 Other crab 5.5285 56.601 16.5429 21.904 0.0 Other crustaceans 0.0 .983 1.1914 6.837 12.1067
- Polychaete .0027 .0048 1.599 84.539 148.8448 T* Echinoderm 0.0 0.0 0.0 0.0 .9788 S3 Chaetognaths 1.3821 59.917 91.5638 151.8757 10.3216 Tunicates 0.0 0.0 .5829 5.994 9.6993 Medusae .0172 .2728 .3415 2.3385 2.6905 Miscellaneous 0.0 0.0 .5755 .2617 67.287 Fish eggs 0.0 0.0 .4856 .8215 0.0 Fish larvae .0854 .3703 .1046 .8483 0.0 Total 9.8017 201.1604 842.786 8572.4277 10842.475
=
Table 11. Average Size x Seasor Standing Crop (Continued - Fall)' 20009 850p 600p 250p 125u
,. Calanoid 17.6151 586.843 1428.098 5349.75 2737.1559 Harpacticoid .04726 2.7599 9.8654 141.703 71.848
- Cyclopoid - 0.0 0.0 1.5485 8.641 25.558 Gastropod larvae .0802 4.59 3.4973 46.341 388.665 Bivalve larvae
.3758 2.28 3.5636 23.77 299.6387 Barnacle larvae .0141 .8642 2.0174 57.099- 183.4689 Penaeid shrimp 1.5827 34.8677 64.6903 190.19 3.8219 4 Other snrimp .6428 7.733 6.291 7.9619 .3353 Stone crab .1392 6.729 8.1879 32.4098 .7253 Blue crab .1538 4.681 11.029 41.14 2.2319 Other crab 2.9994 21.6107 12.09 7.995 0.0
- Other crustaceans .6200 4.3389 2.4969 11.628 24.942 1 Polychaete .0073 .6719 4.5668 42.013 42.194 E3 Echinoderm 0.0 0.0 0.0 0.0 0.0 Chaetognaths 6.6516 57.6387 50.642 62.001 3.417 Tunicates .0151 1.5528 14.705 87.52 286.06
.i Medusae .0086 1.1344 .7624 6.412 2.493 Miscellaneous .0022 .1776 1.575 1.236 4.786
- Fish eggs 0.0 0.0 .3626 .4525 0.0 Fish larvae .0071 1.0409 0.0 .7253 0.0 Total 30.9622 739.5137 1625.989 6118.988 4077.3409 e
. Table 11. Average Size x Season Standing Crop (Continued - Winter) 2000p 850p 600p 150p 125p Calanoid 1.147 6.1304 962.427 5305.065 1525.399 Harpacticoid 0.0 0.0 2.138 282.29 62.726 Cyclopoid 0.0 0.0 3.378 50.859 149.868 Gastropod larvae 0.0 0.0 .079 2.4757 18.328 Bivalve larvae 0.0 0.0 .5395 2.261 .583 Barnacle larvae 0.0 0.0 .4402 8.427 29.145 Penaeid shrimp 0.0 .1577 0.0 0.0 0.0 Other shrimp .1781 .3592 .282 2.665 0.0 Stone crab 0.0 0.0 0.0 .63 .051 Blue crab 0.0 .0027 0.0 0.0 0.0 Other crabs 0.0 .01 .0924 0.0 0.0 ;; Other crustaceans 0.0 .002' .0377 .234 0.0 '
a, Polychaete .0028 .6s73 .4953 14.209 25.12 w Echinoderm 0.0 .0024- .5686 0.0 0.0 Chaetognaths .2459 2.606 4.725 3.92 .3588 Tunicates .002 0.0 0.0 0.0 0.0 Medusae .0257 .0919 0.0 0.0 .7209 Miscellaneous 0.0 .0061 .8435 3.254 148.58 Fish eggs 0.0 0.0 0.0 0.0 0.0 Fish larvae .02 .0015 0.0 0.0 0.0 Total 1.6215 9.3879 976.0462 5676.2897 1960.8797 j e m 1
~
Table 12. Size By Season Interaction Correlations Between CDF's ar.d Dependent Variables Dependent Variables CDF I CDF II CDF III CDF IV Calanoid .083 .204 .080 .323 Harpactroid .171 .139 .239 .450 Cyclopold .208 .511 .174 .128 Gastropod .590 .347 .513 .344 Bivalve .510 .348 .044 .146 Barnacle .109 .120 .082 .144 Penaeid .104 .246 .290 .280 Other shrimp .055 .307 .328 .055 Stone crab .058 .150 .421 .184 Blue crab .003 .279 .648 .214 Other crab .289 .216 .030 .249 Other crust. .193 .174 .037 .160 Polychaetes .043 .067 153 .603 Echinoderm .049 .153 .001 .210 Chaetognaths .192 .097 .403 .208 Tunicates .166 .012 .053 .319 Medusae .035 .025 .110 .170 Misc. .425 .393 .159 .075 Fish eggs .030 .122 .112 .254 Fish larvae .024 .033 .089 .2?S r'
.. . . .. .... . . , , . . ., - .-~.
n 'IV-54
- P
v ., + t Table 13. Euclidean distance matrix
-Size 20009 850p 600p 250p .
125p Season SP SM F W lSP SM F W SP SM F W SP SM. F. W SP RSM F W' 20009 Spring ~ 0 4.' 1 5.2 3.3 15.8 18.6 19.0 6.9 23.1 21.' 23.7 19.7 31.3 31.4 31.3 26.9 40.7 37.9 31.7 30.0 Summer 0 5.0 .5.2 15.4 16.1 18.2 7.6 22.3 20 z 27.7 19.7 30.3 30.4 30.8 26.8 40.6 37.6 31.0 30.5 Fall 0 6.4 13.9 15.2 16.4 7.1 20.2 1~.9 19.2 16.5 28.4 28.0 27.7 23.9 38.8 35.4 29.4 28.9
- Winter 0 16.2 19.4 19.4 5.0 22.7 21.8 23.2 19.0 31.0 31.5 30.6 26.0 40.9 38.7 30.2 29.1 850p Spring 0 12.1 14.2 13.7 15.6 13.8 18.5 19.9 29.0 26.4 27.0 27.6 42.5 39.2 29.0 32.9 Summer 0 13.9 17.2 16.7 10.6 18.3 22.0 29.2 25.7 27.7 29.7 43.4 39.0- 30.3 36.0 i Fall 0 16.8 17.6 13.0 8.3 15.3 23.8 20.0 16.9 20.8 36.2 32.0. 23.7 28.2 !
Winter 0 19.4 18.7 20.2 16.4 29.5 29.4 27.7 24.4 40.9 38.7 27.1 28.0 i l h;600pSpring 0 8.0 16.6 19.1 23.0 .20.3 25.0 25.6 43.3 40.2 27.8 .33.8 . Summer 0 14.2 19.5 24.4 19.7 23.9 26.6 42.8 38.7' 28.4 34.7 t Fall 0 14.8 22.0 18.4 12.8 18.4 36.3 32.8 20.4 27.3 Winter 0 21.2 22.7 20.8 10.4 33.9 31.1 21.4 21.5
- . 250p Spring 0 14.0 21.8 18.5 28.6 28.0 23.5 24.2 j Summer' O 19.9 24.0 34.2 29.2 26.7 28.8 6 Fall 0 20.4 34.1 32.4 15.3 24.6 Winter 0 29.0 28.5 20.6 18.8 4
I 125p Spring 0 ~15.1 31.6 21.2 Summer 0 34:3 23.2 Fall -0 18.8 ' Winter 0 i. e 4
a bimodal abundance pattern. Calanoid cope 250p size range in the spring and summer. pods areare most common in the They always the most comon
- nd fall. category. Harpacticoid copepods are most common in the spring C.yclopoid copepods are most coninon in the summer and winter.
This size class contains the greatest abundance of crab larvae, shrimp larvae, chaetognaths, and fish eggs and larvae. With the exception of fish eggs, these are plankton predators and generally have their greatest abundance in the summer. The crab larvae, fish larvae, and shrimp larvae appear in the plankton as a pulse in the sununer, and drop to almost zero levels in the winter. Chaetognaths are permanent plankton members and their generation time in quite possibly larger than one year. Reeve (1970) found the size class distribution of chaetognaths in Biscayne Bay to be quite similar to what we have observed at Crystal River. The larger size class levels of chaetograths remain relatively constant until the winter. These probably represent large adults that are the remnants of last year's young.
-the fall andThe winter.
smaller size classes oeak in the summer and decline in The smaller size classes probably represent young and ininature chaetognaths that either grow into the larger sizes or are subject to mortality. The 250p size class is most likely one of the greatest contri-butors to the significance of the interaction. This is to be seen as a direct result of the size class being the meeting point of ! several seasonal response patterns. 1 l 600u This size class is almost completely devoid of the smaller detritivores and planktivores such as harpacticoid and cyclopoid copepods, gastropod, bivalve, barnacle, and polychaete larvae. It is made up of mainly plankton predators and large calanoid copepods. In spring it is primarily composed of calanoid copepods with the other major constituents being shrimp and crab larvae and chaeto-gnaths. The sununer shows an increase in these same categories without the addition of any new components. The fall season shows an increase in calanoid copepods, 88% of the total si x class, and a decrease in most showof a the sharpmeroplankton increase in predator the fall. categoriet. Penarad shrimp larvae maintain a steady level during the fall season.The cha cognaths in this size class In the winter, the meroplankton and chaetognaths are almost non-existent. sample. The calanoid copepods are 99% of the total 600u such as a Labidocera either be due to the occurrance of a large species, This could or Centropages, or large individuals adapted i for surviving the winter season, or it could be a product of experi- ! mental technique in sieving. l l l .p. ~ .. . "* 8 IV-56
2000u and 850u The 2000u and 850p size classes are quite similar and may be combined into a single discussion. These size classes are composed almost exclusively of planktel predators. They reach their peak in the fall. In termsthe classes. Following of numbers reasoningthese are not of Steele very)important (1974 the planktonsize systems are controlled by the fate of the lower trophic levels, the primary producers and primary consumers. This is the reverse of the concept for terrestrial ecosystems where the higher trophic levels are typically conside~ red to be dominant. The three way interaction of station-season-size class is not significant and will not be discussed in the results of this report. These data enable us now to think of the Crystal River region as having at least four distinct planktonic regions: (1) shallow inshore region, e.g. Station 1; (2) a region characterized by Station 2 near the gulfward boundary of Carder and Klausewitz's area I; (3) an off-shore region comprising the hydrographic areas 2 and 3; and (4) an intake area represented by Station 5. These results might not have been discerned amongst the wealth of data without the aid of MANOVA. Not only did it porvide the appropriate plankton " system" approach test hypothesis but the multivariate approach has succeeded in pointirg out those particular plankton categories or groups of categories which are associated with ecologic or biological differences observed in the zooplanktonic system. 1 Biomass A univariate ANOVA was performed on the biomass data using the same statistical model as for the standing crop yield. The results may be seen in Appendix A. Briefly summarized, all the terms of the model were significant with the exception of the three way interaction and the size class-station interaction. These results are important since it means that not only are there station, season, and size class differences in biomass but that station biomass yield is affected differentially by the seasons as are the size class biomasses. The following table shows the biomass means in mg/m3 for each station and size class on a seasonal basis. Spring Summer Fall Winter Station 1 60.03 50.48 40.83 47.23 . Station 2 52.05 32.72 62.45 20.44
' Station 3 64.61 58.30 63.47 30.13 Station 4 72.65 102.88 -----
Station 5 58.37 33.61 44.44 17.95 2000u 2.64 3.47 3.56 1.96 850u 6.67 8.12 12.63 2.55 600u 5.80 5.25 8.49 5.12 250u 32.36 23.24 17.58 14.59 125u 13.14 12.65 10.10 4.42 IV-57
This table will produce several types of curves: a spring peaking curve for Station 1, a summer peaking curve for Station 4 and a spring-fall bimodal curve for Stations 2, 3, and 5. Station 5 seems to have a biomass yield that is lower than that of the region from which it is presumably being supplied. This may be due to increased predation pressure within the canal itself. This is important when considering entrainment. If the lower biomass standing crop is the result of increased predation it might be important to de-termiae where this predation is coming from. If the predation is primarily of planktonic origin and the energy obtained is not passed out of the plankton system, then possibly the best estimates for effective zooplankton entrainment would be from standing crops at stations placed within the source waters. This would be necessary since energy not passed out of the planktonic system but retained in the form of plankton predators is most likely run through the plant and the greater part of it turned into detritus and deposited in the vicinity of the thermal pl ume. Thus the biomass consumed by the plant is equivalent to that entrained from the source waters. On the other hand, if the energy represented by the decreased biomass standing crop at Station 5 is to some extent passed out of the planktonic system to non- or less-entrain-able sources, then this portion of the original zooplankton entrained is not strictly consumed by the plant. This energy source could be passed out of the plankton system in at least two ways: (1) The zooplankton could be consumed or be part of a food chain that is ultimately consumed by non-entrainable fish. (2) The zooplankton could be consumed by members of the benthic community, either as detritus, or by direct filtering of the water. The most probable candidates from the benthic comunity would be the infaunal components. Clay Adams (personal communication) has reported as present in the intake canal several types of planktivorous fish that attain a :,1ze great enough to avoid being entrained (i.e., mullet, silversides, anchovies, etc.). However, no quantitative estimates exist at this time of either the standing crop of these fishes or of their feeding abilities. Our data seems to suggest a large portion of this energy is being retained (and/or dissipated by heat and work) by the plankton community itself. The high levels of chaetognaths at Station 5 are evidence of this. This area of research obviously needs to be continued in more depth, especially if one wishes to obtain a realistic estimate of the plant's functions as a planktivore at Crystal River. Until the problem of what standing crop estimate to use in de-termining plant entrainment figures, we will take a conservative approach and give entrainment for only that water which definitely passes through the plant. Table 14 shows entrainment for Units 1 and 2 and that projected for units 1, 2, and 3. In summary, at present operation (units 1 & 2), an average of 283 lb./ day of dry weight zooplankton are being entrained; at projected future levels (addition of unit 3) 592 lbs./ day will be consumed. These figures are based on the yearly / day average. If we assume that zooplankton is 80% water, the dry weight figures can then be converted to net weight figures of 1,415 lb./ day for units 1 & 2 operation and 2,958 lb./ day for projected operation of units 1, 2, & 3. Finally, Mike R,eeve of
.~- ~ -
IV-58
Table 14. Biomass Entrainment Levels as Predicted from Station 5. (/ day) Units 1 & 2 Units 1,& & 3 Date LBS KG LBS KG 07/2t/72 226.04 102.53 452.08 205.07 08/18/72 79.66 36.14 159.33 72.27 09/02/,'2 190.19 86.27 380.38 172 54 09/13/72 401.94 182.32 803.88 364.64 10/06/72 628.92 285.28 1257.84 570.56 10/20/72 141.55 64.21 283.10 128.42 11/03/72 153.78 69.75 307.55 139.51 11/17/72 534.18 242.30 1068.36 484.61 12/01/72 105.84 48.01 211.68 96.02 12/19/72 39.04 1 7.71 78.08 35.42 01/12/73 96.90 43.95 193.80 87.91 01/26/73 59.67 27.07 119.34 54.13 02/09/73 226.64 102.81 453.29 205.61 02/23/73 300.38 136.25 600.75 272.50 03/02/73 163.60 74.21 327.21 148.42 03/15/73 609.78 276.60 1219.57 553.20 04/06/73 369.95 167.81 739.89 335.62 04/20/73 365.98 166.01 731.96 332.02 05/04/73 516.42 234.25 1032.84 468.50 05/17/73 667.32 302.70 1334.64 605.39 06/01/73 107.11 48.58 214.22 97.17 06/25/73 368.47 167.14 736.94 334.27 07/06/73 426.00 193.23 851.99 386.46 07/16/73 343.46 155.79 686.93 311.59 s IV-59 N
the University of Miami has pointed out that we are probably sampling only 1/5 of the biomass actually present due to the large mesh size of our present nets. If we apply this correction factor to our entrainment data, the consumption for units 1 & 2 is 7,705 lb./ day and 14,785 lb./ day with the addition of unit 3. Fish Eags and Larvae Collecting Methods Another important aspect of our study concerned estimation of the standing crop of ichthyoplankton. These are relatively rare in the water column, but are importagt from an economic point of view. The average number of fish eggs /M3 and larvae /M3 can be seen in Table 15. We tested both our collection techniques and enurr,eration methods in an effort to find methods that were both accurate and economical. Fish Eggs and Fish Larvae The paired T-tests were run on two sets of data: (1) estimates of number /M3 derived from whole counts of the reserve sample vs estimated derived from the aliquot method. Fish Eggs Whole Count Method Aliquot Method 7 3.84/M 3 4.18/M 3 s 4.99 9.2 5 .34 tg with 45 d.f. .24 nonsignificant The whole count method appears to have a smaller variance. Fish Larvae Whole Count Method Aliquot Method i 2.12 2.35 s 2.32 4.17 3 .23 .
- l tg with 47 d.f. .382 nonsignificant I Again the whole count method has smaller variance. Although the variance is greater with the aliquot method, the means are not significantly different. We have more data points from the aliquot method ano thus it was used in descriptive statistics and analysis.
m,.. , ....(2).A, test.was_run.tocompare,thefishing.abi.lities.of-the1/2meternet nes IV-60
Table 15. Average Number of Fish Larvae and Fish Eggs per Cubic Meter. r 3 3 Season Station Fish Larvae /m Fish Eggs /m Fall 1972 1 0 0 2 4.68 1.67 3 0 1.74 4 - - 5 2.9 0 Winter 1972 - 1973 1 .008 0 2 0 0 3 0 0 4 .108 0 5 0 0 Spring 1973 1 .44 .27 2 6.94 23.1 3 2.03 3.38 4 .73 1.89 5 2.92 8.99 Summer 1973 1 .09 0 , 2 .12 .72 3 1.48 .54 4 4.31 .48 5 2.86 7.09 l IV-61 W
and one meter net. A whole count method was used on both net samples. Fish Eggs 1/2 Meter Net Meter Net i 7.24 3.12 s 7.33 3.13 5 4.12 tg with 9 d.f. .292 nonsignificant The meter net technique appears to have a smaller variance. Fish Larvae 1/2 Meter Net Meter Net i 1.90 .773 s 1.53 9.4 5 1.12 tg with 9 d.f. .315 nonsignificant Again the meter net gave a lower variance. The average biomass of eggs and larvae along with appropriate standard deviations were calculated: fish eggs i= .02 milligrams / egg s = ! .04 milligrams / egg fish larvae 7= .08 milligrams / larvae s = ! .06 milligrams / larvae The results of the collection technique tests pose a problem to unravel. For fish eggs with presumably neutral bouyancy and no avoidance ability, the two nets should be expected to yield the same results. But why would a larger net give essentially the same standing crop estimates for fish larvae as a smaller net? Much study has gone into the analysis of plankton net design (see Clutter et al.) In general, the results show the larger the net, the faster the towing speed and the less advance warning given, the better able the net will be to collect those zooplankters capable of avoidance. However .in an estuarine systems, small boats with limited power must be used. Thus the larger the net, the slower the towing speed; also bridles are used and again the larger the net, the greater the advanced warning. In our case, the advantage of a larger net opening might have
. .; ,. . . . . .a .
v . IV-62
.1) been nullified by the disadvant' ages of slower towing speed and increa' sed advanced warning,' thus making the meter _ net yield an ichthyoplankton standing crop estimate.that is no more precise than that of the 1/2 ~ meter net. 1
. IV-63 f
1 aw,
Ob.iecti*r #4 Determination of sample Size Required for Testing Hypotheses at Specific Probability Levels In order to determine the sample size required for testing hypotheses at specific probability levels an estimate of the variance of the data is needed. Since a prediction is needed for all stations for all seasons, for all categories, the problem is necessarily complex. Standing crop data obtained in this project were used to estimate the variances of zoo-plankton numbers. Since each category would have different estimates of required sample size, we used the variances associated with calanoid copepods. This category was selected because of its overwhelming importt..ce to zooplankton community with regards to numbers and biomass. We used determine the iterativesample the appropriate formulasize. found in Sokal and Rohlf (1969) to The formula is: n> 2(c/6 )2 { ta,v + t. }2 gp _ where: n = number of replications o = true standard deviation (estimated by 5) 6 = the smallest true difference that is desired to detect v = degrees of freedom of the sample deviation a = significance level for Type I error (1-P)= significance level for Type II error t y
= values from a two tailed t table with v degrees of freedom We chose o = .01 and .05 and P = .50. The error degrees of freedom were 420 ~= so 2 . ables were used in place of t statistics. The coefficient of variation for one calanoid data was 28% and we selected 25% as being an acceptable level of difference for detection. This resulted in n~17 for testing at the .01 level and n=9 for testing at the .05 level. Therefore 9 or 17 desired period samplestoneeded test. to be collected for each station during each time The time scale of a season was selected as being l
i appropriate (approximately equivalent to the quarter). As shown previously, { our data suggest that the seasons do show fluctuations that are interpretable. I Also, many natural phenomena are cycling on a seasonal basis and it is likely that this time scal.e is a more natural one than that imposed by our Julian calendar. i , i
, ,, . . . . . . . ~ . - ' -
IV-64
' Object'ive Summary Objective #1
- 1) Time constraints and state-of-the-art limitations prevented species level identifications.of major food chain and commercially important organisms.
Objective #2 i 1) A general classification system was used to qualitatively assess the planktonic organisms collected within the intake canal area.
- 2) Quantitative estimations of the planktonic organisms in the intake canal included:
- 1) total numbers of zooplankters/m 3
- 2) total numbers of different kinds of zooplankters/m3
- 3) numbers of a specific size range /m3
- 4) total zooplankton biomass /m3
- 5) biomass of each animal category /m 3 These quantitative data are available from the authors.
Objective #3 .
- 1) Zooplankton categories with the strongest positive relationship with salinity included: bivalve larvae, other crab larvae, harpacticoid copepods, and other shrimp.
- 2) Categories showing positive correlation with temperature were:
blue crab-like larvae, stone crab-like larvae, other crab larvae, gastro-pod larvae, penaeid shrimp larvae, and other shrimp larvae.
- 3) Generally, there was an increase in numbers /m3 as temperature increased; this appears to be linked with seasonal changes.
- 4) Stations compared according to faunal assemblages showed an offshore (Stations 3 and 4), an intermediate (Station 2), and an inshore (Stations 1 and 5) grouping.
- 5) Station 3 appears to have the highest standing crop, Stations 1 and 4 have intermediate levels, and Stations 2 and 5 the lowest.
- 6) Station 5 appears to have the highest standing crop of plankton predators.
- 7) Penaeid shrimp larvae (fall peak), other shrimp larvae (spring peak), and chaetognaths. (sumer peak) showed the strongest seasonal pulses.
IV-65
- 8) Winter standing crop was the lowest of all seasons; levels were 42 - 68% of other seasons.
- 9) Size classes serve to some extent as trophic level indicators; the smaller sizes being equivalent to primary consumers with high stand-ing crop levels, the intermediate sizes representing first level predators, and the largest size class (and least numerous) containing the highest level plankton predators.
- 10) Zooplankton at Crystal River appear to use both temporal and size class resource partitioning.
- 11) Analysis of station by season interactions revealed a few basic patterns:
a) Station 1 showed a strong inshore pattern characterized by low diversity, high standing crop in spring and winter, low levels of meroplankters, and low standing crops of secondary and tertiary plankton trophic level consumers, b) Stations 2,3, and 4 were similar across the year. c) Station 5 was unique; similar to Stations 2 and 3 in the spring and similar to Station 1 in the fall and winter.
- 12) The smaller size classes showed more seasonal changes in their composition than did the larger size classes.
- 13) In general, the Crystal River area can be viewed as having four distinct regions: a) a shallow inshore region (Station 1), b) a region characterized by Station 2, c) an offshore area comprising the hydrographic areas 2 and 3 (Stations 3 and 4), and d) the intake area represented by Station 5.
- 14) Present power plant operation (Units 1 and 2) entrains an estimated average of 283 lbs/ day of dry weight zooplankton (202p and above).
- 15) Projected power plant operation (Units 1,2, and 3) would entrain an estimated average of 592 lbs/ day of dry weight zooplankton (202p andabove).
Objective #4
- 1) Sample size as determined by Sokal and Rohlf's (1969) formula was:
- 1) n = 17 for testing at the .01 level.
- 2) n = 9 for testing at the .05 level.
- 2) The time scale of a season was selected as the most appropriate time period desired to test.
. . , , .. , , , .~ .. . - . .
IV-66 l N
- - ~
REFERENCES Adams, C.A. 1972. Food habits of juvenile pinfish (Lagodon rhomboides), silver perch (Bairdiella chrysura), and spotted seatrout (Cynoscion nebulosus) of the estuarine zone near Crystal River, Florida. M.S. Thesis. University of Florida. 147 pp. Baggaley, A. R. and J.P. Campbell. 1967. Multiple-discriminant analysis of academic curricula by interest and aptitude variables. Jour. of Ed. Meas. 4:143-149. Carder, K.L. and R.H. Klausewitz. 1973. Florida Power Corporation's Crystal River environmental progress report to the Federal Inter-agency Research Advisory Committee. Technical Report No. 1 on independent environmental study of thermal effects of power plant discharge. 23 pp. Carder, K.L. , R.H. Klausewitz, and B. A. Rogers. 1973. Florida Power Corporation's Crystal River environmental progress report to the Federal Interagency Research Advisory Committee. Technical Report No. 2 on independent environmental study of thermal effects of power plant discharge. 31 pp. Cooley, W.W. and P.R. Lohnes. 1971. Multivariate data analysis. Wiley, New York. 364 pp. Cuttler, R. I. and M. Anraku. 1968. Avoidance of samplers. In D. J. Tranter (editor), Part 1. Reviews on zooplankton sampling methods. UNESCO, Monogr. Oceanogr. Methodo1. 2:57-76. Heinle, D.R. 1966. Production of a calanoid copepod Acartia tonsa, in the Patuxent River estuary. Chesapeake Science 7(2):59-74. Hopkins, T.L. 1966. Plankton of the St. Andrew Bay system of Florida. Pulb. Inst. Mar. Sci . Univ. Texas, 11:12-64. Hotelling, H. 1936. Relations between two sets of variates. Biometrika, 28:321-377. Morrison, D.F. 1967. Multivariate statistical methods. McGraw Hill, New York. 338 pp. Nie, N.A., D.H. Bent, and H. Hull. 1970. SPSS: statistical package for the social sciences. McGraw Hill, New York. 343 pp. Pielou , E.C. 1969. An introduction to mathematical ecology. Wiley, New York. 286 pp. l l l' l IV-67 m
REFERENCES (Continued) Rao, C.R. 1952. Advanced statistical methods in biometric research. Wiley, New York. 390 pp. Reeve, M.R. 1966. Observations on the biology of a chaetognath. Some Comp. Studies in Mac. Sc. 613-630. Reeve, M.R. 1970. Seasonal changes in the zooplankton of south Biscayne Bay and some problems of assessing the effects on the zooplankton of natural and artifical thermal and othar fluctuations. Bull. of Mar. Sci. 20(4):894-921. Searle, S.R. 1971. Linear models. Wiley, New York. 532 pp. Service, J. 1972. A usert guide to the statistical analysis system. Student Supply Stores. N.C.S. Uni., Ra'eigh. 260 pp. Sokal, R.R. and F.J. Rohlf. 1969. Biometry. Freeman, San Francisco. 776 pp. Steele, J.H. 1974. The structure of marine ecosystems. Harvard University Press, Cambridge, Mass. 128 pp. Wilks, S.S. 1932. Certain generalizations in the analysis of variance. Biometrika, 24:471-474. g g & . g a # ' I IV-68
EFFECTS OF POWER PLANT ENTRAINMENT ON MAJOR SPECIES OF COPEPODS 1 FINAL REPORT October 15, 1974 University of Florida Marine Laboratory i l I i Frank J. Maturo, . Jr. j Principal Investigator j Ray Alden ! Marine Biologist j W1: 11un Ingram III Biological Systems Analyst IV-69 w - - -- ,, ,,
, , , -~,,-~,::,,
~
i e l l ABSTRACT The effects of power plant entrainment on the major species of copepods of the Crystal River area were studied from November 1973 to September 1974. Seven species were observed for entrainment effects, with Acartia tonsa and Oithona brevicornis comprising by far the major component of the zooplankton population of the area. Aspects of entrainment studied were lethal effects, delayed lethal effects, the percent mortality caused by the thermal discharge upon unentrained copepods, the effects on reproduction of copepods surviving atrainment, the effects on growth of entrained juveniles, and the long-; arm effects occurring over the period of time following entrainment. Generally, it was found that all effects were small throughout most of the year until the temperature of the discharge canal waters reached above 35 C. Above this temperature, mortality of copepods in the heated effluent rose in an almost exponential fashion, fecundity rates declined, and growth rates of juveniles dropped below those of control populations.
. ., , . . . 4 > '
IV-70 w
GENERAL OBJECTIVES This section of the zooplankton study looks at the effects of power plant entrainment on the various species of copepods of the Crystal River area. We have examined and evaluated:
- 1) the percent mortality caused by entrainment;
- 2) the delayed lethal effects due to the period of time spent in the heated effluent of the discharge canal following passage through the plant;
- 3) the differential survival of important species and of the age groups and sexes of these species;
- 4) the physical, biological and plant induced factors involved in entrainment mortality;
- 5) the effects of entrainment on reproduction;
- 6) the effects of entrainment on growth of juveniles;
- 7) the effects of entrainment on long-term survival.
Objectives 1-4, Lethal Effects, Delayed Lethal Effects and Differential Survival INTRODUCTION Mortality caused by the power plant is, of course, the most drastic of the entrainment effects. The first four objectives direct themselves at examining and evaluating the amount of mortality, the factors associated with the lethal effects, and the various responses of different sexes and species of copepods to entrainment conditions. METHODS AND MATERIALS Biweekly Field program Intake and discharge populations of copepods were sampled on a biweekly , basis with a 64p mesh plankton net fitted with a digital flow meter. The volume of water sampled, temperature, and salinity (taken with a Beckman I Salinometer) were recorded. The two population samples were each split into i four aliquots with a Folsom Plankton Splitter. These subsamples were sub-jected to various treatments and then tested for mortality by a vital stain bioassay described by Dressel et al (1970). The treatments were chosen to isolate the variables that affect entrainment mortality and to allow multiple regression analysis to test the effect of each. IV-71
- - - - ,,.7
The first intake subsample (IN0) was stained and preserved imediately after collection and was used as a control to show baseline mortality (either natural or due to net damage). The second intake subsample (IN2) was placed in a drift bottle appar-atus similar to that described by Prager (1971) and Gonzalez (1973) and was allowed to drift in intake waters for two hours before being stained and preserved. This population represented a controi for the delayed mortality experiments. The third intake subsample (ID0) was subjected to the thermal shock of the discharge temperatures by placing it in a flow-through bottle and submerging it in the discharge waters until it reached the temperature of the heated effluent. The sample was then stained and preserved. The final intake subsample (ID2) was placed in a drift bottle apparatus and allowed to float through the thermal gradient experienced in drifting i down the discharge canal. After two hours, the sample was stained and ! preserved. This population might reprr.sent a population that has been tidally recruited into the heated effluert without actually going through the power plant. The first discharge sample (DS0) was s'.ained and preserved immediately upon collection to represent imediate plar.t induced mortality. The second discharge sample (DS2) was placed in another container in the drift bottle apparatus and floated dowr. the discharge canal. After two hours, the sample was stained and preserved. This sample population showed any increased mortality caused by the time spent in the heated effluent following entrainment. The remaining discharge subsamples were retained as reserves, since it facilitated computer calculation of volume sampled from digital flow- i meter readingsif both intake and discharge samples were split into four subsamples, even though only two discharge samples are treated. I All samples were labeled and kept cold until counted. The samples were I sieve fractionated into three size classes (above 300p, 150 to 300u and 75 to 150u). This method allowed each size class to be separately split down for counting with a Folsom Plankton Splitter so that the large, rarer animals were not " lost" in the splitting of a sample to countable size. The stain in the organisms was " fixed" by titration with an acetic acid-sodium acetate 1 mixture (5N) and the samples counted under a dissecting microscope at 25X or 50X, depending on size class. The copepods e ce identified to species and sex and notations made as to whether they were alive or dead upon sampling. Nauplii counts were made for DSO samples to provide data for the produc-tion section of the zooplankton survey. The cepepod nauplii were counted and identified to species and provided the information on the population structure for size classes above 75p. Instars below 75u were counted in the growth study. 0 IV-72 l
Computer Analysis Data from the species counted in each sample were taken from raw data sheets and compiled into the tables seen in Appendix A. The numbers per cubic meter (NUMCU), proportion of the total population represented and the mortality (MORT) were calculated for each sex-age class of each species counted at each treatment station. The " net mortality" (NETMT), or the plant-induced mortality above control values, was calculated for all classes in non-control treatments. Additionally, sex ratios, age class ratios, sex ratio mortality indices (SRMI= male MORT / female MORT), and age class ratio indices (ACMI= juvenile MORT / male MORT) were calculated for each species. Multiple regression analysis of various natural and plant-induced variables for the dependent variable NETMT showed that a large number of correlated variables significantly (.01 level) added to the model of any given species. In an attempt to break down these variables into major effects, factor analysis was performed, but so many of the variables were so highly correlated that the computer could not invert the matrix or derive communalities necessary for regression analysis. A second method of analysis was adopted and all independent variables shown to be significant in separate regression analyses, as well as any variable or interaction that seemed likely, were tested by the Stepwise MAXR Regression program from the Statistical Analysis System (SAS). This program tests progressively complex models, to which one variable at a time has been added. The program then prints out the model with the best R2value for any given number of variables. Thus, models with different numbers of variables can be evaluated together and the "best" model with the least number of variables chosen. Generally, the model chosen was the one for which the R 2 value ceased to improve greatly in value with the addition of more variables. Of course, the R2 of the model containing all of the variables and all intermediate models was checked to be sure that no large jumps occurred with the addition of various blocks of interacting variables which may not have individually produced a large R 2improvement. The predictive models formed by this process are the ones that best fit the data available, but can't be assumed to fit conditions other than ones within the ranges sampled. Neither can the models be considered in the same class of cause and effect models as those formed by systems modelir.g. None-theless, these models are formed by a statistically valid procedure which best describes the trends shawn in the , data and may be inspected to discover causality. The regression equations of the models chosen for each species were used to obtain predicted NETMT values for various values of independent variables (see Appendix B). These predicted values were then plotted on contour graphs (see Appendix D) for evaluation and demonstrative purposes. Temperature and salinity being among the major physical factors, were plotted on the X and Y axes, while the NETMT values were plotted on the Z axis in the 3rd dimension (light areas are lowest, shaded highest). All other factors .re then varied one at a time to observe their -ffects on the UETMT IV-73 l 1 l
predicted by the equation for the salinity and temperature conditions. The independent variables that were used in the analysis were of several basic classifications: physical parameters, biological parameters, and plant-induced parameters. Each of these factors showed complicated interactions in their effects on the dependent variable NETMT. The physical parameters were the variables that involve temperature and salinity effects: temperature of the discharge canal (TMP); temperature of the discharge canal squared (TMPSQ); temperature of the discharge canal cubed (TMPCU); temperature of the discharge canal to the fourth power (TMP4); temperature of the intake canal (TMPT); temperature of the intake canal squared (TEMSQ); temperature of the intake. canal cubed (TEMCU); temperature of the intake canal to the fourth power (TEM 4); salinity of the discharge canal (SAL); salinity of the discharge canal squared (SALSQ); salinity of the discharge canal cubed (SALCU); salinity of the discharge canal to the fourth power (SA4); salinity of the intake canal (SLT); salinity of the intake canal squared (SLTSQ); salinity of the intake canal cubed (SLTCU); salinity of the intake canal to the fourth power (SL4); the discharge salinity-temperature interaction (TMPxSAL); and the intake salinity-temperature interaction (TEMxSLT). Biological parameters include variables that involve such factors as the sex of each species examined, numbers per cubic meter, and some aspects of the season other than temperature or salinity (possibly such things as phytoplankton blooms or breeding seasons). These were: the males of a species (SX1); the females of the species (SX2); numbers per cubic meters (NUMCU); numbers per cubic meter squared (NCM2); numbers per cubic meter cubed (NCM3); the suniner season, of June-September (SEAS 1); the spring season of April and May (SEAS 2); and " winter" season of November-March (default of SEAS 1 and SEAS 2). Also included were interactions of these variables with the physical factors and with each other: the interaction of males and females with temperature (SX1xTMP, SX2xTMP); the interaction of males and females with temperature squared (SX1xTP2, SX2xTP2); the interaction of males and females with temperature cubed (SX1xTP3, SX2xTP3); the interaction of males and females with factors in summer (SX1S1, SX2S1); ! the interaction of males and females with factors in spring (SX1S2, SX2S2); the interaction between numbers per cubic meter and temperature (TPxNUM); and the interaction of numbers per cubic meter and salinity (SLxNUM). The plant-;nduced factors were defined by the various non-control treatments set up in the sampling program: the 100 population experienced only the immediate thermal shock (THERM) of being raised to discharge temperature *; the ID2 population experienced not only the thermal shock, but also the stress of remaining in the heat effluent (CANAL), such as might l I
- This situation seems an unlikely case for a population urdess it enters and then quickly leaves a thermal area, but the treatment is necessary as the default for testing mechanical and canal effects.
IV-74
be experienced by a population introduced by the tide into the thermal
- plume; the 050 population experienced the thermal shock upon discharge plus a mechanical-thermal stress within the plant (MECH); the DS2 population represented the comon case, where the population experienced the thermal, mechanical, and canal effects (THERM, MECH, and CANAL). These variables were given a "0" value for treatments where they were absent and a "1" value for treatments where they were present. There were also interactions of these effects with the physical and biological factors:
the canal-temperature interaction (CANALTP); the canal-temperature cubed interaction (CANALTP3); the mechanical-temperature interaction (MECHTP); the mechanical-temperature cubed interaction (MECHTP3); the canal-salinity interaction (CANALSL); the mechanical-salinity interaction (MECHSL); the interaction of the sexes with the canal effect (SX1xCAN, SX2xCAN); the interaction of the sexes with the mechanical-thermal effect (SX1xMEC), SX2xMEC); and the mechanical-canal interaction (MECHCL). RESULTS Ten classes were originally set up for counts and analysis of mortality: Acartia tonsa, Paracalanus crassirostris, Oithona sp., Euteroina acutifrons, Pseudodiaotomus coronatus, Temora turbinata, Labidocera sp., Tortanus setacaudatus, Paracalanus Quasimodo, and a totals category. Two of these classes, Temora turbinata ~and Paracalanus quasimodo, had such low year-round numbers (see Appendix A) that they were dropped from further analysis. The resu!is of the remaining eight categories will be discussed by species. Acartia tonsa i Acartia tonsa is probably the major year-round component of secondary production in the zooplankton community of the Crystal River area. Acartia numbers ranged from 376/m3 to 23,705/m3 with a mean value of 6,292/m3 (see ; Graph 1 , Appendix B). The numbers of this species are exceeded only by : those of the much smaller Oithona sp., which, because of its size, probably does not contribute nearly as much to the biomass production of the area. This species comprises 10-60% of the total copepod population with values of 30-40% for most of the year (see Graphs 4-6, Appendix B). The peaks ! occur in December, February, April, June-July and September. Generally, numbers are higher during the sumer than during the rest of the year. l Mortality of Acartia tonsa sampled in the intake canal ranged from 8.9% to 'a rather unusual high of 33.2% with a-mean value of 15.8% (see ; Graph 1 , Appendix C). It cannot be determined exactly how much of this ' mortality can be contributed to natural mortality and how much is due to net damage from collection techniques. It can be noted, however, that stained , (presumedly alive during vital stain bioassay) ciliates
- were observed from time to time in the body cavities of " dead" (unstained) copepods, so that at )
- least some of the organisms were probably dead before capture. Whether due
- These ciliates were noted in the algal detritus and dead bodies seen in i labora'ory experiuents and are assumed to be feeding on the bacterh in !
the decomposing materials. l l IV-75 l
4 4 to natural mortality or collection techniques, these figures provide a base-line above which plant induced " net" mortality is calculated. Acartia tonsa that were exposed to discharge temperature and sampled imediately (treatment ID0) generally had fairly low NETMT values, ranging 0-32%, with a mean value of 6.9% (see Graph 2 , Appendix C). The trend shows low NETMT in the winter and higher NETMT in the wamer months. Populations exposed to themal shock and then allowed to remain in the heated discharte water (treatment ID2) exhibited a greater NETMT than those experiencing only the thermal shock. The NETMT values for this treatment ranged 0-80%, with a mean NETMT of 15.6% (see Graph 3 , Appendix C). The trend in this treatment seems to show fairly low NETMT (around 10%) for most of the winter and spring months,with a rise through the latter part of spring and a shapr increase in mid-sumer. The Acartia tonsa population that has been sampled immediately upon leaving the plant exhibited slightly higher NETMT values than those exposed only to ther thermal shock of the discharge temperature. The range of NETMT values was fram 0 to 40% with a mean NETMT of 12.4% (see Graph 4, Appendix C). The trend of th9 NETMT values from this treatment seem to be moderately low (0-10%) throughout the winter and spring, with peaks of NETMT in April, June and July, and moderata 10-20%) NETMT through the rest of the summer months. The Acartia tonsa population that is entrained through the power plant and then allowed to drift down the discharge canal exhibits the largest NETMT values, ran
- Appendix C)ging from 0 to 75% and having a mean of 16.7% (see Graph 5, The trend of NETMT values of this treatment show modearate mortalities (10-15%) throughout the year until a sharp rise occurs during the summer months. To show how this trend is related to the higher temperature, a NETMT vs TMP graph was plotted (see Graph 7, Appendix C). NETMT values in this graph show an almost identical curve, with low mortalities at low temperatures and increasing rises with temperature increase above the 35"C level. The differential survival t'etween males. females and juveniles is
' shown in Graph 6 (Appendix C). This graph indicates that, in general, males have a higher NETMT than females and:that both hai e higher NETMT values than juveniles. The repression model closen by the stepvise process for Acartia tonsa shows 17 independent variables that are correlated to the dependent variable NETMT. Although the variables tested are so hiohly corrected with each other that it is, in general, difficult to interpret the significance of any single variable in a model formed in this manner, the majority of variables in this particular model each show fairly high F values and the e.M e regression model is significant at the .0001 level (F = 30.87,17df; R2 .74). The tem-perature related variables are TMP, TMPSQ, TMPCU and TMP4. The salinity related compoents are SLT, SALT, SALSQ, SALCU and SA4. A single season related variable, SEAS 2, is included in the model. Sex variables include SX1xMEC, SX2xTP2 and SX1xTP3. Variables related to CANAL effects were CANAL, CANALTP and CANALTP3. MECH was the single mechanical-therma related variables. The predictive euqation given the the regression is: NETMT = 236.12798241 + (-3.18862679 x TMP) + (.19737132 x TMPSQ) = (.00528293 x TMPCU) + (.00005167 x TMP4) = (.03371347 x SLT) + (-35.35017472 x SALT) + (2.13183226 x SALSQ) + . .
'(.05670728 x SALCU) + (2000561- x x SA4) + (.10168043 'x' SEAS 2) +-
(.07770935 x SX1xMEC) + (.00007589 x SX2xTP2) + IV-76
--leen i
m- . _ ( .00000356 x SX1xTP3) + (1.34134352 x CANAL) + ( .07833226 x CANALTP) + (.00003678 x CANALTP3) = (.03120095 x MECH). Variables in this equation that are connected with emperature or sali-nity used observed or hypothesized values of these measurements for the con-ditions looks at, while season, sex, or plant-induced variables are either ).'~ a '"0" or "1" value, depending on presence or absence of the effect under the observed conditions. This equation was used to predict the values for hypothetical condities plotted by the contour graphs (Appendix D) discussed in discussion section. Paracalanus crassirostris Paracalanus crassirostris is a small calanoid copepod that is the third most abundant copepod in the Crystal River area. The numbers of this species ranged from 66/m3 to 18.260/m3 , with a mean of 3,782/m3 . It has
- been noted, however, in growth study counts that some copepodids of this species may go through a 73u net, and thus some percentage may have been
'ist during sieve fractionation. The percent of the total copepods popula-tion that this species represents ranges from 2-48% and generally acounts for 10-20% of the population. NUMCU and PROPTL values for this species
- both tend to increase in warmer months. Generation peaks for this species are seen in March, May, June and September (see Graph 7 , Appendix 8).
' Mortality of Paracalanus crassirostris taken from the intake area ranged from 3.8-34.5%, with a mean MORT value of 15.4% (see Graph 8, Appendix C).
~ i The populations of Paracalanus crassirostris exposed only to the shock ! of discharge temperatures -(I00) had values of NETMT from 0-78%, with 18.2% mean NETMT (see Iraph9, Appendix C). The NETMT was low (0-2%) from January until May, when it rose to the 16-20% range. From June through August the NETMT rose quite rapidly to nearly 80% and then dropped back in September to the 20% level. :
-Paracalanus crassirostris that were left in the heated effluent for saveral hours showed higher NETMT values, with a range of 0-87% and a mean of 22.7% (see Graph 10, Appendix C). The same general trend was evident, 1 with low (0-4%) NETMT values occurring through the winter and spring season, followed by a rise in May and a very rapid rise by mid-sumer.
Imediate testing of Paracalanus crarsirostris from the discharge area (DS0) showed a year-round mean NETMT values of 24.7% and a range from 0.1% to 81.7% (see Graph 11, Appendix C). The NETMT values fluctuated within the 15-20% range for most of the year, then exhibited a more or less steady rise after a peak 'in April, and finally a steep rise'in mid-sumer. 4 The Discharge populations of Paracalanus crassirostris subjected to 2 hours in the heated waters of the canal following entrainment had a mean c NETMT of 30.8% (0-86% range)(see Graph 12, Appendix C). NETMT values fluctuate from 0-10% until-March, and then show a steady rise until June when a large jump occurs and NETMT values stay in the 70-85% level for the rest of the summer. This trend also shows up in the NETMT vs TMP graph (Graph 14, - steeper Appendix C), with a gradual increase up to 31*C and then The'NETMT vs DATEprogressively graph (Graph
- rises occurring)with increasing temperature.
13 , Appendix C which identified sex shows the same trend and suggests that differences in sensitivities of the sexes are not clear-cut. IV-77 f w ,--g - - - w---cw + . . rm- .r-+e,,,e- --4e-.-- 1-y~ , r--. ,e+=- , e -- , ,
The regression model for Paracalanus crassirostris contains 11 inde dent variables and is significant at the .000l level (F = 38.97,11df; R2 = pen-.70). Temperature components are TMP4 and TEMxSLT. SEAS 2 was the single seasonal variable. Sex variables include SX1xCAN and SX1xTP3. CANAL related variables are CANAL, CANALTP, CANALTP3 and CANALSL. The mechanical-thermal variable was MECHTP. TPxNUM, a density related component, was also included. The full predictive quation is: NETMT = .16661710 + (.00000033 x TMP4) + ( .00046511 x TEMxSLT) + ( .11571277 x SEAS 2) + (.139333712 x SX1xCAN) + ( .00000191 x SX1xTP3 + (1.84786042 x CANAL) + ( .09237988 x CANALTP = (.00004625 x CANALTP3) + ( .01347719 x CANALSL + (.00336525 x MECHTP) + ( .00000061 x TPxNUM).
-Oithona sp.
Oithona sp. is a cateogry made up of at least three species of the genus Oithona: Oithona brevicornis, Otihona nana and Oithona simplex. Oithona brevicornis is, however, by far the most dominant of these species and can be assumed to make up a majority of the category. Species identification was not attempted during counts since juveniles (and males) of all species look so much alike with the magnifications used forcounting. In fact, identification of the males can only be done by dissection of the mandible and observation under a compound microscope. Such operations were not feasible in making counts of fairly large numbers. The Oithona sp. are the numerically dominant species of the Crystal River area. Numbers range from 738/m3 to 66,669/m3 with a mean of 13,055/m3 Some early copepodids of Oithona may be lost in the sieving process, so these numbers may be somewhat conservative. Generation peaks occur in December, March, June and August-September. Oithona represents from 20% to near 80% of the total population (see Graph 16, Appendix B). Both NUMCU and PROPTL seem to increase as the months grow warmer. Intake mortalities of Oithona sp. which have undergone the thermal shock of the IDO treatment go from 0% to 52.1% and have a year-round mean of 6.55% (see Graph 16, Appendix C). NETMT values, however, stay low (below 10%) throughout the year until a single peak in August. The Oithona sp. populations that have traveled down the discharge canal f :h ing initial therma shock (ID2) exhibit an average NETMT of 13.34% f7#.4% range)(sees ph 17, Appendix C). The NETMT values remain low 0-7%) until June when there is a small peak. After mid-summer, these values rise rapidy to the highest levels. DSO populations of Oithona sp. showed mean NETMT values of 8.6% with ' l a range from 0 tc 43.4%. The values remained low (0-10%) for most of the year, with peaks in April, July and August (see Graph 18 , Appendix C). The 0S2 NETMT values for Oithona sp. were higher,with a mean of 15.8% and a range from 0 to 79.5% (Graph 19, Appendix C). The NETMT values remain low (below 10%) until April, after which there is a gradual rise through June. In July there is a sharp increase that ends with values near 80% by the end of the summer. The NETMT vs TMP graph (Graph 21, Appendix C) shows the same type of trend in NETMT, with low values until above 31 C and a 4 IV-78
4 sharp rise above 35"C. The NETMT vs TMP graph for the ID2 population shows the same type of curve with even less fluctuations (graph available upon request). The NETMT vs DATE graph which identified sex classes (Graph 20, Appendix C) indicates that the trend is followed by males, females and juveniles, with males appearing consistently to have the higher NETMT values. The predictive regression model for Oithona sp., shows 12 independent variables related to NETMT (.0001 level F = 44.25, 12df; R2 = .74). Tempera-ture related components make up the major portion of the model: TMP,TMPSQ TMPCU, TMP4, TMPT, TEMSQ, TEMCU and TEMxSLT. Even interactions with other variables contain temperature related aspects: the sex related variable SX1xTP3; and the CANAL variable of CANAL, CANALTP and CANALTP3. The equation for the Oithona regression model is: NETMT = 74.6635119 + (-10.941181773 x TMP) + (.63461625 x TMPSQ) + ( .01161024 x TMPCU) + (.00015111 x TMP4) + ( .77506284 x TMPT) + (.03836312 x TEMSQ) + (.00062738 x TEMCU) + ( .00045185 c TEMxSLT) + (.00000251 x SX1xTP3) + (1.01749009 x CANAL) + (-D6076668 x CANALTP) + (.00003052 x CANALTP3). Euterpina acutifrons Euterpina acutifrons is a pelagic harpacticoid that is the fourth most abundant species at Crystal River. Numbers range from 0 to 7,992/m3 with a mean value of 952/m3. There are three obvious peak periods for this species: one in December, a second at the end of February, and the third at the beginning of June (see Graph 19, Appendix B). The general trend in both NUMCU and PROPTL (Graph 22) seems to be toward decreasing numbers with the summer months. Mean intake mortality for this species was 6.7%, ranging from 0 to 17.4% (see Graph 22, Appendix C). The trend was for a range fluctuating around 10% in the winter, with even greater fluctuations **around the same figure in the warmer months. 100 NETMT values were from 0 to 100% with a mean of 27.7% (see Graph 23, Appendix C). Values of NETMT were quite low (9-10%) until May, when a peak of nearly 40% occurred. NETMT figures in July began to rise rapidly to the 100% level found in August. September values could not be calculated as NUMCU for this species approached O. The NETMT of Euterpina acutifrons subjected to prolonged exposure to heat (ID2) also showed a range trom 0-100%, with a mean value of 23.1% (see Graph 24). NETMT values tend to climb gradually throughout the year until June, when they steeply rise toward the 100% level seen by July. August and September values could not be calculated due to dwindling numbers. The NETMT vs TMP graph for this species (graph available upon request) showed 0-20% net mortality up to approximately the 31 C level, after which it tended to climb rather steeply to the 80-100%* level above 35 C.
- NETMT values between 80% and 100% are hard to calculate since baseline mortality must always be subtracted out. 100% NETMT was assumed when MORT = 100% because subtraction of baseline date would observe the issue.
** Possibly due to the less numbers in each count during the later months.
IV-79
s DSO values of NETMT for Euterpina acutifrons were from 0 to 100%, with a mean value of 20.4% for the entire period of study (see Graph 25, Appendix C). NETMT values fluctuated from 0-10% until June, with peaks in December and May of over 20%. Values.-quickly rose in July to the 100% level, af ter which no further calculations could be made due to low numbers. Euterpina acutifrons populations left in the heated waters of the discharge canal following entrainment (DS2) showed a mean mortality of 33.3% and a range from 0-100% (see Graph 26, Appendix C). These values remained consistently low through April, but then steeply climbed to the 100% level found in July. The NETMT vs TMP graph (Graph 28) shows the same trend with low values slightly increasing up to the 30.5'C mark and then steeply rising to the 100% level by 35*C. The NETMT vs DATE graph identifying sex (Graph 27) shows that adults tend to exhibit higher NETMT values and show the climb to the 100% level earlier in the year then the juveniles. The regression equation for Euterpina contains 24 independent variables (significant at .0001 level, F = 9.49, 24df, R2 = .71). Temperature-rclated variables are TMPT and TEMSQ. Variables falling under the salinity category of the physical paracmeters are SALSQ, SALCU, SA4 and SL4. SEAS 2 is the single season variable. Interactions of sex with other types of variables is the largest cateogry of independent variables, containing: SX2xTP2, SX1xTP3, SX1xSL3, SX2xSL3, SX1xS1, SX1xS1, SX1xCAN, SX2xCAN, and SX1xMEC. CANAL-related variables are CANALTP, CANALTP3, and CANALSL. The i variables related to the mechanical-thermal stress of entrainment through the plant are MECHTP, MECHTP3 and MECHSL. Density related variables are NCM2 and TPxNUM. The predictive regression equation for Euterpina is: NETMT = 11.48945841 + ( .08964009 x TMPT) + (.00232903 x TEMSQ) +
.0980677 x SALSQ) + (.00520640 x SALCU) +
.00007574 x SA4) + (-00000262 x SL4) +
.080982278 x SEAS 2) + (.00030554 x SX2xTP2) +
.00000559 x SX1xTP3) + ( .00001827 x SX1xSL3) +
.00001594 x SX2xSL3) + ( .56076227 x SX1xS1) +
.33571782 x SX2xS1) + (.12656763 x SX1xCAN) +
.15279539 x SX1xCAN) + (.15426654 x SX1xMEC) +
.04516955 x CANALTP) + (.00002891 x CANALTP3) +
.02401825 x CANALSL) + ( .04892255 x MECHTP) +
(.00002259 x MECHTP3) + (.03279629 x MECHSL) + (.00000210 x NCM2) + ( .00000572 x TPxNUM). Pseudodiaptomus coronatus Pseudodiaptomus coronatus is a fairly large calanoid copepod that is at time epibenthic. NUMCU values for this species ranged from 0 to 1,453/m3 with a mean value of 209/m . Generation peaks were shown to occur in December, March, June and September. Percent of total (PROPTL) values for this species ranged from .1% to 4.1% (see Graph 25). Both NUMCU and PROPTL values tended to decrease from winter tc summer.
. o, c .
IV-80
4 Intake mortality figures for Pseudodiaptomus coronatus (designated SPEC = S to avoid confusion with the SPEC = P for Paracalanus crassirostris), show a range from 0 to 60%, with a mean of 18.5% (see Graph 29, Appendix C). The period of higher mortality seemed to coincide somewhat with generation peaks, indicatin9 a possible natural die-off due to " bloom" conditions. Intake populations of Pseudodiaptomus coronatus subjected to the thermal shock of discharge temperature (ID0) exhibited NETMT values from 0 to 60% with a mean value of 10.6% (see Graph 30). The NETMT values were low from January through May and peaked in July. 102 values for NETMT ranged from 0 to 100%, with a mean value of 15.3% (see Graph 31). NETMT values were low during the winter and spring months that this species was present, with the exception of a peak at ! the 20% level for January. After mid-summer (July), the NETMT values rose ! rapidly to the 100% level by September. The NETMT vs TMP graph (graph I
~
available upon request) indicates that NETMT values rise rapidly after temperatures reach 35*C. Discharge populations of Pseudodiaptomus coronatus which were tested for immediate mortality showed a range of NETMT from 0-100%, with a mean of 17.9% (see Graph 32). NETMT values fluctuated around 10% for most of l the year with peaks above 20% in November, January and May. The September sampling period, however, showed that the NETMT had risen to 100%. I The discharge population that was left in the heated waters for two hours had NETMT values from 0 to 100% and a mean value of 22.6% (see Graph 33). The NETMT values of this population, when present in the sample, remained below 20% until June. In June, the NETMT rose to the 40% level and was 100% during the September sampling period. The NETMT vs TMP graph (Graph 34) shows that NETMT levels remain below 20% until the temperature reaches 35*C, at which point they go to the 40% level, and finally reach the 100% level at 37*C. The regression model include 15 independent variables and is signi-ficant at the .001 level (F = 18.9,15df; R2 = .87). Temperature variables include TMPT, TEMSQ, TEMCU and TEM 4. Sex-related interactions included in the model are: SX1xTMP, SX2xTP3, SX1xSL3, SX2xSL3, SX2CAN, SX1xMEX and SX2xMEC. CANAL-related variables are CANAL, CANALTP3 and CANALSL. Also included is the MECHTP3 variable. The predictive equation for Pseudodiaptomus coronatus is as follows: NETMT = 39.32745367 + (-7.84868626 x TMPT) + (.57319184 x TEMSQ)
+ ( .0817133 x TEMCU) + (.00021136 x TEM 4)
+ .08251318 x SX1xTMP) + ( .00006314 x SX1xSL3)
+ .00002814 x SX2xSL3) + (.25932942 x SX1xCAN)
+ .61026345 x SX1xMEC) + (.2360437 x SX2xMEC)
+ (.66467350 x CANAL) + ( .03010903 x CANALSL)
= (.00000664 x CANALTP3) + (.00000440 x MECHTP3).
1 IV-81 l
Labidocera sp. Labidocera sp. is a category made up of two species of the genus Labidocera: Labidocera aestiva and Labidocera scotti. These two species were counted together for two reasons. First of all, the numbers of either species alone were quite low, so that in order to obtain large enough counts to analyze, the numbers were combined. Secondly, although the species identification of these large calanoids is relatively easy as adults, the juvenile species identification is not feasible when counting large numbers of copepods under microscope powers of 12-25X. It is assumed that these species react to entrainment more similarly to each other than to other species of different genera. Labidocera sp. numbers ranged from 0 to 569/m3, with a mean of 57/m3 (see Graph 31, Appendix B), Generation peaks occurred in November and January, with two "1erge" peaks in June and September. Labidocera accounted for from 0 to 2.7 percent of the total copepod population throughout the study with PROPTL values generally around 0.2%. Intake mortality for Labidocera ranged from 0 to 40%, with a mean of 13.2%. The MORT values fluctuated through the year, with peaks in November, May and ouly (see Graph 41, Appendix C). 100 values of NETMT for Labidocera sp. were from 0 to 40%, with a mean value of 8.1% (see Graph 42). NETMT values were high (30-40%)in January and again in September, with intermediate dates having NEMT values of 0. The Labidocera sp. populations that were left in the heated effluent following thermal shock gave NETMT values ranging from 0 to 100% with a mean value of 49.1% (see Graph 43). These values are less than 30% until May, and then rise steeply to the 100% level by June and, except for the large dip at the end of June, remain at this level. The NETMT vs TMP graph (not shown) reflects the same trend, with NETMT values being below 30% for temperatures under 30*C and sharply increasing with temperature until the 100% level is reached above 35'C. Discharge populations of Labidocera (DS0) showed rather high NETMT values throughout the year, ranging from 0 to 100% and having a mean value of 50% (see Graph 44, Appendix C). The NETMT values fluctuate between 30% and 50% for most of the year, increasing to 100% in July and dropping off in August.* DS2 values of NETMT also were quite high throughout the year, with a mean value of 55.2% (see Graph 45, Appendix C). Values generally fluctuated between 40 and 100%, with a dip during the bloom period in June. The NETMT vs TMP graph (Graph 46) showed no real trend of hETMT values with temperature. NETMT vs DATE graphs identifying sex were'not drawn because of the small number of data points available for adults. It was noted, however, that the adults generally had much higher NETMT values than the juveniles.
- The numbers in this sample were extremely small; therefore, this low value may not reflect true NETMT of the population.
IV-82 ) 1 1 1
The predictive equation for the Labidocera sp. regression model was formed by looking only at NETMT values for total numbers (SEX = 4), rather than breaking them down into sex categories for each date and treatment. This was done because it was felt that numbers were not sufficiently high for each sex to provide enough data point observations needed for. program analysis. The data from all sex-age classes were thus combined and sex-related variables were not tested by the analysis. The model, therefore, shows relationships between the various independent variables and the NETMT values of Labidocera as a whole, rather than with the specific sex-age categories of the species. The model contains level (F = 3.02, 21df; Rg1 independent variables signifcant at the .01 Tem
= .78).
TMPT, TEMSQ, TEMCU, TEM 4, TMPSQ, TMP4, TEMxSLT and TEMxSAL. Salinity-related variables are SLT, SLTSQ, SLTCU, SALSQ, SALCU AND SA4. SEAS 1 is the season-related variable which is included. From the CANAL-related category, CANALTP is the independent variable selected. Variables related to the mechanical-thermal interaction are MECHSL, MECHTP3 and MECHCL. NSM2 and NCM3, two density dependent variables, are also included. The full predictive equation for Labidocera sp. is: NETMT = 3876.4635712 + (908.60535312 x TMPT) + (-51.40362501 x TEMSQ) + (1.32627094 x TEMCU) + ( .01769896 x TEM 4)
+ ( .2695408 x TMPSQ) + (.00001660 x TMP4) +
(-1.38705635 x TEMxSLT) + (.61037995 x TMPxSAL) + (-342.48221584 x SLT) + (16.83184065 xSLTSQ)+ ( .24534499 x SLTCU) + (2.91371313 x SALSQ) + i ( .18569173 x SALCU) + (.00288488 x SA4) + (.17162589 x SEAS 1 ) + (.01382356 x CANALTP) + (.03686594 x MECHSL)
+ ( .00001536 x MECHT 3) + ( .42211-79 x MECHCL) +
(.0076544 x NCM2) + ( .0013559 x NCM3). Tortanus setacuadatus Tortanus setacaudatus is a fairly large calanoid copepod that is abundant from time to time in the Crystal River area. Numbers range between 0 and 2,275/m3withameanof118/m3(seeGrapg37,AppendixB). The numbers were generally low throughout the year (0-40/m ),with generation peaks in 1 I April, July, and September. The percent of the total copepod population that Tortanus represented ranged from 0 to 9%, with all values being below 1.8% except during the April bloom (see Graphs 40-42, Appendix B). Intake mortalities were generally low throughout the year, with a peak occurring at the end of April, after the population bloom earlier in the month (see Graph 35, Appendix C). The range was from 0 to 94%, with a mean of 7%. The 100 NETMT values for Tortanus setacaudatus ranged from 0 to 100%, I with a mean value of 18.6% (see Graph 36, Appendix C). The trend was for low l to moderate values (0-20%)throughout the year, except for a peak in August. ' IV-83
4 ID2 NETMT values were from 0 to 100%, with a mean value of 30.8% (see Graph 37, Appendix C). NETMT values were near 0 until June, when ' they began to rise. _With the exception of a dip below 20% in August, the values remain high through the suniner. The NETMT vs TMP graph (not shown) showed that the NETMT values were 0 below 30*C and rose to high levels above 35'C. Populations of Tortanus setacaudatus tested insnediately upon leaving the discharge pipe showed NETMT values which ranged form 0 to 50%, with a mean value of 7.7% (see Graph 38, Appendix C). NETMT values are low (0-15%) throughout the study, with a peak of 50% in August. The NETMT values for Tortanus setacaudatus population allowed to remain in the heated effluent following entrainment were from 0 to 100%, with a mean value of 27.2% (see Graph 39, Appendix C). NETMT values are near zero until April, then rise gradually to approximately the 25% level in August. Values then jump to 100% level at the end of August and remain fairly high (60%) in September. The NETMT vs TMP graph shows a definite trend; zero values at NETMT below 31*C; a gradual increase to moderate (?0-30%) values in the 35-36*C range; and large NETMT values at temperatures around 37'C. The NETMT vs DATA graph identifying sex was not prepared for this species owing to the low numbers and lack of data points, but it was noted that females seem to have lower NETMT values than juveniles during dates with lower temperatures and higher values during the warmer months (July-September). As in the case of Labidocera sp., only the NETMT values for the total class (Sex = 4) for Tortanus selacaudatus were used in the regression model. The model shows correlation between 25 independent variables and NETMT (significant at .0001 level, F = 10.6, 25df, R2 = .97). The temperature variables shown related to NETMT are TMP, TMPSQ, TMPCU, TEMSQ, TEMCU, TEM 4, TMPxSAL and TEMxSLT. Salinity related variables are SLAT, SALSQ, SALCU, SA4, SLT and SLTSQ. Both SEAS 1 and SEAS 2 are seasonable variables included by the model. CANAL variables are CANLTP3 and CANALSL. Variables related to the mechanical-thermal interaction included in the model are MECH, MECHTP3, MECHSL and MECHSL. Density-related factors are NUMCU, NCM2 and NCM3. The full predictive equation for Tortanus is: NETMT = 1456.3201441 + (66.77637828 x TMP) + (-2.25401465 x TMPSQ)
+ (.02458945 x TMPCU) + ( .12059849 x TEMSQ) +
(.00785249 x TEMCU) + ( .00012450 x TEM 4) + (.01032245 x TMP x SAL) + ( .03459697 x TEMxSLT) + (-348.48184795 x SALT)
+ (21.31640042 x SALSQ) + ( .568461 x SALCU) +
(.00572337 x SA4) + (15.30204491 x SLT) + ( .28984861 x SLTSQ)
+ (12.27736482 x SEAS 1) + (5.56867016 x SEAS 2) +
(.00001386 x CANALTP3) + ( .02030898 x CANALSL) + , (.74922578 x MECH) + ( .00000839 x MECHTP3) + l ( .02661469 x MECHSL) + (.21251603 x MECHCL) + , (.00133622 x NUMCU) = ( .00022118 x NCM2) + (.00000729 x NCM3).
, . . . . .s - -
IV-84 1
/ Totals Total numbers for all copepogs collected, regardless of specjes, ranged from 2,070/m3 to 103,918/m , with a mean value of 24,900/m (see
~
Graph 43, Appendix B). Numbers generally rose from winter to summer with peaks in December, March, July and September. Total intake mortality (Totals are designated SP = 10 or SPEC = X) ranged from 7.4 to 24% over the course of the study, with a 14.2% mean value (see Graph 47, Appendix C). Of course, because of the numbers of copepods being averaged to form these values, the fluctuations are quite small and the values generally stayed quite close to the mean value. Populations subjected to the thermal stress of discharge temperatures alone (ID0) showed NETMT values from 0 to 49% with a mean of 8.4% (see Graph 48, A;;pendix C). Values remain low (below 10%) throughout most of the year, with a peak occurring at the end of August. The populations were subjected to CANAL effects after the thermal stre'.s (ID2) showed NETMT values of from 0 to 73%, with a mean value of 15.4% (see Graph 49, Appendix C). NETMT values remain very low (0-5%) until after March, then gradually rise to moderate values (25-30%) in sumer, and finally climb to high values in August and September. j Populations that were sampled immediately following entrainment (DS0) I exhibited NETMT values from 0 to 45.6%, with a mean value of 12.6% (see Graph 50, Appendix C). NETMT values generally fluctuated around the 10% level throughout tne year, with peaks in April, July and August. The DS2 treatment, which most closely simulates conditions that occur following entrainment, produced NETMT values that ranged from 0 to 77%, with a year-round mean of 19.7% (see Graph 51, Appendix C). NETMT values were generally below 10% until after April, when they began to rise in an almost exponential manner to a high of 80% by the end of September. This trend is duplicated in the NETMT vs TMP graphs of not only the DS2 treat-ments (Graph 53 ), but of the ID2 treatments as well (not shown). The NETMT values stay below 10% for temperatures below 3I*C and then rise at an increasingly rapid rate with further increases in temperature. The largest amount of NETMT increase is betweenthe temperatures of 35 and 37'C. The NETMT vs DATE graph identifying sexes (Graph 52) shows that, in general, males have higher NETMT values thn either females or juveniles. The predictive regression model for all copepods, regardless of species, contains 16 indepegdent variables and is significant at the .0001 level (F = 61.2, 16df; R = .84). The temperature-related components of this model are TMP, TMPSQ, TMPCU, TMP4, TEMSQ, TEMCU, TEM 4 and TEMxSLT. The salinity-related component is SA4. The season-related factor is SEAS 2 and the sex-related component is SX1xTP3. Variables related to CANAL effects are CANAL, CANALTP and CANALTP3. Variables in the model that are related to the mechanical-thermal effects include MECH and MECHSL. The predictive IV-85 2
, m a:r a .
~ - -
~ 1 equation for the total copepod population is: NETMT = 39.99852761 + (-6.36953059 x TMP) + (.3811439 x TMPSQ)
+ ( .00997857 x TMPCU) + (.00009637 x TMP4) +
( .00996555 x TEMSQ) + (.00074013 xTEMCU)+ ( .00001402 x TEM 4) + ( .00110950 x TEMxSLT) + (.00000045 x SA4) + (.06359774 x SEAS 2) + (.00000232)
+ SX1xTP3) +-(1.22763063 x CANAL) + ( .07362501 x CANALTP) !
+ (.00003651 x CANALTP3) + (.42489667 x MECH) +
- ( .01427509 x MECHSL).
. DISCUSSION i In looking at the data for the total copepod population, several trends stood out. First of all, there are indications of both mechanical-thermal and canal effects on the net mortality of the copepods. The entrained populations (DSO, DS2) showed higher NETMT values than the populations exposed only to the thermal stress (IDO, 102) and the values for populations-left in the canal were higher than those sampled immediately.
-Secondly, males seemed to be more sensitive than females, which, in turn, were slightly more sensitive than juveniles. Finally, the obvious trend of nearly exponential rise in NETMT with the coming of the warmer months, suggests that temperature of the discharge waters is a major factor in the net mortality of the copepods.
The. regression equation for the totals category was used to produce contour graphs of predicted values of NETMT for various hypothetical com-binations of the independent variables. Temperature and salinity, making major. contributions to virtually every species model, were plotted on the-
; X and Y axes to give the range of possible combinations of variables associated with them.'The other major components of the model (season, sex, MECH and CANAL effects) were varied one at a time to observe changes in
- response of predicted NETMT values pictured by the graphs. In this way, the overall effects of the independent variables and all interactions related to each major. component could be more closely seen.
l Graphs 1-6 '(Appendix D) shows trends of predicted NETMT values for the range of temperatures and salinities (intake and discharge) found through-out the study. Temperature effects are' evident in all graphs, with NETMT values being generally low throughout the 20-38'C range and then increasing rapidly with small increases in temperature above this range. A temperature-salinity interaction appears evident, with NETMT values being elevated with low salinities and higher temperatures and alos with low temperatures and high salinities. The graphs also show that males have higher predicted NETMT values at , . higher temperatures than females or juveniles indicating that this sex is most sensitive to temperature-related aspects of entrainment. Graphs 1 and 2 show predicted values for copepods that have been
- exposed to the elevated temperatures of the thermal plume. There are L
IV-86 z. Y
, ~ -,v. ,w,y ,y r.,,- m -- . _ - - _ - - - - - - - - - - - - - - - - - - - - - - - - - -
moderately low NETMT values predicted throughout the temperature-salinity range for these conditions except above 35'C, where the tolerance threshold seems to have been passed and net mortality rises rapidly. The addition of the mechanical-thermal interaction (Graphs 3, 4) causes a moderate rise (10-20%) in NETMT predicted values throughout the temperature-salinity range, with the conditions at higher salinities showing less effect. Seasonal effects, other than temperature and salinity related ones, are evident when the effects of the heated effluent are observed for SEAS 2 (Graphs 1 and 2 vs. 5 and 6). NETMT values are generally higher during this season (April and May) possibly because of a " die-off" of heat sensitive winter populations, such as has been noted by Gonzalez (1973). In looking only at the observed temperature and salinity ranges for each season, predicted NETMT values for entrained populations ranged 0-10% for females and juveniles and 0-20% for males in winter; 10-40% for females and juveniles and 10-50% for males in spring; and 10-100% for juveniles and females and 20-100% for males in summer (graphs available on request). t Similar analyses were performed upon all species, but for time and space considerations, the contour graphs are not included. These graphs plus pertinent data used in individual species analysis are available from the authors upon request. The trends that were shown for the totals category were observed in the analysis of each individual species, with a few exceptions. Euterpina acutifrons seems to be the most temperature sensitive species, showing the trend of gotng from low mortalities to 100% mortality much earlier in the sumer than other species. The dwindling numbers of this species with the warmer months suggest that this species is essentially a
" winter" species, so that this sensitivity may not be extremely significant to the secondary production of the area.
The mechanical damage aspects of entrainment seem to have little effect on the small Oithona sp. which appear to be mainly affected by the elevated temperatures of the discharge canal. In contrast, Labidocera sp., the largest copepod in the area, appears to be affected year-round by the mechan-ical damage caused by entrainment. This trend seems to indicate that' size of
.the organism entrained may determine the amount of mechanical damage to which it is subjected.
In conclusion, there are several general statements that can be made about entrainment effects upon copepod mortality. Mortality caused by entrainment seems to be low throughout the winter and spring months, moderate in early sumer and rising in an almost exponential fashion after the discharge waters go above 35'C. Virtually total mortality (80-100%) occurred only when the temperatures reached 37'C. Although there seems to be some tolerance to IV-87
y, w . , , initial thermal shock (THERM), those unentrained populations experiencing prolonged exposure to the heated effluent (CANAL effects) in the warmer months showed elevated mortality values similar to entrained populations. This may indicate a possible problem with populations recruited by the tide into the thermal plume during the sumer months. Secondly, there appears to be a general temperature-salinity interaction where the combination of low salinities and high temperatures and the combin-ation of high salinities and low temperatures both cause elevated copepod mortalities while the converse (low mortalities) seems to hold true for the opposite combinations. 4 Mortality caused by the turbul nce and shearing forces encountered in the condenser pipes seems to be relatively low, but probably is the major cause of entrainment mortality in the colder months. The amount of mortality caused by mechanical damage seems to be determined mainly by the size of the organism. Finally, adult copepods seem to be more sensitive to lethal entrainment effects than juveniles, probably both because their larger size makes them more prone to mechanical damage and because their physiological complexity (hormones, enzymes, neurosecretions, etc.) makes them more sensitive to thermal damage (see Jensen 1969). Males were also shown more sensitive than females, possibly for similar physiological reasons. Objective 5, Effects on Reproduction INTRODUCTION The effect of entrainment on reproduction is a more subtle type of problem than immediate lethality. The loss or impairment of reproductive capacity is, however, as important to the population as individual mortality, since affected organisms are essentially " dead" to further production. Objec-tive 5 examines the effects of entrainment on the reproductive capacity of Acartia tonsa, the major zooplankton of the area. METHODS AND MATERIALS Laboratory Work Intake and discharge populations of copepods were collected in a 202u plankton net and brought back to the laboratory alive. The copepods were placed in fresh sea water and kept overnight at intake canal temperatures. Male and female Acartia tonsa were sorted out under a dissecting microscope, utilizing a large bore pipette with a mouth tube. Two males and two females *
- Generally, females without attached spermatophores were chosen in an attempt to start all cultures at the same point and to test male fertility IV-88
were placed in one liter containers of fresh sea water. A mixed algal culture of Rhodomonas baltica, Isochrysis galbana, Monochrysis leutheri and Skeletonema costatum (later substituted by Thalassiosira pseudonana) were fed to the cultures and brought up to proper concentrations every other day. A few drops of a culture of large marine ciliates (probably Euplotes s2.) were routinely added to the food culture before feeding. These organisms help eliminate excess buildup of bacteria and algal detritus in the culture dishes, and provide a secondary food source for the adult Acartia tonsa. The cultures were kept in B.O.D. box set to simulate intake temperatures and photoperiod for one week. The cultures were then vital stained and preserved for counting. The adults, copepodids, nauplit and eggs for each culture were counted under a dissecting microscope. An eggs / female / day ratio was then computed based on total reproductive products (juveniles and eggs) and on the number of living females. It soon became apparent that this method was not the most efficient one, since all cultures in which a female had died during the culturing period had to be discarded from these calculations because it was impossible to ascertain how many, if any, prod"ctive days there were before mortality. A change in experimental- technique was, therefore, made starting with the April experi-ments. The cultures were set up as pair mating experiments, with only a single male and a single female Acartia tonsa being placed in each (the size and volume of the culture dish was proportionately decreased without any apparent affects on reproduction). These cultures were observed daily for any mortality. If one of the adults were dead, the remaining adult was transferred to a fresh dish (for the survival study described below), , while the eggs and juveniles were stained and preserved immediately. To l allow the calculation of the eggs / female / day ratio, the " day" number was ! assumed to be that of the day on which the female was last observed to be alive. Since this concession was made for both intake and discharge cultures, any discrepancies in the amount of productive time that the female might have between the "living" and " dead" observations and the productive time assumed (up to the last "living" observation) would average l out between the populations. The only other alteration in experimental technique was that the culturing period was cut to five days in the warmer months because the higher temperatures shortened the development time to the point where second generation nauplit would be produced within the 7 day time period and would affect the fecundity calculations. Statistical Analysis The eggs / female / day ratio was plotted against date for any visible i discrepancies betweenthe populations. A regression analysis was used to i test the relationship of the' dependent variable, eggs / female / day (EGGS), with the independent variables of sampling date (DATE), the population (intake or discharge) that the adults came from (SIDE), and a date-side interaction (SIDE *DATE). A second regression analysis was done to test the relationship of the temperature at which the adults were cultured (TEMP) to numbers of EGGS produced. i l l IV-89
~
x
.,w .
-w -
RESULTS The year-round results of the reproductive studies are shown in Figure 1. This graph plots the average eggs / female / day for each experimental period versus the date that the adults were collected. The average intake eggs values ranged from .76 eggs / female / day in February to 22.8 eggs / female / day in June, with a mean value of 8.57 eggs / female / day. The discharge values followed the same patterns,with a low of 1.45 eggs / female / day in February to a high of 19.8 eggs per day in June and having a mean value of 5.42 eggs / female / day. The peaks of fecundity roughly trace the numbers per cubic meter graphs for Acartia tonsa (see Appendix B ), with peaks and dips almost coinciding in varmer seasons when development time is short. These observations indicate that the fecundity observed in the laboratory cultures could represent patterns occurring in the field. In looking at Figure 1, it seems that dishcarge EGGS values ere equal to or even slightly higher than intake values on the dates that both values are low, but that the discharge values lag behind intake values on the dates that have high fecundity values. The regression analysis for the depdendent variable EGGS (see Appendix E ) shows that SIDE (.0006 level probability > F), DATE (.0001 level) and SIDE *DATE (.005 level) all have significant correla-tions with fecundity. This regression also provides a predictive quation for determining facundity values: EGGS = 6.623 + (-1.204 if SIDE = discharge) + -3.01 for DATE = 731226 + 1.772 for DATE = 740108 . . . etc.)
+ (1.994 if SIDE = discharge and DATE = 731226) + .590 if SIDE = discharge and DATE = 740108 . . . etc.)
Since Figure i shows that EGGS values of intake and discharge populations were similar until the end of May, the date were divided into two parts: a Winter-Spring section including all experiments from December-May 14th; and a Summer section including all experiments from May 28th-August.* Each of these sections were then put through the regression analysis again (see ; Table 2-3, Appendix E .). The Winter-Spring experiments showed no significant i difference due to SIDE or to SIDE *DATE (above .05 level) with the mean EGGS being 4.46 eggs / female / day populations for intake and 3.22 eggs / female / day for discharge populations. The Summer experiments, however, showed a highly ; significant effect due to SIDE (.0002 level), with mean EGGS values being I 12.89 eggs / female / day for intake values and 7.51 eggs / female / day for dis-charge populations. A regression analysis was also done for the dependent variable EGGS with the independent variable SIDE and temperature (TEMP) (see Table 4 , Appendix E ). The TEMP showed a significant correlation ( 0001 level) with EGGS, with higher EGGS values at higher temperature.
- Experiments for the last sampling date in August and the sampling date in September could not be performed because of the nearly 100% mortality of Acartia tonsa adults in the discharge samples.
IV-90
$T A T I $ 7 1 C A L AN A L Y $ I $ $ y $TE M PLOT OF EGGS VS OTE 23.000oo000 +
g
. e k
k to.conococo + d I l l \ f \ *'s' J.cooooooo +
\ .\
* \
k EGuS W I
/%\\ ,
E E a.osoooooo + l l / e g
\
\
A '/
/
!\d
\ /*
\
J. CooCCooo et e, \
/
,/*y .re
/ .
.g=psed
-2.cccococo .
. l--___+-_._____________,_.._____......._........_________...,_______...___.....,____,,____,,,,__,,,,,_ D J J F F M M A A N M J J J J A DATE FIGURE 1 - Acartia tonsa Eggs / female / day vs date "N"' INM POPMION
% DISCl!ARGE POPULATION
c ..- - DISCUSSION The results from the reproductive studies seem to indicate that entrainment does cause'some reproductive impairment. The fecundity rates of control populations of Acartia tonsa were shown to be significantly different from those of discharge populations. Peak periods of egg pro-duction were the times when discharge population values lagged behind the intake population values, with the greatest discrepancy coming during the sumer months. - The trend for the discharge population to lag behind only during peak periods of egg production may mean that the trauma of entrainment has caused the copepods to cease reproduction for some period of time following entrainment, so that the total number of eggs that are produced once egg production resumes never matches that of the control population which is producing at high rates. If, however, both populations are producing eggs at low rates, the discharge population may be able to catch up to control population in numbers of eggs produced during the experimental period. The lower fecundity rates for the discharge population could also be due to a metabolic response to thermal stress which caused the burning up of nutrients necessary for egg production by raised metabolic activity. This would also allow control populations to get a head start on the egg produc-tion, while discharge populations adapted metabolically and renewed nutrient reserves. A third possible explanation is that the heat had some effects in altering hormones, enzymes or biochemical substrates necessary in reproduc-tive activities. If these components were inactivated or partially inacti-vated, egg production would either cease or be depressed. It must be noted, however, that very few cultures, intake or discharge, showed absolutely no egg production, indicating that complete heat sterilization does not occur. Whichever of these or other possible explanations are true, the overall effects remain the same: the discharge populations have a depressed repro-ductive capacity over the period of 5-7 days and probably over the remainder of their rather short life span. The depression in winter was statistically insignificant, but the summer months showed a depression in mean fecundity rate of 39% (7.81/ eggs / female / day versus 12.89 eggs / female / day for control populations). The largest depression infecundity was found in the August experiment when the temperatures were highest: a 73% depression of discharge population eggs production ! ulow intake values (4.26 eggs / female / day versus 15.74 eggs / female / day for control populations). These trends indicate that discharge canal temperature is a factor in the entrainment effects on repro-duction and that the reproductive depression is only significant during the watermer months (above 35*C). In assessing the effects of entrainment the % reproductive depression should be considered with net mortality values, since populations surviving entrainment have egg production rates like those of a much smaller control population. For example, if 80 out of each 100 Acartia tonsa females survive entrainment, but there is a 20% depression in fecundity due to entrainment, the overall effect on production of standing crop is as if 16 more individuals were reproductively " dead". This figure could be subtracted IV-92
from the surviving numbers to get an " effective" mortality of 26% rather than the observed value of 20%. Of course, these indiciduals are all still living, so that, unlike daed copepods, they remain as potential food sources for.the next rophic level. Objective 6, Effects on Growth INTORDUCTION The effects of entrainment on growth of juveniles is examined, since population structures and standing crop of the copepods could be altered if growth rates (and turnover times) were depressed. Juveniles that have ceased growing (or grow at a depressed rate) are " dead" to the population and secondary production of the area. Objective 6 examines and evaluates the effects of entrainment on growth of Acartia tonsq and Oithona brevicornis, the two major components of the zooplankton community. 1 l METHODS AND MATERIALS ! Field Work l During the biweekly sampling period, juvenile Acartia tonsa and Oithona brevicornis were collected for growth experiments. The collections were l originally made by a fulter pump system of our own design based on that described by Icanberry (1973). The waters from intake and discharge areas were pumped through a container housing a 73p plankton net and then through a large perforated PVC container which was screened with a Su filter bag material. The 73u net filtered out all but the early juveniles stages, while the larger
- PVC container retained these early stages. The water flow was metered and approximately 80 gallows was filtered from each canal. It soon became apparent that, with the relatively small volumes of water necessary to obtain the needed concentrations of juveniles, it was easier to bucket the water through the 73pnet suspended in the mouth of the PVC container (which was hung in the water over the side of the boat) than it was to assemble and disassemble the pump system at each sampling site. Therefore, this bucketir.g technique was adopted, although the pump system technique seems promising in situations where the concentrations of juveniles is relatively low (such as in the open oceans).
The concentrated samples of juveniles were kept in insulated containers filled with ambient (intake and discharge) temperature sea water for trans-port back to the laboratory. Laboratory Work The intake and discharge samples were each split into eight equal subsamples with a Folsom Plankton Splitter that had been covered with aluminum foil to prevent phototrophic clumping on either side of the divider.
- The size cuts down the water velocity and turbulence that might damage the juveniles.
IV-93
~'
The subsamples were placed in culture dishes containing 1 liter of fresh sea water to which had been added the algal diet described above. One culture from each population was stained and preserved immediately, while the remaining cultures were kept in the illuminated B.0.D. box set at intake photoperiod and temperature conditions. Cultures from each population were then chosen randomly at predetermined time intervals (every other day in winter, and every day in summer when the development time is shorted), then stained and preserved. Remaining cultures were fed every other day, with the mixed algal diet being added to bring concentrations in the cultures up to proper levels. - The samples were counted under a dissecting microscope at 50X and the Acartia tonsa and Oithona brevicornis nauplii and copepodids were identi-fied as to stage of development. The mean stage was then calculated for each day that a culture was taken by the following formula. N
- NE N
MEAN STAGE = 3 IX N 1 where N = stage number and XN = the numbers of juveniles counted in stage N. Statistical Analysis Growth curves for intake and discharge populations were graphed for each experiment by plotting mean stage versus day. Linear regression were performed to determine the growth rates (the "B" values or slope of each growth curve) for intake and dischar3e populations of each species on each sampling date. These rates were then compared with T-test designed to compare slopes. The growth rates for all experiments were then plotted against date and against temperature to look for any visible trends in divergence among the populations. Regression analysis was performed to test the dependent variable Growth Rate (GR) against the independent variable set of SIDE, DATE, and SIDE *DATE, or of SIDE and TEMPERATURE. The Growth Rate versus Temperature regression values for the intake population wr.re used in the production section of the zooplankton study to determine the development times of Acartia tonsa at different temperatures. RESULTS The growth curves shown in Graphs 1 -9, (Appendix F ) compare the growth rates of ~ the intake and discharge populations of Acartia tonsa and Oithona brevicornis for each experimental period. For dates on which the initial TDay .3) counts .for a species fell below 20 per culture (2/ gal, or approximately 500/m3 ), growth curves and regressions were not done and zero values were given to the coefficients. This policy was adopted to insure IV-94
that enough numbers were included in mean stage calculations that inter-split variation did not overshadow true growth trends. For the most part, both species seemed to grow quite well together, although both were driven to near" extinction" in the cultures by the bloom of a harpacticold, Euterpina acutifrons, by Day 6 of the June 11 experimental period. Statistical comparison (T-test) of the growth rates of each species on each date show no significant differences between the intake and discharge populations until well into the warmer months (July-September). The Acartia tonsa growth rates on July 9 were 1.1 stages / day for the intake population but only .68 stages / day for the discharge population. This difference was shown to be significant at the .01 level (T = 2.899). The Oithona brevicornis juveniles of both intake and discharge populations seemed depressed on this date, possibly due to high population densities (mean numbers = 14,742 nauplii/m3 ). The July 23 experiments for the intake population of Acartia tonsa showed a growth rate of 1.86 stages / day, while the discharge population had a growth rate of only 1.27 stages / day. The T-test showed this difference to be significant at the .05 level (T= 2.19, 9df). The Oithona brevicornis rates for this date were .66 stages / day for the intake populations, but only .28 stages / day for the discharge population. This difference in growth rate was also significant at the .05 level (T = 2.21, 12df). The experiments starting August 6 gave even a larger difference between intake and discharge Acartia tonsa populations with rates of 1.62 stages / day and .54 stages per day, respectively (significance level = .05, T = 2.19, 9df). The Oithona brevicornis intake population on this date had growth rate of .83 stages / day, while the discharge population actually had a negative growth rate assigned to it. This phenomenon seems to be due to the dying off of later nauplii and copepodid stages present in the original samples and the lack of growth by the first four naupliar stages that persist through theexperiment (histogram series available upon re This difference in growth rates is significant at the .01 level (T = quest). 4.00,12df). The August 20 experiments showed a continuation of the trend of increas-ing differences in growth rate with the wanner months. The intake Acartia tonsa rate was .37 stages / day, while the discharge rate was only .18 stages / day (significant at the .01 level, T = 422, 10df). The Oithona brevicornis population values were .81 stages / day for the intake population, but only
.08 stages /da for discharge populations (significant at .001 level, T = 4.3,12df .
Although the temperatures were not quite as hot for the September 3 experiments, the differences between intake and discharge populations were still quite evident. The Acartia tonsa rates were 1.41 stages / day for the intake population and .76 stages / day for the discharge population (signi-ficant at .01 level, T = 3.55, 9df). The Oithona brevicornis populations had rates of .94 stages / day for the intake population and .44 stages / day for the discharge population. The average growth rate for the intake population of Acartia tonsa over this period of significant differences was 1.47 stages / day, while l IV-95 l I
m ,, . the average diff6rence in rate between the populations was .78 stages / day. Dividing this mean difference by the mean growth rate for the control population shows a 53% average depression of the growth rate for the discharge populatians. Similar calculations for the Oithona brevicornis populations show a mean intake growth rate of .81 stages / day, an average difference of .61 stages / day
- and an average depression in growth rate of 75%.
To better illustrate what appears to be happening, representative histograms were formed to show the percent' composition versus the twelve stages (see Appendix F). Generally, control populations started with the population divided among the earlier stage nauplii. The 0_ithona brevicornis populations at this point usually have a larger spread of stages than Acartia tonsa owing to the fact that their smaller size allows more of the older stages to get through the 73p net during collection. The trend for these control populations is to have the major portion of the peula-tion move into the older stage classes as the days pass and growth teLes place. The discharge populations, however, show another trend where. a portion of the population remains in the earlier stages while the rest move normally to older stages with time. This trend for some of the popu-lation to live without apparent growth :.; especially evident for those dates on which the growth rate depression was greatest and most significant. It is obvious that, if some large portion of the population is not growing, the mean daily stage for the entire population is loured and the growth rate of the population is depressed. DISCUSSION The entrainment effects on growth are seen to be temperature related. There were no significant differences between the growth rates shown by intake or discharge populations of Oithona brevicornis and Acartia tonsa juveniles until July. In July and every experimental period thereafeter, the growth rates of the discharge populations were significantly deoressed below those of the control populations. Thes are dates on which tne discharge canal was above 35 C and the temperatures above this level produced larger and more significant differences between the populations. For the period of time over which the significant differences were noted discharge populations of Acartia had growth rates that were on the average
.78 stages per day lower than control populations. This represents a mean depression of growth rate by 53% (mean control growth rate = 1.47 stages / day).
Oithona brevicornis juveniles taken from discharge populations on these dates shoved growth rates that average .61 stages per day or 75% less than those of control juveniles (.81 stages per day). The apparent cause of the growth rate depression in both species was that a certain portion of the population simply did not grow (see Figures 1-4 , Appendix F ). Increasing temperatures caused an increasing percent of the population to cease growth and thus total growth rates for the population In the experiment that the discharge population had a negative growth rate, the rate was assumed to be zero. IV-96
l were depressed. The cause of this cessation in growth may be related to themal affects on certain homones necessary for molting and growth, or may be due to a metabolic " overshoot" triggered by themal conditions, which burns up nutrients necessary for growth (see Jensen 1969). Similar growth depression was noted by Heinle (1969) in working on a northern f power plant during suniner conditions. The depressed growth rate caused by entrainment may have several 1 important effects. First of all, juveniles that have ceased to grow are effectively " dead" to further secondary production of the standing crop and might be considered as such in mortality calculations. Of course, since they are still living, they may provide a food source for highar trophic levels, but this fact in itself might have important implications. Heinle (1969) has suggested that any population structure shift that causes the size of the individuals to become relatively smaller (as would happen with these " stunted" individuals being added to the standing crop) may cause a shift of competitive advantage to non-selective filter feeders (such as ctenophores) and away from mechanically and behaviorly selective filter feeders (fishes such as anchovies). Also to be considered is the size of the area of thermal impact which may have depressed production for isotherms above 35*C and may have growth effects on the copepods passing through them. Objective 7, Effects on Long-Term Survival INTRODUCTION Long-term survival is examined because it has been suggested (Prager, 1971, Carpenter et al 1974) that some lethal effects of entrainment may not show up immediately. Copepods that survive entrainment but are fatally damaged, are assentially lost to secondary production of the standing crop of the area. We have examined and evaluated the survival of Acartia tonsa populations for the days following entrainment. METHODS AND MATERIALS The culturing of copepods for reproduction and growth studies also provided for observation and testing of differential survival between intake and discharle populations for a relatively long period of time following entrainment. These comparisons, although dealing with artificial laboratory conditions, should show any drastic differences that may occur between the populations in nature. Data from the reproduction studies produced the daily observations of adult Acartia tonsa mortality during the time period of the experiment (5-7 days). Survivorship curves were constructed, plotting the percentage of adults remaining alive versus day. A linear regression was then done for the curves of the intake and discharge populations. These regression lines were then compared with a T-test. IV-97
~
~
A second type of test was also performed to determine adult survival. The total mortality for intake and discharge populati m was calculated for each experimental period. This figure was then divided by the number of days in the experimental period to obtain a mortality / day (MORTDA) ratio. This method assumed that there was an equal mortality throughout the experimental period, whether it was five or seven days long. This assumption may be unfounded, because it is impossible to tell the age of an adult copepod. The same assumption was made on both populations; there-fore, it was assumed any discrepancies woulu average out between the populations. If there are some long-term lethal effects due to entrain-ment, they should show up as higher mortality / day ratios for the discharge population for each experimental period. The MORTDA figures were tested by regression analysis versus side (SIDE), season (SEAS) and the side-season interaction (SIDE
- SEAS).
The growth experiments provided data which allowed the plotting of survivorship curves of juveniles (nauplii and copepodites) from intake and discharge populations for each sampling period. The difference from adult survivorship curves lay in the fact that the exact numbers of juveniles at the beginning of the experiments were unknown. The 100% level was assumed to be the number counted for Day .3 (stained and preserved immediately after splitting, approximately 6-8 hours after collection). Of course, the Folsom Plankton Splitter does not divide the populations into eight exactly equal subsamples, so that daily percent survival values may possibly go above 100 percent. It was assumed, however, that such between-split variation averaged out between the populations and that any signifi-cant effects would show up with all other conditions being the same. A linear regression was performed on the survivorship curves and, as with the adult curves, the slopes were compared with a T-test. RESULTS The survival of adult Acartia tonsa during the pair mating reproduction experiments was generally high. Survivorship curves (see Appendix 6 ) for intake and discharge populations were similar and T-tests on the regression lines for these curves for each date showed no significant differences (.05 level) between the populations. The regression equations gave "B" values which represent the daily % loss of the surviving population. It is easier to consider the absolute value of these numbers which is the daily % mortality. The mean daily % mortality for the intake Acartia tonsa populations during thc pair mating experiments was 8.48%/ day, while the discharge population had a mean value of 8.58%/ day. ! The mortality / day (MORTDA) figures, which were calculated for all reproductive experiments by dividing total mortality by days in the experi-l ment, were put through regression analysis and showed similar results l (see Table 1 , Appendix G ). The side effect was not significant, but the l season effect showed that the summer season had a significant relationship l (.01 level) to MORTDA for the populations, with the MORTDA values of those months being higher (B values = 2.34). Thus, the mean (December-August) MORTDA values of 5.92%/ day for the intake populations and 6.47%/ day for for the discharge populations were not sifnigicantly different from each i IV-98
other, but were lower than the figures calculated for the pair-mating experiments which were performed for the most part in the sumer (April-August). DISCUSSION Long-term survival following entrainment was similar for intake Acartia tonsa populations and the discharge populations of Acartia that had survived entrainment. No significant difft.aences were shown between survi-vorship curves of the two populations durin.1 each experimental period. Neither were there significant differences t twteen mortality per day figures which were calculated for ich population of each experiment. This seems to indicate that, if the at.et copepods survive the first 24 hours follow-ing entrainment, no further delayed lethal affects are seen. Juveniles Oithona brevicornis and Acartia tonsa tested for long-term survival during the growth study experiments showed no significant differences between intake and discharge populations. Even during extreme sumer conditions where growth depression was observed, the survival of both popu-lations were shown to be similar. This indicates that long-term lethal effects are not apparent in juveniles that have survived the first 6-8 hours following entrainment (when the first counts were done for Day .3). IV-99 e __ ,
Objective Summary Objectives 1-4, Lethal Effects, Delayed Lethal Effects, and Differential Survival
- 1) The percent mortality of copepods entrained by the power plant ranged from zero to nearly 100%. The values were generally 0-20% in winter,10-40% in late spring and rose rapidly to nearly 100% by the end of the summer.
- 2) The temperature of the discharge canal apparently was the most significant factor in copepod mortality, with values being low below 30 C, rising to moderate levels bewteen 31 and 35"C, and increasing in an almost exponential manner at 35-37*C. This trend indicates that the time of most concern for lethal entrainment effects would be for days that the discharge temperatures climb above 35*C.
- 3) Thermal death due to high discharge canal temperatures was noted, not only for entrained populations, but also for populations introduced into the area without going through the power plant. This may indicate that high mortality may occur in populations tidally recruited into isothenns of the thennal plume that exceed 35*C.
- 4) There appears to be a tenperature salinity interaction where low salinities and high temperatura., or high salinities and low temperatures caused increased mortality. Un the other hand, high salinities seem to
" buffer" the effects of high temperatures in the discharge canal for most species.
- 5) Mechanical damage caused by passage through the plant seems to account for a rather small percent mortality, but may be the major factor in entrainment effects during the colder months. Size of the copepods seems to be an important factor in the amount of damage caused by the mechanical aspects of entrainment.
- 6) Juvenile copepods seem to survive entrainment much better than adults and males seem more sensitive to entrainment effects than females.
Objective 5, Effects on Reproduction
- 1) The reproductive capacity of discharge populations of Acartia tonsa '
l was depressed below that of intake populations during the suniner months. The fecundity rates of the discharge population was, on the average 39% below that of the intake population.
- 2) Reproductive effects of entrainment seem to be temperature related, with the months that have discharge canal temperature above 35*C showing the largest difference betweer intake and discharge population fecundity, the remainder of the year shoting no significant differences.
l IV-100 l \
s
- 3) Discharge po'ulation p fecundity tended to lag'behind that of control populations during period of peak egg production.
Objective 6, Effects on Growth
- 1) Growth rates for entrained populations of Oithona brevicornis and Acartia tonsa were depressed below those of control populations during the hottest months (July-September). Over this period, entrained Acartia tonsa juveniles grew about 53% slower than intake individuals. Oithona brevicornis juveniles taken from discharge waters showed a 75% depression in growth rates below those of intake populations during these months.
- 2) The growth depression seems to be due to a portion of the entrained population that ceases to grow, rather than a slowing of growth rates for the entire population. This stunting of growth of a portion of the entrained population may have the effect of decreasing the overall average size of individuals in the discharge area, which could have important ecological implications.
- 3) The effect of entrainment on growth seems to be related to discharge canal temperatures. Only populations taken from waters over 35*C in temperature showed significant depression in growth rate below control values. The amount and significance of the differences between the rates of the population tend to increase as the temperatures increase above this temperature level. The temperature aspects of growth depression suggest that areas of the thermal plume above 35'C may affect the growth of copepods passing through them, andmight cause a depression in the secondary production of the area.
Objective 7, Effects on Long-Term Survival
- 1) Long-tenn survival of copepods surviving the first day following entrainment was not significantly different from that of control popula-tions. This same trend holds for copepod nauplii and copepodids, with populations that have survived the first 6-8 hours following entrainment showing a daily mortality rate not significanity different from intake populations.
- 2) There are no indications that the copepods that have survived entrainment are fatally damage in some way that may cause them to have shorter life spans than unentrained populations.
i { I l l IV-101
, e REFERENCES Carpenter, E. J., B. B. Peck and S. J. Anderson. 1974. Survival of copepods passing through a nuclear power station on Northeastern Long Island Sound, USA. Mar. Biol. 24:49-55.
Dressel, D. M. , D. R. Heinle and M. C. Grote. 1970. The effects of thermal shock on the estuarine copepod Acartia tonsa. Master's thesis. University of Virginia. . Gonzalez, J. G. 1973. Seasonal variation in the responses of estuarine populations to heated water in the vicinity of a steam generating plant. Ph.D. dissertaion. University of Rhode Island. Heinle, D. R. 1969. Temperature and. Zooplankton. Ches. Sci. Vol. 10: 186-209. 4 Jensen, L. D., R. M. Davies, A. S. Brooks and C. D. Meyers. 1969. The effects of elevated temperature upon aquatic invertebrates. Edison Electric Institute Research Publication No. 69-900. Icanberry, J. W. and R. W. Richardson. 1973. Quantitative sampling of live zooplankton with a filter-pump system. Limnol.0ceanogr. Vol . 18(2): 333-335. Prager, J. C., 1971. Survey of benthic microbiota and zooplankton conditions near Florida Power ar.i Light Company's Turkey Point Plant, August 23-27, 1971. Report of the National Marine WatE.- Quality Laboratory, U.S. Environmental Protection Agency. J l . .: - I IV-102 l l
l i f 1 APPENDIX A DATA TABLES 1 1 1 IV-103
. ,- - ,y,, c, . .
J- + T a h l' i I For time and space reasons Raw Date Tables are not included. These tables are available from authors upon request. d 4 4 ). I a l. I t I-
- , . . : . ... . . . . . v .
l. L i 'IV-104 i
e 4 APPENDIX B BIOLOGICAL PARAMETERS GRAPHS Numbers per cubic meter and percent of total represented by the species of copepods. 1 i i l l IV-105 -
ST A Y I 57 8 CAL A NALY S I$ $V $TCM
$ Peg
'a PLUT OF NutsCU VS DTE 156'10.00000
- T IP6Cf.CCn0C +
l
/
T
~
96 et'.f 0f" 0 0 + i 7 T WImeCU T 66f'C.00000 + T I
.t.a O
CA
- T 36C0.00C00 T
T T
. l 6*f'.00C0f +
l---------s-------------------,------------------s------------------s--------------. =, ---------------e ..--
'N N D1 D J J- 'F F M N A A M M J J J J. A A 5 i '
, DATE s .
$ GRAPH 1 - Acartta tonsa Mean numbers per cubic meter vs date
$682 8 bt. Ate T*- -' ' v A 4 f A :s. N F84 Are bfANa3460 ULV V AR I A 4 0*t 3OM L 1
- 6 8 4. I L U 5L L t 'e #98 4e1 C.V. A 3 *
.:a/47 +./8's'**etwtC - 3t l '. .
- 2 7. C ' 4J F * * ' *? a' h elu we c23.'.!. ; 'J 4 u l t t**
. . . .' d ', . I I 2 4 *--..-------
8 ... --.- -- - .
- , ' .e=.e-f.8.'
- - - - - - - - . - - - - - ... ' ' t o ' ' '. O .' 3 s
e e
i S TA T I ST I CA L A N A L Y S I$ 5 Y S TEN SIOtml Sp=1 PLui OF huseCu vs DTE A 2*2CC.e*9CC 0 16208.POCCC
- A 122?CeCCCCO +
A NOMCU M 92TCetStre e A A cc s A A2C0e000*C + A A
=
2e".00cco + l------,-------------------+-----------------,-----7=
=----+-----------------.---------
- J F F M M A A M M J J J J A A S' N N D D J
. DATE t
GRAPH 2 - Acartia tonsa Intake numbers per cubic meter vs date
4 SYAY I 5 T I CAL ANAL Y S I S SY 5 Y E 80 51Dt=D 5 pal PLUT OF NuuCU V5 OTE 33rnf.Pa*Ja
- A Y
A ICSCC.norco + A 4-e l A A Br^n r O*,C L + A NUNCU A e l A 55Pa.01rD e *
=::
e o A cn A 4 i 39en.rcret e V
^
A A sea.co'ce + l---------.------__ _-------,- ------- - -- -,- ..---------------,------------- --- - -------------------,---- N N D D J J F[ F M M A A M M J J J J A A 5
. DATE G R A P H. 3 - Acartta tonsa i
Discharge mm6ers per cubic seter vs date i s l
5 YA T IST I CA L AN A LY S 25 5 Y5 TEN SIDE =1 59=1 PLUT t# hiseeCu v5 OTE A 2 a 2C C .P* % C
- A 162na.POCCC +
122 SO .C er C 3 + A NumeC U 92fner1rre e A 4 4 I a A A O A A200.00000 + A A 2ee.cocco + l........._................_=-- - ........................................ _ . _ ...... _ . J F F M M A A 'M M J J J J A A S' N N D D J
. DATE GRAPH 2 - Acartia tonsa Intake numbers per cubic meter vs date
4 5 TA 7 8 57 I CAL A 5YSTE n a 51DtDNSpal A LY S I$ , PLui UF NUNCU VS OTE 13rnc.e**0" + A SCSte.enrCO + A A A A l 8000.r0000 * ' A NUNCO A A l A g 55Pa.01rDC + s s o" os A A 4 3*en.reect N A A A se'.co're + l---------.-------------------,-----. -------------+-------------------,------------------.------------------*----- N N D D J J F.' F M M A A M M J J J J A A S
. DATE G R A P H. 3 -
Acartia tonsa ! Discharge numbers per cubic meter vs date I i I 4
5TA T I ST ICAL A NALYSI S SYSTEN SIOtaD Sp=1 PLOT & PROPTL VS OTE 0.60000C00 + l A e.500COR30 + 0.413"0fC0
- A A
A PROOTL A A A a.30*ecerc
- A
< A s
a a A A A 0.20CC00C0 + A A A 0.acecocco + l........e______---________+_______...,...___.-+--..--.._--------+-__..__----- _*=- -- -_-.______* ___. N N D D J, J F F .M M A A M _M J J J J A A'S j
- DATE
- 4 GRAPH 6 - Acartta tonsa Discharge percent of total vs date 4
l i f . S T A T I ST ICAL ANA L YSI S SY S TED , $3e2 > l PLOT & Nu:CU VS DTE 14 Ct**.C 0f 0 0 + 1 l llMeet9"C0
- e T
800*.C30C0 + 7 NEJNC U T 51re.00P19 + 1
>4 1
w 7 to T T T 200r.*3fc0 +
-T g'N T l
_toon.cocoe + l-- N N D D J J F F M M- A A M M J J J J- A A 5 DATE - GRAPH 7 _ Paracalanus crasstrostris Mean numbers per cubic meter vs date
- - _ _ _ - _ _ - - - - _ - - - - - _ ,...----------- ,n -_
9 gps 2 *x A re F Dete. t.U Ot J 7 e l . r.'so u.*
- Joe '4. 7.'t u 21 kJJdQ4td.5dJ/ 2's780#-
- 6
- f'O Q tt d 'E
- i n '* 3
- I f 3 *e'*.," af 06 Irs #C..*aff0 St.f?6 e
.0 9- _ _ _ _ _ _ - _ _ _ _ - -_ - _ _ _ _ _ _ -
S T A T l' S T I CAL A N A L Y S I 5 5Y5 YE N SIDE =4 SPm2 PLUT OF NuNCU V5 DTE 200CO.C0000 + 16000.00^00 + S ant *.tYC C + NUNCU w Sf1 ** f .8 0 0 0 9 +
< A 8
d A A ecoc.ccoco + A A ._A A A
/ A A A
. e.C0000 +
l_....... ,________..... .. ...... .. -... __.- ,- - ._...........--,............ D a N N D J J F- F M M A A M M ,J J J J A A 3 DATE GRAPH 8 . Paracalanus crasstrostris Intake numbers per cubic meter vs date
1
. ST47I ST tCAL AN A 1. Y SIO $Y$fEO SIDE =D Sum 2 PLOT OF #evasCU VS OTE 12CCo.FCF;C +
A 9590.fo9CC + A e 70C**.c0700 + A
\A 4 S F .P Q10 0
- e
" A
,#^ ^ . .
.: AggummA A I 2C*n o00tCO e
A
^
A A
-Sco.cocc0 +
h......---,--..-...-....--.................................._..__,___________________, _ _ _ _ _ _ , _ _ _ _ N N D D J J. F F M M A A M .* M J J J J A A S.
~
D A T E., , GRAPH 9 - Paracalanus crasstrostris Discharge numbers per cubic meter vs date i a A i 1 l f
> 0
+ 0 4
/
i. I b l4 e 9 8 1 0D ]
- , , l 0
$ l m
l e O 1%
+
0 0 I l 9 D ' I . ' l 8 9 w ! e e
' 4"
> g I I N en tu 8 8 O
> 0
_ < E O
> ss
. c C
. 6 W t= l
< M %u 1 l .H 'E C
- e. w
- e o lE 4 E' E
> m
> a .
a J l J 9 g (N > 0 M e 4 + 21 O e 2 4 $ & n, = 8 " J U 4 o - L n. v f
, o > l 8
W I
" I
" D
> l 8
di i
. 1 .
ta , e
- 0 g 9
0 l
~ x lx O
I e i e -- _ e +_ + _ __ e +- O C O O O O O O 0 Q O G O Q G J L O G O O O > O C O o O C Q O b b O e c o o O t. O q. t 3 (* O t O @ M & N
- O e o e e o e l
v v O c 0 0 IV-115 i I \ l
4 S T AT 8 5Y I CAL AN A L Y S I 5 5 Y S T E et SIDt=8 Spe2 PLui OF PRopTL VS OTE
- 6. 5009e?et +
l A l G.4Cft'O*CC + a P. 3*Cf 00C 0 + a
~P*OPTL A o
A m eAtn'tC*Lrs + b 01 A C$lef *CCC O + A A A
, " b -- A o.tercoccc
- i l...________________________.___________________,______________.____,___________________,____--- -________._____
.N N D D J J .F F M M A A M M J J J
- J A A S DATE -
l GRAPH 11 - Paracalanus crassirostris l Intake percent of total vs date I l l i r
S T A T I S T I C A L A N A L Y S I 3 5Y S TEM 510EsD SP=2 PLuf W PROPTL v5 OTE M.50*90P00 + 4 e.40ernoC0 + a P.39CPCer c + A Pr OP TL C.23COJrCO + e A w A 4 A A r.160*0700 + 4 A A A A c.cneconCo
- h---------+-------------------,---.---------------o-------------------+---.------------+------------------o-.---
i N N D D J J F F M M A A M M J- J J J A A 3 DATE GRAPH 12 - Paracalanus crassirostris Discharge percent of total vs date 4
_ _ _ _ _ - _ _ _ _ _ _ _ _ _ _ _ _ - _ _ _ - _ _ _ - _ _ - _ _ _ . - - ~. . _ . _ . _ _ . _ . SY AT f 5 Y I C A 1. A NAL Y 5 8 5 *
.Y S Y E C3 Spe3 PLOT OF peuCCU VS OTE 41,10 f. C 980 4 +
7 I l I, i l 3 3 t te.f* 0 000 + i l I l i i l 2530C.00ctC + r t NtJetCU T 1 173nr..comeo e f
=:::
4
-a E
O T f g 1 9 t **.C 003 f5 *
. T 1
Y t1Ca.PoeOO + T
---------o-----------------o--------------- _e-----------===_- s--__ = ------- ~ ~-+ - ~ ~ ------------en.--
N N D D J J F F M M A A M M J J , JI J A A S DATE ' ' GRAPH 13 - Otthona sp. Mean numbers per cubic meter vs date e
, , , . _ , . .------ - - - - - - - - ~ ~ - - ~ ~ - - = = - - - -
h I.
,, wru c in n.w 3( e tiss2-(ti-ci naz :2 . .c ie77sv.eacose <- .a e w ar a . < .-
et 7 .nsierc r.e... .cc3c.3 ,,,, . 3 _ 1.
$T A T I ST I CAL AN A L Y S( S SYS TEN bEOt=8 SP=3 PLui OF NuwCU v5 OTE 6*nCcet0tCO +
A 4R*ae.fi?00 . 36eCO.00Pc0 + kunCU A A A g 24***.0100C S
<C I
a ese W A A A A
/gA A
12000.0i*00 ? A A A 8 AMA 0.00000 . l---------.------- --==---.-----.-- - N N D D J J- -F F M M A A H H J J J J. A A 3 DATE , GRAPH 14 - Oithona sp. Intake numberp per cubic meter vs date
\ , 5TA T 1ST ICAL A N AL YS I5 S Y $ T E 83 SIDE *D 58=3 PLOT OF NUNCu VS OTE 3 ?,90 e. C C C 0 0
- A l
24GCCoe?"CG + A A 16000.C00C0 + l A JNUNCU 1 A _ tver,~.*once e A A A N o A f A I I 6 Mea.cocoe
- Ag t
f' e.cocco + l---------,- . - - - - - - - - - - - - - - - + - - - - - - - - - - - - - - , - - - - - - - - - - - - - - - - - - + - - - - - - - = = = = - - - N N D D J J F F M M A A M M J J J J A A S
~
DATE * ~
; GRAPH 15 - Otthona sp.
Discharge numbers per cubic meter vs date __ __ __ - _ _ _ _ _ _ _ _ _ _ _ _ _ _ - - - ------I
5 ST A T I 5 Y I C A L ASp=3 N AL Y S1 S SYSTEN PLDT OF PROPTL vs DTE T 0.76C00C00 + T 0.64000000 + T I
/
n e S2nC0f0C + Y T T mi POOPTL I T P e 4 0 C D O C O + T L i ro a l T C e 28 t'*' 0 *0 t + t
=
a.16SC0000 + " T
- -_.-.-------------------,----------- ..- - - - = = - - - ,_ _ - - - - - - - - . - - - - -
l-M
/'M J' J J A'A S N N D D , J J. F F M M A A J
~
. D A T Ti i
i ? GRAPH 16 - n Ott'ona sp. Mean percent of total vs date I
ST A T 1 5 Y I CAL A pe A L Y S 8 $ S Y S T E 80 htDCat Spa 3 PLOT OF PEupTL v5 DTE C.83000eco
- A A
A e.67C"oeC0 + l A A
+
$)O.5rCOeCO
- A% 4 O'OpTL e ,
m" . N A l9.socao :e + t A
..o rro-:o +
l -- -- -- - . - e - -- -- -=-_-----s-------------------s---------------- +-------------------s------------------+ ---- N N D D J J F F M M A A 'M M J J J J A A. S DATE GRAPH 17 - Ofthona sp. Intake percent of total vs date I
e 5 7 A T I 5 Y I CA L A N A L Y $ I 5 5 V 5 Y E as
$80L*D SP=3 PLUT CF PHOPTL VS OTE t.6Cttoetc + A C.66?N PCS
- a a
- a 4
G.56?nceto e A A A POOPTL A / A A A A.44"nDeCC *
< A 8
a A N A o.3200000o .i 1 A A ' c.2scrocoo + l_________._________________.___________________.__---- N N D D J J F. F M M A A M M J. J J J A A S
. DATE .
GRAPH 18 _ Olthona sp. Discharge percent of total vs data
( 5 T A T I S T I CA L A N AL Y S I S SY S TE 84 SPm4 PLUT OF huMCO VS OTE l 4Sae.mO*00 t-
-s s 1 l
i l 3208.*0000
- y l
t T 2480.00000 + NUMCU
, m 16"*.006C* +
l < l T B f* a l N \ * :; i s. i n ~.*CCC' Tm
%,/ , _.,% _ ,
. 0, c .
l'_________,___________________ ___________________,___________________,____________ N N D D J J F F M M A A M' H J J J J A A S ,
*~ s DATE GRAPH 19 - Euterpina acutifrons Mean numbers per cubic meter vs date it :pr g,g 3 . f, i:
u
% ' D* D I * '" I I ' J 430970**Ca'J37 '* /45.cG.SCJO 141.5 , = 8 74, .11.* <.t 7 ; ,.* , < e t o g t 13g.f s
- - - - - 3 * % * '_'_0 '_D C T___
S T A T I $T IC A L A N A L Y S I S SVSTEM SIDL=1 SPs6 PLOT OF NUNCU VS DTE F16.000CC + 1 A 26**.*CCCC + A A 180".C000C + NUMCU A l
\
1200.r e n* O +
< A b
N LTI l 600.^00C0 + A l A A i a Na A/* A sur A A A 6.cocco +
-----------------+------------------+-------------------+----.
J A A l---------+--_----------------+-------------------+M N N D D J J F F M A A H H J J J 'S
. DATE .
1 GRAPH 20 - Euterpine *cutifrons Intake numbers per Cubic meter vs date i
S TOT I ST 1 COL oNAL F S I S SY STEO f,8 0F.=D Sp=4 PLUT OF NUNCU VS DTE
. 6 r c S .? 30C r. +,
A 4804.CCeC0
- 3hC0ernCCO +
A Pd.'u CU w P409.01POO
- a N
A l N A I2C*.r*e0C + E
~
f* E A A A A N A# -A AquuumeA - A A a.co oc + A l______........ N N D1 D J J F F M H A A M N J J J J. A A S
, DATE GRAPH 21 , Euterpina acutifrons Discharge numbers per cubic meter vs date 1 ! f.,
s
S T AT I 5 i ! CAL AN A L Y S I 5 SV S TEM SP=4 PLUT OF PRuPTL VS DTE 9 13nCCC00 + T . T l 4 l 0 10 50 000 f' + T C.C8C0000C + P10P TL
% 0.055" WOO +
w T N N I O.0 30C 0C 0 3 + 1 y T I c.rc573000 ** l___.__..-+...._____...._____+________.____.____,__.._____.....__...- --..._____.__..-.__.._____...___.Temi_____
D D J J F F M M A A M M-J J J J A A S
' - N N DATE - j' 1 GRAPH 22 - Euterpina acuttfrons Mean percent Of total vs date l t
ST AT I S T ILAL AN AL Y $ I S SVSTEM SIDEst SP=4 PLUT OF PwoPTL VS DTE a.12CC0fc0 + A A ft.095 eof 00 + l i f.070?3COC + A P:0PTL A
- ' A
<: O.a45"Seco
- A 1
g A A co I A A r.cercarc e + 4 A A A A AmuumeA - A
-c .ar sc of er. +
l--......,-------
--------s-------------------+-------------------s-------------------s- __-_ ------s--.--
N N D D , J \ J. F F M M A A M 'M J J' J J A A S
- e DATE k
GRAPH 23 - Euterpina acutifrons Intake percent of total vs date
r l I S T A T I S T I C A L A N A w V S I b 5YS TE N SIDC=D SP=4 PLUT OF PE UP T4, VS DTE C.2CCC0000 + l
^ l c.160C0000 +
A 0 82000CCC + A / A A A
#90PTL 1
0.eA000CC3 + 0 e A N W I A l 0.040C0000 + i A , l
^
.# ^/
A A A M A % A # sumA o.0erconce + l_______._,___-=- __ __ _ -,____--____.. ______...________ N N D D J J F F M M A A M M J' J J J A A S
- DATE GRAPH 24 - Euterpina acutifrons Discharge percent of t?tal vs date 4
a q S T AT I S T ICAL AN AL Y S I S S Y $ T E R$ spsS ^ PLUT OF NUNCU vs OTE Storer0tte
- Y 4
l Sco.CC*00 + , l l AC*e"Cn?C + NUNCU T Antenntec e T 1 / T T w O , 7 29:ornfCC + NT T
, \,s o.noroo + T T l......___,......_._.___..,_.....-.........._,-
- _.._______.........______............____...____.-_._.... .
N N D D J J F. F M M A A M M J J J J A A S
. DATE l:i GRAPH 25 - Pseudodfaptomus coronatus Mean numbers per cubic meter vs date 1
...?-. .. ..--...- --- -
et LP=5 SL A e 4 hu et u se 2 0 = * ( J 3 5 t *> 2 7 J. wi:.,Jt b 7417 C e c3J3 h- SyllJ.14JLO .>f 1 ( 3 =.7 .1916.9
's _ _ . _ _ - - - . . -
0.0 le.>J.t47Cco 13Ce 6C 6
- - - - - - - - - - - - -------------- . -----~~~~---- - - - - - - - - - - - - - - - - - - - - -
1 3
5 i A T I S T I CA L A N A L Y S I 5 5YS TE N 5IDesI SP=5 PLOT OF NuMCU WS OTE SO20.ecc00 + A 820enoCCc + 620.t'nfCo + NUMCU A AWA l 42*.PCJoo + I A Cd 22'.accoe + / A/" i pA l
^
***===^***==
A
.ra.conoe a asem-ma g
=, _--= ........,.....
A "' N J J J J A A S D D J 'J F F M M A . N N DATE GRAPH 26 . Pseudodiaptomus coronatus Intake numbers per cubic meter vs date
'~
ST A T I ST! CAL A NALY S I S SY S T E 88 SIOL=D $Pm5 X e '. 1 PLUT OF NUNCu VS OTE -1 930.*Crre0CS
- A 1
61C.C?nenece + A 51c.CeerSect + A
. Nl swCf3 1
m 35*.e***cnoe e f d w A N A unummu A non.n-eenesc
- A A
A A A i se.co' conc a + , A i' l_________.___.._____________ ..____ . -
=
N N D D .I J F F M M A A M ' M J J J J A A S
*. DATE GRAPH 27 - Pseudodfaptomus coronatus Discharge numbers per cubic meter vs date I
S T A T I 5 Y I CA L AN AL Y S I $ SYS TEN SP=5 PLOT OF PROPTL VS DTE e.C4tcoCCO + 7 1 0.o3303000 + T t.c2590C0C + EM'OPTL T T w 0.01786C00 + I w W y - ! c.9ooneroo + T I T T if o.ccacocco5
/m r
------------..s-----
--_-----+----------- -------+-------------------+-------------------,
I----.----s--- J' F F M M A A N N J J J J- A A S DL D J l N N , DATE , GRAPH 28 - Pseudodlaptomus coronatus Mean percent of total vs date 4
..s S T A T 1 ST 1 CA L A NAL Y S S S SYSTEM h I DE.= 5 SPab +
PLOT OF PWUPTL VS DTF
.. 'P.CSCe0CCr +
. 6 A
l ten 6100PLC
- A P.C3fC$ rot +
FOOPTL A f C2'CorCo
- A W
b
- o.C1090eoO +
^N A A AmmmmmeA
.---,v.,. *- %
l....._...,_______._........_,__..._______..__........._....____..._,________...__......,__ - N N D D J J F F M M A A H M J J J J A A S' t DATE
- f l J.
GRAPH 29 .- Pseudodtaptomus coronatus Intake percent of total vs date
5Y AT 8ST I C A L A N AL Y S I $ SY$T8 M SIDL*D SPdS PLOT OF PROPTL VS DTE e.05000cor + A I G.04eF0ftn +
- A e.C300acco +
A PC OD TL. A 4
- 6.02V aaLO +
a
" A ca A
1 A i A A S.St e00CCe + A AmA A A A A c.aaoeirce + l---...--..---------_ N N D _---+- ......---... ----,-- __ D , J \ J. F F M M
-=.----....................... ....--...........--,-....
A A H H J J J J A A S i
-\ #
DATE GRAPH 30 - Pseu1odtaptom.. coronatus Discharge percent of total vs date 4
i S T QT IS T I CA 1. ONOL Y S I S S YS TE N
$Paf a I
i Y o i A 1 4,60.00CroeG9 + 36".o*Ca0P00 +
'T NtimeC U 242e*Cta22CC +
m . 0 w C) 12".600CPC00 + T Y Y T f l imummet [p.ocoe00CC + 1 I f i I I---------+-------------------+-s-----------------+----------- ----+-------------------+--~~---------------+----- N N D D J J F F M M A A ~M M J J J J A A S DATE GRAPH 31 - Labidocera sp. Mean numbers per cubic meter vs date 8
%b d J, sw e t !** # 2 te 10 0. t * *. s J es !803o 43, 43 3 j c,3g,, g j g g r,c 7,,3 4, g * "' ' 1' D *C **4e =4CC3 isteO7t f
l l l 5 Y A T I $T I CAL A N A L Y S I S SYS TEM blDE=I SP=7 PLUT OF NUNCU VS OTE 80?.000nnC00 + k l 649 e3000000 + 48C.e'reacC9 + NUNCU A l m 32*.Cinconot + I a w N A l 16C 30000PCO + A l O.4*0*0600 + A A A l___ - - - , - - --_______--s--------------= -e-------------- -
-+-------------------e-----------z------s----~
N N D D J J F.' F M M A A H H J J J J A A 3
. DATE .
GRAPH 32 - Labidocera sp. Intake numbers per Cubic meter v' date i
.y.
S T QT I ST 1 L AL A NA L Y S I $ SYS TE M StuE=D SP=F PLOT OF NUNCU VS DTF 2er.eC1tSc00 + A A I'90. 0* f
- C P C O +
140.rCCenrC0 + l
- =
t HvMcu i a , oa. aa- oct , . <:: I e w co l 4c.a-secoca + A
=
A A ummuunA A
~
_ta. cec oacc +
\_________.___________________.___________________,___________________._______
N N D D J 'J F F ~M M A A M' N J J J
, J A A S j DATE O
G RA PH 33 . Labidocera sp. Discharge numbers per cubic meter vs date
ST A T 8 5 Y I CAL A N A L Y $ 8 5 5 YS TEM SP=7 PLUT OF PS.OP TL VS OTE C.030CorCC + l T O.02400rCC + T 3.9181C000
- I PJOP TL m r.rt2P@nCO + T
=::
s a (.ss W i e.CP6P3rCC + T T 1 imi T
) i 0.e!Sc3Cnu + hi s l.....____ A A S H H A A H H J J J J N N D D J J F F DATE GRAPH 34 - Latsidocera sp.
Mean percent of total vs date
S T A T I 5 T I COL ON OL Y S I S SY S TF M k SIDL=I SP=F
-. PLUT OF PROPTL VS OTE CeP6?CCt40 +
A cec 4ncoroo + Ge)36tPPcn t
+
PP00TL m "er24aCDCO +
< A I e w
b O A
./
C e 012* X C + A I A i A A AmAEmiluul A eeoeneocen + A a # ^ *** * ***'ae a l_________+.___________,,______+_____________'a=A _____+___________________+ __________________+____. ________+_____ N N Dl D J J F F M M A A H H J J J J- A A S ! DATE s . GRAPH 35 . Labidocera sp. Intake percent of total vs date 1
S Y A T I S T E CA L AN A w Y S I 5 5 Y S T C se SIDLaD SPs7 PLui OF PFORTL v5 DTE A
- 0. OC 9e 0 6C O +
l A I 0.0eT20000 + c.C9540e00 + P80PTL c_. c.cossocce + a AMA y a A A A 1 ' 7 l c.co l80f 0 0 + A A ' 4 A A A A A e.010c0000 + AammmmmAssummA A ,, l_________,___--- ___________,___________________,___________ _____,___________________,___________________,_____ N N D D , J J. F F M M A A H M J J J J A A S i DATE o i GRAPH 36 - Labid 0cera sp. Discharge percent of total vs date i
6 S T A T I S T 1 CAL A N AL Y S I S SYS TE 88
$PmS e PLUT OF NUNCU VS OTE 2009.09?CO +
s I i
.I T
1608.*0ece + ' i T 12 rte *nrCO + NUN Ct) t 80".60000 + T
=O e
w tm N
,, 40".90C00 +
i m TsumummaT m TaummmmTammmmumT Y i -Y e.acoc0 + e j............_____._..________ ... N N D D J J F.' F M M A A M M J J J J A A .S
. DATE GRAPH 37 .
T0rtanus setacaudatus Mean numbers per cubic meter vs date
?3 Memo se.aza se hv 4 0 6e l l o.0 7F 4 s'.i 323 623;42 I C 4 601. 732 4 2 t, C %") . 3 3 7 C ? o 7rJa317.48476 ?.C /27C.?rsece gj y. 2rs
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 'O ___ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ __ __ _ _ _ _ _ __
ST A T IS T 8CA L A N A L Y $ I S SV S TEM SIDES 1 SPs8 PLui OF NUNCU VS DTE 300?.00600'* A 1 24CO.00CC0 + i A l 1890.00"00 + NuesCU
%
- 208.* 00C C +
f A 4 t.a 600.00PG0
- a A
l A l A
, 1 A muuuum A m Am A-A A em4 A Ae-AEmuummeAsuuuuuum j c.0000C + A i
l_________+___________________+-__________--. -+ _ - _ . _ - _ - - - _ ..___________________+-__________________+_____ N N D D J .J F F H H A A , M' H J J J J A A S 8 . ' DATE
.i i
GRAPH 38 - T0rtanus setacaudatus Intake numbers per cubic meter vs date I 4
S T AT I S T I L AL A N AL Y S I S S Y $ T E tt SIDE =0 SPag PLOT OF NUNCU vs OTF Pa.09CCOP00 + A 320.06* eor c o + A i 2er.nerennoc e A NUNCU l w 160.ec?a0eDO + e u
*=
4t A a?.coarocce . A A A A A Am 0.. . %A/
~
l_________.___________________.___________________.___________________.___________________._________ N N _D D J J F F M M A A M M J J J J A A S DATE GRAPH 39 - Tortanus setacaudatus Discharge numbers per cubic meter vs date i s t i ___ _ _ _ _ _ _ _ _ _ _ _ _______ _ _ - - - - - - -- - - - - - - - - -- - - - --- - - ~ ~
S T A T I ST I C A L A N A L Y $ $ 5 5YSTE N SP=8 PLOT OF Ps.OPTL vs DTE Y C.09030eCO + e.eF200600 + c .054 r O N O + P'OpTL 1 g e.03600CCO + I i 4 u, T T 6.cteaonoc + ) i T emummi c.6anC0000 + l=-- ---, N------------==--___.----------F N D D J. J F M H A A H H J J J J A A S a DATE .g GRAPH 40 - Tortanus setacaudatus Mean percent of total vs date
S T A 7 I S T 1 C AL A N AL Y $ I 5 SY S TE 85
, St0Lat SPm6 '
PLOT (JF PkOPTL VS DTE P.20ffCCCC +
;t '
1 C .16*( 0 *C 0
- A C.12 3rCr C * +
P40PTL m C.'O'ent?C e I e b m 0.04* ROC 00
- r A A A
Ammmmun4 A summme A A-A A AnameA m A m A A c.eeec0000
- A A AmmemA l_________,___.._..___________......_______._____,_...______ ____.................__..__.=-- -- ....____..._...
N N D D J J F F M M A A M M J J J A A S
,J .
DATE - l l- GRAPH 41 - Tortanus setacaudatus Intate percent of total vs date , e j l* i
5 Y A T I 5 Y &C AL A N A L Y S 8 5 SV5 YE M SIDgno spas PLOT OF PhDPTL vs DTE 0.02000000 + a 6.08660f00 + 0.n32"n000 + A PROPTL GeOn80nr00 +
=<
e _,a
%_ A N
A 1 c.0eA0000c + A A A A i
^
~. .
l AmA 0.0cor0000 + A-mum A A A i l_________,___________________,___________________,___________________,___________--- _,_________________,_.,_,. N N D D J J F F M M A A H H ,J J J J A A S l DATE GRAPH 42 . T0rtanus setacaudatus . j Discharge percent of total vs date
- S 7 A T I 57 I CAL AN ALY S I S SY STEM Sputo w
' PLU T 4.F NUetCU VS OTE Taana.cocoe . T i l
Stic3.oococ + 7 4 3366.conon
- T T
NUNCU T 29039.00C00 + r s T w I T be co tscon.cooCn +
/ ,s -
sene.cocco + l__._____,.......______...._._______________........- -_=-.__--+-...--_._..........__...........____ N N D D J J F F M M A A M' M J J J J A A S N -
+ I DATE '
GRAPH 43 - Total Copepod Population Mern nunbers per cubic meter vs date
- 3. . - - - ---_------ - - - _ . _ - .- _ - - - - - -- ..-- . - - . - - _ _ _ _ _ _ _- --....___
a* '
, SPalo Stx=4 is_
kbetu ot 4*200..tC3tb t ide f.te,o J7 9 37C 5J34 tJ e de s Ic9Jdol.eetCC C. a 632 nt. 18 .'s Ic. e s a c r e IC.s pt.npsc33 . 3.-
._-..g.
77.303 I '
STA T I ST 8 CA L A N AL Y S 1 S SYSTE M SIDL=1 SP=10 PL.OT OF NdMCU VS OTE 920CO.rC000 , a 74000.*0eCC + i A 560 $?.r0*C e + NUNCU a 1 3 6 CO*.C orc C +
.-e b A
-$ a \
A A A aceco.coeoe
- A N..\
A l /^ pa#^ 2eca.corce , l___...........___________....,___________________,___ _ ____ .....,_......... .._____.,_._________ ... ___,_____ N N D D J J F F M M A A H H J J J J A A S DATE GRAPH 44 - Total Copepod Populatton Intake numbers per cubic meter 's date
5TA T I ST t CAL A N Y 5 I 5 5Y S T E 83 SIDE *D A 59L.30 PLOT OF NUMCU VS OTE SCCCa.COCCO ,
. A 40c00.00000 +
4 30006.*3rGO + 4 4 A NUMCU A m 2?rfoe0CCCC + k a A l Asummmm accco.Cocco + A
'O.0CoC0
- l____'___...__________________,__________________.___________________,.____________.____.________.__________,_____
N N D D J J F F M M A 'A M M J , J J J A A 3
. DATE i
l GRAPH 45 - Total Copepod Population Discharge numbers per cubic meter vs date
i i APPEriDIX C NET MORTALITY GRAPHS IV-151
~ . -_ _ ._ .. _ -. _ . __
STQT 1 ST ICOL Oh OL Y OI S $ Y 5 Y EN TetT*ING
'} PLOT OF MORT VS DTE
- 0.J3200000
- A l
0 28200000 + l I X A 0.2J200000 + E A , A 1 DOdf A l A O.15200000 + A c.e A A A a j a n m l s N A A rs) l A 4 x r. A
. 0.IJ200000 +
a A 4 a a A 0.08200000 + I A l ==- -...s...........-----s... .-...........,...................,...................,..... N N D D J J F F M M A A M M J J J J A A 3
. s.
. DATE GRAPH 1 - Acartta tonsa Intake mortality vs date SPcC=A
.O* H iiset Cow.
- V AM! bJLE N NEAN STANJARD DEV VARIANCE SUN ((s a htLTLD $5
- 3. 0 an pa r G.CA CCC L.JJs000 3T.tC3 MUHT 20 0.Ib6650 0 059097 0 003492 J.tF7000 1
S T 4 I 1 ST E C A L A N AL V $ IS S VS I EM TRIsIDC PLOT OF Ni.;T M t WS DTE 0.50900000
- E C.40f00000 +
4 C.30C00cc0 + NETMT I 0.20C00000
- m b ^
w w O.3000nC00
- A A M -K A N
* * ^
A c.Cao00C00 + x x a a m l_________+._____________ -,_________. J F F M A M M J J J J A A S
. DATE GRAPH 2 . Acartta tonsa IDO population net mortality vs date TRialDO SPEC =A hETdi le 3. 0c 74 2 9 0 084336 0 007813 0.972000 0 092467 0.0 0.387000 828 474
S 7 AT IS T ICAL AN AL Y S I S SYS TE M Tkt=SO2 i 88LO T (W NE T2S T VS OTE O.83000000
- A A
c.65000000 .I 1 0.496 Con 00
- NrTNT c.330oecoO .
< R z e
u, x 4 c.?r0c'0000 + 4 A R A A enums A R o.c 00coc0 + x-a N A A R l-------...----------------.--------------------------------------.--------------A----.- __ ___ ------,----- N N D D J J F F M M A A M M J J J J A A S. DATE GRAPH 3 - Acartta tonsa 102 population net mortality vs date TRT=ID2 SPECmA VANIASLE N MEAN STANDARD CEV VARIANCE SUM CO RR t:.C T E D SS LOW HIGH C.V. I NETNT IF 0.850294 0.232800 0 054196 2.657000 0.867134 0.0 0.804000 848.950
~
la , g n < l i g 1J u e { i - 1 e4 l4 2 O M d ln 0 O
- O I g
. s. m / O
< 9 I 2 8
< w D E d
! : O i
< < ! x, b 4 I
=
- l lE' t s 8
& . O
. . s d ;
. i.< . 8 82 s
8 3a 1 i l WO ' l / . E i : e O & $
. < - !*yw "
5 a 5 j* 4a . O g 4 O 2
; 85* ,
<e 0 .
z -
=
- a. &*4 l
> ( O E W g at d
> 3 O m
*2.o =
- ik a . :, 2.~ o e a < = ~
t th i u 51
>O
//< !.
*
- D ,
p ! > e l, . < i
< < j, i o s
. 9:
a $J 5
< E
. O O
( M
*E 5~
m - l . I l 2 O O U g a a a O e O O O O o O > O O O h O O W O O h W O g e
- z a ~
u, J J J y J O O 7 m . 4 W*
> 2 IV-155
5 YA T 1 5 T I CAL A NA L Y SI 5 Sy b T E ge - T F. T s O5 2 i PLOT O- t< T NT WS DTE C.50CL0000 + 4 0 6e0C0000 + l l l l l i L.'eh000C00 + Fec. F.4 T 0.'2Co0000 J . e w CD 0.j60C0000 + 4 A C.00000000 + ^ 4 a l + _ _ _ , = ______+.........____..._ .... __ _ ___ ........______ ._________.____________..___.,_.... N. N D D J J F F M M A A M M J J J J- A A S DATE . GRAPH 5 - Acartta tonsa DS2 population net mortality vs date - TRTsD52 SPECS 4 hE_T M T
, 17 0.867588 0 223189 0.049782 2.849000 0.796510 0.0 0.754000 133 835 9
S TA T I 5 Y !C A L A N A L V S I 5 5Y $ Y CM TRT=Ds2 Spot PLui UF NETMT va OTC 4 00300000 + F emf 1 0.80000000 + M
/
O.60000000 + NF. TM T S M 0.40000000 + = w I a:: 8 N i
/,
, 0 20000000 +
- i 4
/. J 0.00000000
- 1 JammmmusJ Jm) N
- J f .........- ......_......... -- _ .. .....................................................
.N N D D J J F. F' H H A A H H .J J J J A. A S DATE ! GRAPH 6 - Acartta tonsa DS2 population net mortality by sex vs date l f
STAT IST ICAL AkALY SIS SYSTEN T2Tuo s2 PLOT OF NETMT VS TMP 0.80000000 . I a a A 0.64C00000 + 1 N 4 C.48C00000 +
\
l
. 1 NETM7 7 ) a C. 32 CC0000 *
<:: i 4
I I a Lit l 4.16000000
- 11 A X-1 _A A
E A 0.00000000 + 1 A- A #l A
, I ___.__ _,___________.__________________..,__________________. __________________._________.________.._____
21.20000000 24.40000000 27.e4000000 30.80000000 34.00000000 37.20000000
~
LEGENDS A a 1 CBS . 8 =2 085 . ETC. TMP GRAPH 7 - Acartta tonsa D52 p0pulatf 0n net mortality vs temperature l l 4
M se
# 9
- O en
- e 6 \
6 4 ,.
- 6 4 ,
k n
' a 8 4
\Af 4
in O o O
*Y k
- e
- in t
t O h".
- t t
O O
** 4 O $
e t e d
- 8
% a t
8, $ e 4 5 y
*< +
8 , l f
- m t - > n 6
- 8 y w >
> + e $
.g
.a s M n m i
> ; $ h
. 5 1
- t - i n O 4 4 u d
+ M ,
M c
# b O is \g 4 8 O
==
O 0 -
> as n
.a 6 J ' N $ R gh , O.
R. o t e t Ih S 0
- 6 gp
+
n * % C $
\ >
C .% S. d J . O h 43 f e
" O w
O g
=
1 eM 8 l 3
> I e m
0 < A 4
> t O s e $ O 1 O i " " -
- 4 I
E 1
-._. I
- 7. o
< 4
*6
.s
\
\
t t O O O o O O ~ O O O O O g g O o O o O O O g Y O o O g O O g
- O o O O O I m O C 4 e 4 i N.
e O, o. O O O O > O O g O S ty459
S T AT IS T ICAL AN AL Y S I S SV S T E 83 T R T = 100 PLOT OF NETNT vs OTE e.800C0000 + P I
- 0. 64 00 0 f'0 0 +
m 0.48cc1000 + NE T8. T i
, P 0.37C05600 +
m m - P O # ,, P t e.16000000 *
"%. 's.
0.00cesec0 + %p
- x x a l l_________.___________________. ==_____________.___________________ ___________________._______ _________._____
J F F M A M M J J J J A A 5 0 ATE e a GRAPH - _ Paracalanus crasstrostris 100 population net mortality vs date t TRT=100 SPEC =P hETNT 14 0 182143 0.217339 0 047236 2.550000 0.614070 00 0.F87033 119.323 b e
$ ?A T I $T ICAL AN AL Y S I $ 5Y S TEM TRTalD2 PLOT OF hETMI VS DTE 0.90000000 + ,
NMa p.72006e00
- I p M 0.54000000 +
P NCTNT 0.36000000
- m f x x a
b P A 0.18000000
- P 4
. 0.0c000cco
- x $ 5E5 m P l_-_ -,---------= -=----_---,-------- _ _=--- ---,- - - - - - - - - - - - - - - . . - - =,=------- - - - + - - - - - - - - - - - - - , - - - - -
N N D D J J F F M M A A M M A J J ,J A A S DATE , G R A PH 10 - Paracalanus crassirostris ID2 population net mortality vs date TR7mIU2 SPEC =P 0.J06745 0 094092 3.8650C. 1 505476 0.0 0.868000 1 J4. 92 0 hETNT 17 0 227JSJ
STAT I57 ICAL AN AL Y SI$ 5Y STE83 TRT =O kt PLOT OF NETMT vs DTE C.82000000 + l P C. 66 C 00000 + 1
- C.50000000 +
1 X hETMT l' C.34 C00000 * '
.-4 p
< p p
I + p ee I p 0.I8000000 + x x p a g# x#^ . 0.C2000000
- i *%x 4 1___ _.. .. - -
A N N D D J J F F M M A A M M J J J' J A A S DATE e
, G R A P H 11 -
Paracalanus crasstrostris DSO population net mortality vs date TRT=DSO S PLC=P NETMT 20 0.246700 O.199055 0.nJ960F 4.934000 0.752536 0.001000 0.887003 80.6F5
*1
'J,-
s
b T A T I 5T 1C A L A N 4 L Y SI S SY S T t- M T57-052
**LUT OF NETNT WS DTE C.5000C000 +
I 2 I C.7dCC3000 + 1 0.S=CC0000 + 6* hr. T M T 0.JoC00000 + l c.-. L cn . W C.lo033030 +
/p ammumusa x / .
lNr .
)
p , 4 0.CC000030 + 4 f___......................................................._.......... J .H A J J J J' A A' S N N D D J. F F H A H
._H DATE' -
~
GRAPH 12 - Paracalanus crasstrostris DS2 population net mortality vs date TRT=DS2 SPEC =P VARIANCE SUM CDRRECTED SS LDw HIGH C.V. E WARIABLE N MEAN STANDARD DEU 0.102246 5.243000 8.6J5934 0.0 0.862000 803.679 AETNT 17 0.308412 , 0.319759
S TQT I 5 Y I C O e. A NOL Y SI S $ Y B T r. M TdTsDS2 SP=2 PLeJT OF pe.- V M T v$ OTh I.COC00000 + r mmme > - t summF l
*, . =
I i-I 0.80000000 + l I J 0.q0000000 + hE TM T 4 1 0.40000000 * *- J aC . e w l Ch 4D M F r 0.20000000 + I F
) n J y 'e J > J
.. F i
. J l >h F J 0.C0000000 +
~
F JmJ J l4
\_--.-,....._.. __.--..-- -- _. ---s...._. _-------e.---------.......--,--......--.........+ ....
s-N N D D J J F F M M A A M M J J J J- A A S J
, DATE N- -
GRAPH 13 - Paracalanus crassirostris DS2 population net mortality by sex vs date 9 8 e
p - em 9 4 O 4 O e O e O l O M
. N ,
I e 4 h 9 fi
>- l j w / i a
w E 2
- 8 a i O e O e I O C O
. n p 8 >
9 h
~
l 5c t I : O g E W sw , O - a 9 O e e
- O " O
.e O e ,
- O M O
- g e W n ; ; = =
. n a l 3 & . a - i e ~ - s E .! a 2 8 W e e g
I
> 0 J
d D !O 0 0 N b g O 4W X e O Q > e O Z 24 + 0 Ch. i O q (a> " * = Il = ; s
- N J to '
O 4 J s O w i O . W 9 O V 4 O >
" w $ O W JM 14 i I O
+ 0 e 1
- , =
a " e e e 3 0 3 6 0 Q l O I
+ 0 N t O O N 4 6
- g I N O i z 4 m
'J
+- --4_- e _e +__--
M I W g _-e-O O O O O O O O O O O O O O O O O O O O O > O O O O O O X u o O O O O > O O O N O q w e e O (P M C 4 n ao O e e e e O O e U O O O IV-165 1
- 5 YA T I ST I C A 4. A N A t. Y S I S SY J TEM TRTalNO Pt.UT OF MJRT WS OTE 0 38000000 +
1 1 e 0.25000000 + A l l 0 1 x 0.20000000 + J NORT r l A X X
).35000000 + 0 0 m x J A
< d ,
a w m a J i , 0 50000000 + c a a
-r A
a 0.05000000 + l __.....____...._ ...,_......____...____.............____....... - -- .._ .......,_........................ s - N N D D J J F. F- M .M A A M M ,J J J J g a g DATE - G R A Pil 15 - 01thona sp. Inake morta1tly vs date 59t0=0 MORT 20 0.lJ7900 0.04d7J2 0.002J76 d.7bd300 0 04bl22 0.0tt000 0.267000 J5.JJ4 4 4
- t. .
I. g
- O ;
q t 4
< a '
1 4 c = j -: w c
= .
l 3u
! E
, i .
. e O e w
= o l
, g i
- a. .
y g *n e c
. L a = c 1' x j g 2 8 e o y C W g i O
- c . x . u
* ; < ~
03 s 40o ., 8 a = Q-
&4
, 4 O W 8 gg
.O.
2.N. , ti U u
<g < < -
B .=. . J to I! a M.
< a e g
a O. ao O t. e '
- . l
=
x . P* (
,e W O l
O M i 6 O o
/
-e
+
- l. ,
O.
* -+- . . _c =
~*'"
O O O O o O O e g O E O L' K D 8 C 3 A $ k k $
' E E a l '
f* IV-167 1 \ l
_ _ _ _ __ _ _ . _ . . _ . _ _ . . _ _ . . . . . - _ . _ . . . . _ . _ _ _ _ _ _ . . _ _ . _ m_.. - _ _ __ ST AT I ST ICAL ANAL T $ IS SY S T E 83 TRT=ID2 PLUT OF NETMT vs DTE ,, 0.En 00;300 0 + 04 Ama 1 0.6.odUCCS *
- 0. 810SoCO +
l NFTNT-c.3 ace'cco o + x w
?
O a cn o.nsoooooo + x N= o.ceos oco + SM5 m b ^ " l---------+-------------------+-------------------+--------- =
----+------~~~---~~ ~ ~--*----~~-------------* ~~~~
N N D D J J. F F M M A A M' N J J J J A A S
~
j DATE. - GRAPH 17 - 01thona sp. I 102 population net mortality vs date i l l i
- TRT=102 SPEC =0 i
,a , , ,, ...u... .
.. w ... ....,an .....o.. ..., m . ... ..,..... .....,,
l l
, a o # .
=
o .$ t 9 $
~
,. O i
$ M o S. O e
8 D
< o 0
l D
; c . 4 x 5 O e
n a 3 O
\=
l!= c i i6 E 1
! l hl 8 +' O
> 8 D 0 N W O 8 h C
> ! . 3 W '
8 243 O 8 4 N w = o l 9 5 o e o e . l, < as , c e O l
> d = 9 4 o o i
> 1 co 18 m
- U = ]
J X W P*
<3 $ z S .* l 9> .I =
%y -
g 8
<a> si, o
- u. O w C a O >
.J 6- 0 m f >N
< W S s' . ,
v , -
*O $
_ / \o i, b. 8 8 k
>. u 3 4, o W =
= lA J
<a *
.// . l- n 4 $ e 3 9 O 8
- I l
.-- . _- ._- l O O O O O O O O O O O O 7 O O N O O O O s' O O B. O O O O O O 3 O u O O O O > O U O O U U uJ O U O WD @ 9 Z N
- O O O O k I
t-IV-169
, , - ~ , --n -
--n .-
+ , w
- n T A 7 I S T I COL A NO L V SS S 5Y S TE M
,. ThisD$2 PLOT uF Nt. TNT VS OTE
,C.eC0Cou00 +
w I 1 4
' 0.6400C000 +
I
' 0.*aucoc00 .l n l
3 N TMT l i m
.C.J20C0000 +
e-o
=<
e a g N O 1 .. l '.C. 3 6 C O 3 0 G b + 4
,%"4
~
7 % ~ .%
.C. C C 3C OC 0 0 + 4 __ %4 I------------------------------------------------.----------------A N N D D J J F F M M A A M M .I J J,,J A A S s . .
DATE - GRAPH 19 - 01thona sp. DS2 population net mortality vs date TNT =DS2 SPEC =3 , NETMT 17 0.858412 0 259052 0 067808 2.693000 1 073728 0.0 0.795033 163.534 4 I
5 YA T I 57 8C A L A N ALY b 1 $ b Y $ T ti k Tie f =OS2 epa.t PLUT OF Na.T N T v5 teT E 5.00000000 e n. p 1 2 0.80000000
- s 1
l 0.60000000 + s hE TM T m 0.40000000 + < 1 I & *e u 0.20c00000 . 1 p =# .
=
1 5% ., , O.00000000 .
, ,/
s a J-J s l; ' l .._..__ - . - - - N N D D J J' F F M M A A M M J J J J & A 3 DATE ' GRAPH 20 - Olthona sp. D52 population r.et mortality by sex vs date
e O e O 9 0 0 0 Ow 9 O e O Oma e O e N e >= 8 M e W D I e 4 e 8 e at O 9 3 e s O W 3 e O e $ O 34
.s O '
s O e O
!e
. M W
b l 3u O b \ l O M 3 0 0 4.s as e o es 8= 4 O C 1 O C 6 O n l O
> . . . h.
g o a M e M e O 0 M g "$ l 8 &
. = o i e ~
e - m w & O O r g g 14 > . g > e
> n I D i O
.3 9 O N" i O 4 I O E O > + O CL Z 18 W O
> 2 eC 42 4 g e
> IL I P. 49 O 4 ,1 N
.) e-O a 4 =J
{ IL e U O 8 ee I k O e WD 1 O U e O > o gw 3 g= *
* + O '
4 \4 0 / l g e C
=
k e e O N l
, .I N N
O i e W O D 1 e o 3 i O M $ O se l O
+ O 8 8 O l N 4 I )
s .e I N 3 I Z 8 W
+_- ___,.----:_-- _e- e- .*
w \ W __+==== g O O O O O O O O O O O O O O O O O O O O O D= 0 O O O O O Y O O O O U O > 0 O O O e e W N O O e e e Z M ** O e e e -e o
.g*, * 'e
'O . -
O. . O .- . O- - 'O -
'OS.* +
- IV-172 l
5 7A Y ! 5 T IC A L A NA L Y 5 8 5 3Y S TCN TRT=I6a0 PLOT OF N 0 sti vb JTE O.25000000'* 1 N 0.it0000000 + 1 A E x t
- K m
0 15000000
- MOAT '
, a ,s N a O.80000000 + t n N, s e- 9 - A N G2 { L e 0.C5000000
- t.
t-. 0.c0c00000 . f-------.--------------...------------.---------. _; '
. ammmac samme t N N D D J J F F M M A A N H I J J J J A A S DATE GRAPH 22 - Euterpina acutifrons Intake mortality vs date 1
SPECat MOhT 18 0.067444 0.06 9 9 e a a aa** ' I f
w
$ T A T I S T I CA L A N AL Y S I S 5 Y $TE 4 TRialD3 PLUT OF htTNT v5 DTE 1 000eca00 ,
[uummu'muut: 0.8h000000 , 9 s l
} 0.6tecS009 . , NtTNT
- E M a
?
O.40090ec9 . E m e a w n i c.20aeocco , 1 E F
=
E [ r " "'""amex e.odoconoo x a l.---_____,__.-____.---_______! m t _----+------------------,-------_..--._---,----- l J F F M A N M J J J J A A $ DATE
/
r GRAPH 23 - Euterpina acutt' frons i ! IDO population net mortality vs date TRT=IDO SPEC =! hETNT 12 0 277250 0.370937 0 837594
, 3.327000 8 58J538 0.0 1.000003 133 792 4
'e a s k
5 T A T I$T I CA L A N A L Y 5 I $ $Y$ TEN TRT=102 . PLui OF NETNT VS OTE 1 000000c0 + g 4 0.80000*c0 + g smuuma 0.69en3000 . E NETMT 3.40001000 + M t e n 0.20000000 . E E E 4 r e %.a n' i a 4 = a e a 0.cn0c0000 + n e n e n l_________.___________________.___________________.___.__________
= ____________. __-.___________________._____
N N D D J. J F F M M A A M M J J J I A A' S. I DATE
- GRAPH 24 - Euterpina acutifrons ID2 population net mortality vs date s
- TNT =ID2 SPEC =E
- mpYuv 34 + n.71s597 n.itanno n.tonese 1.719n00 1.305445 0.0 1.000000 836.970
?
STA T I ST 1 CAL A N A t. Y $ IS SY $ TEN ist TuO $3 PLOT OF NETasi VS OTE E k'.00000000 + 1 E l 0.d0000000 + 1 0.60000000 + 1 l hETMT x I
.c. 40000000 +
- 4 1 a
a
-e K E
$ \
? E t n l 0.200C0000 +
\
E L E x x 4 1 A A l E ' E-L 4
. l A ,
O.00000000 *
, l_ - -s---.- . _ --..-.s--.==_ -------+--------.~..--...e...................s........-..........s.....
D D J J F F M M A A M M J J J J- A A S M N DATE - GRAPH 25 . Eut reina acutifrons DSO population net mortality vs date TRT=DSO SPEC =E J.463000 1.J24498 00 1.000003 148 248 17 0.203706 0.287737 0.C82783 NETMT i i
e sT4 T 1b T I CA L A N A .f S I5 S Y 5 TE N
- TJ. T =O S2 PLJT OF teiTMT v5 UTE t t 5 003000C0 +
1 E l 0.ts0000000 + a C.60C00000 + a retTMT i C.400C0000 +
.-. E a t
a N N I d.20 000000 + E
*% 7 E
x s a a L. .
^ A a 0.00000000 + -
h---._.s..............--......=-- --e = - . - .....-....- ~--...........+--.................e..... N N D D J J F F M M A A M M .J J J J A A 3 DATE GRAPH 26 - Euterpina acutifrons DS2 population net mortality vs date TRT=0S2 SPECsE 0 332F33 0.38868'T 0.858078 4.991000 2 815091 0.0 5.003333 186.887 NE Tv7 15
5 YO T I $T 10 OL OMA LY $ 15 h V h T E > _ TR ima a2 $P=4 PLOT OF NtT4T v5 UTC 1.00000000 *
" l 4 J f t
+. F J
0.80000000
- l l "
l J l , [JC.60 0000 00 + - l hETMY I l O.40000000
- Y, M Y
l N J fn 1 4 i i C.JCC00000 + J f N4 J r3 J 0.00000000
- r i l J J
)
____. . = _ N N D D J J_ F F M H A A M' N J J J J A A S DATE GRAPH 27 - Euterpina acutffrons DS2 population net mortality by sen vs date i d.'
I O O
- a. 4 - - l
#
- O
- O M
0
. a g d s
t
. I e
- i w
4 *. ,
\O i
O g e 4 I O ~ O 4 P s i y
. 3 i
I O W i
' % g s e ;
e {aO
. o O 4 . .
*O j
s
- In 5 t
W d 5 " w g e *t k
" 4 P O ,
h . I A
<g i *il 4 O S
5:. 1 . 4 4 e 6
*? h
- tS
\M
\ .
4 i 4, o
* \
IO 3 a
\g%%- - O p
- go C
+ \
\
'e s
\$
, * \:-
\
*t.
g4 M "5 1 4-- ,- * . .- d .- 6 O O o O O O O O O O O O O
- O O w O O O , O g O O g w O O O O O O O
e. 4 h e %. g
- O V
- ty-V9
l 5 TA T I 5T ! C O t. A e O t. Y SI 5 5 y STE M =
.- TET=INO i 4
, PLOT 06' MORT US Of t;
$1.60C00000 + S s
, l 5
.- 1 0.48000000 +
i l 5 0.360C0000 + 5 NORT S m
'O.24000000 + 1 I 1 x g
e- e a:: e n ( X l A 0 82000000 + smb e A-A - a 3 = n 2 3 0.00000000
- a a s l.____..._,_____ ___ ___.__, ..__.___.___.___.....______...__....,____................__.......................
~
N N D D J J F F M M A A H H J J J' J A A S DATE -- GRAPH 29 - Pseud 0dfaptomus coronatus Intake mortality vs date SPL C = 5 MOHT to 0.165313 0.158395 0.0J5493 2.105000 W.332J38 J. 0 0.300003 101.eae4 9 9
ST A T 1 5Y ICAL A N AL Y S I5 5 Y$ TEN THf=IDO PLOT OF NLTNT v5 DTE S 0.600ce000 + 2 i 0.48C00000
- 1 e.36000000 +
j NETMT [ n.24000Cfts *
.t e CD N
E
- K f* .12 0 0000 0
- s E
- x x
A I l # 4-s x x a.ocoo0c00 es -------.----- l-----_-.-__--------------.------------------.------------------.------------------------ . M J J J J A A 5 M A M J F .F 0 ATE
- i GRAPH 30 - Pseudodlapt0mus coronatus 100 population net mortality vs date TRT=IDO SPEC =5 0 290blo 00 0 600000 192.198 v.147 8 0 106000 0.203719 0.041501 0 848300 f
S TA T I S T I C A t. A N A L Y $ I $ SY S T E4 f - Th T = I Dd I ' f g PLUT OF NETMT VS OTE t.700noc00 + S 1 I
. r' O.00000000 +
l ( l ME t l l 0.dn000"C0 + NE T M T S 0.E0a00000 + m
*:0 I
w , CO , x N R 0.200Coe0C
- x x
s s x s x== s e.0co00cc0 + xdx- s-s x x x S r l_______....___________...._,___....__s--___.._,_________-____ ..__ -_ ......._________,______.....___.____,.....
,N N D D J J F F M M A A M M J J J 3 A A S DATE l
, GRAPH 31 - Pseudodiapt0mus coronatus ID2 population net mortality vs date TRT =ID2 SPEC =S hETMT 12 0.153083 0 305598 0 093390 1.837000 1.027291 0.0 3.003030 199.628 e
e
. = i
- a
- n
=
1' <. s. . . I
< ; 4 O
- i. O M @
lD o O
, O.
/ ! n
! 3 n 4
\ ,o l
O J
\ l
.x i
~
=
2 e 6 g
. O o = "
l
- 1 s x :<
g 2 3
's 3 O
4 e e m L
- j &
.i 4 m o a w '
w m a
.o w . s x < . 2
- i. m a O 2 l M 5%
w n i a
<31 .! *
=
a.
&4 :
i O - ZN 9 4 Y
*g = a ca, =
!=
\
1 g > >h ' O , O 4 Ise "O
.J ,
o ! 8
. x in , 4
- I I a ,
i ! _ . . 1, i
/ i . I l
ia .- i n
, e O
a w a $
~
\ < z
~
O. W i i E h
+
l!. I O O O O O O 89 O O O se O O O O O O O O O O O O > O O O O O O I O O O O O O > O U O O O ig O O O U S O. d 9 N O O O O O O P 5 w 2 1 IV-183 ;
, wr,-v
9
~
5 fat I&T I COL O te O 6 Y S I E 5 Y 5 Y2 M YkT=OS2 Pt. J T L# Ne'V4T vb OTL 1.C000C000 + , 1 l C.eGC30000 + a l O.00000030 + l f4L TM T a E C.n.0 000 0 0 0 + 3 m K 9 a CD b
- 0.4C00CC30 +
l
'N ,
O.C3000030 + Summme, A A A l.... .__... ....______.._............_________....... ______.__...................___........... . . . .,..... N N D D J J F F H H A A H H J J J'* 3 A A S DATE 9 GRAPH 33 . Pseudodlaptomus Coronatus DS2 population net mortality vs date
=
TRT =DS2 S PEC=S NE T,M T 12 0 225917 0 293634 0.086228 2.781000 0.948431 0.0 3.000000 829.975
i 1 e O 8 emo 8 O 5 O e O s O
- O e 'M j** e 1-
$ M ,
. n 9
4 9 I 8 8 i
'l f%
\.
g %, W i
. O t'
3 i O 0 0 h e O 9 O a O 9
, n .
8 8 w e B. 6 2 i 8 8 O b 3 4 O O M W W . O il O p ll C C 9e
- O O M
M S O ", I
. ! E E
' 4 &
M A I h N
- l 2 8 .
se & 4 & E E , W > lO - ap
> d I O M D I, O J
(N I !8
+0 [
O e- O < 2e W i ll 4 CIC
> Z
- g 4E ; k
> 4
- N Q
J g
- 4 J 4 M I
Q M i i
** I e
l O e
> e o u.
W g O n W A I O W es e O W +0 e to e O g 9 d 4 W -W G e B s 4 O
> t N W l l
S e O h a O o b o 09 W g O se O O e O N 4 e l+9
= ee i
N O Z W O WX W
$ -+_ -+ _- _ + .+ ._--- +- J O O O O O O O O O O O O O O O O O O O O O ea O O o O O O E O O O O O O > O O U O O O W O O O O S 4 d 4 N O' e e e e e e
** O O W u O IV-185 l
l
S TQT I ST IC 4L 'A fe O L Y S8 h S Y $ 7E h T AT = gr40 PLOT E7 NJ47 v5 J(t 1.00000000 + T l l
*- s
~0 00000000
- I 0.60000000 +
NORT l
\
0.40000000 + <: -s e "cn
\
I ! I a 0.20COC000 + X I-g*%
"~*p A T
.s*%
0.00000000 + TmimT-T Tmi T I-i-imi Tmf l_______,___________ ..,____________.._____.-----
=___....__.................... ____.......... ____...___
N N D D J. J F F M H A A H H J J J- J A A S DATE s.. -
. GRAPH 35 . Tortanus setacauaity Intake mortality vs date wtic=T MO4f 17 0.070471 0 228482 0 0S220* 8.17d000 0.335200 00 0.9+J000 324.22*
C
'I 5 T A T I 5 T IC AL A NA L Y S I 5 5YSTEM TRT=IDO PLOT OF NETNT v5 OTE 1 00000000 . T 0.80000000 .I c.60000000 +
NETNT x C.40000C00 + l M 1 Y I (n l c.20c00000 + T T x x x
%s T
- T 0.cc000e00 . x x x x Tammmuner l_________._____ _ _
J F F. M A M M J J J A A S DATE G R A Pit 36 . T0rtanus setacuadatus IDO population net mortality vs date a TRT=IDO SP EC = T hETNT 10 0.886300 0.307779 0.094728 8 863000 0.852550 0.0 1 000000 865.200
a 5 Y A T I 5T 1 CAL A N A t. Y S I 5 SVS TE N TkT=Iu2 PLui UF NETMI VS OTE 4 1 00000CC0 + r , C n.80000t00 e E x T c.6fe00000 + NETMT T 0.4boe0000 + 8 _.a x 8 CD CD 1 A e.29900eC0 + x i r , a l 0.ccorScoo . x/T *
%x *
-I x l_____..__,___________________,__________...______.________..._______,___________________,____...____________,__,,__
N N D D J J F F M M A A N M J J J J- A A S DATE ' GRAPH 37 - T0rtanus setacaudatus ID2 population net mortality vs date TRT=ID2 SPEC =T NETMT 8 0.308325 0 362499 0 838406 2.465000 0.919848 0.0 8 000000 817.647
l l l STA T I ST 1C4L ANA LY $8 5 5YSTEN TRTmDSO l PLOT OF NETNT VS OTE C.50000000 + M L 0.40000000 + c.acc00000 i X hETNT I C.20000000 + a
< T M
I m l C.t0000000
- l K
l 0.00000000 * " " " 4 I i T umusum i T l _ _ _ , _ ..__..--_,__.. -. N N D D J J F F M M A A M M .J J J- J. i, A A S DATE i GRAPH 38 - T0rtanus setacaudatus DSO population net mortality vs date TRTmDSO SPECai NETNT to 0.076600 0.159049 0 025297 0.766000 0.227670 00 0.S00000 '207.636
S T QT I S T IC A L 0 M Q ~L Y S I S S Y S TE N T837=352 PLOT OF PP. T MT t15 OTE 1.C003C000 + +
's T
~ 0.eC000000 +
I a x l [ 0.oc JCC000 + 5 I
. M T4T g
0.00000000 + 1 e-.
= d ' 9 ** g , > M
- I' ns
- L x
', { & W ! t u 4 I #$ . s e e $ " . # $ \O S > ia I I O N " O % 0 O. d$, 1 a O, 4
- Z . a 4 cL " <a
" w " b J > O % e # J 4 s o
- I l
P o
- iO O i O W " t O U 1
t $e P \iOt :* .s N . e S e e O 3 h \OO +O a N 4 e \~ \ 1 "1 = I *- * + +' - +- -,- "i O O O O O O O O O a 3 O E. O 3 p O O O a O o O E O a O O o e Q O G 3 t O e io
- O a
iv-191 5 TOT I ST I C O L. 'ONOL Y S 8 S SY , T F. M s TRT=Itc0 PLOT OF H J,4 7 t1s DTti ' 0.48000000 + L s i 8 L 0.33000000 + L ' O.25000000 + l w l x MOe4 T s L L O.37C00000 *
- b i
A X 4 N ) i < A , a -d L * ' W L 4 A hma _ A 0.09000000 + A I l A l l .C.C1000000 + L-L 1_ -. .... ..............................................................._. ............ ..... 6mme. N N D D J J F F M M A A M M J J J r J A A S , r DATE GRAPH 41 - Labid 0cera sp. Intake mortality vs date S PEC a t MOET 16 0 1385C0 0 129342 0 016982 2 80430C 0.2SJ274 c.0 3.4C6033 96.485 5 T A T I 5 T I C AL A N A L V 5 iS SY STE M TNTutuo PLOT OF NETNT v5 OTE 0.SeOn000L + E I 0.400C0000 + L L , , n.3C0*0000
- NF TN T P.20C00000
- m v e w
e w x c.noco0c00 s i 1 x 1 1 0.ee000900 + x x L L-L x L ammmm-mu l_________,___________________.x J F F M A M M J J J A A S DATE , GRAPH 42 . Labid 0cera sp. 1 ! IDO population net mortality vs date l II TRT=IDO SPEC =L NETNT 9 0 081444 0 162477 0 026J39 0.733000 0.20tI90 0.0 0.403030 199.494 ____ -____ - ____-____--__ __ -l
- S T O T I S T I C A L ONOL Y S I S S Y S TE M TRI=lp?
5 PLUT OF NEIM7 VS DYF I.30000CCO + L L L ' L f , 1 0.80000000 * ,t- L A8uumex 1 0.A0000000 + NETMT L C.400010C0 + e w L M L o.acacea0c . i. l C.66cn0cc0 + x#* %x ^ [ l____.___.__.._______________,.___.[____...______,______m- .N N D D J J F F M M A A H H J J J J A A S DATE l GRAPH 43 - Labid 0cera sp'. 102 populatt0n net mortality vs date 1 8 l TRT=ID2 SPEC =L hETMT 10 0.490700 0.415299 0.172474 4 907000 3.552262 0.0 4.000000 84.634 l I l i l l l [ . l l STA T 15T 1C AL A NA LY S SS SY STEN TR T=D SO PLOT OF NETNT VS DTE , 8.00000000 + L l I 1 , I i 0.80000000 + ij L s l C.6C0C0000 + I L hETMT i L L l C.4 C 000000 + x s ; f. w I LML X V3 U1 0.20000000 + L " L v s N ~/ \ s.. - 0.00000000 + - /.- ......----------------+-------------------,-------------------+----- { ___,__ - . _ - , _ . N D D J J F F M M A A N H J J J J A A S N DATE . GRAPH 44 - Labidocera sp. DSO population net mortality vs date e TRT=DSO SPEC =L 0.35972T 0.304365 0.0920Ja 4.287000 0.926380 0.0 1 000000 78.037 re. TN T 11 S T OY I ST I C OL QN OL Y S I b $ V 3 T E N T> T *D S2 PLOT OF Nr. T M T US DTE 4 . 0 0 3C 0 0 C,0 + L L L L, I C. e 3 CC C 0 0 0 + . I Ag C.60CC0000 + NL TM T a 1
- 3. *C C000 0 0 + L i
m . L E aC b ' e 01 '0.d0000C00 + ~ Na L JimAm 4 AMX l .7 0.0000000C + , A A- A l ....._ ........_.................................... - .............................. ............. .,..... N N D D J. J F F M H A A M M J J J J A A S DATE . GRAPH 45 - LabidOCera sp. DS2 population net mortality vs date TR T =D S2 SPECst. NETNT to 0.Sb2J00 0.404338 0 16J490 S.523000 1 478406 0.0 1.C00000 73.250 S O 3 O 9 O g O 5 9 0 l e O J g g O 1 N I
- e >
i O W J e 8 9 1 4 m % J _W O J u O & J # s 0 . O % g O g 4 O . y 4
- h i
= l t , Nu 8 K O y W 9 0 a g O w 8 c O . O . C
- 4 e O ch O I
* *g e a e e g O W , n @ v 3 O-6 ,
- 1 W 2 ~ ;
ee b . A 3 8 E e E bl > 8 e y W i O > , O SO W O N > O <g 5 0 g g
- x. w G 8C w Z 0 42 -
b a x a a g 4 J , 4 Q - = 1 9 e tO a O V e W } O V W e O M " S g O J + 0 e g O a e W 4 l e g " 4 4 8 W U.N 9 l 6 e W OO O O O x a O * \ O +0 0 0 N 4 i .e. o kN Oz 4 3 + _ _ _ , -- . . .J. s O O O O O , O O O O O O O O O O O O O O O p O O O O O O g O O O O O O p O O O O O e O e e O g e ) O e, O N e O O e e O Q O O O IV-197 i I r l -o r, _ _ _ _ . . _ . - _ _ _ . _ . _ , . , , _ ST A T I5 T I C A L AN 4L Y S I > S Y ST1 M c-TNTalN) PL JT OF M04 7 [VS D TC O.25000000 + ' l '1 l 'I I A ' l 10.28000000 + O.I7000000 + l x A a x .,' MORT = */ A A 1 'f0 4J000000 + m o x < A h # ~ 0.09000000 + \ A !n/" A A '0.05000000
- +
l---.___+-__...__ ........._-- __ _.___,_____. ._ .....__,.._____............,_............... ..,_.... N N D D J J F F M. M A A M M J J J J' A A 3 DATE - GRAPH 47 - Total C0 pep 0d Population Intake mortality vs date -.-------------------a-_--------....------ _- . - . . .--....._-_ ______ .._--________ 5PLCaX Mod 20 0 142 00 0.04:251 0.00t702 2.aoa30o, 0.0JeJ31 c.o76o00 3.a,3333 g e, , j ,, e S T A T I 5 Y I C A L A N A L Y S I 5 5 Y S T E N 1RT=Ipo PLOT OF NETNT VS OTE .0.50000000
- x 0.40000000 +
e 6.30000000 + NETMT C.gnenn000 e m 0 a w x x I x 0.80900000 *
- x x x x i o.cecoseco + x x x x l---------,------------_ --s--------------- =__-s--------- __- ,------------= _ _=,------ ----...e-----
F M A M M J J J J A A S , J F t DATE
- 4 GRAPH 48 - Total Copepod Population l IDO population net mortality vs date 3
' 't T RT =IDo SPEC =X i: NET"Y to o.o84357 o.3244:e o.085460 1 88 000 0.20:239 00 0 49000o 347.49o il , 1 S T A T I S T I C A L A N A L Y S S S S Y S TE N TAT =ID2 PLUT OF NEfMT VS OTE C.S'8060c0 0 + xWE s
- 9. 64 $ 6'3 c 0 0 +
2 4 0.48D00000 + NCTMT 0 320e0000 + w x x e N o x O e 0.Is,'90 orc o + x x x a.neceorce + x# x x l -- - -,____________ __ __._________________ _ .._________________, --_ __________ __ ____.___.________. ,_..__ N N D D J J F F M M A A M M .J J J J A A S DATE - GRAPH 49 ~ Total C0pepod Population - ID2 mortality vs date TRT=802 SPEC =x NETMT 17 0 154948 0 235401 0.055454 2.634000 * , 0.886689 0.0 0.732003 858.929 's m a am 8 a e 8 l M E $4 !
- j<
O n I o. d ln l f e 4 I 8 D ln 3O w e N I wt 8 > o n lE w
- e e :
1 l' g% k !* - t = %8 W 5Y e * , f"e m n n .A i EA
- n x e 2L es
> g $M O a e aa ! < as - 8. - W x l l H 38 ~ o e o l A e ,g j a 0 Nh x a e 4 x e Z
- N O >
- A Ze W I o aC a
+42 Ise E o" > E e L1 l E o ,b -O e < g ~ - l ~ * ~ ~ l . , l" J - i 4 W 6 = 4 6 e se m -
- n i -
N 1 X g O t 0 I W l , = O. 1, S ! n 1 l O 6 l l $ _- .4 O_--_ -ummms 4 amame O O O O O O O O O O O O O N O O O O O O O O O tm O O O O O O K O O O O O O > O O O O O O W O O O e 4 n 2 N
- U e e e e e e e o O O O O ;
IV-201 - . ._ _ . - - s - _ -- S 74 7 I S T 1 C4L A N4 LY S1 S SY nT CM TWT =DS2 6* LOT OF f(;T Mi VS Di r. .C.eC00J3Jo + E fa t ..e i l 0.cwo03000 *
- C.420dJ000 + K ,
I P.L T M T C.J2000000 + E e-. a::: B N O A N C.8c,300000 + i Na a amtma 4 XMA 0.0C00JC00 + A A A l................ = - . . . . . . . . . . . . . . . . . . . . . . _ , - N N D D J J F -F M M A A M M J J J J A & S DATE GRAPH 51 -- Total C0pepod P0pulat10n 052 populat10n net mortality vs date ' TAT =DS2 SPEC =X NETMT 17 0 197'176 0 248143 0.061575 3.352000 0.985200 0.0 0.FF3003 125.848 e A to A L V h I $ $ Y S TL M S T A Y IS T 1C A L TRTaDS4 SPal O PLOT OF tod TM T VS DTt-. = 1 00000000 + l r I 0 80000000 * ,W' M 0.60000000 + l NETNT l M 4 C.4 0 C00 0 0 0 + t N J O W # ~ [ ( l 0.J0000000 + p 4 w/ ' J 1 J J > J t 0.CC000000
- J _____________,___________________,_____
.____________..._____,___________________,___________________,______H J J J A .A S l H A A H J D J J F F H N N .D h DATE GRAPH 52 - Total C0 pep 0d Populatton DS2 population net mortality by sex vs date . O . O . O O O 9 O . n. ~ n 1 M I "N, i.:O e = , , O - 3 O as O ' . O . O. n . l .D. ! 83 = : d L l. 3O 5 .E aO . O O C
- O O a
O ,g l3 Rs j 4 14 .
- a aa
= C I 2~ O sa - i a -o . ,i. x g i O 8 i <: t, = 2 ,E W .!g O8O O. .g > Z 8 e cg 55 c. =.4a! e a >= I Q < s I. e a i v = ~ 1 . l . O . g 4 O U O > . . O w . O - O . < \* l 3= * ! 8 .N - . N l i e i . l a e i i O Q i i OQ . O W l O
- O
.O O N i l O I l N. 4 3 g N O i z i = q y, M . W _ . _ _ _ .-- _.- a O O O O O O O O O O O O O O O O O O O O O g. O O O O O O 3 O O O U O O e. U O O O g e d N @ O . . .- z n - O a . .. . . .a i - a.: a . s- .- - - .r '.1 IV-204 l APPENDIX D CONT 0UR GRAPHS Temperature versus salinity versus predicted net mortality. IV-205 GRAPH 1 Temperature vs Salinity vs Predicted Net Mortality juveniles and female copepods, summer or winter season, discharge population with no mechanical effects 40 , amovs se 1 ,g oc n ........................................................wm. w , :wus wu.w w.v.v.wu.v. v.w. g v.v.
- w. .w w.vu u:.wu:nuuuusunus.vuur.venarueusn:
m ysm: - + 35 , s_:re:*- w. ... , 30 TMP "" 25 ~ l 20 J %i; . w .- ' ' . "% t , . . . . .M.:. w;.:... . , m+: :sp . ~ ~ , . , , - - . - ~ . , l .:.x. x. . .x. w,x.x 1x ,,, ,,, .,n ,3,w. , :.,xn.x<.n. w,, n.w..n. ,. - -~n.v t 15 ~ l I 17.4 19.8 22.2 24.6 27.0 l , 29.4 SALT GRAPH 2 Temperature vs Salinity vs Predicted Net Mortality i male copepods, summer season, discharge population with no ) j ( mechanical effects i i 40 . ASOVE GO T, P 84 % ,.f .w.v. . .v.v.gegw... .. wa. G .t - - .. .w . . . ne.w. ea. ....w.. .. ... .v.w. .v, . . ....v . . .wa.v. . .:.: .,. e. . . e. a.v.w.xv. .s x .we, w.46hwwww.we.ww w.v.. . ., ...- -.. .s-au . . <.v.yys,wx.x, - f, f w.., we.wufa.v.wa<. u,w...w.v.w. u.- 35 > aJ Sm.s: s.;s w. . ^ .: m.;- m i:2 , w: .. .... h 30 46;s.!! MP f6::+. ?5 .:.:. iCs g::. - o% + _ ] l 'O . w.:. %::.m.;.; ... 93.. ..; l . .s.- :- --~..s-a ~;. m..x.s-a...,.. . . . . :m^~' '~~ m+ :.m . ...m L.J . .J-Si4:^ - ~_~' - - - ~ " " 5 .s '17.4 19.8 22.2 24 4 2M SALT 2N4 IV-206 l l \ \ GRAPH 4 Temperature vs Salinity vs Predicted Net Mortality l male copepods, summer or winter season, discharge population with mechanical effects 40 t ..ov. c se s .^ .' ~. '.. .+:: .% . . :. =.6 :+ . w,s .. ..'.**. .. . .. . . . . . . . . . ~. . . . . . : : : . . .:.:. ,,. ;;::.% . . . ~ '. ' :. f, '_ '. ' ' ' ' ' * " " ' " ' ' " ^ < +:+:+:+=:**** " "' ~~'~~" 35 - 5::. . ... :: . . . S C. .. . -~...:." I !!!Miiii!!!! T fE ............ ;---- \ i:j29..%!:j ttO%;:i 25 20 N:.:.:.:
- m. .. . :s ;, ....i
.m.;.; "::'::?i??i? t". :.c.'.t'.'.'.??? ^::. . . . . .. .. . . .,7,, 5.,. . _ , , , . , , , , _ , , ....___.,_....~~~~n 1 i 15 - 17'.4 19'.8 22'.2 24.5 27.0 ~ 29!4 SALT GRAPH 3 Temperature vs Salinity vs Predicted Net Mortality juvenile and female copepods, summer or winter season, discharge population with mechanical effects 40 ABOVE 80 S N .I.. . .. .ee- u re % .I . . . . . . . . . . ... . . . . . . . . . . . . . . . . . . . . .. .. ~. ~. .1. . - - . .. .= ,.:.:.:.:.:.:.;.:. . . +:+:+:+:+: +:.:.:+:.:o:.:+:o. = . - = . . ~ ~ " Q.
- .:;- W.
O% .. S 1 30 !!N !!! TMP ilN!iii!!!! p Ds#E 25
- sm 20 7
v" - - - - - - ~ - - ...,s. g.g:s .:.W5: 45.i. '15 . 17'4 . 19.8 22.'2 24.6 27'0 . 29.'4 SALT IV-207
- l. ._ .___ .__ . _ _ . - - - --_. .._2---.--.--._-:=a-~ - - - - - - - - - - - -
__ . . _ . _ _ _ ~ _ _ . _ . _ . _ _ _ _ _ _ _ . _ _ _ _ i 1 i 1 GRAPH 6 Temperature vs Salinity vs Predicted Net Mortality l male copepodo, spring season, discharge population l 40 , with no mechanical effects ] A SOvi 90 ' I .. e ,..s y ..f [ ~ ' . .[ ? . , .' . ':) ~. . ' ' ' ' ' ' ' ' .l..'..'.'....'..'.'.'.'.'.'l..',('.,'..,~.~.'.,'..".,'.,'.,'.,';..';.,'.,',..,'.,'.,'.,'.[J.';.[J,';;;.,'l';l'l[l'.,'J[JJ,!,)
- W=
35 li ~ - - - "N: '~ i::se:z:i: :xg l # !!!b!! i j 30 Te !!M i l 25 i J to s :: i ' l 20 ........... W . . . . . . . . . . *ne: .. I i 15 17.~ 4 19.'8 22.2 24.6 27.0 SALT 29 4 !i I GRAPH 5 Temperature vs Salinity vs Predicted Net Mortality i juvenile and female copepods, spring season, discharge population with no mechanical effects l. 40 ' I A SOvi 90 % .......... .~.. ......,.....:.....:.0::::::?:.L.0~...l,'0'f.0:0':.0'.0:0.C'C'.C. !b.i.;.;:;:y:$$:;d:0;r' .. 0::.$:0:0::.'d:0:d$.0 ) 35 - -
- x sx l . ....H. %
l l 30 ! gg Whn id !! 25 g : N: 20 N" *+ " ~u.:. "w"+: \ .xQ.;. __._. e.--} 15 .e 17.4 19.8 22.2 * *^ S AT,T IV-208 (v e APPENDIX E FECUNDITY RATE ANALYSIS i IV-209 O e e r = > e 4 O k 40 e A V h w e 3 M ooc A 000 . 4 O 06O e W e 3 eee O @h O 000 e @ $ W E W NOhS6MOdhN=ecedee9pSesOdhNh W m=N@@mhOd@e4MeO=SNNhM9N9NhM J ee=4he=0e=O@heOOeath=eN=eOM W h=N J 4 =@hahmhNSAmmh9 mat %CeM90 meat 3 Nha > e @ d eNSW49Wh009e e De=O Omo eNeo WG > M J knd ==ACONomO4N=4DO=Ahd99eNOhJn E O WO 4 NNm d4940AONNommad@MhechooN9=h= S 4 m o O > =me Oeseee =
- M Nee M = "e e e e sONmM4MNOOQUuGOOOOOO=
eeeeeeeseoeeesee 3 N e see o O000000000000000000000000000 W O N MO E nan > gg 3eeggga p g e40 g WG > = =N W 6 @ g e WO O E e S W e e O e W 4 = E wm W Nee G OOmetS=Me@OceN99deE=MeAOceNm 4 O W Ne= OceO@mmeome=et440J9944=e=#3@ D A n W ei e S etE?#JhSMG@NNh@@@AOhCADONNN@ n E = = O m * = N o -M
- f S O N A O m S = N O @ @ @ F O N O J mNN W @@hO@chhoch=M@@ho@Ahhsch=90@
S e 4 900 ddhC9dMh940N@ambs969h900N@@m 2 3 O = NNS O e e@e =4 4 4-@hus N eN Ae =4 C4 =d h mN9 N E > =@h *W9mem e so m e e e *N ie d O @ m e m e @ = d e .W N M O * = & E meO eeeeoeoeeeeeese eeeeeeeeeeeee 4 eee OO===O=======O====O=======Om W & Ohm & 9ec W G NNN G W W J O e O J m 4 4 = > m ) a > = e ' b e WE = E ==O 00@ = ~4cN==NO=he===mmechM=pMNcO=m > 4 A 000 > OO4eOOphNO=oOOO-Nhongege@hgG Q 000 W C eee = OOoNOO=99000000COh9NNet94che - - W O 000 A OOO=On omho-OO O O N 4 NNC 9 7 9heh=O b u E eoeeeeeo e eeeoeeeeeeeeeeeesee = 1 S 0000000000000000000000000000 W > c
- 5
= W a = g 2 O @ W 4 4 N O > 3 4 @ = W 3 O 9 W g N.ON 4 = W@N J ehe WO @N 4 kna 2 Z D @ > Ohe > 4 4 e e see W e 9 L 9eN J e I W.N NN m + 4 W k 2 Z O @N@eO9 AWN 3O=h09mchdea9Nh9@cm # W n eeNeemN@@Neh@s=4OS-v9me9 hoe @ 5 CNtMAkmNNotDhodoveNEONahma=N O 4 = U = e W94&Me @e me 04 =O m-noc cunk m C9 AM C = O me@439e394e#NA=NAONO@Og9eN93 E K eeeeeeeeeeeose eeoeeeeeeeeeee + J E W G e @ W Oec E @Mamm@N=OM=4ehe=Q==OOOnouOma =3l l3ggggg =g g g ggg g # W W 4 e N W De= 0 % 4 O E A N 9 494 L M V 4 h N @ g Och B Q 3 4 = b 4 WOO k Z 3 e N O = Oc? E = Q W h h e > 49k = N e h 2 @NO h k W L e e e W ese # M O e e O 3 m== W W R M S N T e b e 3 CNd W @MN =@ 3 @ e e W 9 d W W = 4 > g E 4
- W m e =Mah = = 2 h @@@ @ e * = e P e e O eO4 AO S @
W @a@OdfM4=m===DN3%SNJOhhh9994 N b W 3 N S N N N 9 N N
- d @ e h a # U d V 4 & r e d d a m e9 N \O J J D C
- e e f d *J O N o 9 7 = = 9 = 4 = = = h = P 9 t
- Wm 4 h O h &
=99 4 > OdO90eOOhNkJ7ChW4wo==hehM9hd W W e O N' O N O == MO N e 9= N hC *909fe9?Phh9ASO@9* 0 0O*9*JA7PmC 'S = m e O S ** m e = N h p N h 3 9 @ # > N N 3 CN O A A = T 9 9 9 0 k N e g d S A = m p o o d k 9 9 0, W eeee eeee oeeo,eeeesee eeeseee C @=M a med M N O e N J e D e = O -= Omu = U O O-N O U 18 Og88000 =g 2 1 6 8il 4 C 4 O & = w & g J e W 4 4 > > m ye Q b 8 E > M9 3 E *
- O O W w m e e .= 'd > EmN994cha*OmN9e@@hSPO=N94@0h
= W > 4 WOOOOOupOO=mm=======NNa3NN4NN
- W WW U J & W U D O D O 9 u O u P o iS o e 'J O O c o pO n t e uC UO O U W'2 W V W Q Sk>>>>=>>>>>>>>>>>>>>>>>>>>> M J
4 4 3 O d 2 4WW e MFM1E*T*?FFhesia& 25777277Ft1 4 J v T E 3 ok3 3 >IT73t391372T72Sa37kve72377I A F Q .g. a o O =<=
- *4. 'w.W,WJ+ .**
- M' WOW 0 43373333733333J3p3333333.3p3
'M =O3a3D303DOJ3a33s30030333J3 4 ' 4* s ... . . .y IV-210 at e e 2 e > N 4 e E a==
- a w M SOM ,
W e I k A == O 4 as e NON e e e 99 eee d @M O 000 h @ T W & W N m.e d .sh e M e t e O S 9 d as desse3NONNmNNeem 3 Mme44 ewe #MOMOde J ee9me@QNOJ@.i*Da es @ON e so m e M e d e se @ @ *l O C e 8 3 MNe > 9N34kakMMafe4@m W k ) m J Ode SMmM@NekMede@he E N W@ q Nee A OQueMMOON==00UOm 4 e Q S D @NN eeee eoeee eeeoeee 3 O @ ese Q 0000000000M00000 ' O e Q n u an == =s k et 004 I O 8 W W e > 9 m W @ 6 O d == 0 C e @ W e o O N W 4 - S MMkOOOkkOh000hhO E E = d Nae @ == @O SPON 4 O d 04e 2 = e de Nk SN&as O N. enOeep A N. N = e p. > A O e= M en s k en h N 9 en k h m h ane *t S h h M W mageeM4e@Se#94Se O J NON k 3 e a Nme as se e o h c e ep h e c h e e e p= 2 O O = eeO Q @@@kakO@m@kah@@m W z k e@ > NNE33@JS93488098 Q & g @@M d oeoeeoeoeeoeoeoe 2 4 eee 0000000000000000 W & N pm 4 .* e p= W 4 Q W d a 3 e Q J pm E 4 4 D b 2 > e = E 4 WE E @mm @OO == m e d s= O m o d O S == W % M P. S k E A Nod > OmkomO4hhmONDhhm O OON == 0.e d O O O =e e n e M 9 9 0 e e e d e *** ONkOOO94ONO9CNeN d Q 000 A eeoeeo eeeee ee oee . > U E 0000000000000000 as & S W > 0 W 2 m W@e & k 5 N N d 4 4 4 @ t- 3 @ e W NMN ee W C N == 3 so m 4 *de a e J S4e WQ M e 4 a>S 2 Z @ @ D =* e N > a 4 e * *ee W k e L WNm , J e I d a 9 4 e = f k O MennomeNNoseeNem # 2 2 N eco@N09404dkksNS e W 3 @@@OmO9Deee9hhMM 4 m = (MOk@O9@'AeNeN#4N 9 U O MNMoMmNehNm4@Okm + = Z *eeeeee****eeeee E M ae O d m e O O N m N O O *e o .e = J %w W O k k W a@@ 2 =ga ge3 3 8 3 W M k O W #9e Q 9 4 O e e 9 3 d oe k E ** Q 4 m M N J MMN e Q 3 @ N N 4 mee > 2 O e N m = kmo E se Q W e me k N 9 se - e e. z Omm y . d W
- 8
. a @ 4 EJJ O @ @ ,- = = N C e w a g e e e W k b me J 3 =e W Os W W > 2 9 d =@memem@NG4@mm4@ =0 4 e as c9modedeD9=90A99 %N > W 3 AOc4AcoehoeOsmON J emadahmm9memosmN N n* = J 4 ceOMoeOWNdefe4*h WC W 9 L en e at E se p= pm > >= 3 h D N e N M m O +n e 9 h N O MW 4 Q m @ = Q 9AkOkdJa9#9@cNSm # I > se 3 4ONA k 9 m e N == e 4f M pm O S eeeeeeo e e eeo eeee C b W M O O e N *4 3 9 N m =eO O O O O OO V 08 850 1 8 i N e 3 8
- E J e 4 4 4 e y
) k e s Q b e E oo =9 0 t e we Q Q W > E e W == W k 4.e q M e d e h e @ O = N M e a N a d W 4 WOOO"7COOOma==== d W d Q W O W V000wa0000000000 > U W E W W G W 2ht>>>>>>>>>>>>> M J E G 3 m a 44J g %227tAITyrrv2233 4 4 3 J a 2 3 Q>o 3 ex22marIT77tFZ7a 4 u *g a q o =g* c 23J3733333337333 E4 4 d a W w d $3'A A == Q Q O u 3 0 0 0 EJ 3 3 G O iJ O IV-211 s...-+ , . , C , e M E e D N 4 9 & Nm@ e d W@ SON WG E O A OO@ n N mon e e e S eee m O O 000 d a a W & W epN9NOdeNM9 W emmoePe@m9h J O@@hMNeh@@N Nhg J deeO@@e=See d cOOmmeopeh? mhe D MNkedONoeeS Wm > b J hDN 4eh9meeN@@@ S @ W@ q 098 3 ON9mbdOOOOmo 4 9 O P D 809 eeeeeee*: ee* 3 M o eet O OOOOOOOnoncO O
- O mN N thN > a8 8 8 M e m > mN W
@ l N WN @ $ e N W e e O e W 4 = E 3 khS9990mme90 6 = W M9M @@@@m=3D@mm@ 4 O e One g d@cNNekcNNph D A O e#9 4 de?@ademdae= 0 J @Ne W OSN4ee@N#@@@ > e 4 One @pok@eNokumN 2 O = 99= 0 OOOS*OGO@eO9 6 > New > @@@@NN904NN9 & g 99h d eseeeetseeo e 4 eee 00m========= W & Mmm & hMe W @N@ O W E W J M G O J u E 4 N D N I > e m E O= WE 4 mm9 OON = mNNemmhem=== > b A Cme > OOOADOhNNmMO O 000 = OOODOObeNet> ~ $ O eee M O 000 A Oe O O.ee O O O 4. D D DeO O eeee > U E C00000000000 m & S M > 0
- E
= W c O & > 3 4 N M 4 4 m e > 3 0 9 W NN9 mW 7 @ N 3 PNS WGm J ==N d O e W 4 Som 2 2 m h 3 NUS > 4 4 e e see W O 9 & a@N J e r a m NN 4 W > 0 echN*eeNemme e E E a mw9m4NONeena a W S MOdehhNde@hN g 4 m ** @@NMDCadNdem g U O OqDSnphom004 = 4 z eeeeeeeeeeee + J A WM@N M Wh9 499mah00 GONO W W = C O E m3$ 0 $ e W 006 O g 4 O M @ h enO L m W 4 m 2 O J med a p 3 e # 9 4 cee > F 3 A
- b = nem g
= 0 W m N d > M9m = 0 D q Z mhh k M e e e up eoe @ m N 9 3 @te WW m O e O NO@ E f e P @ W SNW g = J 3 4 9 O e p W W = > E g 4 e a NOmoccO4000m o W $9medSNMmM*O % b b bl J 94@Dum7NA4NO e*9dMMOODmOm %o J ONOO*Tk@NN4U W 9 4 m N 9 L med 3 mahm9m@hnmos u 4 O = 0 m O hM9he=A=9NoN e m > m a C N9k000@e9G4*> W esee oeeee eee ON@9=9CouuNO c h V O O =0$ al 1 % C a O e m a g J 4 e d > b m O e b @ E h M e O 2 e m O O W > m e-M = W > E=N99$8htmOm m W > 4 WOOOOOOOOnma m M WW U W O W UGOOOOOOOOOO > WWEW u e W Gh>>>>>>>>m> M J T T O g 2 W4W 2 Jy3XY7 FIT 2/F A 4 3 @ 4 4 3 O*G 3 *F3F172KTXf7 m '. 2 , -Q W 2 C D J oet a. *.' 0.3333 37 3 33 g # ** , '4,' M S- W"W .@ WOA- W =u00 33 O C3 p IV-212 5YA T I ST I CAL AN AL Y h I S SY STE M ANALYS IS OF W ARI ANCE TA N A.E . REGhESSION COLFPICIENTS e AND STAftSTICS OF F8T FOR DEPENDENT WAR 8AtsLE EGGS F WALUE PROS >F H-SQUARE C.W. DF SUN CF Suu ARES MEAN SOO AR E $3UNCE RF.3332J 0 0001 0 83350388 805.S5678 4 kr.W ESbt0N 8926.22469992 964.68 2 J 4 9 h _2 ERROR 225 12495.52209832 SS.53565377 ST) 3EW EGGS MEAN COEf=ECTs!D TOTAL 22F I4420 74679825 7.45222475 F.33398 PARTIAL SS F WALUE PROS >F DF' SEQUENTIAL SS F vALuE PROtt > F SOURCE 0.0009 10.85493 0.0086 63J.24644873 St.402S2 SIDE 1 56J.960SOSSO 0.0005 8361 26419412 24.51IS4 0.0005 )- TLMP I 1361 26489412 24.68854 I PROS > lTl 500 ERR S STO e VALUES SOURCE o VALUE S T FOd H03B=0 0.0438 2.98805753 0.0 S N TE RCE PT =7.24 0268 04 -2.48819 0.49409020 -0.20978090 - 1.bbJ 39 t t 9 -J.J7676 0.000@ 0.30747289 Du dMV 0 0 5 4.95098 0.0001 0.I1631SR4 Teimp O.S754660s o _ f #. N C TABLE 4 Regression on eggs / female / day = side + temperature e 9 9 .e.. O e 9 APPENDIX F GROWTH CURVES I l l IV-215 l l
- w. .
GRAPHS 1-9 GROWTH CURVES A - Intake - O - Discharge l l l P IV-216 GRAPH 1 PLOT OF D AILY MEAN DEVELOPMENT STAGE I2 000 + - - - - - - - - - + - - - - ~ ~ - - + - - - - - - + - - = -+-- - -+---------+-------+--------+--------+---------+ t I I I I I I I I 1 8 8 I I I I 1 1 8 8 8 I 8 I I I 8 I I I I 1 I I I I I I 1 8 I I I I I I I I I I I I I I I 8 I I 1 1 8 8 I I I I I I I I I I I I I I 8 I I I I & I I I I I E I 8 I I I I I I I I f' 9.610 +- -- I - +---- -*-- -+---------e--------- 8 --------+---------+---------+----~~~--+--------+ 8 I I I I t 1 I I , I 8 I I i I I I I I I I & I I I I I I I I 1 I I I I I I I 8 I I 8 I I I I I I I I E I I 8 I I 1 8 8 I E 8 I I I I I I I I I 1 I I I I i 8 8 I I I 8 I I I I I I I 8 I I I 7.200 + - - - - - - - - + - - - - - - - - + - - - - - - - I- - + - - - ----+------ --+ - --+--- + ----*---------+--------+ I I I I I & I I I I E I I I I 8 I I I I I I 8 8 I E I 1 I m <m 5 8 I I I I I & I I I I I I i I I I I I 8 I I 1 8 e I I I I I N " g 8 I I I I I I I I I I I I I I I N
- I I I I I I I I E 8 I E I 5 -
I I
- 8 4.800 +---------+- --
I = -- - - - - - + - - - - - - - - - + - ------+---------+---------+---------+---------+----- I & I I + I I 4 I I I 4 I I I ^ .. I I I I I I I I I I I I I I I I I I E 8 I I I I I I I I I 8 1 I I I I I I i I I i I I I I I I I I I I I I 8 I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I * - - - + - - - - - - - - - + - - - - 2.400 +-- == +--- --+---------+---------+---------*---------+------- -+-- f I I 5 I I I I I I I I I I I I I I I I I I I I I I I I 8 8 8 8 I I I 1 A I i 8 I I I I C I i 1 I 1 I I I I I I A I I I I I I I I I I I I p I I I I I I I I I I I I T I I I 'l I I I I I I I I I I I I 8 8 1 1 +---------+---~~----* I A I 0.000 +---------+-----~~--+---------* --+---- --*--- - -+---------*- -- 0.0 1 400 2.8CO 4.200 h.600 - F.000 8 400 9.800 11 200 82 600 14.000 DATE - - - 740709. TEMPE R ATURE = 28.8 C fop SPECIES A ON THE INT AKE SIDE, THE MODEL IS AVG a 1 10056* DAY + 1.oS 3J F F 09 SPECIES A ON THE DISCHG SIDE. T HE MODEL IS AVG = 0.6840b* DAY + 2.54042 FOR THE REGRESSION DN SPEC RES A THE Y VALUE IS 2.59937mITH 810EG95ES OF FREEDOM I s GRAPH 2 PLOT OF DA8LY MEAN DEVE OPMENT STAGE 32 00C + - - - - - - - + - - - - - - - - - + - --+- --- -- ^ " " d 8 I I 8 a N 7 ..*---------+--------+---------4--------+ I I I I I I I I I I I I I I I I I I I 8 I I I I I I I I I I 3 I I I I I I 3 3 I g I 8 ? I I I I 3 I I I t 8 8 I I I I 8 3 1 I I I 3 I I 3 g I I I I I I I I I I i 3 3 9.6eo +---------+---------+----- --+--- I I 1 8 -+---------+---------.I---------.I---------+---- I I I I I =-- .I---------* 3 I I 3 I I I I I I I I I I I 3 I I t t i I I I I I I I I 8 I I I I I I I I I I I I I I I I I I I t I L 1 3 I I I I I I I I I 8 I I I I 4 I I I I I I I t I I I I I 8 ------+---------+---------+---------+---------+---------+I t i 7 200 +---------+--- ----+- I I I I I I ~+-~~~-----+ w . I I I I I I I I I E I I I I I I t i 1 * *- I I I I I I I I I I I I
- I 1 I I f
N Z I f I I I I I I 5 I I I I I I I I I w 00 6 E I I t I I E I i 8 I 1 I I 1 I I I I I I I I I I I I I I I I I I I I I 8 I I I 4.800 +------ ' I -+-- I ------+---------+=- =--- +----~~---+---------+---=== -+---------+ - - -+ ---* I I 8 I I I I I I I i 1 8 I I I*. 8 I I I 5 I I I I 1 I I I I I I 4 I I I 8 I 8 I 8 I I I I 8 t 1 I I I I 8 I I I 1 I I I I I I I I I I I I I I I I I 5 8 I & I I I 8 I I I & 2.4bo +I ------+---------+---- =+---------+---------+- I I I I I 8 5 I -==-+--------+---------+---------+-~~~---+ I I I 1 8 8 I I I I I I I I I I I I I I I I A I I I I I I I I I I I I I I I I I I C I I I I 1 8 I I I I I . A I I I I p t i I I I I I I 1 T I I I I I I I I I I I I 8 5 .. I I I I I I I I I I I 1 I 8 I ==+------.--+---------*---------+---------+---------+---------+I ---------* I I A 0.000 + - - - - - - - - - * - - - - - - + - - - - I I 0.0 1 400 2.800 4.200 5.600 7.000 8.400 9 800 . 11.200 12 600 14.000 OATE - - - 740723. TE4 P ER ATURE = 27.8 C FOR SP ECAVG 1E S= A DN THE INTAtE SIDE. THE MODEL IS 3.86382e OAV + 2.78069 FOR SPEC AWG IES A e ON THE DISCHG SIDE. THE MODEL IS 5 2784 8 e DAY + 2.76298 FOR THE REGRESSION ON SPECIES A THE T v4LUE IS 2.tS986u!TH 90EGREES OF FREEDOM i l i l o o o e e se se se .e mese me e.ee e me en me see. eene se m. e l 0 oo me se me.ese { se e .o en esse sesese 4**se se e. se.e se me se s l I I $ s t l 0 1 4 i e 4 I I I g g e i 6 0 $ 1 0 0 g g g e ] v o o e 0 1 I I 8 8 ,4======-m=,9=========e=========e=====-===+=========*f N
- g e es O I g u g 0 I w e u e J w i I I* i N g 4 0 I N g
4 ll o I $ 6 I I o g e t 8 ' g g g 9 6 4 N. O sosas emesem os , so me es se ss ee
- e. es se ee ee esse e. e . ee e. se me ne e. as esee0 me - esme es es . se e. <
0 0 0 0 0 " W 9 1 0 $ 0 W 0 e $ le 5 8 1 i l I 9 0 0 o W e 4 0 le o o 0 0 0 G a .$ . = I $ 0 .e == ~ .s e$ as .s e e ss es ee == = + e W ** w + .e s enes ee .s ee + .e = = .eee es .o .e ee + e= == = e .e .s a. i' 0 $ I I @ ** . W ** 4 I h t i 8 J J g 1 > I e I I I I W W U l W 4 g g g I 4 g 08 C# E l 0, . a b> * , g g > g g =~ z . 1 . . .
- 1 w . . . o ,
I e 1 f I t t i e ee WO Z* W# Z* . . g *a -N a 1 0 e.=========+.mm=======e.=========+.=========+.=========+. G W t 0 0 g I 4 I m > 8 0 $ t 6 0 g+ 8' e6 1 W i 4 0 $ 8 8 0 I o b .> W 1 1 o 4 4 " 2 l 8 e WO 03 y 4 w +=========,. ===== I =+=========e = =====+=========+ 3 i
- o. W 4*
I V8 3 I 3 s e it e i e > -9 WO J ' 3 g g g 28 N 4 I > g 8 0 0 g == "Oc > J t I g 4 8 m i $ B 9 We WN N g 4 3 i o 1* Z* O g u u, gg , ,. o. -o -o y s ., . , , 4 e.ee ee ee .e e e me , se es es see. eese e.ee + == =as se me se ssee e -e mo me.e e. .ean e m es se ne sses.e ee me + . 2 2 Q e s O O g e e
- a. g g O
> 9 9 0 t. O O g O . O i e O O .J l I 1 t el 8 .I 0 . W. W, = a ., . 1 . =< w< i . - - u i i . w 0 . . . . i o u= = o .e en . ..e_= w e. m.n e. .. ...e=__ N. g a ...e_. .e _ = _ .e e. 3 I. W I. i. k4 = = 2 0 !. !. I O O = I e 4, 3, . = O l . I. e . .o 3 i g i g e.ee 0 me _ _ _ _ _ e. 1 _ ~ .e .e m._ Lee _.e ___e.=.- . o., ., . . e . .ee. I i i L
- 1.
- g
. . ,. .t . I ., . . . . i o . s e e e e o p
- e. . . . . ..
- e. t
.s ee . .e s. . . .t ..o .e o. . . _ = _ .e = s. es . . . ___ _ e O . . . w e. . . . = I. - .l e ,. , , i . . 1 <
- i. . . . l i . . . 1 l l
.-------,-=-------.e-----=---.e--------=.i---------.e o. ; o a o . o o o o 1 o .a, . o o o e
- o. . e. N. e. . o.
n o n e u o 39Vl$ NV3W o.>zOz< IV-219 O O e me eeee eeee ee ee ee es e se ns es am ee O 8 e e mo me ne es ee eeee ee ee g ee ee eeee ee ms ee = se e ee ee ee ee ee ee eeee e ee e 8 9 4 0 $ ) 0 0 # e i m 9 0 I 4 0 l 0 0 0 0 il I t 6 { ii 4 0 e i 6 [e 0 e l $ 4 s t 6 [ O 4 I s O l e ee eeee ee m on es em ee e ee ee ee e== = ee .e e eeee ee @ I i .s ee ee e ee ee eeee ms .s ee +I =e se se m os s eens as e e g 9 e t ) 0 N G le 5 s 0 " O 5 e il W 0 0 I tg I I i 0 i i i g i 1 I g 9 0 $ i I e , 4 9 e 0 O m l e 9 e O Q e e. .s ee ee es ee ee ee e se en es ee m ee ee ee ee e i 8 i O N 0 t . = es seee me ne e es s om os ee ee ee me ne s ee em me ne em me ee.e e gpg i 4 9 8 8 1 8 ** eu 9 i i 6 e su 0 n 4 I e 4 0 0 i it y o 0 I O w 9 0 e i e O o 6 4 I O p 6 W 0 ) e O +=========+0=========e=========+$ =========+=========+ 8 i e @ e g g .en. .og 4 i l l 1 0 @ > s 0 0 g g 2 M 0 0 I t 4 8 g w w > i 0 n I I og og me k 4 9 0 0 E 0 8 w og n, 3 2 0 8 8 8 0 }e g 3 pl 3o O W 0 0 I t 3 p g == I l l 9 0 4 O, > 8 _ g 0 0 1 0 I I O q W g O, g1" P,. e O J e=========e=========e=========e=========e=========0 e I l 0 e, a 34 p, se B B i n 9 e e N W lt' 8 I e e g y > i w W W l G 0 I 0 i 0 W oe ce > es se g O I l 9 I 0 8 i' en ge e> M 2 i q q me 4 0 ' 0 i i O e wo oo 8 i I t t O e w r W Ig ]p osse ss ee me e me so am m ee ee ee ee ee e se ee I OO O 4, ge 3 3 ee ee ee ee ee ee en e me ne aeme me se ee em os e me e.sne se ne e eeee e 8 A O be o th @ J > 8 ' O 2e = = = 4 J l B h' i 8 e es= e oe > k I li ,9 , m 4 8 gg g gg e e. o 1F 8 8 8 1 l Ze 4e >= W G p 9 >O z L I 0 0 O I e l i i O i z > Q e l I I 4 r wee ee ee m es es se e me ss es se ne m eeen e so mm es eems ees<ee ne e .e w O i O O l 0 I i e mmeneeseeee eeeeee e e g g q l I 'l 9 O w q q U 0 j ' to G g 14 4 ' g 4 WD e> W O l s O W< m =A 4 9 1 e i . "Q 6 0 $ 0 t i i O y w w 0 4 0 0 0 O w qu CL + =e ewee m 0 0 I 1 g g e =e es es e m es ee ee ee ee ee ee ee e esenemmee===*ee==e.. 0 N g 6 8 e$m es se s ee nw om es se e e qpg 6 3 0 0 W 2 0 0 1 e g g C l 0 l 0 0 i 8 g a 0 0 0 g g Z l 9 1 0 l Q 0 0 me I I I i 0 M 4 8 9 I O in 4 i O O qu +====mw=== =========e== 0 0 N I C I O f =====em========*=========e I i e g q I i i e N w 4 0 0 1* 4 9 8 8 8 4 i 8 O I w I I I I g I 4 I 4 0 e 8 0 O > 4 8 D 8 9 9 O E +=========+======== 0 I I O W g i 1 i 0 ========e=========+========m4 4 9 i e se g 0 0 t \ I 4 ll l I i i 3 e i n i B i ll 8 4 0 I O t t I e 9 8 8 i 0 t t I I e se meee ee ee ee ee me ne e se ss en ee se em en ee ne e es eeee eeee eeee eeasee O
- e. e .
8 e .e m en e e e O O O O O O O O O 0' @ O e ,o c= i e N O 9 o e o O N @ b e e se 4 N O 3'JY1S NY3W <v<c*-< IV-220 e O O O O 4 se.e se me.e ee e se se e se se me seassese se me $ es aoes se se was.e se e ao se se se se se se sese e .ose se se me.e erme .e 4 * $ 9 0 $ 0 4 g 1 $ $ $ # s 9 $ $ 0 $ H 0 $. , l. I I i I .O , I . I O. - -.e---- ..,-.e.e.. , -....,.e.e.e--..,.e-.e..e- , I i s 9 I l 0
- l. ** 8 I.
.I = W ,. . . . . .o = , . . . . o 6 . . . ~ r -------=- , - - - - - - - - - . - - - - - - - - - . , = - - - - - - - - ,--=------.. = b , . . a , . . W e . . .W = o . . . . W . . . o W ,--=------.,---------.,-=- - - - . , - - - - - - - - - . , - = - - - - - = - .. e. ~ o e . - 4 li i g 9 l $ em * > s i I 4 M i 4 9 8 J J Z I I I e W W W i t i OM Qe ** Z 9 3 i 0 3 O 09 O st B W $ e ll 0 g i O EM EN 9 2 0 8 0 0 8 1 4 as *W N g e -==--===+--======-e-====-=-wem-=== --mem =--==..+ e Wk WN e O 1 I B I e 0 # Z* Ze # J l B I I I HM he # W 6 j f 9 0 0 4
- I t e 6 e e M W5 W G
I e i I W W e II I t C+ e l $ O se .C* 6
- 2 9 1 i 9 O W> W> A 4 0 0 i 9 i 4 4 m W e e .e .e.n ee.e em se es ee se .e== + .e ee ee ee ee ee en .s e so me e eee + .e e. .e- se ne se e O. Wo @g 3 'l g
44 we3 0 e a a h w 9 0 4 > 0 9 4 >@ ab J J t I I e
- 20 =4 4
= v s t 9 3 -> Co D 4 9 0 0 0 0 II N O O I e 4
- 4 o We WM >
0 I I a 1. O Eo 4e L 0 0 t I e t 4 >O >O W Q + sese em semo seee ee ee e ee ms me ss ee ms e se m ee se .e ee -e .e 4 en s es aseese s e me.e me.s esses esses e e s Z $ 0 3 1 1 O I A > > 0 0 0 0 Q Q Q 4 e i I E WC J e e , s O O & ls l ) I $ @ GW v I I WD WD W 4 { t O W4 W4 ** 9 I e i I O == .* U $ 8 ,9 .m ee e e ,. I $ N U Q W $ esse.eeeeeeeee e me nee. e e. + e. .s ee .e en en e .e ee eee. .e e. 4 e p y a 9 0 1 I I @ m & W 8 I O O I W W I I I 4 I e e e 5 = Q 8 4 8 9 O Q O 1 0 $ $ % b Z I e a I e e o O se l 9 8 1 8 0 O 9 I I I e I e M e .see.e ases am me.e ee e .m as se.s ee.e ee ee.e e me se es se ne se se meae me ss es sees + = e .e ee.e es se me + e W 4 i I e O N W 4 u I 8 9 1 = l l 6 I { $ @ s I 4 .I s I W = i i 0 8 9 I I i I 8 0 0 W !s i i e e e o Z 'I 5 9 8 $ @ > + me e es on.e se se es se + se se se se .e .e se se as 6 as se e en se se se.e =e + en en es se me em .e se + e 1 O O I O ** 8 8 8 0 0 5 $ 1 9 b I I I I e 1 0 il ls O B 9 9 9 0 I e 0 $ e +--- --.e ..e- .e- . --.e . .se .i- - .s ee -- .i .e - .e- .e -- .e o. e o o e o c. e G O c O O u n
- o. e. o e. e. o.
n e n e n c. se 30V1S'NV3M o,e,ga,, IV1221 GRAPH 6 PLOT OF OAILY MEAN DEVE OPMENT STAGE 12.000 + - - - - - - - - - + - - - + - ---=+--------+---------+---------+--------+---------+-------+ _-.---+ t I I I I I I I I I I I I I I 3 I I I I g I I I I I I I I I I I E I I I 3 3 3 3 I I I I I I I I I I I I I I I I I I I I I I I I I I I I I 3 I I I I 1 3 I I I I I I I I I I I I I I I I t 3 9.69? +---------+-------- *--- ----e- -------+---------+-- - =+---------+---------+--------+---------+ t I .I I s I I I I I I I I I I I I I I i I 8 - I I I 8 8 I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I 1 I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I 1 4 I I I I 7 200 +---==- +---- ---+--- --+--------+--------+---------+---------+---------+------~~*-~~---~~+ w I I I I I I I I & I I w >- $ 8 I I I I I I I I I I I 1 I I I I 1 I I I " I <ll I I I & I I I I 8 I 8 z I I I I i 1 I I I I I N $ I I I I I I I I I I 8 $z 8 I I I I I I I I I I I I I I I I I I I I I I I I I I I I I 4.800 +----- - - - + - - - - - - - - - + - - - - - - - - - + - = - - = - - = + - - - - - - - - - + - - - - - - - - - + - - - - - - - - - + - - - - - - - - - + - - - - - - - - - + - - - - - - - - -
- I I I I _ E I I I I I I I I I & .
^ 3 I I I I I I I I 8 I I I I I I I I I 1 I I I I I I I I I- I I I I I E I I I I I I I I I I I & I I I I I I I I I I I I I 8 8 8 I I I I I I 2 400 +-------- I --------+--=- ^ C I --+=--- I I I I I E I -+---------*----- - - - + - = = - - - - - +---------+---------* - ------+ I I I I I I I I I I I E I I I I I I I I I I I I I I i 8 I I I I I A I I I I I L & I I I I
- C I I I I I I I I I I I A I I I 1 E I I I I I 1 4 I I I I I I I E I I I T I I I I I - I I I I I I I I I I I I E I I I I I A 0.000 +---------+---------+---------+ --=--+---------+---------+---------+----=- +-------~~*---------*
0.0 1.430 2.000 4.200 5.600 - 7.000 8.400 9.800 31 200 12.600 14.000 { OATE -- - 740820. TEMPERATURE e 34,0 C FOR SPECTES A ON THE INTAKE SIDE. THE MODEL IS AVG = 1.36525* DAY
- 3.99200 FOR SPECIES A ON THE DISCHG SIDE. THC MODEL IS AVG = 0 88914* DAY
- 2.3J279 FO# THE REGRESSION ON SDEC IE S A THE T VALUE IS 4.22496WITH 10 DEGREES OF FREEDOM
GRAPH 7 > LOT OF DAILY MEAN DEVELOPMENT STAGE 12.000 +---------+=====----+---~~-===+- ====+===------+---- =--+==-------+- =----+----- +---------+ I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I E t t I I & I I t I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I E 1 1 I I i 1 I I I I I I I I I I I I I i 1 1 1 I I I I 9.60C +---------+---- --+-==- --+---------+- - -+---------+---------+---------+---==- +--- ==- -+ I 8 I I I I I I I I I I I I I I I I I I I i 1 I 1 1 I I I I I I I I I I I I I I I I I I I I I I I I 8 I I I I 8 1 1 I I 8 I I I I I I I I I I I I I I I I I I I I I I I I I I I . I I I I I I I 1.200 +--- - - = - + - - - - - - - - - + - - - - - - - - - * - - ==+--- -----+---~~----+---------+==-------+---------+ I I =-----+ w I I I I I 1 1 I e I I I I I I I I I I y$a M I I I I I I I I I I I I I I I I I I I I I I I I I I e E I I I I I I I I N < I I I I I I I I N W U I I I I I I I I I I I I I I I I I I I I I I 1 1 E I I I I I I I I I I I 4 00C +--= -+---- ----+- ==*---------*---------+---------+---------+---------+---------+---------+ 8 I I I I I I I I I I I I I I I I I I I l a ' I I I I I I E 8 I er I I 1 I I I I I I I i I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I t I I I I I I I I I I I I I 2.400 +---------+---------+---------+---------+---------+-- ----+------- -+- - - -+- - -+--------+ I I I I 8 I I I I I I I I I I I I I I I I I I I I I & I i 1 1 1 I O I I I I I I I I I I I I I I I I I I I I I E T I I I I I I I I I I I H I I I I I I I I I I I O I I I I I I I I E 1 I a N I I I I I I I I I I I I A 0 000 +---------+---------+---------+---------+- - ===--+---=- -+---------+---------+---------+---------+ 0.0 1 430 2.000 4.200 b.600 . 7.000 8 400 9.000 11 200 12.600 14.000 FOR SPECIFS AVG = O ON THE INTAKE SIDE. TtE MODEl. IS 0.80503e DAY + 2.63582 FOR SPECIFS AVG = D DN THE DISCHG SIDE. TBE MODEL IS 0.C848b+ OAV + 3.!!I84 FO9 THE PEGAESSION ON SPECIES O THE T VALud IS 4.29073stTH 12 DEGREES'DF FREEDON GRAPH 8 PLOT OF DAlt.Y MEAN DEVELOPMENT STAGE l 2' . C0 0 +-- ------+---------+- - ---- - +-------- +---------+----- I I I I I -+ - - - - - - - - -I+ - - - - - - - - - + - - - - - - - - + - - - - - - - -
- E t !
I I I I I I I I I I I I I I I I I I E I i i I t I I I I I I I I I I I I I I I I I I , 1 I I I I I I I I I I I I I I I I I I I I I I I I I 1 I I I I I 9.600 +---------+---------+I ---------+------I I I I I I L 3 -*---------+---------+---------+I---------+---------+---------*I I I I I I I I I I I I I I I I I I I t t 1 5 I i I I I I I I I I I I I I I I I I I I I I I I I 8 I I I t L I I I I I I I I I I I I I I I I I E I E 1 I I I I 4 I I I I I I 1 7 2 e cI + --- ------ + --- -- - -- +I --- ----+---- I I I I I E E w ----+------- -+-- ------+---------+--- I .*---------.I I I -----*. y i I I I I i 1 1 I I I w e I I I I I I < W I I I I I I I I I I I I e z I I I I I i 1 I I J 1 N x$ I I 1 I I I I I I I I N 43 I I I 1 8 I I I I I I I I I I I I I I I I 1 1 1 I 4 I 4.800 +-------~4--- I I I I I 1 I I I 8 ----+--- --+---------+---- I I I I I I I I I & 4 +---------+--- - - - - * - - - - - - - - - * = = -- 3 I ~*-~~---~~~* I I I I I I I I I I I I 1 I I I I I I 5 I I I I I I I I I I I I I I I I I I I I I I I I I I I I I t I I I I I I IO I I I I s t_ i 1 I I I I I I I I I 8 I I I I 2.400 +--- I I I I ----+---------+--------+-- I I I I I I I I I I -+---------+---------+---------+---------+---------+--- I I I I I I I I I I I I A 1 8 I I I I I I I I I 1 1 I i 1 1 C I I I I I I I I I I I O I I I 1 1 I I I 1 Q t I I I I I I I I I I I Y I I I I I I I I I I t I I I 't ! I I 4 I I +---------+---------+I-- I
- I A 0 000 I I I I I I I
---+---------+-- - --+--- -+---------*= I I I 0.0 1.400 --+---------+---------* 2.800 4 200 5 600 , 7.000 8 400 9.800 11 200 32.600 14 000 DATE - - - 740903. TE4PERA TURE = 30.9 C FOR SPECIES AWGA= ON THE INTAKE SIDS. THE MODEL 15 1 40588+ DAV + 2.05579 FOR SPE CIAVGE S=A ON THE DISCHG SIDEe THE MODEL IS 0.7605te DAY + 2.46628 FOR THE REGRESSION ON SPECIE S A THE F WALUE IS 3.54846WITH 9 DEGREES OF FREEDOM f I e l O ' O . === +-===o.m===+ O. 4 == e. e.= .. .ee. . . I i + ) 1 i e = 0 0 0 i 0 0 8 8 ) 4 1 8 i 0 0 0 9 l 6 4 4 i !e s 1 0 f g g e 0 I 1 O 0 $* 1 8 l 4 1 0 4 * + en en es es e == es se 4 as se me e en se
- es a. en e ee == =. + es es e. ee == as se + == es == == => e.== == e.
- I g a N a
l m 0 l g I I O t i e i W 4 'I \\ M G l 4 - E I 4 I i e l O E g e I O e 9 O I 1. i N L g 3 ee ms em es me se + es e. as em es en e.em me e es em os om en esse ss em + s me e .m e. me + e Q
- es.e am en e. neae asse + es 3 m
$ 1 0 $ 0 m N i 9 0 0 1 0 W 9 9 e W i 1 4 8 9 1 E 9 I i l # d 9 0 g e e O W ls 3 8 8 9 O O 9 I I e 0 O O. O o*e. +es +ese .see +m. e. essee. +.smo ee emeses.se e W W e W + memen.eeo. @ $ 9 $ l $ ) 4 e e u ( 0 i l i eJ J r , e i I il -l W W > M t I 0 g e OM OG g g g 8 QW O P= W > 0 $ # # i O XM = Z e* O 2 0 I I e 0 O 4 O ly 0 t l* e 3 g g e WM WO N g 1 0 g 3 e Ze Ie m & +=========+=========+=========+=========+a=========+e 9 WM kN N o I e e e e 4
- J ll t i 3 'I
*
- f'l W il 8 0 t ll @ It) e > ll 8 6 4 g 0+ 0+
W I e e e e g O 8 0 I I g s O W> WW 4 4 W == Z g g O 4 8 g I e i O WO 00 ========+=========+ e w I W W Z ,=========4 e p======+e=========e s > 4# U# M4 Mk 3 # 8 p J 0 0 0 3
- 2N =G 4 0
i 3 e*
- Q CD >
J 4 il e 1 M M = f f f # g , ii WS We > 4 0 t e
- I O Z* Z*
t e 0 t ] 4 3 ) g g o MO MO W $ 0 g 9 4 Z 4 t* 8 0 e Z > O +=========+=== =====+======== +=========+=========+I 0 # $ f oO O 2 > t e, 1 8 5 O 0 0 O O O e (e i J e t e ii G O W 4 l MD WD W 8 9 9 1 W4 W4 ee I O ** = U 4 0 I i 4 0 O U V W S
- i. 8 8 N W W 4 8
+ me .~ ee se 0 .s ee se e em men ee s e as se == + ee ne .e as e. es ee s. + w e.no me + .e .e es.n e. ee e a a w O f A O 1 I l l 1 2 l' 1 0 1 B 4 0 0 0 E E O I e a Q O 4 8 0 8 6 4 2 3 e a a u t O ** e I $ 4 I t e O M I 8 e O W 8 I I i e e W e s.ee ss ee m osom ee n. + .e ee ee ee ee mme + es se ne + me me ss en e..s m + == es ee mm e.ee ee se + I I e e N E G $ 0 0 1 0 6 # $ 8 8 8 i e N 0 t t E i 4 l 0 0 1 s 1 ls , 0 W ' 8 8 8 4 I O Z 3 0 9 1 I I O > 0 t 0 8 0
- g
.s e.ee me m e == me . e..+ e + on es ee me ne em me e s ee + e .s e. e= == mo s. + 8 0 i e i = 0 0 $ 1 0 4 0 $ $ $ il $ I e e a e e 0 4 4 8 0 8 e e i i 1 4 s t 0 i e i i e O +e.mm =me.+a.=m me + e +==mmmm m +.em === u O O O u o o n L O O C 8,,a O. e. N. e. e. O. P P. e N O N. e 3DV15 NV3W o=>Iez( IV-225 4 For space considerations, only a representative series of histograms for the time that growth depression occurred are shown. Histograms for other dates within this period may be obtained from the-authors. 4 0 'T 4 i g -. IV-226 _ . . . _ . _ , _ _ . . . . _ , _ . . _ . . - _ . _ _ - - - - - ~ , _ . - .. . .__ / i s . I ) MIST 04eam POe Oaft 74C920. S*ECIESA S10E8 oaf 0 3 Misf oceau fos D ATE 740s20.* *SPECitsa = =
- S10ER 04v 4.0 10C.6*O + - --+=====+** t ~ +*=-~*8 3**eae* *===~-+=
8 8 ~ -+===--*I I I I i 8 I I i I I I E I I I I I I I t I 8 I 't I I 8 I I I 8 I t 8 8 8 75.Pe* -----* TS.80P+===~~+=====+----=+====-*I ******+=~~~~***=**+I I I I I t I I I I I 8 I 8 t I B 8 I t 8 I I 5 t t t I t t t I I I t I I I I a f
- -----+--+
-- -
- C S0.9fi + = = + I C
0 S*. pee += ==+==---* I - --+I I O I I I I I I I e t i I e I I i t e i I I e t I le a i I
- I I t 0 t I to
- I ====+ I O t
- I $ 8 9 25.P90
-==--* *=====+===*=+*=**-+t I S 2 5 0 a f +I= e = = = *= * = = ===I + = == == +t I I I I lo e I 1 e t t 7 I t le * *te ? 8 e i I f I le e ele I t * * ** 18 88 I 88
- ele i o e*** to el i O I i
*=+eme=e+*=e-e+ 8.P *=****8*e n 9.a *=eae=*=*-e=+e****+e**-** N 1.Pte 3.e7S 6.75e 9.62SI2.50C t.900 3.875 6.7C0 9.42Sta.Ste F40e29 SIDEI 0A7 S.O Mtsinceau gee.aaa Po.e.D.AT += . . .E..F..4e.9 21. ..S*E+ .- . ASIDEI 04T 20 utsinceau C I E.S Po.e+ ..D..A T ..----+=. teo.ata P $8tCttse .=+-----+ I I I I t i I I I I 8 8 I I I I I I I I I I I I l f I I I I I I t i I j t t t I I
- 75. PO 4 I
*=====+-----+ - +-+ - -=* - ==+== = + I I 79 .8 0 9 +I = *- = +I =====+I I I I I I I I f f I I I t et I f I 5 I I I et i S I t i 8 I I I I I el ! 8 8 8 C t i S*.809 + = = = = = + = = - - - + - * * * = + - - = *
- I*
C S0.0Ce +=====+- - -+=====+===~e+ t I O I I I l et og 0 I E I m t 3 3 u 8 8 e i I e 8 to et 1 I I e i f 8 I I I O I I e I .I ..e I O g gg.pen+. I 8.- ..+8 ... - +lee...ee+t g 33.cee +...-.+.e..-+-.. ** 88 I I 8 8* *t t I 8* f f t I I* og ** T t T t I* *t I 8 8 I* *I t I ** 8* *t t t I te 88 . s t O I t I O I e e B* *I *=e********=+#**********+ N 0.a *********-+************** N *.* t fCe J.eFS 6.753 9 62S12.S00 3.e00 3 075 6.750 9 42582. SOC SIDE 8 DAY 4.0 wt qtnceau r ne S83EI DAT 3.0 wgSTOGeau Po.p D t .7 4.c-+ S .2 c.. Sof ** C..I E.S .+ A gar,nga +... O.af.t...f eas +== Se* C..I .+ t e .-+. E.SA 3ene aea + .--.a f e-- 8 =8 I I I I 8 8 8 t 3 8 t I C D t l 8 I I I I I l t 8 8 I I I I I I I i 1 8 = = = + 8 == - + 79.*0*+==*-=+---+=*-+I t t I 8 7 5 . c 6 e 3+ -== ==I + = = =*-+ t t = ~ +i et I I ( l I 8 I t I et 5 5 8 8 t I E t i I I I I t et t I I I I I = = *+ -= = + C C 0 S*.C0f ***---+ --***-----+I I I O Sm. fee I+====+ 8 ----+== I = +I et t i I t m I 8 5 t el u 9 I 8 e I t 8 I e et e t i 8 8 I O I I I
- 88 O I I I I t I
-=+==**** 25.f
- 1 S 25.000 + - ==+=====*
9 t *****=+-----+--*-***-****8 t 8 I *to
- 8 I 7 1 I t 1
I I t
- 88 I * *t 7 t t t '88 e 85 T I I I e of I t *R* * *t I I
- of t t sie * *1 O t t t n
= e.0 i I *-*=+=*-e-e-o-*-*++**-*+ I = *.9 e-*=e****-**+e=+-*++-e-** g 3.000 3.stS e.?SO 9 42582 500 8.ce0 3.sFS a.75C 9.e25 2.S00 FIRURE 1 Histegrams fornAca"nn. u'eca.s. y intCaa juveniles, %C0irposition ve stage i l l l IV-227 p N *" T C/- Stoto pay g.3 ,Matingoam Poe Daf t feests. SeECIESA SIDED DaV 5.0 MISf0Geen t?O.9Ca rom +== Daft - -+ - = + 74eS.20. +Sat C.ttSa =* 4eaeeC0 *== ==+= = =+ ~ ~-+ - -.* I I I t I I I I 8 I 8 I I I i 8 I I I I I I t I t 5 I I I I I I FS.eee + ..-+ .. I. t I i t t +I-t I + I I 7 9 .P 0 0 + =.*- + .=-+ -- + --* I I I I t I e I l 0 e 3 I I I I I I I I l e f 8 3 e s s 8 I I t 8 I I I C 99.0*P e --+ - .-+= +. =.+ C S0 0P0 *= -+-..+.--+- -+ M e f 8 8 I e t I t t I C e t I I m I e Ie 8 8 8 0 e a I I I e I e 8e 3 I I m e 3 3 3 e ie 3e t 8 3 9 2 9 . 0 *eCe e .-.-+ =.-+ ...-. 3+ = = . - + S 25 0** * *-- +.*-~~*==. -+ - -.* l 8 I I e 3 1 e 8 e i et t ? e o i I I 3 y I e 8 e B el a e e e I I I 3 8 I e i e I eI t @ e e I ee to g 0 t e i e 3 et e.e.e.e.e.e.+e.e.ees.o.e+ 3 i e e.e w e.e e.e.e.e-e.e.+e.e.e+e.e.o+
- 4. eft 3.075 a.7SC 9.62S$2.SCO t.eet 3.e7S 6.FSC 9 62St2.500 M I ta*ST nc.e 0 aeam Stoto Da7 20 Se SIDto Day e .0
+ .poo + 04T.E 749920 .. ..$*t..C.I . .E.S.A MI Iea.9ee St9Geam FO.e +. .. D.AT.E + F.40920.. . - t C.a .o .t.$,a 8 I I I e I I I I e I I I I i a a I I I I t t I I I I I I I - - t I ...,I - I ?Seega e... + I I I - + - - . + I FS.eep .t .-+i I = . + . - + - - . + I i t $ I I I I I I I I I I .I t I B t O 4 I I I I 3 8 I I I I I e i I I I I I I I I C S*.ett e.e...+-....+ .-.=*=...-+ C SP.0PP +-.a.=+==.--+= .*.*===* O I e I I e 3 0 9 8 I I e f e i I I I m 8 I I I I e I e i I I I e I g I 3 e i e S 35.99c e.e....i.....+I... e e .I....-+I O S pS.ege I e e +.e.e.3.... I... +I... ,I t B 3 8 I e I , e I I I e ? ee I to 3 I 7 3 e e e o I I e I I e e I to 8 I t I e e e o I I e 8 $W ee I e ae t I O I o ee e t I e I w 9.a e.e.e.o.a.o.ee.e.e+e.e.e+ w a.a e.e.e.e.e.e.ee e.ees.o.e+
- 8.*ee 3.nFS 6.75e 9.62S$2 600 9.eC0 3.875 6 750 9.62St2.S30 m t te ST O.';ses a m....a.pe D.a.f
. f.7404. 2..' ..Se7.C.a .. ..er S a SIDt3 Dev 3.0 SIDED Dav F.3 wtStoscereau tem +rn.e..D..A.T.E.740.+8 . . 2Set.C.i e .t.s,a e. 4 I t t t I I I I I I 1 I 9 6 I 8 I 8 I I I t I I I I 3 I f 9 0 8 8 I 79.80a +..===+.-=.-+..*--**-*.-* I I t I I t I 7Setta +....+=....+=====+...e I I I I t I t t I I I I 8 I I I I I 3 I 8 I 8 t 8 i I I I C t t Sa. Sap + = ==+- - *== ~ e - .** t t t I i 3 I I @ l
- I e 3 8 C Sa.ee**=*.+---+..--*===+
e i e 0 t
- I I I I e
I I I I e I e i I I I e t I I 3
- I e i I I
fl g -pS.eee .I.ee--..I ...,I . +i.. .+8 0 g y S. e n 0 t * +.e . e..I . I +I .--.e +I ... .I t t e e
- 3 1 4 3 I e o t I eI T e *
- I t i I I Y 8 e o I 3 et t i e e e i te B I I e e I I 6 ee e t el 3 w a.e e.e.e.f.e.e.See.e.ees.o.e*I 8 O w e.0 t e e I t e.*.e=8-e.e.**=*=e+e=*=e+
*I $ 3.ece 3 075 6.750 9.42542.530 4.ceS 3.s75 s.7Se 9.62512.Se0 uttfoceag MtstnGea4 FDe SP gno,aea p..o.s..O.a.f.t.74 ..eS 2 0 ..S* + E..C.i .+.t S A SIDEO Dat 4.0 ten.ec* +.e..D. aft.741520 +. ...+.. - E ..C.t r..$ a St0ED Daf e .0 I I I I I I e I I I I I I t t i I
- t I I I t 8 I e I I e t 8 I I I I I I I I
- I I 3 yg.*0a +=..*=+.a..-+.=.--+-**==+ 75.00* +=e.==+--.--+-----+I ==---+
t I I I I t e I I I I t I 9 I I I e i I I t. 3 3 8 8 I I 3 3 e 3 I I I t t I I I I
- I I I j
C SS. POP +.e..=e.==.*e---- +===*-* C S c . P a a + .e -..+ +.--.+ = = +..- ,I e, I e e i I t O I e ! I I I e g e e t 8 I m I e I I t 3 e t e e t t 3 e t
- I I I I O e t I O I e
+.e...I +t... +t.....,I t I g pSeces +..e.........+ . +.-. + g 3S aea . t I *
- I I I
- I I I I T t e o I B I T t e I I e 8 I I e o I I I t I e i I I I O I e e t I I O I e I m c.e e.o...e.e.e.ee.e.ooe-o.e+ w c.e e.e.e.e.e.e.+e.e.e,8e.e.e,3 1
t.eco 3.erS s.7Se 9 623:2 500 8.e00 3.stS a.vSe 9 62S.2.500 FMCRE 2 Histograms for Aeartse tensa discha.rge juveniles % composition vs stage IV-228 * > a a MIS 70Geam MIS 70ceam se.e pate face 20. SPE Ci t SO SIDES Oav S.O tea. tee F0s+=..Cafe + 7ese20.+ .. .S.et +- C.I+E SO SIDER 047 0.3 la0. Pee + . - + = = =*~ *= +- - + I I I t t I I I I 8 I 8 I t E I t t i t E I $ 8 8 I I I I i t I 3 I I I I I i I + 75.ca9 + ..-+ + .. + --+ 7 9 . a n e + .. . .. + ...+ . .. + . t I I I I I I I I I 0 8 E 8 I I t t t 8 8 e a e a. e 8 : s t I $ 8 I f f f I i C Stee09 + - - - - + .-+ + + C $6.n00 *-~~+-..+--h--+ 0 t I g g I O I I I g I
- e t I I I e 3 I I I I I
I ea I I I e t I I I I O i t 8 0 t i I f 8 3 as. cia + .ee.+.... ~ + - + 3 ====* S I I e e t I I I 25.0Ce I+ ~ ~+====+== I *8 = +I I T t e e *
- I 3 3 7 I I e* Io 8 0 t i e oe e 3 3 e t i e e e to ete i e o e e e O e e oee e e ete e I O
m e.c Ie 3 e.e .e-o .e. e =+ e.e-e e s.o .e+ g m S.0 e.e.e.e.e.e.f +e.e.e+e.e.e+ e teace 3.879 6.FSO 9.625ta.See tea 0? 3.87S 4.75C 9 62$l2 500 MIS 70Geam p0s Set $8088 04T 33 8 ene Daft Sf388 047 e.0 tPe.0ee + - . ATE - o - .-+.C..it.SO c.+ 74C020. -+ $90OG. '0f + " t ST "9- =+ - .6es20.7 .+ - - +=SetC.tf.S3 = + I I I I t t I I I I 8 8 8 I I I I I I t t l I I 4 I I [s [ I I l s t i I I t E 79.*9e +. ~ .+.- - +=* - += = =+ 790en*.....+-..+..-.+.....+ I I I t I I I I I I I I I I t t 8 I I E I 8 8 8 8 I E I t I t t I I I I I I f 3 C I S*.pae*-~+.-.+---+.--+ I C S a . P e e + =. ..+ .--+ = .= * + I 0 g g 0 3 8 I t u t i 1 I 3 I # 8 I I I I I I I I
- I I I 3 8 0
E B I O I I I I I $ 2 9. ng e .t ..... +. e...+3 . - +3 ... + S 25.000 + ..+ - .+-.. *--- -+ t e e t I I I t I a f 8 8 7 e t o e i I 3 7 8 3 e
- I t I I e e se e 8 g i e oe e e e I g g 8 3 e O e e e e e e I g O 3 e o e o e le e eIe o et
- m P.9 e.e.e.e.e.e.i ee.e.e+e.e.e+
e M 0.0 e.e.e.e.e.e +e.e.ees-o.e+ t.090 3.07S 6 750 9.62%I2 580 le*00 3 975 4.750 9 42S82.503 Slott Oaf J.0 S$ DEI DaT F.0 M S S f MGiam fos.D.a t t..f eaq 2 0 I e r . ne a +-. . +. ..+ ..+ S.et.C t t.S3, . MIS?"G.meaeau I** + Pn.e..D.a . +. f t F.4 $st 0+. 8.C..I . E.SO 78.. + t I B B I I I I I t t t 8 8 I 8 I f 8 I I t I t I t I I I I... I 7S.0ae +... I I.....I + t +-....+......I I I I 75.eaa +.....+. . I I I I +I....-+E B t t t i I t , t t t t 3 S I I 3 g t 5 5 I 3 I e 3 C t t St.n** e ....e...-.+.. 8 8 +. -.e 8 C S*.e** +... I I I+=====e.. t.....+i + 0 t I 8 I I O t t t 3 8 m t I 3 I t
- I I t t E 8 I I I I
- I I B B I O I e I
I 0 t 3 8 i I i S 25.e*0 +=..-.+==.e=+ .. +..e..e . S 25.00e e..-..+-o...+.-=-.+.....e e I e E I e t I I I I I t I 7 I 3 e e e to I e 7 3 I e t 3e e et e ge o ese e og I e e ee e to I I O I t I I e e ese o et O _e e oe e e e.e.e.e.e so e.+e.e.eI e .e.e,E n nn o t e e.e.e.e.e.l e eeeeeeee.e.e+ u een t.C0c 3.eFS 4 750 9 42S32.330 , 1 000 3 47S 6.FSO 9.42932.S00 fe MIStoceau Fce O. ATE 740820. S8ECIESO SIDEI Oav s.O ..n.af.f-MISfnceau F0.stea.aae. + +.e12a. SPECIESO ..***+***=*+ $80El 047 40 30a.eGe *--. + * - * * * + * * - - - * * * * * - +1 t I I I I I I t 8 1 I I I I I I I 3 I t t I I I I t t I I t I I I I 79 ega .I.....+I ...+B -.. +=.. + B t yS.ppe +.....+. .+ ....+....-+ I I 8 I I I I t i I I I 3 8 I I I I I I 5 I I I I $ s t I B B f I I t I I I 3 I I I C Sc.0c0 e . *=.- +....-+===
- C S*.e*0 e.-..-+=.- +...-.+- *.**
O I I I t t O 3 3 8 8 8 m 8 I I t 8 m I I I t t
- I I I $ $ e t I t t t O t I I I I O t t t I I S 25.nga +.--..+...e.+... +.....* S 29.ee*+=..--+=.---+-**=*+--****
t I I e
- I t I I t t I e ele e 8 7 8 8 8 e1 I I T I I I
- eto e I t I eee*to I t t i I I e 6te e i O t * * *ee le e 48
- et O I I le e ele
- 88 m 9.6 e =e- e-e .e. e .+ e . e- e + +. e s* w 9.e e.e.e.e.e.e.+e-e-e+e.e-e+
1 000 3.e75 a.75e 9 625:2.See 1.e00 3.o75 a.7Se 9.4t5 2.500 FIGURE 3 Histograms f08 cithens breFicornis intake juveniles. A composition vs stage l W IV-229 l l I Kl gtec taa.eau pO.e.D.a.f cap *. . . t.74.04 80. . SP.E.C..t.f.SO SIDtB Oav 6 3 nf Stogeae Pos 04TE 74C920 SIDED Dev 6.0 e 3 g a . co e +--+ --+ = -+- SerCi.t.SO e I i 1 I I I I I I I e t i 8 l l 8 I I 8 I 8 I I s s 3 I 75.g9ee ..o... + .I... +I t I
- 73. g e s + -.-+--+ --+-
i t .t I 1 8 9 3 I I f f I I I I I I I I I I C 0 t i I I S I I I I I I I f I t t t C 0 90.e9e+=.==+- ~*= m = t I l 3 +I I CO S 3. 0 0 0 * - + =.-+ = - +- ~+I B t th I I 8 8 p { t t t I e- 3 I I s t I t e 3 n 8 8 8 8 i 0 Is t t i S ft.00* *...e.e.~ ..+ ~ + ~ +8 I e* I I S ts.ae9 + - + = =+=. - +== =+ t t T I *e e e 8 I t e
- 8 I I oe o e 3 I t ? 8 ee e e i I I 6
e i I i 8 e e ee o e to eo ee e o to I o eo e e e e to I I g a.a e.e.e.e.e.e.ee.e.ees.o.e.I N 9.e e-e.e.e.e.e.ee.e.ees.a.e,I et t.P00 3.879 4.FS* 9.42512.900 8.*C0 3.47S 6.7Se 9.62512.S00 pett60 tf 0G.eae tae Fne ft + ~ Oa.e ==742020. =e-m-m SeR C 8 tS3 $83E0 Dev 4 0 Mistoceau por 7409 I 8 ta*.0C* + -. Daft-** --*.2.e .-Set + -C .=*I E SO 88040 Dev 7.0 I 8 I I I I I I I I I t I I I I 0 t t t t i B I I I I t 79 09a *=-+=.-+==o~.-+ 8 8 8 8 t I i 8 8
- 75. e c e + .--+--e . *-..o I t I l 9 I I t t 8 8 8 I I I e
I 9 8 8 9 I I ~S I I I I I t I I I c 6 ce.eea .I ...+B - t - - + ~ ~ = + ~ -
- I C
t
- 91. ? e e * ~m ~.-+ = ..
I +I ..-+I . 8 8 8 3 I e e t I 8 8 8 e i I I I I I G I I I I I
- 1 t 8 I O I I I 8 I I I I n f I I I I S 29.*f9 *..**.+.- -*..- +~ ~+ S PS.P** **ea..+.....+.+.*.*
I I I eo e
- 8 8 I I e e t Y e o e e 3 1 t I 8 7 e e I e I 3
t ee e eo B I 4 3 I gg eo e o e I t I e a e o e e B 4 I n eo ee e o e.e.e.e.e..e..e......I . .e,I g g pea e.e.e.e.e.e.ee-o.eee.e.ee n c.m . I.fe* 3 47S 6.799 9.43983.SJE t .P*e 3 875 6. 7SC 9.orsta.50t StOtp DAT 30 M Igme, ageeea e .. S T OG F O..s - . O.a.t .t .P ..S e ae. s.2 0.t..SO. et.C.i MISTnGeas t eo.eC pne 6 +~ .D.af.t..P.e.te20 + e- MetC.IESS +.. ..e SBOED DAY e .C f 8 I I I I t 8 I I t t t 8 I I I I t g I I I I B f I t i I te. nee e.....0......I.....+t.....e t I I I i I 79.ae* +.....+.. I .I......I... t e e I t 8 I 8 8 8 8 I I t 8 I I I t I I S
- S t i I 8 8 I I t i I I I I I I I C
t#. PAP + .==+..--.+. t I I +...*-+ I I C O St.0c0 +....-e I I . +I. t .I.....e t t M I I I I I " I
- I I I
- I I I I I
- t
- t I I O
q pg 49e t .t...e.e......I... t e I
- I I...
I .I I O t S 'RSeeaa e.- .-e.... eo 3... . +I-....e I l 9 I e oe e T I I
- I t t i I I t e e o I g 3 t ee o e o e t 8 I I i * *
- I I a
- . eo e ee e 8 I I O t e oe o e le t 3 e.e e.e.e.e.e.e.se.e.eee.e.ee N F.n e=e=e e e-o.eeee-ese.e.ee l.POS 3.875 6.791 9 6rSl2.See 1 009 3 678 6.?SO 9.43552 500 399 0 0 MISTOG. e ane eau * ~D.- aft
-* ~74*92e. ~+ -* - SetCit +"- *.S +O SIDED 047 40 wi9tocene 10C.0e* Poe+--.- o.aT.E.740.s.t.e + . Set.t.tt.S3 .. . e Stoto pay S .0 t 8 I I I t t I I I I I I I 3 t I I 8 t t 1 1 1 1 I I I I I I 73.p0P *.-.+.--+.*.**--o I I t I I I I I ~ ?S.Sen + --.-+. ~ .+t-.. +......I I $ 8 I I l I I I I 3 I t I t t 3 8 8 8 8 9 I I I $ I I I I I I t t C Cf.PS? ***...+....****-.****- + I W 8 I I I I C O S*.eC0 +=.-..+..-..+I =..- 3 g 3
- e. ..+
3 g c t i I I I # 8
- I e- I I I I I
- e I I I a t
- I I I t g
t pS.maa e.e...e.--...I... +I. I e O S I 2S.9a 9 +=....e-.. e t +... .I.....e t 3 i
- I I t t I e
- T 3 e e e e l I t ? I e e e I I I I ee e e e I I 3 i 1 I t g eo e e e 3 I 8 G e o e eo e I I I m 0.a e.e . e-o .e.e .e e. e .e e e-e.e+ e e e e e e t t e 4 P.O e.e.**e.e.e.ee.e.ees.o.e.8 3.ae6 3.s7S s.7Se 9.e:Sta.90e 8.e00. 3.e75 s.75o e.o Sta.Soo FIGURE 4 Histograms for Otthone brevicerwis discharge juveniles, %eosposition vs stage 1
i e~, IV-230 d i APPENDIX G SURVIV0RSHIP ANALYSIS E I IV-231 1 Survivorship curves for intake and discharge populations ' of adult and juvenile Acartia tonsa were shown to not be significantly different from each other. For time and space reasons, these graphs are not included, but are available upon request from the authors. I
- l. IV-232
H . 2 , > ,.3 < . U . .0 I .ao k A .- .-o -N
- n. . .. e a. n. n.
, o e 3 ooo M - t M L o, 4. , se ~ .d,, s .~,o ncee n, m ,,~ # :: 4; . .......................... 3 ... > ,., . w . > ~ .-- .o .~~ . . ~ . ~ a. .o o , e . ~ o ~ >
- c. e.
o... o. o. o. n. . a a 3 .~ o .e s * * * - o ~ ~ . .. - nonooo. . . . n, . . ~ w d . . = ~ ~4. e oooooo ==~ . - a . . < o. .o , =~, == ~ > 4 ~ 2 w .o .o ~. - ~ . .= ~ .,2 ~ , o. a. coo. .n oo. a . .z o o - ~.- - un o ~~.o.. - v . 9 I 2 ~~.e .- ...... ...
- o w oooooo o 2 .>- . .
o - - o a , = 3 o . 5 8 ; ~ > z s < . . . > ~ . . - s ~ . . . . .-o . . .o- , . a . = o 4 .. -- o .,.,~~ > u a 3 o ooo a.a. n. a o...,o. ...-~ .... am e onoooo a . > e . . o . o ~ ~ o. . a aa . - e o . s - , . . <- < ~ - . < <- o o o ~,~ . z 2 o. .o o . -o 3 ~- ~~ 3 m . > > > . . o . n ~ = d e. . 1 2 1 1 , z
- 3 a
-. o. o. 8oo . a . W ~ ~. o - s ooo . . < u a 2 . + < a o - ~ . . n o o o c zzw
- o. o,.<..
no.o,_ . v 5 w n Yo o - o o o = < - a . - z 2 r , , .
- v
~ .., a -~ . . 1 , , = - o , - < a 3 s s = = z .. . ~ . . . * > a - < o oo + a 6 o o . = w 4 a 4 o = . <. = omooo. . e < o x ~ . a ... o - . - o < o o. . ~~~ s * * . o v 3 = . ~ a ooo - . . a < c n * < .
- z o 4 . - - .o.., < o a =
- e ~ s a - .o . ne. . * - e = a. ~ n m . B ,. ~. ~, .- . .z . ~ . . = o x w a .L . w ~ 3 =. > - . a a : - ~ n w - . . - 3 .o . - - - x - a
- 9
- 6. ,,o nos~oo o.o .o oo on . x
= o - w a .
- a ,ooooo = a a . . <
d < no 2 a n . o
- m. . L o
a e s o ~ ~ s -~~ > t, o o. . < < o ~ ~ o 3 o . ,o o,-.~,oo ~ < - - = o o o a < o < . v w u.. m k W ~ . ~ .... ooNooO .,, 2 < < . 4 3 > .e U W d a - w .o w< -o N o u W A A d j g C o o g C 4 < . . Q
- I E -
- o z **
9 Q o n M # W 4 &_N,.c - d - Wooooo t A W d J , V w w Voonco P V N M 4 u M. J I E 3 2 u 3>>>>m M l 7 JAd 2 'JTTTTY Z J 3 4 T 2 T 3 J o<u 3 :J-3 S d hT232 3SS3 d N i < M 4 w W W DAW e -ooaco l N 5 IV-233 EFFECTS OF POWER PLANT ENTRAINMENT ON MAJOR SPECIES OF COPEP0DS Measurement of Zooplankton Mortality Using Adenosine Tri-phosphate as a Viable Biomass Indicator. Final Report October 15, 1974 , University of Florida Marine Laboratory Frank J. Maturo, Jr. Principal Investigator Richard D. Drew Graduate Assistant - IV-235 - r > . _. l Measurement of Zooplankton Mortality Using Adenosine Tri-phosphate as a Viable Biomass Indicator INTRODUCTION In studies of conditions of stress on zooplankton comunities, it is desirable to measure the viable biomass changes over time. In certain circumstances, such as the passage of water through power plant cooling pipes, the stress is sudden and the impact on the microbiotic community is immediate. In the past, the methods of estimation of zooplankton biomass included microscopic detemination of viable individuals by photo-taxis, movement, respiration, and other similar techniques. These methods are tedious, time consuming, and usually representative of an underesti-mation of the true live count. Recently a vital stain technique developed by D. M. Dessel, et. al., has proven to be a reliable indicator of the existence of living or dead zooplankton; however, this method still re-quires much time spent in counting individuals of the community under study. Biomass parameters used to measure viability of a given microbial zooplankton population, such as total organic carbon content, have always suffered in tems of their being non-specific; that is, related to both living and dead factions, and thus unable to reflect microbial responses to varying environmental conditions. Adenosine tri-phosphate has been shown to be a rapid indicator of viable microbial biomass in bacterial and algal cultures, marine and freshwater phytoplankton, activated sludge, and sediment environments. Although the bacterial and phytoplankton ATP relationships have been extensively studied, infomation on such relationships with micro- or macro- zooplankton are scarce. Only two reported studies have involved ATP levels in zooplankton, and these experiments took place only under very restricted laboratory conditions. The organisms involved wem two marine pelagic copepods, Calanus hegalandicus (0. Holm Hansen) and C_. finmarchicus (N. Balch). In both studies, the investigators employed starvation as a stressed condition to observe possible changes in ATP as percentage the total carbon that is not related to copepod mor-tality. Their work suggests that a constant proportion of ATP to total organic carbon is maintained under conditions of prolonges starvation. Two independent studies recently conducted at the Florida Power and Light Turkey Point Power Plant incorporated the use of the ATP assay as a zooplankton biomass index (Prager,1970; Lackey & Lackey,1972). Although in both projects the actual application of the assay remained limited, the results of the technique were oromising enough to warrant consideration of the assay as a major zooplankton mortality parameter to be incorporated in the 1973-74 Florida Power Crystal River Power Plant project. The use of a viable biomass indicator such as the ATP assay is based primarily on two assumptions:
- 1. The cellular ATP levels must be pmportional to some cellular IV-236
unity, such as'to carton or unit biomass ~(dry we' ight7; par-
- ticularly during varying environmental conditions.
- 2. After the zooplankter death, the cellular ATP is broken down (hydrolysed by adenosine tri-phosphase), so that no ATP is associated with dead organic matter.
The purpose of this report is the evaluation and application of the ATP assay as cogared to current vital stain technique .in zooplankton mortality studies. METHODS The samples were taken from the Crystal River Power Plant intake and discharge canals on a bi-weekly basis. As seen in Figure 1, the intake samples (I) were taken immediately adjacent to the retaining screens in the intake canals; the discharge samples (D) were taken in i front of the second series of pumps in the discharge canal. The initial intake and discharge samples were taken approximately 15 minutes apart to increase the probability that the same zooplankton population was being sampled. Both samples were taken using surface plankton tows with 89p mesh nets. For purposes of ATP-zooplankton analysis, this size net proved too small, allowing a large percentage of phytoplankton to be retained in the samples, especially during periods of phytoplankton blooms. It was decided to go to a 202u mesh net for samples to be assayed for ATP. After the samples wem obtained, they were taken to i, the field trailer and divided into sub-samples using a Folsom Plankton Spli tter. Six aliquots of 15 ml. each (three from intake, three from discharge) were filtered again through a 202u filter pad. After the aliquot was filtemd, the filter pad was imediately dropped into a hard
- plastic scintillation vial and the filter apparatus rinsed with 10 ml.
of 0.025 M M0PS buffer (pH 7.8) into the vial. The vial was capped and quickly dropped into liquid nitrogen to freeze the sample until ATP extraction and detemination at a later date. The samples could then ' be' stored at -20*C for several months without the loss of ATP activity. ' Sub-samples from the initial intake were placed in two floating bottles, described by Ray Alden earlier in this project report. One floating bottle was placed in the intake canal adjacentto the screen; this bottle received the designation I-C. The second floating bottle was placed in the discharge canal and contained two sub-samples--one from the intake
- area (I-D) and the other from the discharge area (0-0). The second bottle was relecsed and permitted to float freely in the discharge canal for two hours. A third intake sub-samle (I-T) was placed in the dis-charge canal for approximately five minutes to represent the passage time through the power plant.
F The rationale behind the sampling procedure is that the zoo-plankton can be studied after their encounter with stresses occurring during their passage through the cooling pipe. The difference between the initial intake (I) and discharge (D) samples is related to the effects of rapid temperature, salinity and mechanical stresses on the zooplankton population. The intake sample placed in-the discharge for y IV-237 ---c- % y e 9 /', g- g- g py--p g- p y h - l^ri---. s .. _ _ . _ . H , M 1 \\ \ \ \ INTAKE CANAL ! DISCHARGE CANAL l p I 'f a l
- wN I
r l t l 4 , Plant Fig. 1: Stations. I. Site D IV-238 -r five minutes (I-T) represerits an initial' t'mperature e and salinity stres's divorced from mechanical pressures of the cooling pipes. The floating bottle in the intake canal (I-C) represents a control for I-T and the other bottle floating in the discharge canal. This second floating bottle contains I-D undergoing prolonged teriperature and salinity stresses without passing through the plant; D-D experiences all the stresses of a_ zooplankton population two hours after passage through the power plant. All the samples were taken back to the lab for secondary filtration and freezing as previously described. At the time of the extraction, the vials were emptied into 35 ml. of boiling (+ 100*C) 0,02 M MOPS or 0.025 M TRIS buffer at pH 7.8 and boiled for ten minutes. (Extraction efficiencies of ATP are approximately the same for both buffers.) It is important that the temperature of the extraction be kept 100*C or greater to insure rapid and complete deactivation of Atpase. After boiling, the solution was cooled with running water and the final volume brought up to 40 ml. The solution was centrifuged at 2000 rpm for ten minutes, and the supernatant poured into acid-washed test tubes and frozen at -20*C until ATP determination. The ATP was assayed using a duPont Bioluminescent Biometer. Ten ml. of the ATP extraction solution was injected into the machine, using a Harcilton microliter # 705 syringe. This syringe conducts the material to be assayed into a buffer solution of purified Luciferin-Luciferase, and the initial light peak was measured and converted into grams of ATP per milliliter. Two values were taken for each extraction, resulting in six values for each station on one particular sampling date. Copepods were counted and measured on a Sedgwick-Rafter cell. Three entire fields for each sample were used to determine the number of copepods per ml. and the units of biomass per ml. One unit of bio-mass was defined as a cephalothorax length of approximately 189u. This size classification is based on a technique developed by Heinle (1966) relation length of the cephalothorax to volume. Balch (1972) ] also used this size approximation when separating individual copepods into equal biomass groups for subsequent ATP analysis. Although in using this counting technique the width is assumed to be relatively i constant, it is felt that this assumption is c compromise between only ! counting the total nurgwr, or measuring the exact dimensions of each ; zooplankter; the fonner being extremely variable, the latter ex-tremely tedious. ! ) The vital stain technique (Dressel, Heinle and Groth,1971) is described in ' detail elsewhere in this final report (R. Alden). Briefly, neutral red stain is added to the copepod sample, staining living copepods a deep magenta when acetic acid is added, while dead copepods are white or light pink. RESULTS The mlationship between ATP levels and changing environmental con-IV-239 s ^^ ditions imposed on the. zooplankton community by the power plant is ex-pressed as changes in ATP per milliliter, ATP per number of zooplankton assayed, and ATP per zooplankton units of biomass from ov station to the next. Eleven sampling dates are included in this rt, ort, ranging from April 4,1974 to September 3,1974. Table 1 from each of the sam-pling dates represents three foms of ATP levels mentioned above; that is, grams of ATP/ml, zooplankton, and unit for each of tne stations sampled on that particular date. The second table (Table 2) groups the zooplankton assayed by size based on the length of the cephalothorax (as described earlier in the methods section of this report), and the percentage of the total number of zooplankton at a particular station i that the size group represents. The last table (Table 3) from each sampling date represents the percentage change in ATP levels between designated stations: for instance, on April 4, the ATP levels as ATP per unit biomass decreased 39% from the intake to the discharge canals. - Assuming that any percentage decrease between these two stations would represent approximate mortality of zooplankton, then this decrease would suggest an approximate 40% mortality ra!te on this particular date. However, this may not be the case, as later sample dates exhibit increased changes of ATP levels. These patterns will be discussed later in this re Suffix letters following intake (I) and discharge (D) notations (port.i.e. O, L, or S) represent one of the various filtration methods used to detemine phytoplankton interferences on a particular sampling date. The change in temperatures (at) ranged from 5.3*C to 7.3*C, with a mean of 6.5'C (Table A and Fig. 2) with 7aximum temperatures encountered during late August and early September. TABLE A begrees *C at Intake (I) and Discharge (D) Canals and Temperature Change Across the Condensers: Date I D AC Date I D AC , 4/2 24.8 30.7 5.9 7/9 28.8 35.7 6.9 4/24 23.9 30.6 6.7- 7/23 29.7 36.0 5.3 5/14 25.1 31.3 6.2 8/6 28.9 35.1 6.2 5/28 27.5- 34.8 7.3 8/20 30.1 37.2 7.1 6/11 29.5 36.0 6.5 9/3 30.9 37.0 6.1 6/25 27.2 34.2 7.0 The change in salinity (0/000) ranged from 0.2 0/00 to 2.5 0/00, with a mean of 1.4 0/00 (Table B and Fig. 2). IV-240- = .p- - j 7 .. ,1.-; , - 7m -;
- . :p,l ;.-:,l 3--
- - ,l 7 - -. l.i.- - q. , ..t. . mi . .l. . .. .j . 2 i 1 ii rlJ h j.: ! .l , 1' L l !PICm E 2" '! ! ! 5 i ii i ; i. !! '! ' l ..] ; { 6- . i- - . * ' i :
- i; i i
l- l , , ' ' I . ; .q : 't . : i i 4 .. ; -I .iIl , g , ! ; ' 39 . t I ' ! - I .. ! ' ' - 31.0 !]- .}'t 1: .l' -fl ' ' ' ': ! -l -l.' .i . ! i i l 'i j l i 8 i
- i. . ,
4 i l . j' , i i, - . . i I , , i . - . i i , i , i 1 !- i [l I. 8' l :16+'f l l l i. : { . ! .l i. . ! -l .I [- l l l.j.I<!j i j. I i l ; l'. l 2 l i ' j l./li s .x. l- . 3,. . ! i : i , i . : -i_ 4 , - 29.0 i . .' - i t-l ' i i : i . i, i , , i , ; i . > /, . ; i-l f , ini 'i [i l . ' : sl:jsi .ii:1 :.l ' l ! ' l , Li ; i !I - I ' f l i i- -l,--{ ..., :j il' ' ' o- -.,'.,fds!;;j ; l: - ! ' ! -' N si , I /l t X ""*i i ! !' ; si i! s o
- r p' 1; .: , ,
. .s s,- 4 ! i i 35 - -.M - !/ ' ' X i-27.0 q . ! .:Ic. . i i . ;t o.v'!7i'x' .t. /.M t i #l1. ! s ' ! .L ! .i hM' % m [> , p! ' _o -l - l - 1.i l ' ; ; - i .i] [ ' ~ ' /; . ' i 'l. l 'i \' i j .' [ , ].! , '. l - fi , !. / ~ ' ll v i: i i 33" ! .1 i .;,4 .f ,cl d g .[ .s "i ' ! ! j, 1 - / / ' i g /\ i g i , !i
- ,g: C l I i
-25.o m J g ' / p / i , , \ .j -s i. a v(F m .
- o. I< .X i .
\ . ./ & 31. l - 1 , -23 : mL m >t- ~ &# \ 'l . s /- M i - a z l ';' / ; !i i , ! t \ ' li '/ i i 3 [; o d ;,i I/ .:ci - ., , j \ '
- 1. . .
.l - - , i+ .! l e ' g , / ' ' 29' - ! l .i ! ; ' i i - ; 'i ' ! i. ' i '21.0 . . . ; / .j i j ,i ! ; - i )( i .' gX/ ' , , j '- l ' [ ; .~ I '/' , i .j: . ;i,. i I j .; l{ i .g./; }.l , '
- .l '
/ ' , i , , i , . U . * ;q / x , ' ; i i ,. , 27, !! X ' i I - * .19.0 j ; 'o - 2-I I/' , e i i 1 , , j - - - [ l. l , . ;- i i o I j - i ! i 25 * - , i i ' 17.0 ( X - X) intake temp. ! ' l-( X---X) discharge temp. ;' 2 (0 - 0) int'ake salinity ! (0---0) dischkrge salinity 23 - - i . t'5 . 0 4/2 . 4'/24 5/14 / 5'28 6/11 6'/25 I/9 7223 8'/6 8/20 9'/3 SAMPLING DATES - Y L 6, TABLE B Salinity in Parts Per Thousand (0/00) at Intake (I) and Discharge (D) Canals and Salinity Change Acro:s the Condensers: Date 1 D A0/00 Date I D A0/00 4/2 17.0 18.5 1.5 7/9 24.1 25.9 1.8 4/24 23.0 23.2 0.2 7/23 25.1 25.7 0.6 5/14 24.1 25.6 1.5 8/6 17.8 19.2 1.4 5/28 25.8 27.4 1.6 8/20 24.2 26.7 2.5 6/11 25.1 27.2 2.1 9/3 25.2 26.6 1.4 6/25 26.1 26.8 0.7 In Figure 3 the negative change in ATP levels (-AATP) between the intake and discharge canals is used to indicate mortality (a decrease in ATP = -(-ATP) = + mortality), whereas an increase in ATP in discharge canals would actually represent a negative mortality. This method of re-porting ATP levels pennits a graphic comparison with the mortality reported using the vital stain technique. For example, in Fig. 3 on April 2, the ATP levels graphically represent a 35% mortality as compared to 25% net mortality using the vital -stain technique. The actual change in ATP from I to D was a 35% decrease, or -35%. In the Turkey Point Power Plant study (Prager,1970), ATP was chosen as one of several methods to evaluate the condition of plankton populations in tt.e intake, discharge, and canal areas. The ATP levels (ug/l) for a two month sampling period were lower in the discharge and canal areas, than the intake. The report concluded, "The ATP concentrations above, taken as proportional to mass of living zooplankton, follow the mortality data." However, during s an increase of ATP (99/1)pecific sampling occurred periods, such in the discharge. asincrease This August 17 was ignored, in the discussion of general ATP changes in relation to zooplank-ton mortality. However, when similar increases were noted in phytoplankton - ATP relationship in an earlier section of the report. This pattern, according to the author, reflected greater metabolic rates during the lower temperature changes, and with subsequent increases in temperature, "Either energy reserve depletion, death of canal plankton, or some com-bination of both" was occurring. This pattern, that is, an increase of ATP levels between intake and discharge was observed furing two months of the six month sampling period discussed in this report. Four sampling dates report a negative mortality, 6/11, 7/9, 7/23, and 8/6. Since ATP is recorded as ATP per zooplankton and ATP per unit biomass, the increases , are not due to sampling varying populations, but due to some '.netabolic ! ' overshoot' creating an instantaneous-surplus of ATP to a constant biomass --ttflected by the increases of ATP levels in organisms passing through the condensers. It_ is interesting to note that these increases occur in the discharge i IV-242 l l 11 !I 1 ll 1II -l. -;', . ! i i. -l j - r e n k i >\ - a ,a - t) t r nss i n ol nia et t eai wt v t s 3 e go m ,/ X X b eo gr 9 \ ,rf g sa lh y \ ect vsi eil 0 N l d a 2 X t ,/ A Pd r Tno 8 - Aam ' t - ( e n 6 \ [ XoO D t - ! ./ 8 g - I - \x \ - _ \ X 0> 3 \ 2 _ ./ O/ X 7 / f / / _ 3- j / 9 - E i/ R f o ,N 7 U G - I F ~ S E T ~ 5 A 2 D O ' i/ ' 6 G N , i I L P M - 1 A N ./ 1 S . X ' 6 ' / - 8 2 _ O . -/ 5 N ~- yk 0, / 4 1 5 4 X X y / 2 4 \ \ \ \ 2 W O -/ 4 ~ - _ - . - - - _ 7 . 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 8 7 6 5 4 3 2 1 Q T- 2 3 4 5 6 7 8 0 9 .i ,;' ^ temperature range between 35.0*C and 36.0*C. When the temperature dropped from 36.0*C on June 11 to 34.2*C on June 25. a plus mortality, or decrease in ATP was observed. As the discharge temperatures increase to 37.2*C and 37.0*C on August 20 and September 3, respectively, a plus mortality was observed, perhaps indicating a temperature shock inhibiting ATP production as well as increasing metabolic functions--a ' dying state' thus existing. In sumary, due to complex metabolic responses to rapid temperature and salinity increases particularly suggested in discharge temperatures between 35'C and 36* C, the use of ' initial' changes of ATP levels immediately after passage through the condenser pipes is questionable. Unless one is interested in the metabolic response to rapid temperature increases, the variation of ATP levels is not recommended for an initial mortality index. In Figures 4 and 5, the delayed effects of temperature and salinity on the ATP levels exhibits a stabilization of initial responses. In the first of these two figures, Fig. 4, the intake control (I-C), which re-mained for two hours in the intake canal, is compared to the intake sub-sample exposed to the discharge canal environment for two hours (I-D). Previously to July 9, the second filtration was accomplished with an 89u filter; and the filter size was shown to retain large numbers of phyto-plankton during blooms. Therefore, subse tuent to these findings, a 202u filter was used in place of the larger size. Therefore, the sampling dates 4/24, 5/28, and 6/11 may have shown an increase in ATP due to phytoplankton responses in addition to changes in the zooplankton population. It is also possible that the intercellular ATP level stabilizes at a higher metabolic level with the increased temperature. However, in the sampling dates from June 25 to September 3, the ATP changes or mortalities corre-spond with the mortality shown in the vital stain technique. Again in Fig. 5, or the change between I-C and D-D (discharge sub-sample in dis-charge for two hours) similar nortality levels are exhibited as those occurring using the vital stain technique. These last two figures, (4 and 5) suggest that af ter the initial metabolic responses of zooplankton to temperature, mechanical and salinity stresses, an equilibrium is reached, and the difference between tnese equilibriums will reflect the mortality. Assuming that the difference between I-C and I-D or D-D is indicative of mortality caused by passage through the power plant condenser pipes, then during the two sampling periods 8/20 and 9/3, discharge temperatures of 37'C or greater would suggest a 100% mortality of the zooplankton population. CONCLUSIONS
- 1. The initial changes in ATP levels, based on ATP/ zooplankton and ATP/ unit biomass resulting from passage through the con-denser pipes are related to metabolic stresses and not to changes in viable biomass. Therefore, changes from initial intake to discharges would seem to be a poor mortality index.
- 2. The delayed or long term stresses pennit the intercellular ATP levels to reach steady state. The changes between these two IV-244
m. 907.' " M rlil: : ilt: ihl4 idli! niME -i lf i ::ii . h!? Sie 'i%il.id:ily i!:!.lai l r ! ":!I%l"iE l ihi! l : OI!! -' - !!!I i id.".iiib ' ilJiil#-l : :4 =. :i. aix q; 80 5l: q;: = - ar.giziye E gi .. 4' g. 37 i . PIGUR;E n i 7piC.;a;4np::ja . ' ,. J . j.......'.'. !, i=i.;:.]m4!..
- 3 ~
qi_ i7 '.J m '.. i ' ~ . - i,c. , . 4. - ' .; i z. n ri . 1. :. ..i:
- . e..i- _ ._.! .: : vi: :-
...:. 7 '..i.- . . . j . ..
- r-C. .c_r .1 - - F. -
l ~ l. . . ' I ."m r - l : v -- s 70 ;I
- ix:rn:!-'
- i. :. I .r.
'i .e l. .i.=. ii i-t+ l..nj:. .. !.: -! . . .J.i' - : 1..; l - t iiT i '! i i .; .t DI !: ] G.. iP .i. c = 4! s
- 1:-4. '
T.l. lh ii "I :1: i- .-i,i ++-];._ i - 'i 1 i f =l '
- {-
q'. .j; =i f.;, ' " 'fli!/D, . p-r 1 /! ~~ ii. !' l ;i l a i' , .. . j i I ~ g~ . i:O. i.pu1.i..h,il ..: rh ; =.ji-..ti.::: o.: i.. 4 , i. . i, i.n. ' : 1. i .an ..o. .. iii. 4.. :.4_ .:1 i a . m: . :- - ! :..j T,I . ' .ni : , ..ti _c,i :_ 4_:. ;_[..:i :. . l i i, .i.._ l F.' !" ' i !:.]:i] ; [i.7 k nl"3- l~3 . j . I ;d . :j ' l . i'"!! !!]'_,_ _ q_: . ~ ' i - I I" ! - lJ ; Ti Jgi"' i 50 * ! o )) T '" ; rl .. i[ _' II J t .!: .ti ' 'l : i ri- ! :i 2 f_ -{ i :!'r ' " g 1, - ; :l ' !. : 1 -r l"!. ?! .. ti .; j/!-- :-r-b i , g i . .t- .# .FI. . . . . . . .l' ' 'T. ----*I B Y. . .F ' 40 " I . . . . . . h. . Yi ...i . . . - .D .h E M 1 X- Ed { ~ I l i I' - ~ ;;mijhEj . - . .i . c l R 3. -
- jui. -l ..! i- ; ii ri
- ga in . .: . . ;i:.: t 4. - . . ! . . 8 . ;. ; i l - , :l : j '4..t. n;: .!-l.....8. cp i _ l rIn , !: I - ij l-..! , 1 r- .! . -!E4 . - jf a. ; f r- I ! . I m . i ,j-l . Dit i-
- i:. ii:=im!
0 tel .i i . ~! * !. _i. .i._ _ d _! ! . L;_ S _ ' !. : j 9tih, "a 4 : 20 , 1 -ti l+ 1 - M -i :x vi l- ' h ' - r :; t,: ' ! t ,! - :i i:t if : j4 imi El:ipi:. 4h] ~ !:.1. ! hlJj4p il :}} ' ::!1 opi l' j-: ;; !/4 j q - [ . j .. j i i - -f l- ! ! .l:
- i : !. nj" to risti ii:
- p"! : c-i!- .NXl Mt'i "0..i i:1:
9 . +1' n!" li i W -b.. ^ -- - k f . I2 M' I I il " V71. X 1 "I' 1 fi' i il .l . ; iY.i i' ' d I I- I ilh b : p: I l"l "I' ' i ' 'i 'm il 1 ' 2- ' D: . - 2. p} . 4aj ir.] : iT:d.lj ..!E ::13 7 mo t i l:: 'j:iiihy:: ) $@l '. ! i hii 4 "i. uiaEf ; i l - in i"l; ri l :: i I jf t aj ~ l: j g r!.ip jiu41 yi j n; "!J :] :h!:i iijf 4.p;i:9] ]p:]fl . .. . 4: ni1 ];- i .i ;j' - l 1 :. l .!4' , !Gji E!" ; (! F j . i ...;. ; .1 - ] j[: I ' l' j 'T' .]- .I jl. .l.n[i) . ]:i T:}l "- : ! ] l- . I . l L ! .] l. - n ! -- nlm j.. 1. ] .
- . l j'.!. u!- .-p: :.i . l - In :i- .! : [ 1- _t cl.:::!.cl - t- ; :-
; ;jn:!u % ; [: ' i 1] .:4.. jf ((;.9; .g nf ] 1.j . 4 i'" {l :I: :.!" ' l. El i l l : ..I i ;i? . h -20 ; ,; , , , 43 ;. ~~ ; ;..qpg; ;: . F 1:
- x. j 7; ; ; ,
;.; i;; - : .; , ; , .! ; ; . jq s -30 . "il l!L{ k!E ' I-x !~ :1 4 -- x J d ii /: lE l .'. Li.^i 4.* thi . 'l' ii ' - I l-in:.l- .-. ' i. l : : i j."j;; iy 1! Ji .! . ;7.g i .i : * . i, . [. {i .l-i.i I . lt . ' l,
- . i
-40 Ei~!' "!..i".I""! I. l i rEl I 3. in . 's l' l ',E i ' ! 'l ' - ! . mi - 2 3 .: nj- , q. "i ' ! . .p.'..; . l: . X. ; .i .j ;; . . .j ; ;- ! I j i !E 2 4; .---j ;- 1 u_ :. 2 8 8 t-q l F , L-l ", 4 g!:.s ...!l , 7 ' i ' ' ' ' i ! ' -50 l ! - ' 3 i I !' ,z : .:in .i . gj : : l 1 ; _.t: 3 y . ' [' , :1l .i y j , :.l- . .!. !.i I j j . l j i . , . . . e . ! ,. m
- j... .ln.g. e <:.!. .t a.i . nl.u*. . - .. . f.-.
t- .].' .:0: . l. . :: j . I -
- t. l ..i .I-l- 7. 1."ic. ... l .i
-60 ..'m ] ..- - [
- ~ a
- - - 8 - - - - n- .I.. ..i .., ;. - l: . - , i .s- - 8 1- 'l..j. - . . .l ' .-l .-;
- 1. .
.j: l' i yl - -j' . . l.. I } j. i: 1-i -' 1 I i i . _. jx ' -70
- !j: .!- ! : !
5
- .j -
j ! . I . jn j -l' :) - lgi i j j t- X j .( 'ATP l'eveld , be tweenL ititake: 3 . -] . .]. . .!._ 1, ...!j..!l ,_ . i:..H. . J._:.n3 ; .ii. , gm.:: j1:. j.g:nc ..j . {I. -C t.o I-D. . knd . dischqrge btatipns)] . - ? g -80 ' il :P 51 3:'dHl: I +l Oi I l#l- h F Ii@i 102 b t-Oh_jetjm6rtal'ity'fkoinvithllstiain ! ni:. ..iy n!:. - j:Hi , . i _ . . ig : q :l i.- : !. i .: j -!D i : i 1. ; . I i: F! :i _ ; 1._i.iT.!.i ._ i mi : { i : p.. 8 " - -t- "I ' r=l U d. d . Y : 'r " ~ *2='I" ,"'I = i ' " TkI~i ! 1 ! - '! < FIN ' Ii ; - I <- ' -! ' "I ~ F ~ ' ' I' -907. . . . ' d. 5 4/2 .4'24 / 5/14 5 /.28 6,/11 6/25 7/9 7/23 8/6 8/20 9/3 SAMPLING DATES ) ) ) 90 7. _ _ . _ . _ _ . . ~ . 1 .. __ . _ 80 . . . . . . _ . _ _ . . _ . _ i _. _ _ _ . _ . . _ . . . . --o 70 - . FIGURE .5_-..- x ,0 60- -- --. - . - - - - . - 7 50 j - . . . _ . . . . . _ . . . _ _ _ . . ~ . . . _ . . _ . o )( x p ex 40 . . _ _ . - _ . .,/ ,. _ _ . . - . . . . _ ._ __. _.. ....__. . _.X .*. __ . /
- 30. OA_ ' .
. . / _ X / f N I / 20 . . _ . - _ - - _ . _ -. X- - _..._.f._ . g-. . X / - 'n . . - . . . ./.._.__-_..._____.\ ..y..___.... _ _ . - ' b-~ . --. . - . . .- -. - g - . 10- - - -- -- - - <L - - - - - . ~ - - - . - - . - - - - - .._g..;._.... /, . _ . _ . . _ . . . . - _ _ . . ._ 20 g - - - - - - - - - - - - . -- - - - - m .. .____ . - .-_l _. _ . y. _ . _ . . _ _ . - . .. . . .- - . . .. . -- - . . . - . . - . . . . . - . . -10, __ _ _ __ _. _ . _ _. _ _ _ _ _ _ . . _ _ . __.x._.__. . . -20. _ ___._____._..._._a.._.. - -50 . - _ . . _ _ . . . _ . _ _ . _ _ _ . - .. . . . _ . . . - - ~ . ~ . . . - . . . . . - . .y. _. -. _. . - . . . _. . . -60_ ..._;_...____ _ _ . . ... . _ _ . . . _ _ _ _ _ _ . - . - . _ . _ _ . _ . . . _ . . _ _ _ _ _ _ _ . . . . . _ . . _ .. ._ _ _ . . . . . _ . . _ _ ...__. _ . . . . . . . . . _ . . . . . - . . . - . . ..~ _ -70 , . . _ _ _ . . _ . _ _ _ _ . _ _ _ _ . - _ _ . . . - _ _ _ . _ _ . _ _.. _. . __ . . ._ . _ . . _ - . ~. . . _ . . . . ..__..__:..__..._. . - . . - . - _ - - . - . . _ . -- ..--- . X X -(. ATP levols, between . intake __.! -80 - - - . - - -- - - - - - - - - . - - - - - - - - -- - - - - - --- -- - I-C t o D-D - and discharge stations). . _ _ _ -- - 0------o .._ .. net- mortality from vital s tain-- 4/2 4'24/ 5/14 5/28 6'/11 6/25 T/9 7/23 876 8/20 973 SAMPLING DATES ) ) . .... .,.y .'. , , hour steady states (I-C to I-D or D-D) correspond to changes determined by the vital stain technique. Therefore, this similarity in mortalities by these two methods would suggest that long-tenn ATP variations reflect viability changes in zooplankton community.
- 3. Based on data generated by the two sampling dates 8/20 and 9/3, temgratures of 37*C or greater result in 100% mortality.
2 IV-247 ^ .. __. . . . . - . ~_ g..# Ww m... -- Sampling Date 4/2/74 Temperature ('C) Intake: 24.8 Salinity (0/00) Intake: 17 A T: 5.9C Discharge : 30.7 4 S: 1.5 0/00 Discharge: 18.5 Table 1: ATP levels as related to volume (ml), number of zooplankton (zoo) per millileter, and units of biomass (unit) neasured by length of cephalo-thorax in um. Samp. Sta.
- g. ATP/ml g. ATP/ zoo g. ATP/ unit I 1.21 x 10 -8 4.26 x 10
-0 2.15 x 10 -10 1.32 x 10 -0 ~ ~ 9 8.54 x 10 ' 2.20 x 10 I-0 4.49 x 10 ~0 4.55 x 10 -0 2.72 x 10 -0 D-0 3.62 x 10~ 3.60 x 10 -0 2.13 x 10 -0
- all values represent mean, n=6 Table 2: Size distribution based on cephalothorax length (one unit represents 189am), expressed in number of zooplankton per milliliter and per-centage of a particular size grouping of the total number.
Samp. Sta. ** 0-189 190-378 379-567 568-756 757-945 Total I number 14.22 10.78 3.56 3.33 0.11 32.00 % total 44 34 11 10 ---- D number 20.00 13.11 3.67 1.78 0.11 38.67 % total 52 34 10 5 ---- I-0 number 54.50 37.00 7.67 6.17 105.34 % total 52 35 7 6 D-0 number 49.75 39.32 6.86 4.80 0.54 101.27 % total 49 39 7 5 ---- ** all values represent mean, n=9 Table 3 : . Percentage change in levels of ATP per ml, zooplankton, and unit biomass from sta tion A to station B. (+) - increase (-) - decrease A B ATP/ml (%) ATP/ zoo (7.) ATP/ unit (%) I D - 29 - 48 - 39 I-0 D-0 - 19 - 21 .22 IV-248 , ,, . Sampling Date 4/24/74, ,, , , , , , , , , , _ , Temperature (*C) Intake: 23.9 Salinity (0/00) Intake : 23.0 o T: 6.7'C Discharge: 30.6 aS: 0.2 0/00 Discharge : 23.2 Table 1: ATP levels as related to volume (ml), number of zooplankton (zoo) per milliliter, and units of biomass (unit) measured by length of cephalo-thorax in ua . Samp. S ta .
- g. ATP/mi g. ATP/ zoo g. ATP/ unit
~ -0 I-L 2.29 x 10 4.92 x 10 2.34 x 10 -0 -0 I-S 3.14 x 10 -8 3.06 x 10 1.58 x 10 I-0 1.47 x 10 -8 3.96 x 10 -0 2.19 x 10 90 3.03 x 10 -8 -0 D-L 1.13 x 10 0.57 x 10~ -0 D-S 5.44 x 10 ~0 1.40 x 10 -0 0.75 x 10 ~ -0 -0 D-0 2.50 x 10 1.94 x 10 0.99 x 10 -0 -0 I-C 1.94 x 10 ~0 6.58 x 10 3.02 x 10 ~ -10 I-T 2.28 x 10 9.29 x 10 4.40 x 10~ -10 I-D 1.31 x 10 -8 8.06 x 10-10 4.15 x 10 D-D 5.83 x 10 ~9 4.03 x 10 -0 2.03 x 10 -0
- all values represent mean, n=6 Table 2 ! Size distribution based on cephalothorax length (one unit represents 189ag), expressed in number of zooplankton per milliliter and per-centage of a particular size grouping of the total number.
Samp . S ta . ** 0-189 190-378 379-567 568-756 757-945 Total I-L number. 9.22 15.22 3.44 2.00 (1.33) 31.21 % total 30 49 11 6 (4) D-L number 84.78 202.62 39.64 22.95 1.40 (0.67) 352.06 % total 24 58 11 7 1 ---- I-S number 26.33 36.67 5.00 3.33 71.33 % total 37 51 7 5 D-S number 101.11 220.56 35.56 22.23 0.56 (0.50) 390.52 % total 27 58 9 6 ---- ---- I-0 number 10.33 9.22 3.12 1.39 24.34 % total 44 38 13 6 D-0 number 37.06 69.34 14.22 7.95 0.84 129.41 % total 29 54 11 6 1 (continued) IV-249 .n m e- , - ;s . :, : l -4/24/74 '(c:nt.) l l t ' Samp. Sta. 0-189 190-378 379-567 568-756 757-945 Total I-C . number 4.45 8.78 3.89 2.55 0.22 (.33) 19.89 % total - 22 44 20 13 1 2 I-T number 5.22 6.89 2.11 1.67 0.22 (.33) 16.11 % total 32 43 13 10 1 2 I-D number 2.41 4.37 1.00 0.92 8.70 % total 28 50 12 11 D-D number 4.04 7.38 1.89 1.55 0.11 14.97 % total 27 49 13 10 1 ** all values represent mean, n=9 ( ) = number of zoo. Larger than 945 . Table 3: Percentage change in levels of ATP per ml, zooplankton, and unit biomass from station A to station B. (+) - increase (-) ' decrease A B ATP/ml' (%) ATP/ zoo (%) ATP/ unit C%) 1-L D-L + 72 - 77 - 76 I-S D-S + 73 - 54 - 52 I-0 D-0 + 70 - 51 - 55 I-C I-T + 18 + 41 + 46 I-C I-D - 33 + 23 + 37 I-C D-D - 73 - 39 - 33 I-D D-D - 59 - 50 - 51 L IV-250 m ~ y w Sampling Date 5/14/74 T'e mperature'(OC)' "Intak'e : "25 .'l Salin'it'y (0/00) Intake : 24.1 ~ A T: 6.2*C Discharge : 31.3 o S: 1.5 0/00- Discharge: 25.6 Table 1: ATP levels as related to volume (ml), number of zooplankton (zoo) per milliliter, and units of biomass (unit) measured by length of cephalo-thorax in um. Samp. Sta.
- g. ATP/ml g. ATP/ zoo g. ATP/ unit
-0 -0 I 7.45 x 10 -9 1.62 x 10 1.37 x 10 -0 l.46 x 10 -0 D 5.28 x 10 ' 1.13 x 10 -8 -0 -0 1-C 1.44 x 10 6.10 x 10 4.60 x 10 ~ -10 I-T 2.81 x 10 ' l.17 x 10- 0.94 x 10 ~ -10 -0 I-D 6.61 x 10 ' 5.78 x 10 4.10 x 10 -10 -0 D-D 1.55 x 10 -8 3.74 x 10 3.17 x 10
- all values represent mean, n=6 Table 2: Size distribution based on cephalothorax length (one unit represents 18Sg), expressed in number of zooplankton per milliliter and per-centage of a particular size grouping of the total number.
Samp. Sta. ** 0-189 190-378 379-567 568-756 757-945 Total I number 36.42 6.41 0.13 0.40 43.36 % tota! 84 15 ---- 1 D number 29.98 5.99 0.27 0.56 0.43 (.29) 37.52 % total 80 16. 1 1 1 1 I-C number 19.92 4.90 0.64 0.41 25.87 % total 77 19 3 2 I-T number 21.04 4.74 ---- 0.15 (0.15 26.08 % total 81 18 1 1 I-D number 10.14 2.96 ---- ---- 0.11 13.21 % total 77 22 1 D-D number 35.39 .7.59 0.11 ---- ---- 43.09 % total 82 18 ** all values represent mean,- n=9 ( ) = number of zoo. larger than 945 . (continued) IV-251 , a:n, w ~ wc 5/14/74 (cont.' Table 3: Percentage change in levels of ATP per m1, zooplankton, and unit biomass from station A to station B. (+) - increase (-) - decrease A B ATP/ml (7.) ATP/ zoo (7.) ATP/ unit (7.) I D - 29 - 10 - 17 t -I-C I-T - 80 - 81 - 80 I-C I-D - 54 -5 - 11 I-C D-D +8 - 39 , - 31 I-D D-D + 134 - 35 - 23 Sampling Date 5/28/74 Temperature (" C) Intake : 27.5 Salinity (0/00) Intake: 25.8 o T: 7.3 #C Discharge: 34.8 A S: 1.6 0/00 Discharge: 27.4 Table 1- ATP ' levels as related to volume (ml), number of zooplankten (zoo) per milliliter, and units of biomass (unit) measured by length of cephalo-thorax inp . Samp. Sta.
- g. ATP/ml g. ATP/ zoo g. ATP/ unit
~ 4.68 x 10 I 2.67 x 10 ' 2.12 x 10 ~ D 4.16 x 10 -8 2.46 x 10 -9 1.71 x 10" 4.56 x 10 -0 ~ I-0 3.94 x 10 ' 4.12 x 10- 0 D-0 4.27 x 10 6.21 x 10 -0 4.46 x 10 -0 1.20 x 10 -9 0.78 x 10 -9 ~ I-C 1.23 x 10 I-T 9.23 x 10 ~ 1.17 x 10 -9 O.90 x 10 1.02 x 10 ~0 I-D 1.73 x 10 1.26 x 10 -8 D-D 1.07 x 10 1.30 x 10" 0.81 x 10 -9
- all values represent mean, n=6 (continued)
IV-252 5 /28/74 . (cont.) ~ *> r. . - . , , , , . . , Table 2: Size distribution baseu on cephalothorax length (one u it represents 189e), expressed in number of zooplankton per milliliter and per-centage of a particular size grouping of the total number. Samp. Sta. ** 0-189 190-378 370 567 568-756 757-945 Total I number 16.75 3.41 0.26 ---- (0.26) 20.68
- 7. total 81 17 1 (1)
D number 12.25 3.33 0.72 0.91 ---- 17.21 % total 71 19 4 5 I-D number 8.00 1.23 ---- ---- ---- 9.24 % total 87 13 D-0 number 4.50 2.17 0.28 ---- ---- 6.95 % total 65 31 4 I-C number .7.70 4.15 0.44 ---- (0.15) 12.44 % total 62 33 4 (1) I-T number 6.81 2.07 0.29 0.15 ---- 9.32 % total 73 22 3 2 I-D number 4.89 1.62 0.44 ---- ---- 6.95 % total 70 23 6 D-D number 5.18 2.22 0.44 0.29 (0.15) 8.28
- 7. total 63 27 5 3 2
** all values represent mean, n=9 ( ) = number of zooplankton larger than 945 . Table 3: Parcentage change in levels of ATP per ml, zooplankton, and unit biomass from station A to station B. (+) - increase (-) - decrease A B ATP/ml (%) ATP/ zoo (%) ATP/ unit (7.)
- 1. D - 11 -8 - 19 I-0 D-0 +8 _ 36 +8 I-C 1-T - 25 -2 +15 I-C I-D - 17 + 44 + 62 I-C D-D - 13 +8 +4 I-D D-D +5 - 25 - 36 IV-253
3&pm :+ ~ Sampling Datt 6/11/74 LTemperature (*C) Intake: 29.5 Salinity (0/00) Intake : 25.1 A T: 6.5"C Discharge: 36.0 a S: 2.1 0/00 Discharge: 27.2 Table 1: ATP levels as related to volume (ml), number of zooplankton (zoo) per milliliter, and units of biomass (unit) measured by length of cephalo-thorax in om. Samp. Sta.
- g.ATP/ml g. ATP/ zoo g. ATP/ unit I 4.17 x 10 -9 2.99 x 10 -10 1.68 x 10 -0 D 7.43 x 10 ~9 -0 3.45 x 10 1.95 x 10-10 I-0 3.18 x 10 -9 3.41 x 10 -0 2.45 x 10- 0 4.64 x 10 -0 -0
~ D-0 6.74 x 10 3.48 x 10 I-C 2.62 x 10 ~9 3 16 x 10 -0 1.94 x 10 -0 I-T 2.20 x 10-9 5.00 x 10- 0 3.07 x 10 -0 I-D 2.08 x 10"' 4.70 x 10 -0 2.54 x 10 -0 D-D 4.12 x 10 ~9 4.63 x 10 -0 2.99 x 10 0
- all values represent mean, n=6 Table 2: Size distribution based on cephalothorax length (one unit represents 189m), expressed in number of zooplankton per milliliter and per-centage of a particular size grouping of the total number.
S"mp . S ta . ** 0-189 190-378 379-567 568-756 757-945 Total I number 17.44 16.16 4.09 1.49 0.15 (0.48) 39.81
- 7. total 44 41 10 4 (1)
D . number 14.00 6.31 1.19 0.30 (0.31) 22.11 . 7. t o tal 63 - '29 5 1 (1) I-0 number 18.26 7.82 1.33 0.37 27.12 % total 67 29 2 1 D-0 number 11.17 3.12 ---- ---- (0.17) 14.46 % total 77 22 (1) I-C number 9.55 8.68 2.20 0.45 20.88 % total 46 42 11 2 I-T number 6.57 5.23 0.60 0.45 ---- 12.85
- 7. total 51 41 5 3 I-D number 11.25 8.66 1.45 0.78 ----
22.14 '% total -51 39 7 3 D-D number 5.78 1.33 0.59 0.29 ---- 7.99
- 7. total 72 17 7 4
** all values represent mean, n=9 ( ) = number of zoo. larger than 945 . (continued) IV-254 6/11/74 (cont.) ~ r. .. .- . .:.e. .. ...,a,....,... .. ..,s., . .,s,. . Table 3: Percentage change in levels of ATP per m1, zooplankton, and unit biomass from station A to station B. (+) - increase (-) - decrease A B ATP/ml (7.) ATP/ zoo (7.) ATP/ unit (7.) I D + 89 + 15 + 16 I-0 D-0 + 112 + 36 + 42 I-C I-T - 16 + 58 + 58 I-C I-D - 21 + 49 + 31 I-C D-D - 57 + 47 + 54 I-D D'-D + 98 -2 + 18 Sampling Date 6/25/74 Temperature ('C) Intake: 27.2 Salinity (0/00) Intake: 26.1 A T: 7.0*C Discharge: 34.2 6 S: 0.7 0/00 Discharge : 26.8 Table 1: ATP levels as related to volume (ml), number of zooplankton (zoo) per milliliter, and units of biomass (unit) measured by length of cephalo-thorax in9m. Samp. Sta.
- g. ATP/ml g. ATP/ zoo g. ATP/ unit
-10 I 5.04 x 10 2.81 x 10- 0 1.71 x 10 -0 -0 D 3.59 x 10 l.46 x 10 0.85 x 10 l ~9 -0 -0 I-0 1.99 x 10 0.98 x 10 0.75 x 10 ~ -0 -0 D-0 3.02 x 10 ' 1.79 x 10 1.37 x 10 I-C 5.77 x 10 -0 2.12 x 10-10 1.41 x 10 -10 0 -0 I-T 1.14 x 10 ~9 1.70 x 10 0.84 x 10 l ~ 1-D 5.72 x 10 1.78 x 10~ 1.11 x 10" D-D 7.90 x 10 -0 1.89 x 10 -0 1.06 x 10- 0
- all values represent mean, n=6 (continued)
Iy-255 _ _ . __ j , ufA .: a:6 > , , , .. m .. _ . _ _ 6/25/74 (cont.) Table 2 : Size distribution based on cephalothorax length (one unit represents 189as), expressed in number of zooplankton per milliliter and percentage of a particular size grouping of the total number. Samp. Sta..** 0-189 190-378 369-567 568-756 757-945 Total I number 14.28 10.23 2.61 0.94 ---- 28.06 % total 51 37 9 3 D number 12.09 8.27 3.16 0.71 0.27 24.50 % total 49 34 13 3 1 I-0 number 24.11 7.50 0.73 0.22 32.56 % total 74 23 2 1 D-0 number 12.23 4.22 ---- ---- 16.89 % total 72 25 3 I-C ' number 1.99 2.22 0.25 0.13 ---- 4.59 % total 43 48 5 3 I-T number 4.15 4.59 . 1.48 1.33 ---- 11.55 % total 36 40 13 11 I-D number 3.11 1.78 0.59 0.44 ---- 5.92 % total 53 30 10 7 , D-D- number 1.63 1.18 0.29 0.29 (0.15) 3.54 % total - 46 33 8 8 (4) ** all values represent mean, n=9 ( ) = number of zoo. Larger than 945 . Table 3: Percentage change in levels of ATP per ml, zooplankton, and unit biomass from station A to station B. (+) - increase (-) - decrease A B ATP/ml (%) ATP/ zoo (%) ATP/ unit (%) I D - 29 - 48 - 50 I-0 .D - 0 + 52 + 83 + 83 I-C I-T + 98 - 20 - 40 I-C I-D -1 - 16 - 21 ,C D-D + 37 I - 11 - 25 I-0 D-D + 38 + 6 -5 IV-256 - . . , ~ ' ' " ' ' ~ ~ ~ ~ ' ' ' ~ ' -- <- ~ s- ... ..- , S'ampling'Date //9'/i4' ' Tempera ture (#C ) Intake: 28.8 Salinity (0/00) Intake: 24.1 .1 T: 6.9"C Discharge : 35.7 ti S : 1.8 0/00 Discharge: 25.9 Table 1: ATP levels as related to volume (ml), number of zooplankton (zoo) per milliliter, and units of biomass (unit) measured by length of cephalo-thorax in us . Samp.'Sta.
- g. ATP/mi g. ATP/ zoo g. ATP/ unit
-0 I 8.97 x 10 3.61 x 10 1.04 x 10 -0 -0 D 1.00 x 10" 5.70 x 10 1.77 x 10 -0 ~ ~ I-C 2.24 x 10 O.92 x 10 0.54 x 10 1.06 x 10 -8 -0 ~ I-T 3.69 x 10 2.04 x 10 0.54 x 10 -0 ~ I-D 2.09 x 10 ' O.80 x 10" D-D 7.47 x 10 -0 0.74 x 10 -0 0.50 x 10 -0
- all values represent mean, n=6 Table 2: Size distribution based on cephalothorax length (one unit represents 189/.su), expressed in number of zooplankton per milliliter and per-centage of a pcrticular size grouping of the total number.
Samp. Sta. ** 0-189 190-378 379-567 568-756 757-945 Total I number 0.45 2.89 6.00 6.67 0.67 (0.67) 17.35 % to tal 3 17 35 38 3 (3) D number 1.49 2.12 5.97 5.27 1.27 16.12 % total 9 13 37 33 8 I-C number 9.38 5.11 1.61 1.18 ---- 17.28 % total 54 30 9 7 I-T number 9.24 3.34 0.89 1.75 ---- 15.22 % total 61 22 6 11 I-D number 12.74 4.74 1.04 0.89 ---- 19.41 % total 66 24 5 5 D -D number 6.22 2.09 1.35 0.30 0.30 (0.15) 10.39 % total 60 20 13 3 3 1 ** all values represent mean, n*9 ( ) = number of zoo. Larger than 945 . (continued) . IV-257 x> .. - __ _ _ 7/9/74 (c nt.) Table 3: Percentage change' in levels of ATP per ml, zooplankton, and unit biomass from station A to station B. (+) ' - increase (-) - decrease A B ATP/ml (7.) ATP/ zoo (7.) ATP/ unit (7.) I~ D + 12 + 58 + 70 1-C I-T + 375 + 330 + 278 .I-C I-D -7 - 13 0 I-C D-D - 67 - ?r -7 I-D D-D - 64 -7 -7 Sampling Date 7/23/74 Temperature ("C) Intake: 29.7 Salinity (0/00) Intake: 25.1 4 T: 5.3"C Discharge: 36.0 AS: 0.6 0/00 Discharge: 25.7 Table 1: ATP levels as related to volume (ml), number of zooplankton (zoo) per milliliter, and units of biomass (unit) measured by length of cephalo-thorax in . Samp. Sta.
- g. ATP/ml g. ATP/ zoo g. ATP/ unit 1.92 x 10 -8
~ I 1.81 x 10 7.18 x 10- 0 2.28 x 10 ~9 ~ D 1.01 x 10 6.47 x 10" I-0 2.00 x 10 -8 4.04 x 10 2.24 x 10 D-0 4.44 x 10 -9 1.66 x 10 -0 1.15 x 10"
- all values represent mean, n=6 Table'2: Size distribution based on cephalothorax length (one unit rapresents 189.um), expressed in number'of zooplankton per milliliter and per-centage of a particular size grouping of the total number.
Samp. Sta. ** ~ 0-189 190-378 379-567 568-756 757-945 Total I number 0.39 1.07 0.84 1.20 0.15 (0.15) 3.80 l_ 7. to tal 10 28 22 32 4 (4) D number 0.59 1.19 0.59 1.33 0.15 3.85 % total 15 31 15 35 4 (continued) IV-258 l l ) I 7/23/74 p (cont.) . - ~ ,.. . - Samp. Sra. ** 0-189 190-378 379-567 568-756 757-945 Total I-0 number 10.27 4.20 0.47 1.40 (0.47) 16.81
- 7. total 61 25 3 8 (3)
D-0 number 16.44 8.89 0.89 0.44 ---- 26.66
- 7. to tal 62 33 3 2
** all' values represent mean, n=9 ( ) = number of zoo. Larger than 945 . Table 3: Percentage change in levels of ATP per m1, zooplankton, and unit biomass from station A to station B. (+) - increase (-) - decrease A B ATP/ml (7.) ATP/ zoo (7.) ATP/ unit (7.) I D - 47 +;25 - 10 I-O D-0 - 78 - 85 - 79 Sampling Date 8/ 6/74 Temperature ( C) Intake: 28.9 Salinity (0/00) Intake: 17.8 o T: 6fC Discharge: 35.1 A S: 1.4 0/00 Discharge : 19.2 Table 1: - ATP levels as related to volume (ml), number of zooplankton (zoo) per milliliter, and units of biomass (unit) measured by length of cephalo-thorax in u8. Samp. Sta.
- g. ATP/ml g. ATP/ zoo g. ATP/ unit
~ 0 -0 I 2.26 x 10 ' O.77 x 10 0.57 x 10 1.00 x 10 -0 ~ D 1.54 x 10 ' 0.66 x 10 I-C 1.01 x 10 8 9.76 x 10 -0 4.97 x 10 0 ~ -0 -0 I-T 9.70 x 10 ' 4.94 x 10 2.36 x 10 -0 1.22 x 10~ 2.51 x 10 1.47 x 10 -0 I-D D-D 1.14 x 10 -8 2.02 x 10 -0 1.40 x 10 -0
- all values . represent mean, n=6 l
(continued) i IV-259 ! l x ,,, .; _.v . , . . E 8/ 6/74 .(cont.) T'ble 2: -Size distribution based on cephalothorax length (one unit represents 189pe.3), expressed in numbe r of zooplankton per milliliter and per-centage of a particular size grouping of the total number. Samp. S ta . ** 0-189 190-378 379-567 568-757 758-945 Total _ I ' number 14.48 4.68 0.57 0.27 ---- 20.00 % total 72 23 3 2 D number 10.25 4.44 0.62 0.59 ---- 15.90 % total 64 28 4 4 I-C number 2.61 1.82 0.54 0.38 0.15 (0.13) 5.63 % total .46 32 10 8 2 (2) I-T. number 4.74 5.44 1.48 2.12 0.15 (0.15) 14.08 % total 34~ 39 10 15 1 (1) I-D number 11.72 9.45 1.21 1.78 0.56 (0.15) 24.87 % total 47 38 5 7 2 (1) D-D number 38.81 15.97 1.45 2.67 0.22 59.12 % total 66 27 2 4 ---- ** all values represent mean, n=9 ( ) = number of zoo. Larger than 945 . Teble 3: Percentage change in levels -of ATP per ml, zooplankton, and unit biomass from station A to s tation B. (+) - increase (-) - decrease A B ATP/ml (%) ATP/ zoo (%) ATP/ unit (7.) I D - 32 + 30 + 16 I-C I-T -4 - 49 - 52 I-C I-D + 21 - 74 - 70 I-C D-D + 13 - 79 - 72 I-T I-D + 26 - 49 - 38 I-T D-D + 18 - 59 - 41 I -'D D-D -7 - 19 -5 , ~IV-260 f a Sampling Date s/20/74 ~- ,' 5 Tempera turs ' (#C). In take : 30.1 . .., - + . w-Salinity (0/00) . Intake: 24.2 A T: 7.1"C Discharge: 37.2 aS: 2.5 0/00 Discharge: 26.7 Table 1: ATP levels as related to volune (ml), number of zooplankton (zoo) per milliliter, and units of biomass (unit) measured by length of cephalo-thorax in p%. Samp. Sta.
- g. ATP/ml g. ATP/ zoo g. ATP/ unit
~ 8.81 x 10 9.02 x 10 -0 ~ I 2.48 x 10 ~ 9.72 x 10 ' 7.48 x 10 -0 ~ D 2.19 x 10 I-0 2.83 x 10 -8 5.72 x 10 -0 3.23 x 10 -0 D-0 8.98 x 10 3.65 x 10 -0 2.10 x 10 -0 I-C 2.70 x 10 ~9 7.94 x 10 -0 3.43 x 10~ 2.24 x 10 -0 ~ I-T 6.13 x 10 ' 1.12 > 10 ~ I-D 1.67 x 10" 0.66 x 10 -0 0.44 x 10 -0 D-D 2.94 x 10 l.10 x 10 -0 0.73 x 10 -0
- all values represent mean, n=6 Table 2: Size distribution based on cephalothorax length (one unit represents 189 /as), expressed in number of zooplankton per milliliter and per-centage of a particular size grouping of the total number.
Samp. Sta. ** 0-189 190-378 379-567 568-756 757-945 Total I number 0.69 1.04 2.62 1.33 % total 5.68 12 18 46 23 D number 0.74 0.30 1.03 1.78 % total 19 (0.15) 4.00 8 26 45 (4) I-0 number 36.62 18.16 11.22 6.40 % total 50 (0.22) 72.62 25 16 9 ---- D-0 number 12.67 7.56 2.67 1.78 % total 51 24.68 4 31 11 7 I-C number 2.06 1.02 1.14 0.69 0.15 % total 5.06 41 20 23 14 3 I-T. number 17.43 10.40 7.08 5.40 % total 40.31 43 26 18 13 I-D numbe r 22.88 12.27~ 4.83 2.20 0.13 42.31 % total 54 29 11 5 ---- D-D number- 20.00 6.96 2.22 1.48 ---- 30.66
- 7. total 65 23 7 5 all values represent mean, n=9 IV-261 ( ) = number zoo. larger than 945 .
. w. - w < . , _ . -_. - .- -- 8/20/74. (c:nt.) Table 3: Percentage change in levels of ATP per ml, zooplankton, and unit biomass from station A to station B. (+) - increase (-) - decrease A B ATP/ml (%) ATP/ zoo (7.) ATP/ unit (%) ,I D + 10 - 12 - 17 I-0 D-0 - 68 - 36 - 35 I-C I-T + 127 - 72 - 67 I-C I-D - 38 - 92 - 87 I-C D-D- +8 - 86 - 79 I-D D-D + 76 + 67 + 66 J Sampling Date 9/3/74 Temperature (#C) Intake : 30.9 Salinity (0/00) Intake : 25.2 d T: 6.1 C Discharge: 37.0 aS: 1.4 0/00 Discharge: 26.6 Table 1: ATP levels as related to volume (ml), number of zooplankton (zoo) per milliliter,and units of biomass (unit) measured by length of cephalo-thorax in4.e. Samp. Sta.
- g. ATP/ml g. ATP/ zoo g. ATP/ unit 2.44 x 10 -9
~ I 3.41 x 10 0.87 x 10 1.72 x 10 -0 ~ D 2.82 x 10 0.58 x 10" 5.58 x 10 -0 ~ I-0 6.34 x 10 3.77 x 10 -0 D: - 0 7.87 x 10 ~0 1.64 x 10 ~9 1.11 x 10 ' I-S 4.29 x 10 -8 2.96 x-10 0.86 x 10" D-S 1.27 x 10 -8 0.66 x 10- 0.22 x 10~9 I-C 2.93 x 10-8 1.33 x 10 O.96 x 10" I-- T 7.02 x 10" 0.72 x 10' O.53 x 10 I'- D 1.06 x 10 -8 1.16 x 10 -0 0.85 x 10 -0 l.30 x 10 -0 ~ D-D 3.69 x 10 ' 1.02 x 10 -0
- all values represent mean, n=6 (continued)
IV-262 4-e-9/3/74 (cont.) . . *
- P .-r " Table 2 : -Size distribution' b'ased'on cephAlothorax length (one unit ' represents '
189 tut), expressed in number of zooplankton per milliliter and per-centage of.a particular aize grouping of the total number. Samp. S ta . ** 0-189 190-378 379-567 568-756 757-945 Total I number 1.44 2.67 2.41 1.59 (0.27) 8.38 % total 17 32 29 19 (3) D number 1.37 3.70 8.92 1.74 (0.66) 16.39 % total 8 23 54 11 (4) I-0 number 46.00 17.33 4.33 1.34 (0.67) 69.67 % total 66 25 6 2 (1) D-0 number 34.87 7.03 13.03 2.77 (0.34) 48.04 % total 73 15 6 6 (1) I-S number 0.69 1.83 2.27 3.19 (1.13) 9.11 % total 7 20 25 35 (12) D-S number 2.16 2.51 8.85 5.51 (0.67) 19.70 % total 11 13 45 28 (3) I-C number 10.87 2.81 0.69 0.59 ---- 14.96 % total 73 19 5 4 I-T number 44.64 10.98 2.13 1.96 ---- 59.71 % total 75 18 4 3 I-D number 42.67 8.07 3.21 1.87 (0.45) 56.27 % total 76 14 6 3 (1) D-D number 23.14 3.64 1.49 0.45 ---- 28.72 % total 80 13 5 2 Table 3: Percentage change in levels of ATP per ml, zooplankton, and unit biomass from station A to station B. (+) - increase (-) - decrease A B ATP/ml (%) ATP/ zoo (%) ATP/ unit (%) I- D - 17 - 30 - 33 I-O D-0 7 24 + 194 + 194 I-S D-S - 70 0 78 - 74 I-C I-T + 140 - 46 - 45 I-C I-D - 64 - 91 - 91 I-C D-D - 87 - 90 - 89 I-D D-D - 65 _ 12 + 20 IV-263 4 Balch, N. 1972 ATP content of Calanus finmarchicus. Limnol . & Oceanogr. , 17(6):906-8. Dressel, D. M. , D. R. Heinle and M. C. Grote. 1970 Vital Staining to Sort Dead and Live Copepods. Chesapeake Sci., 13(2):156-159. Heinle, D. R. 1966 Production of a calanoid copepod, Acartia tousa, in the Patuxent River estuary. Chesapeake Sci. 7:59-74. Holm - Hansen, O. and C. R. Booth, 1970 ATP levels in algal cells as influenced by environmental conditions. Plant Cell Physiol. 11:689-700. Lackey & Lackey. 1972 A Study of Thermal Effects on the Plankton Community at Turkey Point Power Plant. Unpublished. Prager, J. C. 1970 A Study of Biscayne Bay Plankton Affected by the Turkey Point Thermoelectric Generating Plant During July and August, 1970, Report National Water Laboratory, U.S. EPA. I IV-264 s- . -- - . . . .. .. . . ., . . . .. ., ., .,., , EFFECT OF POWER PLANT OPERATION ON SHALLOW WATER C0ASTAL ZOOPLANKTON 1 FINAL REPORT October 15, 1974 UNIVERSITY OF FLORIDA MARINE LABORATORY Frank J. Maturo, Jr. Principal Investigator John W. Caldwell Marine Biologist William Ingram III , Biological Systems Analyst tw e IV-265 . . ~ , ACKNOWLEDGMENTS The following personnel were involved with various aspects of this report. Their hard work and efforts are greatly appreciated. Marine Biologist I's: Tom Chaney Richard Cullen Ron DuBose Betsy Dupree Lu Garrigan Eric Hallquist Frank Hearne Herb Hickox Bruce Krepley Alex Smart Art Wenderoth Secretaries: Libby Coker Joan Rech Linda Hogaboom Student Assistants: Linda Haskins Debbie McManuels Computer Programming: Mark Pittman Statistical Analysis: Tony Antonie111
- Data Coding
Jan Ingt-am EDP Equipment and Facilities: l North Florida Regional Data Center-l IV-266 i P 1 Data are available at computing center costs. Requests should be addressed to: Dr. Frank J. Maturo, Jr. University of Florida Marine Laboratory 313 Bartram West Gainesville, Florida 32611 i } I i r e Y l IV-267 f + - + _ - - - , . ~ ~~ . , . _ , . _ _ . . _ i PROJECT SUPNARY
- 1) The region of study, approximately 7,330 hectares, was established so as to include within its boundaries the area of power plant influence.
- 2) Three distinct areas were noted- an offshore area and two inshore areas, one of which showed power plant effects in addition to natural influences.
- 3) The portion affected by the power plant operation (the thermal plume) was 3% by volume of the total area studied.
- 4) The power plant thermal plume enhances zooplankton standing crop in the fall and winter, but depresses it in the late summer.
- 5) Zooplankton production is thought to increase in the thermal plume area because of increases in temperature until thermal maxima are reached in late summer. However, late sumer is not a peak reproductive period for zooplankton.
- 6) Overall estimates of productivity were comparable to those reported from other estuaries.
- 7) We were unable to account for all components of natural predation, therefore, our figures are underestimates. Our deteminations of power plant predation have a high degree of accuracy.
- 8) Although the power plant was the major zooplankton predator in the region, the total estimated predation was quite low compared to pro-duction.
- 9) The fluctuations in zooplankton populations in the overall area studied are not controlled by natural predation or power plant operation.
- 10) Projected estimates of the effects of the addition of Unit 3 operation show no appreciable change in zooplankton populations. However, multi-variate analyses suggest that increased intake flow rates will have differential negative effects on meroplankton categories.
IV-268 EFFECT OF POWER PLANT OPERATION ON SHALLOW WATER C0ASTAL ZOOPLANKTON GENERAL OBJECTIVES The purpose of this project has been to estimate the effects of Florida Power Corporation's Crystal River power plants (nos.1, 2, and
- 3) on the zooplankton in the immediate area.
We have:
- 1. described the source and discharge areas of the power plant's cooling water in relation to the zooplankton sampling stations;
- 2. estimated the standing crop of zooplankton;
- 3. estimated the production of zooplankton;
- 4. estimated standing crop of natural zooplankton predators (a. ctenophores, b. chaetognaths, c. decapod larvae, d. fish larvae, e. juvenile fish) and the amount of predation of each;
- 5. estimated the power plant " predation" on zooplankton and compared it with natural predation;
- 6. performed a statistical analysis of the data and determined a) the significant factors affecting zooplankton in the area, and b) the significance of the power plant operation on this comunity;
_ 7. compared zooplankton diversity at Crystal River with that of other areas in the eastern Gulf of Mexico;
- 8. identified many of the fish larvae found in the zooplankton samples.
METHODS Field sampling routines were based on the findings of our preliminary survey. Stations sampled biweekly were divided into six areas: 1) Inshore Intake (Stations A and Z), 2) Intermediate Intake Area (Station B), 3)Off-shore (Stations C and J), 4) Enclosed Intake Canal (Stations D and E),
- 5) Thermal Plume (Stations F, G, and H), 6) Intermediate Discharge (Station I)
(Figure 1). All stations within an area were randomly sampled twice within a given biweekly sampling date. The number of samples taken, the net mesh size used, and the sample depth are shown in Table 1. Depth samples, which were taken three feet above the bottom, were not taken when the depth was less than ten feet. Depth samples at Station A.H. and Z were never taken because the water was too shallow. Station F at the discharge pipes was not sampled at depth because local mixing of the water column was assumed. The numbers IV-269 f .; ;n y,r ie i % 7 "' k., 'I.' . ,.C )k M.I-[q j , C ,; }. 'm~ti g ; c. , .% *ce _. - y 'j. *. . e 4 ."G n .-
- '- r[2 L:{u*\f*;_Q- :
' y s( 6 . .U .- EE,~: U t .: **I*> M -g. e N N. t s.,,,D ] b:~ . ' r. ,*o. h v eg = u. c N.h
- v. ag e
t.. . -. : g g ; sy$e e e,, i * ~ ., L. ., 2 a 4: 5 2 e k o i es i.,e Ie 4 -s * & M**'. ~n ; * .; }. c? ' W* , .i O ',;s .. .;, *-' -g y- - CE \n '- J - i i Cp o-5 h. $,. .' i. b O4 ._5 8, = -, s _$e l- ; ~. ~ t.,,, u e l' 7 - 4J * :* *** , ' '* - .C.n , C1 t/t 7 s.-I- "s b,3 ,,, g g& 3'O "- %D * .* -* 9 4 --- " "=n ' , , . ..::. Q' f' ;,s
- mU
. , .? .1 f.~... ..* t , q ' - y '*g e. ,u e g g j 1, fj oN * = e[M., . j .r. .; - , (1- . y., n *
- n, V a 8a c.. . .. .: 4 s
g g1 .,4
- 4 er 4 7 . (4 I
ho a e g 's k ,g [ c4 I n .s . '] . ,,, ]
- Iof I",,'q.f:
q f.; . {\ 4./ / . d. / ., f - 5 j . , )k,. z ~ ag / / . .. ,-. r.. .g, j \ "& ....o .%.0pi, 1 - d' .t u x, .~ %'c::M i Ia, : 0,.\ ufW ".'r * . o f,'o , j 7# M I % . j. e ~ N'h. 'Q,, bl. J. .. . k...:-)\ JI p..:.'d * .b. } h.. ;[,&) , - *'?
- r ?e .:n 21 y.In ~ It . -
g ,p@.% T : * ,.f,, h / 0 .# p f..'.$ \ '[\q*C: s 6 ' . . . ."** :4 ! 3, M - e <. - >&.#%.i'MMW ~ Q .} ",4 e% tt, g *3 , 3 *. . h. .p 1 s'd'(A 0 s ; t r 4, '(,. c C MM'\gJ o 'Gt.,. 0 g, p3 2,,, , 1 .- T '* Q%N 3 b ) %,?. .- - g ,p ay oGJ,I. + ( < ' ' ' = .j 's n\. ': g .'g-3 , , e = < g s ,e,,. g, c :,- .
- 4h.. n , y,p. ,,%y- - } a ~t '.,,i4 v
- j. \ \ of na__.- ~ : (&-,
o .g. ,m . s %,4.,q ;.2 6.L , . .g e x . .e , m < * ~, o e - ~' , p S y Q ,] ,.. .glj $,.,>.. , . !. [:: . 4 a o* (\ c.f- - e i ! & c. *- :, ..()y!.s y.p.y'<eR C.w,,'U y-o M...,fq,>o=1 \ /k - v. , >. Q p ., is .p" - ne - e - C- p - e it sn z. , < . 'nW:. , emQ e s, \e\ - t m 1 )., pll[,f. . . ~. %.y. }.**g& '. f, i ~!)
- .:q:.p", .
.h . 9 = 'Q' a- . 7 :a a c ~,.vv v .o *j,,f;<i, i';,g:of \* qr'Q.,f E ,), \Q ,k o - N! y , c , ,3 i .ii*.. .
- o. p. - v, . < -
oc.'/a $ .t = f - .- A
- V ,
Q G
- co
*N. . ! . *
- . ("tc.
',9. cql;.v-, - v., ,,9 .a t .'> i 1 s / ,eN - . - t -.\ :. :-: t:2. a - - g,g* oo'i .% ,i i Vo-
- r_ .- e cm , % - w.
< .\\ = 4 'N * /Y $d e 1< >~ \ l '. * , . O [./ / ,' -' - # ss i,y ~
- r -<
.~\e.h .. (~ M c : -;n,t, -5.q ;, ,a ;y. ,'Aq .. '**e ,'e./..c-~~~ - . , , - . a .>
- y. 9- e w oi ,\%/ = "/'/ % j; a ->.,, o_
n 4 ' - - ' e -, . .. s .- ,. . p _u _ - y * * ~ .b. _ I .o :,g.- s y- 9 . . . -- , ; e ' o<-siM..- q ..t./ S'v % ,, , - T. =
- t
's s - x) 4io wcel. - gr s, e.s.. 'N~_ % oc i . d__s g'R;_,. - t '* _ 1 Wg 4 +r - - A, Do. #' ~- '\ ,l [ - -- , ,. v N' N
- 5. ' - -- N ) ' s.eC,e s'3 2'%s[Ne
<h e k .=., *gM.Ng s. < so.-Q,, , \ gs <~-- g,, a] i, Mi n a \. ~ .N.a (\., (d', <v* ' a,
- N
'i. i v's */ 0 .*g< A , '{ on % t - (* _ .,.a i% j, 9 .4,# . }, ,D e ) Ygi.i'M,5 ~ I. '*o,/.o o }* .*x- *e 51 c . s 2 r r,,.s.\ ,% * , ' = .~~;) . 4 * , . 3 g ,. Ny O g .. .- e g h) ' (1 ., =) s 2 2 e gg's J. 4:s 0 g ,4 A s_, c t-s x- e / , v-270, , .g.g..s e,. , d g.% 4j s shown in Table 1 are based on samples taken at each visit to a station (multiply by 2 to obtain actual number of samples taken during biweekly period since each station was. sampled twice.-)< ' ' ' * ~ Prior to processing the camples in the laboratory, the two paired samples from each depth collected by the 202u net were pooled in order to obtain a better estimation of the zooplankton standing crop. Table 1. Number of samples, net mesh size, and sample depth taken/ visit to a station. 202u 202u 64u 64u Station Surface Depth Surface Depth A 2 - 1 - Inner Intake D 2 2 1 1 area E 2 2 1 1 Z 2 - 1 - B 2 2 Outer Intake area J 2 2 1 1 F 2 - 1 - Discharge G 2 2 1 1 area H 2 - 1 - I 2 2 1 1 The Fall and Winter diurnals (Nov. 12-15, 1973, and Jan. 21-24, 1974, respectively) were sampled as follows: Station E, 8, I, and G were sampled on successive days, each over a 24 hour period. One surface sample with the 202u net was taken each h-hour during the 24 hour period with all environmental parameters taken hourly. The Sprin 29, 1974, and July 25, 1974, respectively)g wereand Summer sampled diurnals (April as follows: Stations E and G were sampled l simultaneously over a 24 hour period. Simultaneous surface and depth 1 202u net tows were taken twice per hour with all environmental parameters l taken hourly. Samples from the quarterly diurnals were not pooled. All environmental parameters were taken at each station for each set of i samples obtained. These parameters included: salinity, temperature, dissolved 1 02, turbidity, current speed and direction, wind speed and direction, tide stage, cloud cover, precipitation, sea conditions, and station depth. Surface and depth tows were taken using half meter plankton nets equipped with General Oceanic flow meters. Tows were of one minute duration taken at approximately two knots. Depth samples were taken by using a subsurface depressor which was designed to create down pressure on the towing line to which the net was attached and lowered to the desired depth by means of a pulley system. All samples were preserved in 10% formalin in the field. Samples were then transported to the lab where they were processed. Laboratory procedures were modified according to the findings of our previous sampling program. Samples were split using a Folsom plankton splitter into a reserve half and a half to be used for biomass determinations and for counting. The halves to be counted were then sieved to sort out l the different size classes. Steve sizes used for 202u net tows included. No. 10 (2000u), No. 20 (850p), No. 30 (600u), No. 50 (300u), and No. 75 (202u). A No. 75 (202u) sieve and a 64u sieve were used for 64u net tows. Each sieve size sample was split in the Folsom plankton splitter to a countable size. IV-271 l - . - l .# + .o ,. - Two hundred fifty to two thousand animals per sieve size were counted according to the abundance or scarcity of animals in a particular sieve size and according to the time avai:ble for counting. Samples were counted and placed in the following categories: 202u Net Samples Calanoid Copepods Penaeid shrimp larvae Acartia tonsa Lucifer g.
- Labidocera g.
Taracalanus quasimodo Other shrimp larvae Paracalanus crassirostris Temora turbinata Porcellanid larvae Pseudodiaptomus coronatus Gravid Female Crab larvae Tortanus setacaudatus Centropages hamatus Megalops Other calanoids Zoea Cyclopoid Copepods Other crustaceans Oithona g. Polychaete larvae Other cyclopoids ._c Harpacticoid Copepods Tunicates Euterpina acutifrcns Gravid Female Medusae Longipedia helgolandica Metis g. Miscellaneous Other harpacticoids Fish eggs Gastropod veligers Fish larvae Bivalve veligers Barnacle larvae 64p Net Samples Acartia adults and copepodites Evadne g. Calanoid adults and copepodites Ostracods Harpacticoid adults and copepodites Other crustaceans Cyclopoid adults and copepodites Bivalve veligers Copepod nauplii Gastropod veligers Barnacle cyprids Polychaete larvae i l IV-272 ,
- v 64u Net Samples --
(Continued) Barnacle nauplii Chaetognaths Crab' larvae Eggs Shrimp larvae Miscellaneous The method of biomass determination involved removing 5000-10,000 animals from each sieve size (depending on the availability of animals), placing them in tared weighing containers, and drying and weighing the sample. The average weight / animal / sieve size was then determined. Therefore, as the animals were counted, dry weight biomass estimations were attained from the pre-determined weight values. The average weight values were determined from samples taken during all seasons. i I I IV-273 OBJECTIVE 1 : SOURCE AND DISCHARGE AREAS OF CRYSTAL RIVER POWER PLANT'S COOLING WATER IN RELATION TO ZOOPLANKTON SAMPLING STATIONS Richard Cullen and Ron DuBose INTRODUCTION Carder, Klausewitz, and Rogers (1973-74) have done extensive studies on the physical oceanography of the areas surrounding the Crystal River power plant. However, the information contained in these reports is incomplete with respect to total area and volumes which are influenced by the plant. It is important to our zooplankton survey to know these areas for detennining standing crop, predation rate of the plant, and the effects of plant predation on zooplankton standing crop. Therefore, we have attempted to define the boundaries of plant influence and determine volumes of these areas. METHODS Volumes of water were determined for all areas shown in Figure 9, Distances and depths were taken from Coast and Geodetic Survey Chart Number 1259(Scale 1:80,000). Dimensions for areas in Figure 2 were measured in centimeters and then converted to square meters by multiplyig/cm 640,000 m .g tge Thearea in square conversion centimeters factor was arrivedby the conversion at by factor squaring the conversion from centimeter to meters (lcm =800m). An average depth fot 'ach area was taken from the map and was converted to mean sea level by adonig .37 meters. Using the adjusted average depth, the volume of each area was determined. The percent of an area's volume contributed to the plants' cooling water for a given resident time was determined by the following: percent volume of area contributed = percent cooling water [ contributed from an area) (plant cooling water consumption) per resident time volume of area Area 2, for example, supplies approximately 30% of the plant's cooling water. Assuming a one day resident time for the water in this area and using the values in Table 2, we can make the following calculation: 6 percent volume of area , (30%)(3.48 x 10,3/ day) contributed 25.7 x 10 ,36 percent volume of area contributed = 4% RESULTS From our calculation,we have determined the total lolume contained in the area of influence shown in Figure 2. This is approxim acly 250 million cubic meters of water. Volumes for individual areas can be found in Table 2. IV-274 s a yg ea !: [. inn %).1 r'* .~'l *f l e::'MVM D ~ 8 MH
- l;E 2
i "C nil ' A,ypa. s _- G y> .. "" l ": ia- .a.s ' '.' o %, ue 2.s. ._ i. hh a .,,y p u *g, ' 7 c. s ,, . . ,: e, <
- c 1~_ L. ,
i vi s.- wo l5,
- t. ..- / .c,I~ :
"" f.; s[*'# *C:xa : *y . I[g . '. ' ' S' .Y% 5 e -;J ' ' ..** ;, - > ,t Q L _.,, " '** 5 a ". A mo- ,, .c rem .n Q, * - ", Et (e/.'...') l' . ,\ ' .s o /,::yg@.%*,~ * ~ : lll m-m
- , . . ". . ~: o 1 % w .s,( o
.r 1 - a n m s
- u. s......
. a - 8 ,, , . o,, w .... .-=c.,c ! =s 1 v.4 e . ., e.g ~ - t 0,, ] j 3's., \ h ~ g4 ,/ ~ , -. ,' E 'e,, ,f, l. }j [I . i 9 . .k . ... . ; go' i ' gMe, a*(, *%,*w=7 g, { ; J.'Q *N,',* l U d,[ i s* ,.., . , f,d,, ,h's .e <, /,# .. e. . 3 , 5 v . 1 1 e g.. . <._4S. i in 3 . : p'Nthz.4 ,%.. % j i & {g{ 'fc < ' i, ~ I E e. ; T . .l '.. Q* *' vW > *.'- .)h . s;g (S $i'3~jr U e.N ~ r6' k - i , ; _feffy." y .. " > ,,; , ,, $@j c ff .~ ~ 5 J G-!) l ., , 3 g q ?,*yl g$$$l.',.'.\" ,&'V. ,h -l' .2, W v.. . 4. . ,.' n ? *,- en >p f= fh,.b s . j&* .. ;.,,, ?w~.$ 'Y,.' h & * . ~ h_ y.Yt$ " mms & _ gw J* : 'o $ f d s gm ' 3 " Ld h.. t.</p;% w ' ', j / *ii , s ! f.~l.4.,yi, p%',! s , n-t'4 = ~ . 2- ; gra ./ . . y- - , c - 8
- ,,0's\
(.N% ., .- .s._ I s,,,4,' "m' '%jg c e ' .5N 8'" y ,, 2 Q ~ . e. = rp de , . . ,, . . ~ ..s. +- . i a-ie e . -sx s : u U lHC = '
- v."" ,4, e =',g U..gs 3
9 *.,"\'e u I * *",'N ., # g g. f,4,, 4. '.,,3'se 0 %'*## a, .N 8 *yN ,- .b ' ./'f g .\ l = \U pa-- ;. . [ *h ,4 C N . ,-$ '*+#g .D a sf- $,: \s\ e g c c *r *Er \ /* s'. ' .* y 4 l,, ' 's ~~ ' ; .\.'q) ,.*)ep I .\. * *h.9 T , J - 9 , / - ~, A *r, ~- 2 d E ?, - Os\. $, l e ... ~~ .~.N u2 4 vd . . . . ~ . . . , ,, - y\g f
- P er-
\I '.(' *y;G 4 u -
- f. ~\ # 1 ~
8'N + 3'., k -~ % ,, ', '~ kD q, e k ' 42. ',','h2 . h'<[. LT
- g s~{ mdN[
N, @) .g- a . '*r, O +e" 1 ,y- \n ~ 2 ,v, s} u' \\> t)', ~( , ,/ls)w $ *G . - , . c , , o, .Q \ ,, 0 e I 8 (~',~'e '"-kb \Q ~' s . ad* \* e. 8 *N Q - 2 t s ig gr.@ sp[@ a" h N 4 g(j sdt 'gqt l g o ~ ~ e 1 = \y ; e h~Q.'},\e,;(R *N84~ Q e y l~\* .\ i .I r- I e s ,. y - ,is - - k, o .e 9* e N ~ .[- A 4\) ,, / ge ~ ,\ ./ T e ,. e n p i Table 22. Crystal River Power Plant's Monthly and Seasonal Percent Predation, Monthly Percent Mortality, Entrained 5 Biomass, Killed Biomass, Weighting Seasonal Standing Crop, and Projected Effects of Unit Number 3.+ Mean*** Weighted Seasonal Standing Power Plant Projected + Projected + Entrained ** Crop for Percent Biomass ++ Biomass Biomass Entrained Biomass Killed Plankton Area Predation Entrained Killed by-Units 1,2, dnd by Units 1,2, and Percent Influenced Units Units, mg/ day, mg/ day 3. mg/ day 3. mg/ day-Season Mortality by Plant 182 1,2,3 lb/ day lb/ day lb/ day ""* Fall 27.58 518 mg/m 3 .35 *3.1 x 105 *6.9 x '02 *8.5 x 10-7 *6.2 x 108 *l.7 x 10e .70 *j ,9 y < 92 3 ,4 x . 03 *3.8 x '02 Dec. 25.75 [gx: gj {g x; gj ggx; gj ((x; gj Jan* Winter 21.97 394 mg/rr.3 2.3 x 109 5.1 x 108 4.6 x 109 1.0 x 109 .45 .90 s _1 x ' 03 1.1 x ' 03 1.0 x ' M 7.2 x 'a3
- 2 Fe b. 24.31 j.gx {j
- .g x ; g8 {.{x: g ?g x : l j
kMarch N 8.97 8.9 x ' 08 .0 x ' 07 ,a x - 1:'9 .6 x :18 2.0 x ' : 3 .8 x '~ :'2e 4.0 x :2 ' April Spring 13.39 802 mg/m 3 .09 .18 x ; x ;
- 13 -.0 x
; ,4 x 3.? x. v ] -; 3 ; u . J..f
- x. g2 $,,gxx u l3 s. x 2 May 24.84 1.L x t3 3.5 x u u 2. :. x 'M 7. x .o 3.' x' 03 7.7 x' 02 6.2 x ' 03 1.6 x ' 03 t
""' 37.39 Sunsner 376 mg/m 3 *45 *90 July 38.45 j.g gj p.gx gj x g.g x : ] .ig x .; j
- No 64p-202p biomass data were available. Nov. data not used for fall's power plant prediction. '
Final Zooplankton Report. I ** Taken from ***ttultiplied Alden by 2.56 x 10(1974)'m 8 for standing crop of area + Assuming same percent mortality as for Units 1 and 2, and twice plant cooling water consumption. ++ Standing crop estimates taken from Station E for Feb. because Station F data possibly in error, 4 0 N-Table 2. Data on the Volume, Area, and Depth at liean Sea Level for the Area Influenced by the Crystal River Power Plant. 2 3 . Surface Area (m ) . Mean Depth (m) . Volume (m ) Area 1 6,460,000 1.37 8,850,000 Area 2 10,220,000 2.51 25,700,000 4 Area 3 37,940,000 4.80 181,990,000 Enclosed Section of Intake Canal 400,000 5.41 2,140,000
- Exposed Section i of Intake Canal 650,000 5.41 3,500,000 Dischare Area I 10,920,000 2.37 25,890,000
, Plume Area 5,370,000 1.37 7,350,000 Discharge Canal 200,000 5.00 1,000,000 Total Surface Area 73,300,000 m 2 18,000 ac.res Total Volume of cooling 222,000,000 m 3 4 water @ M.S.L.- Plume Area 5,370,000 m 2 1,300 acres Plume Area Volume 9 M.S.L. 8,350,000 m3 Plant Consumption' 2,417 m3 / min.; 3 3,480,000 m / day IV-277 ~ - . -. . _ --~ g Since no hydrographic data were available for resident times for the cooling water areas, we have constructed a table based on assumed turn-over times of 1, 3, 7, and 14 days (Table 3). For instance, over a period of 24 hours, 3.48 x 106 ,3 of cooling water will pass through the plant. Using percent contributions of 12.5%, 30%, and 57.5%, for A eas 1, 2, and 3, respectively, we found that Area 1supljes0.435x10m6b or 5% of its volume, and Area 2 supplies 1.00 x 10 m or 4% of its total volume, and Area 3 furnishes the balance of z.001 x 10 63 m or 1% of its total volume. DISCUSSION Cardar, Klausewitz, Rogers (1973) divided the sources of cooling water into three areas and gave a description of flow with respect to these areas. Area 1 (Figure 2) includes all the water bounded on the north by the south bank of the intake spoil, on the east by natural shoreline, on the west by the last gulfward string of oyster bars, and on the south by an imaginary line drawn from Black Point parallel to the intake canal. Area 2 includes all the water bounded on the north by the intake spoil bank up to channel marker 21, on the east by the west boundary of Area 1, on the west by a line drawn south from marker 21, on the south by the same line as Area 1. Rogers (personal communication) stated that the plant would have no effect south of the imaginary line drawn for the southern boundary for Area 1 and 2. Area 3 includes all the M ter bounded on the east by the west boundary of Area 2 and a line drawn nor th through marker 21. Carder et al. (1973) left the north, south and west sides of Area 3 unbounded, However, for lack of better information we arbitrarily selected the Cross Florida Barge Canal for our north boundary, extended the southern boundary of Area 1 and 2 to enclose Area 3 on the south, and the east boundary was drawn south from channel marker 8 of the Florida Barge Canal. This area encloses Station J. Based on data gathered by Carder et al. and presented in Florida Power Corporation's Technical Reports nos.1-4, we are making the following assumptions about the plant influence on our sampling stations:
- 1) Area 1, which contains Stations A and Z, contributes the least water of any area. This is probably due to the physical characteristics of this area. It is shallow, traversed by many uyster bars, and physically bounded on two sides. During ebb tide some of the water leaving the area flows into the intake canal where it is subsequently entrained when the tide begins to flood. Once the water reaches the mid-way point in the enclosed portion of the intake canal it will pass through the plant. During flood tide the water remaining outside the canal is pushed shoreward. Rogers (personal communication) stated that 10-15% of the plant's cooling waters are contributed from Area 1. Station Z, being the most landward station will contribute less water than Station A.
IV-278 ~ ~ .
- Table 3. Percent Contribution by Areas Based on Assumed Resident Time.
, Area 1 Area 2 Area 3 Volume Percent Volume Percent Volume Percent Contributed Of Area , Contributed Of Area , Contributed , Of Area 1 0.44x106 ,7 5% 1.04x106 ,3 4% 2.00x106 ,3 1% 3 1.31x106 ,3 15% 3.13x106 ,3 12% 6.00x106 ,3 3% Res t Time in Days 3.05x106 ,3 7.31x106 ,3 14.01x106 ,3 7 34% 28% 8% 14 6.09x106 ,3 69% 14.62x10 m 03 57% 28.01 x106 ,3 15% l l IV-279 . - ~ - - - - - e., .
- 2) Area 2, containing Station B, has few physical boundaries to restrict flow. The main contribution of cooling water from Area 2 occurs during ebb tide. Most of this water enters the canal at marker 29, where the plant is able to maintain an eastward flow against the ebb tidal tendencies. Approximately 30% of the plant's cooling water is supplied from this area.
- 3) Area 3, containing Stations J and C, is a large, deep body of cooler, higher salinity water. This area contributes the major (50% plus) portion of the water going through the plant.
Water from this area forms a wedge along the bottom of the canal and maintains an eastward flow toward the plant regardless of tidal cycle. Since the majority of water drawn into the plant at the screens comes from between the depth of 5 meters and the bottom, this bottom layer of cool, high salinity water is very important. Carder et al. in Technical Reports 1-4 have consistently found this to be true.
- 4) The plume area (Fig. 2) is bounded on the north by a line drawn east and west just south of Drum Island, on the east by the natural shoreline, on the south by the north bank of the intake spoil, and on the west by a string of oyster bars extending north of marker 31. The high salinity water characteristic of discharge water stops south of Drum Island, clearly indicating the north boundary of the plume area. This area contains Station F, G, and H. Station F is in the 5.5"C isotherm above ambient, Station G is in the 2 C isotherm, while Station H is in a 1.5 C and 1.0 C isotherm during ebb and flood tide, respectively.
The plant discharges 2,417m3 / min of water heated to 5.5 C above ambient into a discharge channel which empties into a shallow basin. During ebb tide there are three types of water converging in this area. These are: low salinity water from the barge canal /Withlacoochee River, cool saline gulf water, and heated saline discharge waters. Plume water is confined to the south and takes a southwest course once it leaves the canal. During flood tide, discharge water velocity decreases and the plume water flows onto the mud flats lying north of the canal. There are two types of water converging during this tidal i cycle. These are 1) warm discharge waters, which are moving eastward in the channel at the surface and westward on the bottom forming a high salinity wedge, and 2) cooler gulf waters. These two water systems mix well vertically due to their relatively close density, causing a horizontal temperature gradient rather than a vertical one. The entire tidal plume shifts to the northwest during flood tide. During all tidal conditions water in the discharge canal maintains an out-going current without eddying. IV-280
- 5) Discharge Area 1, containing Station I, was out of the limits of the physical oceanographic study. This area is bound on the east by the west boundary of the plume area, on the west by the east boundary of Area 3, on the south by the intake canal spoil, and on the north by an imaginary line drawn east and west of Drum Island. This area is the intermediate area between the plume area and intake Area 3. There is a chance that some discharged water may oe recycled into the intake canal through a pass in the southern boundary of this area.
Also,some water will be recirculated once it enters Area 3.
SUMMARY
- 1) Boundaries were defined and volumes were calculated for the areas that supply and receive cooling water.
- 2) Percent contribution of cooling water from each area were: Area 1 (10-15%), Area 2 (30%), and Area 3 (50% plus).
- 3) Area 3 supplies the greatest amount of cooling water. However, the calculations have shown that the plant, on a percentage basis, has a greater effect on Areas 1 and 2 than on Area 3 due to Area 3's greater volume.
i e IV-281 i i
' = ~ -
\
OBJECTIVE 2 : STANDING CR0P ESTIMATES Tom Chaney INTRODUCTION Several authors (McIlwain, 1968; Gillespie, 1971; and Hopkins, 1972) have documented that inshore Gulf coastal zooplankton populations show seasonal pulses which generally occur in the fall and/or the warmer months. In addition, Cuzon du Rest (1963) and Gillespie (1971) have shown that stations within a given area do not always show zooplankton peaks occurring during the same seasons. Total zooplankton standing crop, numbers and biomass in the Crystal River area were examined to determine seasonal and areal fluctuations. METHODS Stations that were similar hydrographically were combined, transforming the eleven stations into six areas (see area map Figure 2). Stations A and Z are in Area 1, Station B is in Area 2, Station C and J represent Area 3, Station D and E represent Area 4, Stations f, G, and H are in Area 5, and Station I is in Area 6. The nine sampling months were grouped into four seasons for the total data: November and December were designated fall; January and February were designated winter; March through mid-May was designated spring; and mid-May through July was designated summer. Total zooplankton numbers and biomass were transformed by averaging stations and biweekly samples to fit these seasonal categories (see Figures 3 and 4). Our statistical analyses have shown there is a significant season by area interaction in the region influenced by the power plant. (See Table 2 forareasinfluenced). Since the areas are not equal in volume, these areas were given weighted values according to the volume of water contained in a particular area. This was done using the following formula: Weighted mean = 1 (A) I, + A II + ... + A VI) AT 2 6 A T
= I A 4,) i Where A l-6 represents the volumes of Areas 1 through 6. Roman numerals 1 through 6 represent variables, for which a value will be calculated over the total volume influenced by the power plant. The variables are averaged values for station contained in an area.
RESULTS The total ~ zooplankton biomass data (mg/m 3 ) shomin Figure 3 and Table 4 l indicate that Areas 1 and 4 have fall peaks with weights of 527.67 mg/m3 and 825.61 mg/m3, respectively. Areas 2, 3, and 6 have spring peaks with ! ! 1 ! , I IV-282
, y s p ee, # en s values of 644.49 mg/m3 , 571.81 mg/m3, and 2807.12 mg/m3, respectively. Area 5 is low in the fall (263.40 mg/m3) and increases during the year with a sumer maximum of 509.34 mg/m3 The total zooplankton data for numbers /m3 (Figure 4) show the same areal and seasonal trends as the biomass data. Areas 1 and 4 have fall peaks with values of 8,181,435/m3 and 1,058,907/m3, respectively. Areas 2, 3, and 6 have spring peaks with values of 967,328/m3 , 857,412/m3, and 4,377,002/m3 Area 5 is low in the fall (390 the year with a sumer maximum of 711,309/m3.,257/m3), and increases during The weighted seasonal means (gm/m3) are shown in Table 4. These show a low in the winter (393.635 mg/m3) and a high in the spring (801.778 mg/m3).
DISCUSSION Areas 1 through 6 in the Crystal River study area do not all have the same seasonal fluctuations; Cuzon du Rest (1963) and Gillespie (1971) have shown this in other Gulf coast areas. This occurrence is not unexpected since sampling areas can vary in respect to salinity, temperature, depth, and depth-related environmental parameters. (See statistical analysis, Objective 6). The number and biomass of the 64p data comprises the majority of the total zooplankton, for this reason 64u data is extremely important to stand-ing crop. SUttiARY
- 1) Areas of sampling at Crystal River do not have the same seasonal peaks.
- 2) Area 1 had the largest number and biomass peak (in the fall) with values of 8,181,435/m3 and 5,217 mg/m3, respectively. Area 6 had the second largest number and biomass peak (in the spring) with values of 4,377,022/m3 and 2,807 mg/m3, respectively.
- 3) Weighted seasonal biomass means are: fall - 518.042 mg winter - 393.635 mg/m3; spring - 801.778 mg/m3; summer /m3;375.506 mg/m
- 4) - The 64p zooplankton comprise the majority of the biomass for the total zooplankton standing crop.
l l IV-283 l
. _ ~._..-- __.
I Figure 3 J Seasonal Distribution of Total Zooplankton in mg/m3 by Area 5,217 mg/m3 1 5000-2,807 mg/m3 2800- - s
~ ~
/\
10005 900-4 800- 8 mg/m 3 700- g 2 4- " 600-2 500- ,s 5 400- u 3 3 5 300- 3 2 200-100- - t t t t Fall Winter Spring Summer SEASONS IV-284
Figure 4 s Seasonal Distribution of Total Zooplankton in Numbers /m3 by Area 8,181,435 __ .L 4,37;,903 6 __ \_ k
\ 1 12x105, ',
i 1 x105- 2 9x10 5, 3 8x105, i 7x105 - 5 g 6x10 5, 5 5x106 . s 3- 2 4x105, g , 5 ~~ 3x105 , 2x105, lx105 O. . , Fall Winter Spring SunEer , I l I 1 IV-285
s Table 4 Total Biomass (mg/m3) by Area and by Season and Seasonal Mean-Areas Weighted Seasonal Seasons 1 2 3 4- 5 6 Mean mg/m3 Fall- ~ 5217.666 254.065 311.713 825.610 263.397 677.796 518.042 Winter 267.416 331.068 354.950 389.326 337.995 793.620 393.635 E Spring 550.353 644.490 si' ?07 617.546 465.366 2807.107 801.778 Summer 899.553 523.433 297.863 631.947 509.344 503.586 375.506 e
OBJECTIVE 3 : PRODUCTION OF ZOOPLANKTON POPULATIONS AT CRYSTAL RIVER Ray Alden and Frank Hearne - INTRODUCTION The problem of estimating production for zooplankton populations is complicated by the fact that the most important component of the population, copepods, are polycyclic or have continuous reproduction. Various researchers have employed different methods to estimate the production in planktonic populations. An excellent work edited by G. G. Winberg (1971) summarizes many of their methods.
"The production of a species-population for a known period of time is considered to be the sum total of growth increments of all the individuals existing at the start of the investigating period and remaining to the end, as well as the growth of newly born individuals and thoce which, for various reasons (due to being consumed, dying or other causes), do not survive to form part of the final population biomass existing at the end of the period.
As in studies of individual growth, the growth increment is defined as the increase in the amount of living or organic substance of the species in question as well as the energy included in it." (Winberg,1971) An estimate of production in the area under study is essential if relative importance of plant predation is to be assessed. Acartia tonsa was the major component of the zooplankton year round. Since growth rate versus temperature information was available for this species, the production of Acartia tonsa was used for zooplankton production es timates. METHODS Research concerniag production of Acartia tonsa in the Patuxent River estuary (Heinle 1966) estimated production based on the computation of turnover times. Turnover time was estimated from the instantanenus death rates which were in turn calculated from population age structures in the field and development time as determined by laboratory experiments. To arrive at turnover times Heinle began with equations for the instantaneous death rate:
-dn = (inctn -inn)/t n (I)
-dc = (InAtc-inC)/t (2) where
-dn = instantaneous death rates of nauplii
-dc = instantaneous death rate of copepodids t n= time required to grow from mean age nauplii to mean age copepodid t c= time required to grow from mean age copepodid to adult inn = the natural logarithm of the density of nauplii at the beginning of the interval.
IV-287
.. > =
]
\
l inC = the natural logarithm of the density of copepods at the beginning of the interval. InC = natural logarithm of the density of copepodid extrapolated tn to time t n* InAte= natural logarithm of the density of adults extrapolated to time tc-Equations (1) and (2) require the computation of the mean age copepodid { and mean age nauplit which is explained below. ' Next, finite death rates were obtained by the formula: 0 = 1 - e-d (3) from which turnover times were computed: Tt = 1/D (4) Turnover time is the time between equivalent stages of subsequent generations (time for the offsprireg of an adult generation 1 to grow into an adult of reproductive age in generation 2); turnover time is used to mean the amount of time the population requires to be replaced by a new set of individuals (Woodmansee,1958; Heinle,1966). We wish to arrive at a percentage figure of daily population growth .if predation were not taking place. If T t is the number of days in which the population replaces itself, then 1/Tt x 100% of that population is produced per day. Let K = percent produced of the standing population per day where: 1 K=I xt 100% (5) Numbers of nauplii, copepodids, and adults were obtained from the studies of field populations. Laboratory studies produced growth rates (stages / day) over a temferature range of 19.0*C to 34.0*C. In order to determine the mean nauplii and copepodid stages it was necessary to detennine the number of individuals present of each age for the six naupliar stages and five copepodid stages. These allocations assumed an equal death rate for all stages. Based on this assumption, the numbers of members in each stage can be found by the equation: N
= N(n-1)e (6) where N
n = numbers in the stage of interest Nn-1 = numbers in the preceeding stage
- t = time per stage (from laboratory studies, see Alden) d = death rate i <
i For the six naupliar stages the equations are: IV-288 Y
Nj=Nj N 2=N j e-dt N3=N'2 N4=N83 N S=Ne# 4 N6=N'5 Thus (N total) = N) + N2+N3+N4 *N5+N6 N(total) = Nj+Ne-dt j + Nj e-dt,-dt + Nj e-dte -dt,-dt + Nj e-dte -dt,-dt,-dt
+Nej -dte -dte-dt,-dte-dt N
total = Nj [1 + e # +e +e- M +e- M +eO] Thus N N(total) 1 = 1+e-dt,,-2dt+e-3dt,,-4dt+e-5dt N g -dtN (total) 2 = 1+e-dt+e-2dt+e-3dt+e-4dt+e-5dt N
, dtN(total) 3 = j,,-dt,,-2dt,,-3dt,,-4dt,,-5dt N
,-3dtN (total) 4 = 1+e-dt+e-2dt+e-3dt+e-4dt,,-5dt e -4dtN (total) 5 1+e-dt,,-2dt# ,-3dt,,-4dt ,-5dt
,-5dtN (total) 6 j,,-dt #,-2dt,,-3dt,,-4dt,,-5dt l
A similar system of equations can be found for each of the five l copepodid stages: ! IV-289
< ~
. ..m C
C total 1 = j,,-dt,,-2dt ,-3dt,,-4dt
. (9)
-4dt e C(total)-
[5,j,,-dt,-2dt,-3dt,,-4dt When the number of individuals in each stage of growth is known, then mean ages for nauplit and copepodids can be calculated by formulas (10) and (11). 1(Nj )+2(N2 )+3(N3 )+4(N4 )+5(NS )+6(N6 ) N* = (10)
" total
=
7(Cj )+8(C2 )+9(C3)+10(C4 )+11(C5 ) C m C (11) total Values for t and t can then be computed by equations (12) and (13). Recall that t an0t ar6thetimerequiredtogrowfrommeanagenauplii to mean age c8pepodiff and from mean age copepodid toadult, respectively.
=
t n t(C,-N,) (12) t c= t(12.5*-Cm) (13) This set of equations was solved iteratively by using best estimates to compute tn and tc and substituting them into the equations for dn and d c (instantaneous death rates for nauplii and copepodids). First estimates for Cmand N gwere taken as the difference between mid-naupliar stage and mid-copepodid stage and from mid-copepodid stage to ist day adult. Since there are 6 naupliar stages, the first estimate for N was 3; since there are 5 copepodid stages, the first citimate for Cm wasm 8.5. Counts of nauplii, copepodids, and adults were available for only a fewmonths(December,May, June,andJuly). Using the population counts for these months, production was calculated over a ran and a regression computed which related production (K)ge of temperatures to temperature (T). The regression for May, June, and July is presented in equation (14). For December a separate regression was computed. This regression is presented in equation (15). K = (.0125977)T .136449 (F),jj = 114.6 R2 = .912) (14) K = (.0128374)T .132658 (F1,37 = 330.0 R2 = .899) (15) s .
*The mean stage at reproduction was taken at 12.5. This equals the number of naupliar stages and the number of copepodid stages + .5 stage.
IV-290
For the spring (March through mid-May) and summer (mid-May through July), regression (14) was used to determine production values at each station for each sampling date. The regression (15) was used to compute production values for fall (November and December) and winter (January and February). These regressions were applied to yield a season average production for each area. Table 5 shows the six areas into which the affected area of Crystal River was divided and the stations which represent each area. The percent volume of each area of the total area was used to weight the contribution of each of the smaller areas to the total seasonal averages. Because of equipment limitations, growth rates were not obtained for temperatures below 19'C. This meant that some caution was necessary when using the regression to obtain production values for dates when temperatures were belce 19'C. For the months November, December, January, and February when temperature observations at some stations were lower than 19'C (in some cases .anging below 10'C), it was noted that the average temperatures over all stations did fall into an acceptable temperature range. In those months, the regression based on December naupliar, copepodid, and adult counts were used [ equation (15)]. Average temperatures for the area and season were used (see Table 6). It was felt the production values were not unreasonable when average temperatures were used. When station by station data were used.the regression yielded unreason-able results for the temperatures which were below 11 C. An alternate method was used to corroborate these production figures using a method much like that of Conover (1956). Conover asserts that about 50% of the zooplankter's body weight is carbon and 50% of this weight is consumed daily. Of this amount, about 80% is made available to the animal (Conover 1956,1966). Oxygen consumption data are available (Conover, 1956) which can be converted to respiratory costs for the zooplankton. This is done by assuming a carbohydrate metabolism with glucose as the substance oxidized. If 1.4 is the den:ity of oxygen and .375 is the ratio of carbon to oxygen in the glucose equation, then the carbon cost of respiration can be computed by equation (16). ml 02 consumed x 1.4 x .375 = mgC oxidized for respiratory costs (16) Then, assuming that: production = carbon assimilated - carbon respiratory costs carbon weight of animals the production c m be computed. RESULTS A production of 10% per day would mean that in a one day period biomass or the numbers of individuals of average size would increase by 10%. Since the , time period for which this production was computed was one day, the reciprocal ' of the production is the turnover time in days. This is the amount of time from any lifestage of one generation to the same lifestage of the next generation. l l l IV-291
- 2x; _. .
~ - - . - --
A production of 10% per day would amount to a turnover time of 10 days. In Table 7, weighted daily seasonal averages are expressed. Heinle's method computed for each area for each season is presented in Table 7. The values range from a low of 8.7% in ,, inter in Area 1 to a high of 23.4% in Area 6 in sumer. Seasonal daily averages for fall, winter, spring, and summer were 10.9%, 9.8%, 14.6% and 22.8%. The value for Area 5 for summer was computed for temperature average in excess of 34 C (see Table 6 ). This temperature is so high as to be near the point at which heat death, growth depression and fecundity depression take place. (See Alden, this report). As a result of this, the figure presented perhaps somewhat overestimates the real value. A typical calculation by the metabolic method for Area 2 for Season 1 would be as follows: Biomass average for the month per m3 = 331.07 mg; average temperature = 19.09'C, from Conover the 02 consumption is 10.9 micro liters per dry weight / hour. mg C oxidized to respiratory costs per day = 10.9 microliters 24 hours x 165.5 mg dry weight carbon x 1.4 x .375 = 22.7 mg mg dry weight hour mg C carbon assimilated per day = (.5)(.5)(.8)(331.07 mg) = 66.2 mgC Therefore, Production = 66.2 - 22.7 x 100 = 13.17 331.07 When production of production were was computed by the metabolic method seasonal averages 16%,16.5%,17.4%, and 14.0% for fall, winter, spring, and summer. These are presented in Table 8. i DISCUSSION Note that production by Heinle's method for Area 5 (the plume area) is consistently higher than other areas. This is because this method of computing ' production is based upon temperature and yields increased production based on increased temperature. The residence time of populations in the heated waters must be considered. If higher temperatures act upon populations for only a short time then the increased growth may not have a significant effect on the development time of the individual.
-The figures for production are very near those found by other researchers for other estuarine areas. Conover (1956) found yearly average production to be 16.6% for Long Island Sound, while Deevey (in Conover 1956) found Block Island Sound to have a production of 16.7%. Heinle's.Patuxent
! IV-292
River average turnover time for summer was 4.48 days. This would correspond to a production of 22%. Woodnansee's (1958) estimate of a 4 to 7 week turnover time is thought to be too long a turnover period. Zillioux and Wilson (1966) estimated a turnover time of 21 days at 16*C for laboratory populations. When the December regression is extrapolated to 16'C a 25 day turncver time is produced. Time and state-of-the-art limitations dictated that the entire zooplankton community be approximated with the production of Acartia tonsa. This is fairly valid approximation because Acartia is the major component of the zooplankton standing crop at Crystal River, making up 50-90% of the total numbers. Extrapolation to other zooplankters, such as chaetognaths and larval fonns, is reasonable because in terms of biomass these types of organisms represent a very small percentage. The technique of Heinle used in this study has some shortcomings. Laboratory growth rates were used which were not governed by the population density and food pressure which would be found in the natural conditions. The only variable which growth rate was measured against was tempera ture. Also, equal death rates were ascribed toall developmental stages of nauplii and copepodids. Each stage is also assumed to be of equal time duration. The metabolic method of estimating production based on Conover's work (1956) also has shortcomings. Conover's feeding rates and oxygen consumption were based on more northern popu'idtions which appear to be mainly feeding on phytoplankton. Crystal River organic carbon as measured by Hopkins (1974) was much higher than measurements by Conover (1956). Detrital feeding by zooplankters may alter figures of percent body weight assimilated per day. Generally, Conover's technique appears to be less sensitive to fluctuations in productivity. This is probably because oxygen consumptions do not differ as greatly over the temperature range considered as growth rates do (Conover 1956, Alden, this report). Furthermore, lack of information necessitated a single estimate of percent body weight consumption through the year. Of the two methods, we believe Heinle's method to be more reliable because it takes information directly from the populations' age structure as well as laboratory work. S'JMARY
- 1) Seasonal by area production values were computed. They ranged from i a low of 8.7% in winter in Area 1 to a high of 23.4% in Area 6 in sunener.
- 2) Seasonal averages were found to be for fall, winter, spring, and summer -- 10.9%. 9.8%, 14.6% and 22.8% , respectively.
- 3) Generally increased temperatures in Area 5 (the plume area) caused this area to have increased production of zooplankton. The high production figure for the plume area in the suniner was thought to be unreasonable since temperatures were near heat limits of the organism.
IV-293 l 1
.&an. , , ~ . - , . M. -- - - - ' -
Table 5. Volumes Represented by Stations in Areas 3 Area Station Volume (m ) 1 A,Z 8,850,000 2 B 27,450,000 3 C&J 181,990,000 4- D&E 3,890,000 5 I 25,890,000 6 H,F&G -8,735,000 1 i J I l
.IV-294-
,, ,0 - . .
Table 6. Temperaturc Averages for Area and Seasons at Crystal River (*C) Area
, _ , ~_
Weighted Averages
- Seasons 1 2 3 4 5 6 for Seasons Fall 19.06 19.09 18.33 18.21 23.27 20.46 18.81 Winter 17.10 18.05 17.67 18.24 23.47 18.22 17.94 Spring 23.22 22.15 22.16 22.85 27.75 22.79 22.45 Summer 29.18 28.51 28.65 29.02 34.83 29.39 28.93 Table 7. Zooplankton Production at Crystal River by Heinle's Method (Percent Per Day) -
Area Weighted Oaily l Average for i Seasons 1 2 3 4 5 6 Seasons i l Fall 11.2 11.2 10.3 10.1 16.6 13.0 10.9 1 Winter 8.7 9.9 9.4 10.2 16.9 10.1 9.8 l Spring 15.6 14.2 14.3 15.1 21.3 15.1 14.6 l Summer 23.1_ 22.3 22.4 22.9 30.2* 23.4 22.8
- This value is computed for a temperature near 34*C (see text).
A e i IV-295
. . . , _ ,- .m . _ _ . _ _ _ . . _ _ . _ . _ . _ _ . .
Table 8. Zooplankton Production at Crystal River by the Metabolic Method Percent Per Day Area Weighted Daily Average for Seasons 1 2 3 4 5 6 Seasons
' Fall '19.7 14.6 15.8 18.4 13.6 17.8 16.0 Winter 15.4 16.1 16.4 16.6 15.0 18.3 16.5 Spring 17.0 17.5 17.2 17.3 15.7 19.4 17.4 Sumer 17.7 16.1 13.1 16.7 15.1 15.8 14.0 i
s d i s IV-296-
Figure 5 Heinle's Method-Percent Production by Area by Season 5 30 - 28 - 26 - 24 - s d 22 - 20 - 18 - 5 16 - 14 - 6 1 10 a f 9 FALL WINTER SPRING SUMMER IV-297 . _ _ _ ._ _ o _ . .. .
.. _ .nr _
Figure 6 Percent Zooplankton Production at Crystal River Computed by Metabolic Method 20 -. I 6 19 -. 4 4 e 6 18 -- 6 8 e [ 17 -- Y 4 U N 16 2 3 5 5
~15 --
14 - l 3
. .WINTER ' SPRING SUMMER 1
SEASONS
.IV-298
OBJECTIVE 4a : CTENOPHORE STANDING CR0P AND PREDATION Eric F. Hallquist INTRODUCTION Ctenophores are known to be voracious feeders. Several authors (Nelson, 1925; Cronin et al.,1962; Cuzon du Rest, 1963; Hopkins, 1966) in various studies have reported a general decline in zooplankton when large numbers of ctenophores are present. There has been much speculation as to the possible importance of ctenophore predation on standing crop (Fraser,1962; Cronin et al.,1962; Hopkins,1966; Gillespie,1971). After measuring the respiratory rates of Hnemiopsis leidyi, Williams and Baptist (1966) concluded that the organic carbon requirement of a 20 ml Mnemiopsis (in estuaries near Beaufort, North Carolina) was equi-valent to the zooplankton contained in 4 to 100 liters of water. Bishop (1967), after determining the feeding rate of M. leidyi experimentally, calculated that ctenophores in the Patuxent RTver, Maryland, consumed approximately 31% of the standing crop of Acartia tonsa daily (average density 70,000 Acartia/m3). These studies indicate that large numbers of ctenophores can have a significant effect upon zooplankton populations. The most abundant ctenophore in the coastal regions along Florida and the northern Gulf of Mexico is Mnemiopsis mccradyi (Davis,1950; Woodmansee, 1958; Cuzon du Rest, 1963; Baker, 1973). The etenophores found at Crystal River are predominantly this species. Ctenophores were caught throughout the sampling period at Crystal River. Therefore, we wish to determine their predatory effect upon the standing crop of zooplankton. METHODS Between January and July of 1974, ctenophores caught in pMnii.a tows at Crystal River were measured in terms of total volume. nfe of individual animals were not recorded. Biweekly tows were made at surface and depth at eleven stations using 64u and 202u nets,1/2 meter in diameter. The volume of water filtered during each tow was measured with a flow meter mounted in the mouth of each net Standing crop was measured as a concentration of ctenophores in ml/m$ which was calculated by totaling the volume of ctenophores caught and dividing by the total volume of water filtered at a station. Predation by ctenophores was estimated using two methods. The first, fonnulated by Bishop (1967), measures the feeding rate of a ctenophore (Mnemiopsis leidyi) as a function of the copepod concuntration and the size of the ctenophore. The second method uses production values to estimate predation. Production values were calculated using procedures and conversion factors formulated by Baker (1973) for Mnemiopsis mccradyi. I L Bishop's equation is expressed as Y = 69 + .012X, where Y is the feeding rate of the ctenophore (copepods /ctenophore/ hour), and X is the i product of the copepods' concentration and size of the ctenophore. In order l l l IV-299 _ l
to use this equation we assumed an average volume ctenophore caught at Crystal River -to be approximately 20 ml. This estimate was based upon field observations. The zooplankton data were converted to animals / liter for use in the equation. Since ctenophores are' generally considered to be miscellaneous feeders, all zooplankton that fell within the size range of 64 to 600u were acceptable prey. Nagabushanam (1959) reported that the ctenophore Bolinopsis infundibulum, a short-tentacled species much like Mnemiopsis, had trouble capturing larger prey (larger copepods and decapod larva). For this reason the upper limit of 600p was established. The majority of our zooplankton consisted of copepod nauplii, copepodites, and adults, with the remaining portion generally composed of mollusc veligers, barnacle nauplii, small decapods, and chaetognaths. Feeding rates of a 20 ml ctenophore were calculated for each station and date using the corresponding zooplankton concentrations. Bishop carried out his experiments at a temperature and salinity of 27 C and 16 ppt, respectively. Since the majority of our ctenophores were caught in temperatures between 25 and 30'C and salinties ranging from 16 to 26 ppt, no adjustments were made in the feeding rates calculated. Ctenophore concentrations (ml/m3) were divided by the 20 ml average size to obtain the number of ctenophores/m3 for each station and date. The product of ctenophores/m3 and the corresponding feeding rate (animals /ctenophore/ hour) equalled the number of animals consumed m3/ hour. These values were then converted to percent of zooplankton standing crop consumed daily. Sample Calculation Y = 69 + .012 (animals / liter x 20 ml) = animals /ctenophore/ hour animals /ctenophore/ hour x ctenophore/m3 = total animals consumed / hour 3 Total animals consumed /m / hour x 24 hour / day x 100 = percent consumed daily Total animals availabie/m The second method assumes that 33% of all food ingested by ctenophores is utilized in production (Reeve, personal communication). Therefore, total carbon ingested is estimated by tripling the production values for carbon. Estimates of predation can then be made by dividing the total carbon ingested by the total carbon available from the zooplankton standing crop. Production was calculated using methods formulated by Baker (1973). All calculations weie done using a specific growth rate of .071/ day. This value, calculated by Baker for laboratory reared ctenophores 7.0-56. ml in size at 24 C, was considered the most applicable to our ctenophore size range and environmental conditions, production values were calculated as IV-300 e
.s. .
carbon /m 3 / day and tripled to estimate the total carbon ingested /m3 daily. The carbon available from zooplankton was converted from dry weight data using a conversion factor (carbon / dry weight) of 40%. This is an , approximation for general zooplankton estimated from valueg reported by Beers (1966). These values were calculated as mg carbon /m . Both the carbon ingested and the total carbon available from zooplankton were calculated and computed to yield a percent of the stand-ing crop consumed daily. Standing crop of ctenophores and predatice vr. lues for both methods were calculated for each station and date. The mure riate data were then combined and averaged to obtain values for ee.n aie; and ceason. Seasonal averages (over all stations) of standino nop and predation were calculated by weighting each area according to tha volume of water it contributed to the total estuarine system. RESULTS Standing crop data are suwarized in Table 9 and Figure 7 . The weighted seasonal averages shod that the ctenophore standing crop reached a peak at the Crystal River area c'uring the summer, with an average vslue of 2.49 ml/m3 Avenge ctenophore concentrations for winter and spring were .33 and .80 ml/ms, respectively. Predation data are summarized in Table 10 and Figure 8 The values obtained from Bishop's method were lower than those calculated using the carbon method. Predation along with ctenophore standing crop reached a peak during the summer with values of .161% of the zooplankton standing crop consumed daily for Bisbep's mcthod as compared to .525% for the carbon method. Average predation rates for winter and spring were .023% and .047%, respectively for Bishop's method, and .010% and .150% for thc carbon method. DISCUSSION Since predation was calculated as percent of zooplankton standing crop consumed daily, the values were often controlled by the zooplankton densities as well as the ctenophore concentrations. A comparable ccncen-tration of ctenophores would have a lower percent predation in & higher density of zooplankton. For this reason, the largest ctenochore concentra-tions did not necessarily yield the greatest prc. cation rates. Both methods of measuring predation yielded low average seasonai values (as compared to findings by Bishop,1%7). The average values of .101%, .160% and .525% of zooplankton standing crop consumed / day (which occurred during winter, spring, and summer, respectively) a-detennined by the carbon method are probably near the m.x1wm rue of IV-301 _ __ ~ . ,a __ , _ j
. .a-- ,. , _.d , __ J J _- -
Table 9. Standing Crop of Ctenophores (ml/m3) for Each Area and Season Areas Weighted Average 1 2 3 4 5
+ 6 For All Areas 0
jg 1.05 .53 .05 .51 .05 1.95 .33
.s 2
EE 1.63 1.67 .64 .96 2.82- .03 1: .80 u 23.77 7.55 .78 11.45 2.68 .46 2.49 i w No data available for f611 I f t i 4 l l
-IV-302 1
t.
Figure 7 Weighted Average of Ctenophore Standing Crop vs Season
- 2. 5 -
Ctenophore - Concentration ml/m3 2 -
- 1. 5 .
1 . l
.5, Winter Spring Sumer Season i
Figure 8 l Weighted Average (All Areas) of. Predation Rate vs Seasor; ; I 1 i
.5 -
8
% .4 , l 5 Carbon Method SS '
---- Bishop's Method OD .3 '
at
-a
?8 2* .2 3%
e s. / 2* /
. E' .1 ,'
$N yB
<m 0 .
Winter Spring Sumer Season l IV-303 1 1
- g. - . . . - - -. --
x . a _.,. m . . ,w: . . ~ - .~. . - . - ~~- e < v
)
b., d
- : Table 10. Comparison of Ctenophore Predation Rates.
'(% Zooplankton Standing Crop Consumed / Day)
- . For Each Area and Season, Calculated Using Two Methods J
I Weighted Average For All
, 1 2 3 4 5 6- Areas
, Z g .... .... .... .... .... .... .... b .74+ .026 . 011 .022 .003 .994 .023
- g. g .325** .093 . 072 .048 .006 .277 .101
.081 .072 046 .037 .095 .001 .047
{IA m EP
.282 .196
. 175 .077 .114 .003 .160 u .916 .782 . 045 .547 .108 .021 .161 1.852 3.050 . 144 1.340 .219 .050 .525 No data available for fall
+ Method from Bishop (1957)
** Carbon method i
{ l IV-304 i
$ q g -- ,, ,w- "
-,y .-- e --
I l I 1 1 predation because they were calculated using a specific growth rate measured , by Baker (1973) for ctenophores with an unlimited food supply. For this I reason they are the most pertinent to our observations. When these average ! daily predation rates for each season are compared to the corresponding average daily production for zooplankton (9.63%,14.42% and 22.47% of the zooplankton standing crop produced / day), it appears that even under optimum feeding conditions the population of ctenophores at Crystal River was never large enough to have any significant effect upon zooplankton standing crop during the seasons sampled.
SUMMARY
- 1) The standing crop of ctenophores at Crystal River was measured (as concentration of ctenophores ml/m3) during a seven month sampling period in order to estimate the predatory effects upon zooplankton standing crop.
- 2) Predation was estimated as percent of zooplankton standing crop consumed daily using two methods. The first utilized an equation formulated by Bishop (1967) and the second was based upon production values for the ctenophores that were calculated using procedures and conversion factors found in Baker (1973).
- 3) It was found that the ctenophore standing crop and predation reached a peak during the summer with an average standing crop value of 2.49% ml/mJ and average :,redation values of .161% (Bishop's method) and .525% (carbon method) of the zooplankton standing crop consumed daily. Predation during the winter and spring amounted to .023%
and .047% respectively for Bishop's method, as compared to .101% and
.160% for the carbon method.
- 4) It appeared, after comparing the average ctenophore predation values for each season with the corresponding average daily production values for zooplankton, that even under optimum feeding conditions the ctenophore population at Crystal River was not large enough to have a significant effect upon zooplankton standing crop during the sample period.
1 I IV-305 P emo m, 0ee m -w4r . . am ew+
m .u _ ._ _ . _ . _ _ _ l OBJECTIVE 4b: CHAETOGNATH PREDATION Alex Smart i INTRODUCTION Several different types of chaetognaths were found in the plankton at Crystal River but they were not identified to species. Sagitta hispidus, l a small,co non chaetognath ranging in size from 5 to 12 mm at maturity, ! prefers water of reduced salinity (Pierce,1951). Reeve (1966) reported that the population of Sagitta hispidus in Biscayne Bay, Florida, could : be recognized as a fairly isolated and distinct population. The same probably hnids true for the Crystal River population. Pierce (1951) reported that Sagitta hispidus was always taken at inshore stations and never at offshore statioas off Cedar Key, Florida. Since Sagitta hispidus is probably the most comon species and the only one for which we have quantitative food requirement data, we will assume all chaetognaths have food requirements similar to S. hispidus for the purpose of this predation estimate.
\
Sagitta hispidus eats only live animals whose movements produce ' vibraB 1s picked up by receptor hairs on the chaetognath (Reeve, 1966). ! An earl .er paper by Reeve (1964) showed that 55-85% of the food avail-able to S. hispidus consists of copepods of the genera Acartia, Paracalanus, I and Temora; the remainder being made up of polychaete larvae, barnacle nauplii, small medusae, decapod larvae, and fish larvae. He stated that an 8 mm S. hispidus preferred food 1.0 - 8.0 mm in length and reported a 10 m chaetognath eating a 15 m Lucifer. Reeve noted a slight tendency toward cannabilism in this chaetonath. Reeve (1966) reported that fluctuations in copepod numbers are l reflected in S. hispidus numbers. He reported that the presence of 1 numbers of juvenile copepods in the water cause Sagitta to produce a large brood of young. { t Temperature and salinity variations affect feeding rates of Sagitta (Reeve, 1964). It was found that Sagitta hispidus would not ! i feed in water below 10 C and above 30*C, that the optimal feeding temperature was 25*C and that the probable lethal temperature was 33 C. Reeve reportea that the heating of shallow water grass flats during the summer is responsible for a decrease in Sagitta numbers during August. He also noted that feeding rates increased slightly when salinity varied from that of normal sea water. Reeve states this faster ; uptake of food may be due to an increased metabolic demand imposed by an active osmo-regulatory effort. Salinities of less than 20 /oo or over 50*/ . were found to be fatal to Sagitta hispidus in the laboratory, but Reeve doubts that the salinity variations found in the natural environment influence the population, unless stress of inadequate food i supply or high temperature restricts its salinity tolerance. Reeve (1964) measured the 02 consumption and conversion efficiency l of Sagitta hispidus and estimated the food consumption required. He found that i l l i IV-306 !
the minimum consumption required by Sagitta hispidus per day would be 24-59% of the Sagitta weight per day. In times of stress this figure may go as high as 70% of the chaetognath body weight per day. METHODS The equations that will be used for determining the chaetognath predation on zooplankton are: P = CB, where P = Predation by chaetognaths in mg/m3 C = Consumption rate of .59 (from Reeve,1964) for the purpose of a maximal estimate B = Biomass of chaetognaths/m3
%P = (100)(P)
W where %P = Percent Predation P = Fredation by chaetognaths/m3 W = Total Plankton Biomass /m3 RESULTS and DISCUSSION The chaetognath biomass for each area for all seasons is shown in Table 11. The weighted seasonal average shows chaetognath biomass starts low in the fall (.255 mg/m3) and steadily increases until it reaches a peak of .658 mg/m3 in the summer. However, not all of the areas show this trend. Areas 1 and 2 show a small peak in the winter and a large one in the summer. Area 3 remains fairly constant with a peak in the spring. Area 4 shows a fall and sunner peak. Areas 5 and 6 show winter and summer peaks. This implies localized environmental factors are affecting the standing crop biomass in each of these areas. Figure 9 and Table 12 show a mean daily percent predation by chaetognaths for each area across all seasons. An area by area description is as follows: Area 1 - This area shows peak predation (.054%) to be occurring during the summer season. Spring predation values are lower than winter which shows a slight peak. Area 2 - This area shows a fairly stable predation rate across fall, winter, and soring. In the suniner, however, a significant increas< to .179% 13 shown. Area 4 - This area shows levels that are somewhat higher than the two previous areas, but the same general trends are exhibited. The area shows a lower value for fall ( 041%), reaches it's highest level in the winter (.141%), declines in the s sur.ner (pring)(.027%),
.118% . and reaches another peak in the IV-307
F Table 11. Chaetognath Biomass (mg/m3) by Area by Season and Seasonal Mean h Areas
..s. ghted Seasonal Seasons 1 2 3 4 5 6 Average Fall .0249 .1940 .342 .488 .078 .0 31 .265 4
i y Winter .119 .299 .386 .111 .194 .560 .354 ,
' cn 8
Spring .075 .193 .669 .176 .199 .097 .517 ; t Summer .616 1.173 .562 1.160 .295 .845 .658 r 4 i
i -j. t Table 12 % Chaetognath Predation by Area by Season and Seasonal Mean
- i l
Areas Total Weighted c Seasons 1 2 3 4 5 6 Seasonal Mean 9 Fall 0.0000 .0458 .0976 .0410 .0274 .0157 .0773
~
Winter .0209 .0355 .1151 .1412 .0474 .0400 .0939 Y
$ Spring .0077 .0265 .1233 .0273 .0389 .0140 .0937 Summer .0540 .1786 .1288 .1182 .0766 .1330 .1301
* ^ , . .,c...
Figure 9 Percent Chaetognath Predation by Area by Season
.18 { _ 2
.17
.16
.15
.14 6
.13 3
.12 4
.11 8
g .10 Te 3
.09 E.
U ,08 w 5
.07
.06 1
.05 2
.04 4
.03 7
.02 6
'. 01
.00 Fall Winter Spring Sumer i
Season IV-310-I l I
- ~., . , . . . ,, . . . ,. . m .,..,,.. ,,. w. ,. . . .. ~g.,....,y......
Area 5 and 6 - These areas are similar to Areas 1, 2, and 4 with highest predation rates in the summer (.077% and
.133%, respectively), and secondary peaks in winter.
The predation in Areas 1, 2, 4, 5, and 6 appears to be reflecting a combination of changing chaetognath and zooplankton biomass, since percent predation is a function of both chaetognath and zooplankton biomass. It also appears that these areas are showing a lag effect, as they have high predation rates in the sumer, following the normal spring zooplankton bloom. Area 3 - This area is unlike all other areas in that it showed its lowest level (.098%) in the fall, increased steadily throughout winter and spring, and reached a high of .129% in the summer. It is not readily apparent what the governing mechanism is in this area. The biomass dta show a spring peak while remaining fairly constant through-out the other seasons. It is possible this deeper water area is more environ-mentally stable than some of the shallower areas and this stability would tend to compress the amplitude of population fluctuations. The weighted percent predation means for eact season are shown in Table 12. The data shows two smaller peaks in winter and spring (.0939% and .0937%, respectively) and a high value of .130% in summer. The lowest value of .0773% occurred in the fall. The slight decline in spring. predation rates does not reflect the increase in chaetognath biomass during this period. It is possible that the Crystal River area as a whole is showing the previously mentioned predator prey lag; the zooplankton population undergoes a " bloom" which is followed by the predator chaetognaths. SUlHARY
- 1) The weighted seasonal chaetognath biomass showed a low value in the fall of .265 mg/m3 which steqdily increased through winter and spring, reaching a high of .658 mg/ma in the summer.
- 2) Areas 1, 2, 5, and 6 appear to show similar trends: a small peak in the winter followed by high levels in the summer.
- 3) Area 3 showed a gradually increasing predation rate beginning in the fall and continuing through the summer.
- 4) Area 4 was similar to Areas 1, 2, 5, and 6 except that predation rates in the former area were highest in the winter.
- 5) The weighted seasonal chaetognath predation rates showed a general increase throughout the seasons. Fall was lowest at .0773%, winter and spring predation rates were nearly equal at .0939% and .0937%,
respectively, and sumer had the highest value of .130%. IV-311
. . -. - ~
l _ _ . - i
. .m, . -~ __
OBJECTIVE 4c: DECAPOD PREDATION Alex Smart INTRODUCTION Only qualitative date exist for feeding of Atlantic decapod larvae. e Bour (1923) reported crab zoea and shrimp larvae feed primarily on Jiatoms and secondarily on copepods. She reported that crab megalopa contained chewed decapod larvae. Porter (1960) reported that crab zoea fed algae did not survive, but those fed Artemia larvae did. Lasker (1966) found that the Pacific euphausid, Euphausia pacifica, ate copepod nauplii and algae. For the purpose of obtaining a maximal estimation of zooplankton predation by decapods we will assume that zooplankton makes up the entire diet of the Crystal River decapod larvae. Lasker(1966)measuredthefeeding, growth, respiration,andcarbon utilization of the pacific euphausid, Euphausia pacifica. He estimated that Euphausia pacifica needs to ingest 5% of its biomass per day to meet its total carbon requirements. Salinity and temperature stress in the near shore environment is believed to increase energy needs of invertebrates. To estimate predation conservatively we will use 300% of Lasker's estimate. METHODS We will calculate maximum decapod predation by the formula: Decapod predation /m3 = (.15) (Decapod biomass /m3) and % decapod predation by the formula:
% Decapod Predation = (Decapod predation .n3) (100)
Total Zooplankton Biomass /m3 _RESULTS and DISCUSSION Decapod biomass for each area for each season is presented in Table 13. Decapod biomass was found to increase steadily from fal to spring. The Weighted
- . summer.
seasonal mean biomass increased from .004 mg/m} in fat 1 to .677 mg/m3 Percent decapod predation by area by season is presented in Table 14 , and Figure 10. Percent predation in all areas except Area 4 reflect the increase in decapod biomass. Areas 1, 2, 3, 5, and 6 had the lowest per-cent predation in fall of 0.0%, 0.0%, 0.0%, 0.0002%, and 0.0019%, respectively, and highest percent predation in summer of 0.027%, 0.0516%, 0.0385%, 0.0282%, and 0.0230% respectively. Area 4 showed a bimodal distribution with a peak of 0.0296% in winter and 0.0512% in sumer. The winter peak coincides with a decrease in' total zooplankton biomass in this area which probably resulted ; in the peak in percent predation. The weighted seasonal mean percent pre-dation increased steadily from 0.0002% in fall to 0.0378% in summer. IV-3.17 l
. - 1
j - f p. Table 13. Decapods Biomass by Area by ,
-j Season and Seasonal Mean J' (mg/m3) s.
Areas ' (
. Weighted '
Seasonal Seasons 1 2 3 4 5 6 Average _ Fall 0.000 0.000 0.000 0.034 0.006 0.029 0.004 ' i y Winter 0.033 0.089 0.090 0.111 0.040 0.049 0.082 . Spring 0.303 0.596 0.364 0.509 0.204 0.295 0.377 - Summer 1.125 1.018 0.627 1.291 0.760 0.398 0.677 i-5
?.
-i
?
.h Table 14. Percent Decapod Predation by Area by Season and Seasonal Mean ,
Areas k Seasons Total Weighted 1 2 3 4 5 6 Seasonal Mean i Fall 0.0000 0.0000 0.0000 0.0010 .0002 .0019 .0002 Winter .0028 .0040 .0074 .0296 .0021 .0020 .0065 E O e Spring .0100 .0315 .0207 .0265 .0090 .0130 .0204 i Summer .0270 .0516 .0385 .0512 .0282 .0230 .0378 l
w-Figure.10 , , Percent Decapod Predation by Area by Season i 24
.05
.04 3
8 2 E .03 4 E a 4 1 5
'E '
8 6 L ' E . 02. 3 6 1
.01 5 6-
.0. 2{'# l l
l l l Fall Winter Spring Sumer Season I -315 l
4.c , ,..._; .w . . - - , . . . _ . l l SUM 4ARY
- 1) Weighted seasonal mean percent predation by decapods was found to increase from fall to summer. The seasonal means were 0.0002%
in fall, 0.0065% in winter, 0.024% in spring, and 0.0378% in sumer.
- 2) Percent predation in all areas except Area 4 reflected increasing decapod biomass. Highest predation occurred in the summer. Area 4 showed a bimodal distribution with a secondary peak in winter caused by low total zooplankton biomass in that area and a high sumer peak.
1 j I ( e IV-316 wwv -
OBJECTIVE 4d : FISH pkE0ATION
, ,. . . .- ' ' * ~^ ' '
'ATex^ Smart
-INTRODUCTION Clark (1967) and McHugh-(1966, 1967) state that 70% of the economically important Atlantic species of fishes inhabit estuaries during part of their early life. Twelve percent of the annual Gulf l of Mexico comercial catch for 1958 - 1960 was landed on the west coast of Florida (Power 1960,1961,1962a,1962b). Sykes and Finucane (1965) studied the Tampa Bay estuary and concluded that the algae-sea grass ecosystem appears to be essential for survival and growth of 3 many comercial species. They found juveniles of 8 species whose {
Florida catch makes up over 90% of the yearly Gulf of Mexico. yield. Sykes (1965) estimated that some 7,500 square miles or 4.8 million I acres of estuarine areas exist on the periphery of the gulf of Mexico. l The present study area makes up 18,000 acres of 0.4% of the total l Gulf of Mexico estuarine area. Larval Fish' INTRODUCTION _ It is difficult to get a quantitive estimate of larval fish density. Bridger (1956) and Tibbo et al. (1958) found that Atlantic herring larvae are capable of avoiding plankton nets. Pearcy and Myers (1974) caught more herring larvae at low tide and in the dark. From this, they concluded that only weakly swimming larvae are effectively sampled by plankton nets during the day. The spawning location of fishes is important in determining the amount of predation due to fish larvae. Studies have shown that fish spawn in many locations. Hildebrand and Cable (1938) took larval forms 12 - 13 miles offshore. Hoese (1965) took larval forms 10m in length 10 miles offshore. Harrington and Harrington (1960) found tarpon leptocephalus larvae-150 miles offshore. Pinfish spawn offshcre and the larvae stay offshore until they develop self directing power (Caldwell, 1957). Tabb (1961) found spotted sea trout spawning in deep holes and channels. Flounder spawn at the head of estuaries (Mulkana 1966). Pearcy and Myers (1974) found that herring spawn in shallow water or intertidally. Moe (1972) presented spawning data for 21 families. These data are sumarized in Table 15. Very little data have been collected on feeding habits of fish larvae; only qualtiative data exists. Morris (1955), Houde and Palko (1970), and Detwyler and Houde (1970) report that sardine and c'lupeid larvae eat cope-pod eggs and nauplit of 40 - 509 size. Fish larvae have a capacious stomach which enables them to pursue a " feast or famine" type of existance. Since no quantitative estimate of larval fish food consumption exists, we will assume that the fish larvae consume 40% of their body weight per day from the 64 - 202u plankton. This high value was assumed because these fish are in a period of rapid growth. l IV-317
- 2. a , n,- -- - -- - - -- -
METHODS Predation by fish larvae will be calculated by the formula: P = (.4)(B) where P = predation and B = biomass of fish larvae and percent predation by the formula:
%P = 100P ,
W where %P = percent predation and W = total biomass. RESULTS Mean fish larvae biomass (Table 16) was found to be highest in spring with a vague'of .0023 mg/m3 and slightly lower in summer with a value of
.008 mg/m . F1sh larvae were present in the fall and winter, but the biomass
-was extremely small.
Mean daily predation rates of larval fish in each area are shown in Table 17. Larval fish were rarely captured in the estuary; no one area was dcminant. Weighted seasonal means showed predation rates were lowest in the fall and winter, highest in the spring (.0004%), and tapered off to .0002% in the summer. DISSCUSSION iue low larvae fish predation values are due to the scarcity of larval fish in the plankton samples collected. This may be due to etther net avoidance or spawning location. Some net avoidance is probably taking place (Krepley, fish identification section). The majority of the immature fish coming into Crystal River probably enter the estuary as juveniles. According to spawning locations from Moe (1972), most of the fish common to the study area-fall into the groups that spawn offshore (Table 15). These fish would pass through their larval stages before entering the study area. Caldwell (1957) supported this by reporting that pinfish larvae stay offshore until they develop self directing power.
SUMMARY
- 1) Larval fish were captured rarely in the Crystal River area; no area was dominant.
- 2) Predation rates were lowest in the fall and winter (no larvae collected),
.0004% in the spring and .0002% in the summer.
IV-318
Table 15. Spawning Data from Moe (1972) Families Families Spawning Offshore _ Spawning Inshore Elopidae (Lady Fish) Engraulidae (Sardines) Megalopidae (Tarpon) Syndotidae (Lizardfish) Albulidae (Bone Fish) Cyprinodontidae(Killifish) Clupidae (Herrings) Centropomidae(Snooks) Serranidae (Groupers) Lutjanidae (Snappers) Carangidae (Jacks) Pomadaysidae (Grunts) Sparidae (Porgies) Chaetendidae (Butterfly Fish), Scombridae(Mackerals) Labridae (Wrasses) Scianidae (Drums, Sea Trout) Scaridae (Parrotfish) Mugilidae(Mullet) Acanthuridae (Surgeon Fish) Sphyranidae (Barracuda) ! l l l l 1 IV-319
F t
*'(')#)# _ _ _
TEST TARGET (MT-3) k*$k 1.0 lll m at 5 I.I [!!f EI NELE 1.8 l g5 1.4 1.6 i
& N e
MICROCOPY RESOLUTION TEST CHART i k $+w 1 y A'/%,
/7
/Aa4
+ 8p O '
4 #' d'
+Mfr
\ /////p 4*
%> $77,#
\ IMAGE EVALUATION
(# NNN TEST TARGET (MT-3) i i i
- 1.0 sfEH UM l# !;Y E!!!L2,
( l.1 E ?? Ulll2.0 e. l 1.25 1.4 1.6 p -- 6" > l idiCROCOPY RESOLUTION TEST CHART 4 J p% %//// + //p r,4t./, h ae,s4\
+,<#3,e
/
4'ew 6 y g
- o ,
4+ gas, t
-. - ~ .- . - . - . . - . - .-. -
o- , W
- .C 52 O O M ~
M.C O O
~
O O wO em . O. O. O. OW
>= M 8
8 8 8
- 8 $. 8 8 a
m W L. O O O M aC O O O O la O O O O bi m O. O.- C. C. m MC Ee NE F - m O O O O M M O O N O M C 0 4 O O O O eO b Mm <C O. O. O. O. - W
-M O
W
. E.
t
- 8 S 8 3 8 $. 8 8 in 6
" O O O O O O Ch M-N O O O O W
C. O. O. - O.
~
O g O C
$. 8 $. $. .
'e C L CD L O W C to & W -
4 M C L W 4 - Q. M .A 3 M M IV-320
P
. ~ f ,
( r. k -
. a 't - -
..T
.i
~ # *.
d en t a . he gM i el 0 0 4 2 Wan 0 0 0 0 0 0 0 0
- l o 0 0 0 0 as
.- t a n oe o TS s a e S y 0 0 0 6 b 0 0 1 1 6 0 0 0 0 a 0 0 0 0 e r A y b n 0 0 0 0 on 0 0 0 0 ia 5 0 0 0 0 te 0 0 0 0 aM d el
. ra Pn s o a es e aa r 0 0 1 0 ve A 0 0 0 0 rS 4 0 0 0 0 a 0 0 0 0 Ld n
ha s i F t 0 0 2 0 n 0 0 0 0 e 3 0 0 0 0 c 0 0 0 0 r e P 7 1 0 0 4 1 0 0 1 3 e 2 0 0 0 0 l 0 0 0 0 b a T 0 0 0 0 1 0 0 0 0 0 0 0 0 s n r g r o e n e n s l t i e a l n r n e a i p u S F W S S . m.
,) ! is I 1g 4 $ l
. ~- _ . . . .m .. .- .- Juvenile Fish INTRODUCTION Juvenile fish migrate into the estuaries after reaching a size of approximately 10 nm (Caldwell,1957; Springer and McErlean,1961). This occurs year round. Table 18 summarizes the observations on the time of influx of juveniles into the estuaries. Since the influx of different species of juvenile fish continues differently at ording to season, we will assume that some juvenile fish are present at any given time. The question of what these juveni es eat has been answered by several authors. Harrington and Harrington (1960) reported that copepods make up 73% of the diet of young tarpon, with fish and caridean shrimp making up 23% and 3%, respectively. Mulkana (1966) found that harpacticoids made up most of the diet of 10 - 30 mm flounders. The most complete study of the diet of juvenile fish was done by Carr and Adams (1973) at Crystal River, Florida. Of the twenty-one species of juveniles studied, zooplankton was fouad to make up a major portion of the diet of fifteen at one stage of growth. They stated that it is likely that most, if not all, of the species studied would have been found to have a planktivorous stage if smaller juveniles or larval ~ specimens had been collected. Most feeding data are given in number of animals / stomach or % of diet. Much quantitative data exist for freshwater fisheries species but these data would be of doubtful value in predicting feeding ratas for juveniles of saltwater species because of different osmoregulatory conditions and food habits. The avera Calline and Hill (1973)geisfood 3.5% requirement body weight value of this
/ day; Ricker will(1946) and be used for feeding rate calculations. In order to determine a body weight from lengths, the formula log W = -4.373 + 2.914 log L (Caldwell, 1957) will be used.
Zimmerman et al. (1973) reported that seagrasses are generally restricted to depths less than 1.5 meters at mean low water. Juvenile fish associated with grass beds would be restricted to this water depth. Area I and the plume area of the present study are the only areas with average MLW depths of 1.5 meters og less. These areas were found to have a total volume of 16,200,041m#. Snedeker et al. (1974) give an average density value of 2.25 fish /m3 for their drop net samples in Crystal River. This density will be used for our calculation of juvenile fish predation. METHODS The feeding rate for individuals of each species was calculated by tile fonnula: IV-322
Hansen
. (1969) .EO Harrington and Ha.rr,ington..(.1964 ,
' * ~'
. e, . i-Caldwell (1957 a".
cu. Hoese (1965) Oh x Cameron (1969) til;
=x Mulkana UC (1966) egg Hilderbrand and Cable UE (1930) o<-
Hall and Woodwell dd
. (19711 ^*
Moe, Lewis a and Ingle M 5 (1968) S Bellinger and e L Avault (1970) O j Sringer and u C McElean (1964 g
] Dahlberg and , , ,
y Odom (1970) A A L u. o Sykes and Finucane dd d Am dd d' C om o (1965) mu- '3 **' i
$ Springer and
% R:Elean (1961) 3E?E O zz U
p e L - E ! m m a b ^
& ^
E iC E E - QJ fU M M e O O C ^ as g t
*v-M 3
3 C m 3 C O
*e=
U
'e=
e- W c f5 W O W O f5 p O L .O e- ut f5 - C b "3
.c 3 L'O o O O @'O - 'O 80 D
% E .C h b m C W m 43 -
O O & C M O > b 3 U m M
.C g C U *C O b C g O b m h
o L M C 4 m v 4 *e= *e- Q **" M M l 3 W U O iO M i O L 8
> g O
2 #O 3 E
>=
O 8 C O 3C 5 % *e-O - M m 8 8 M e J U 2 O CO 7: . O 3 we M E w
- W 8
W 8 y O t W .c O E
.C 4 O O to t m C 3 b 'O 80 c
g M M L M e C 5 A to 0 M .M M L 'O 'O C O b V -r- O 'O *e- t 80 "J ""J O e= @ O a O M t'- Q. M 0. Z U
.C 3 M 3
.c 3 E G
M E O IV-323 k
t, i Table e 18.Sumary of Time of Appearance of Juvenile Fish in Inshore Waters (continued) - WW 2@ RF W@ FE E* @ EF 0 5 OF O2 O F O O Of O I 01 EK BE 01 at La at **Za *# ER ** a- al ia 2a 2"
- Y =a 37
- r 4. SE* S2= 5* di *77 38
=a ==" ma a Oz :: Og = =
0 - a. O. a' R 88 Ra G a sa as S. G S" 88 Species O $ $ "" a M E
- - a a
- Pemit (Trachinotus fallatus) Su. $ Pompano (Trachinotus carolinus) $0pt bOE Crevalle Jack (Caranx hippos) Su. Sheepshead (Archosarus probatocephalis) Sp. Sea Catfish (Galeichthyes felis) su* F Tide Water Silverside (Menidia menidia) ! Tarpon (Megalops atlantica)
$NS Atlantic Croaker (Micropogun undulatus) Jan Juveniles in General Apr dul Oct Aug ;
/
f a
F = BE where F = feeding rate (mg) B = biomass (from log B = -4.373 + 2.914 log L) (from Caldwell, 1957) where L = mean length consuming zooplankto's (from Carr and Adams,1973) E = Daily food requirewnts (3.5% body weight / day) The mean (E) was calculated by averaging the feeding rates for all the species. The calculation of mean feeding rate is shown in Table 19 The number of fish occupying the grass bed areas was calculated by the formula: N = DV where, N = number of fish occupying the grass beds D = density of fish (2.25/m3', V = volume of grass bed areas (16,200,041 m3 ) , In order to compare juvenile fish predation to that of other predators, a study area density was then calculated using the formula: S=Nv where, S = study area density N = number of fish occupying the grass beds V = total study area volume (256,000,000 m3 ) , The total daily predation has then calculated from the formula P=SF i where, P = predation by juvenile fish (mg/m3 ) S = study area density (fish /m3 ) E = mean feeding rate (mg/ fish / day) and the daily percent predation was calculated using the formula: 100 P
%P = total zooplankton biomass IV-325
-e m .-
Table 19. Feeding Rates for Juvenile Fish Feeding Rate Mean Size Body Weight at Mean Body Consuming at Mean Size Weight Species Food Food (m) (mg) (mg/ day / fish) Menidia , beryllina 12.5 6.61 x 10-V 23.13 Hyporhamus p unifasciatus L 17.5 l.77 x 10 6.19 Diplodus V holbrooki C 18.0 1.93 x 10 2 6.75 Lagodon rhomboides 6.98 x 10 2 i C 23.0 24.43 Stronqulura sp. C 22.5 6.63 x 10 2 23.21 Haemulon plumeri C 23.0 6.98 x 10 2 24.43 Orthopristis chrysoptera C 25.5 8.96 x 10 2 31.36 Bairdella chrysura C 23.0 6.98 x 10 2 24.43 Cynoscion nebulosus C 15.0 1.13 x 10 2 3.95 011goplites sarus C 17.5 1.77 x 10 2 6.19 Leiostomus xanthrus C 15.0 1.13 x 10 2 3.95
-Eucinostomus sp. C 20.5 2.82 x 10 2 9.87 Mean 15.65 V = veligers L = larval crustaceans C = copepods IV-326 l
l l 1
, 1
l i l
.. r, .. ., ,
Table 20. Seasonal % Juvenile Fish Predation Season Percent 'redation Fall 0.26 Winter 0.25 Spring 0.07 Sumer 0.12 l l 1 i IV-327 f
where,
%P = percent daily predation P = daily juvenile fish predation RESULTS 36,450,000 juvenile fish were estimated to be in t The juvenile fish density over the entire 256,000,000 mgegrassbedarea.
study area was 3 then estimated to be 0.142 fish /m . Multiplying this density by the mean feeding rate from Table (15.65 mg/ day / fish) yielded a total daily predation value of 2.23 mg zooplankton /m3/ day. Table 20 summarizes the percent predation values. The percent predation values ranged from 0.03% on April 18 to 0.78% on January 29. The seasonal means were 026 % for fall, 0.25% for winter, 0.07% for spring, and 0.12 % for summer. DISCUSSION Juvenile fish appear to be one of the major categories of predators of the zooplankton populations over the entire study area. The influence of these fish would be much more noticable in the grass bed areas they inhabit, but the movement of water throughout the estuary makes consider-ation of juvenile fish predation over the entire study area the most sensible approach. The December 18, January 29, and May 21 percent predation values shown in Table 20are five times larger then the values for the dates before and after them. These peaks in percent predation are also shown in the decapod predation values and are probably the result of abnormally low zooplankton biomass on these dates creating artifically high values that are not in the same range as the other 14 values. SulHARY
- 1) Juvenile fish were found to be one of the major predators on zooplankton, with a daily percent predation value of 0.16%.
- 2) Disregarding three abnormally high values this percent predation value is 0.08%.
- 3) Seasonal percent predation was found to be .26% in fall, .25% in winter, .07% in spring, and .12% in summer.
IV-328
- a
.e ,, ,
Total Natural Predation
SUMMARY
- 1) Total natural predation showed a bimodal distribution with a secondary peak of .45% in winter and a major peak of .82% occurring in the summer (see Table 21). Total natural predation was the same during the fall and spring with a value of .34%.
- 2) Juvenile fish were found to be the major natural predators in the fall and winter. Ctenophores were found to be.the major predators during the spring and summer.
- 3) All planktonic predators showed an increase in predation during the summer, while fish larvae peaked in the spring and fish juveniles in the fall and winter.
- 4) The estimate for total natural predation is probably lower than the actual value. The planktonic food web is of such a complicated nature that all of the natural predation cannot be accointed for. Planktivor-ous animals not included in the estimate were aoo it fish, coelenterates, carnivorous copepods, etc. These categories were not included eithir because of a lack of data, as in the case of adult fish and carnivorous copepods, or because they were considered to be of minor importance to the overall natural predation.
i 1 r l IV-329 1'
.,,.x .~. w .. . .- --. - - - . . . - - -. ~.
-Table 21. Natural Percent Daily Predation on Standing Crop of the Area Fish Juvenile Adult
- Natural Season Chaetognath Decapod Larvae Fish Fish Ctenophore Total Fall .077 .0002 0 .26 -- ---
.34 Winter .094 .0065 0 .25 --
.10 .45 Spring .094 .0200 .0004 .07 --
.16 .34 Summer .130 .0380 .0002 .12 --
.53 .82 No data available i
i I IV-330
,a - , , , . . - . - - - -~
OBJECTIVE 5: A COMPARIS0N OF POWER PLANT PREDATION AND NATURAL PREDATION .- i . Richard Cullen,an'd Ronald DuBose , , ,, INTRODUCTION An estimate of power plant predation was needed to compare its consumption with natural predation and for determining total predation on th' tooplankton standing crop (total predation is the sum of natural and rw plant predation). METHODS The total amount of zooplankton killed per day by the power plant was calculated by multiplying daily zooplankton biomass entrained by the average monthly percent mortality attributable to the plant, and dividing by the estimated weighted zooplankton standing crop in the area influenced by the plant (see Table 22). This yielded the percent of the total zooplantkan standing crop killed by the power plant per day. 3 (standing crop at F(mg/m ))(percent mortality)(daily water entrained (m3 )) (mean weighted standing crop for all stations (mg/m3 ))(area influenced by plant (m3 ); (biomass killed by plant / day) (standing crop of area) 3 Standing crop (mg/m ) at Station F multiplied by the power plant's daily cooling water consumption was assumed to be daily plant entrainment. The mean weighted seasonal standing crop multiplied by the total volume of the area of influence was used for the total standing crop estimate. The monthly mortality figures were supplied by Raymond Alden (Alden 1974). Since the output of cooling water for Unit 3 is approximately the same as Units 1 and 2 combined, the entrained biomass, biomass killed, and percent power plant predation can be arrived at for Units 1, 2, and 3 by doubling the figure for Units 1 and 2. This is done assuming the percent mortality will bs the same for Units 1 and 2 and Units 1, 2, and 3 (no better data are available at this time). RESULTS Seasonal average plant predation rates were determined for November, 1973 through July, 1974. These data are shown in Table 22. Average seasonal mortality rates during the time' period were 26.7 , 23.1 , 15.7 , and 33.6 for fall, winter, spring, and sunner, respectively. The average seasonal percent plant predation was .35 45 , .09 , and .45 Total natural predation over the respective seasons is . 34 , . 45 , . 34 , and .82 % . Average daily biomass (lbs) entrained and power plant killed biomass (lbs) per day are also shown in Table 22. Projected biomass entrained, power plant killed biomass, and plant percent predation for Unit 3 are included in Table 22. We believe these projected data are under-estimates because of increased aT and possible increases in mechanical effects. A comparison of power plant and major natural predation is presented in Table 23. Power plant predation is 51.1, 50.0.20.9 , and 35.4% of the total IV-331
} ~i- !
l Tabic 23. Natural and power plant percent daily predation on standing crop of the area. Total Power i Fish Juvenile Adult
- natural plant Total
i -
- Season Chaetognath Decapod larvae fish fish Ctenophore predation predation predation y- Fall 0.077 0.0002 0 0.?6 0.34 0.35 1.69 bl
~
Winter 0.094 0.0065 0 0.25 0.10 0.45 0.45 0.90 Spring 0.094 0.020 0.0004 0.07 0.16 i
- 0. 34 0.09 0.43 Summer 0.130 0.038 0.0002 0.12 -
0.53 0.82 0.45 127
- No data available 2
't
predation during the fall, winter, spring, and summer, respectively, in the area' influenced by the plant. If we look at the largest natural predator for a given season, we find that it would make up 37.7,27.8. 16.3, and 41.7% of the total predation. Total predation (' "ral plus power plant) was
.69, .90, .43, and 1.27% over the seasonF DISCUSSION Entrained zooplankton mortality rates appear to be lower than the 100% previously assumed. In the time period of this study (November, 1973 through July, 1974) percent mortality did not reach 40%, but in the following two months @ugust and September, 1974), percent mortality was reported to be 75.5% and 94.41%, respectively (Raymond Alden, personal comunication).
No standing crop data were available for these months, but percent power plant predation on the area influenced by the power plant is probably under 1.3% per day. This was calculated by determining the ratio of killed entrained biomass per day to standing crop of the area influenced by the plant, assuming standing crop at Station F to be equal to the average stand-ing crop of the area and using .9441 as the mortality. Power plant predation was equal to total natural predation during fall and winter, but the natural predation was 3.8 and 1.8 times greater than plant predation during spring and sumer. Of the naturt.1 predators studied, the power plant had a greater predation rate in the f all and winter than any single natural predator and during spring and summer it was second to ctenophores' predation rate. Since all natural predation cannot be accounted for, our total natural predation figures are underestimates of the true natural predation.
SUMMARY
l
- 1) Zooplankton mortality rates appear to be lower than the previously assumed 100%. Average seasonal mortality rates for November, 1973 ,
through July, 1974 were 26.7, 23.1,15.7, and 33.6% for fall, ! winter, spring, and sumer, respectively. l
- 2) Average seasonal power plant percent predation for the study period !
were .35, .45, .09, and .45% for fall, winter, spring, and sumer, respectively.
- 3) Biomass entrained by the plant ranged from 2,000 lbs/ day to 5,100 lbs/ day and the entrained zooplankton killed by the power plant ranged from 180 lbs/ day to 1100 lbs/ day.
- 4) Projected biomass entrained and entrained zooplankton killed for Unit 3 are equal to the combined figures for Units 1 and 2. This assumes the same percent mortality that is now estimated for Units 1 and 2.
- 5) The power plant appears to be one of the major predators in its area of influence. It contributes 51.1, 50.0, 20.9, and 35.4% of the total predation. Total and natural and plant predation for the respective seasons are .69, .90, .43, and 1.27%. '
IV-333
OBJECTIVE 6: STATISTICAL ANALYSIS OF NATURAL AND POWER PLANT INFLUENCES ON ZOOPLANKTON COMMUNITIES AT CRYSTAL P.IVER. l l William Ingram Data Analysis Methods All samples were compiled so as to consist of: 1) a set of observations on environmental variables which include: salinity, i temperature, dissolved oxygen, water turbidity (as measured by a ' Secchi disk), wind speed, wind direction, water depth at point of collection, incident light as measured by a pyroheliometer, moon-rise and moonset, sunrir,e and sunset, length of dawn and twilight, tide height and direction, a qualitative estimate of cloud cover, precipitation and sea condition; 2) a set of biological variables which include: numbers /m3 for each of the twenty-nine zooplankton categoriesineachsizeclass,Shanngn-Weaverestimatesofdiversity in a size class, and total numbers /m across size classes; 3) a - set of observations on the levels of micronutrients and phytoplankton standing crop (supplied by Mr. R. Gibson) including: phosphate, nitrate, nitrite, ammonium, silicate, and total organic carbon; and, 4) a set of power plant activity measurements including: gross power load, flow rate of water through the plant, change in water temperature, and discharge temperature (supplied by Florida Power Corporation). Because of the impossibility of measuring all the variables in a syctem, the choice of variables is extremely important. We tried to collect data on a set of variables that would encompass the aspects of the environment that were important to the planktonic community. The compilation and coordination of data from various and diverse sources was performed to provide (as much as is possible) synoptic data sets. This resulted in three data sets which were analyzed: -(1) a biweekly data set of 202p to 3,000p sized plankters, (2) a biweekly data set of 64p to 202u sized plankters and (3) a seasonally collected diurnal data set of 202p to 2000p sized plankters.
.These data sets ranged in size from 3,045 observations on 83 variables to 539 observations on 71 variables to 4,602 observations on 57 variables.
In general, we had two basic goals in the multivariate analysis portion of our program. The first was to attempt to determine whether the operation of the Crystal River power plant was significantly related to temporal and spatial variations in zooplankton standing crop. The second was to investigate the relationship of temporal and spatial fluctuation in zooplankton to the temporal and spatial fluctuation of the non-plant driven environmental variables. Because cf the inter-relationships of many of the important variables, we chose a multi-variate analysis approach as the bes' technique for analyzing a set of variables that must be considered together as a system. Our approach may be phrased as a generalized statistical model: 1=p_+'(jbjX_g)+( 63 Z 3) + Sz + Sp + T + Interaction + c IV-334
Where: Y_ = a vector of standing crop levels for the zooplankton categories (the dependent variables). 3 = a vector of generalized mean values for the zooplankters, b_jX j = a vector of variables (X ), which contain the non-plant 4 driven environmental variables and their relative relationships (b j) with the dependent variable set. E6 Zj j = a vector of covariates (Z4 ) which includes the plant driven environmental variables, and their relative relationships (63) with the dependent variable set. Sz = a general term signifying the size class effect on the vector of zooplankton standing crops. Sp = a general term reprsenting the spatial effects on zooplankton standing crop. T = a general term representing the temporal effects on zooplankton standing crop. Interaction = a generalized term used here to represent the various interactions possible. c = the error term comprising the unexplained variance that can be partitioned into two categories: (1) the variance
~
that could be explained, and thus removed from the error term, if the appropriate variable had been included in the model, and (2) the experimental error which is a result of various random effects. By a slight rearrangement of a model of this general type it is possible to determine if the effect of the plant, as expressed by the variables measured is of statistical significance: Y - (Ebgj + Sz + S_g + I) = (E jZ)+c j One can then test to see if the portions of the variance accounted for by [(I Z ) + c]is significantly different from e itself. The data collected during the diurnal program have a slightly different goal than that collected during the biweekly program; the statistical models proposed are formulated differently to reflect this. The diurnal goals center around the investigation of diel temporal patterns in the zooplankton and the relationship of the plant to them. Thus, our diurnal data consist of hourly observations on all variables. l IV-335 i
~ ~ ~ ~ ~~
Variables that change little or not at all during a 24 hour period are not included in the diurnal model. Seasonal effects on diel cycles are investigated by observing how the pattern changes from one diurnal sample period to the next. The goals of the biweekly program are oriented towards the investi-gation of changes on a much larger scale. In actuality, any periodic fluctuations of less than a month could not be detected by this program. Therefore, microvariations in the environment are not included in this model and most variables are averaged for the biweekly period preceding the time of sample. The 202p biweekly model and 64p biweekly model are the same except for the absence of a size class term in the 64p model. These had to run as separate models because the set of zooplankton cateogries for 64p samples is not the same as for the 202p samples. The various models will now be discussed in detail. Biweekly Models The specific model for the biweekly sample statistical analysis is: I klmnop
- 25kiklmnop) *( Ej -j klmnop *-(+B_j+((j )+D +E_g+_BD
-n in* E o*Eno+BDElnoklmna)p l
where. Y = the dependent variable vector, u_ = overall mean level of the dependent vector, (E b_g x j ) = the vector of nonplant-influenced covariates. (E6,3 Z 3) = the vector of plant-influenced covariates, Ak = the vector of effects due to the depth factor, k = 1,....a. B_) = the vector of effects due to the area factor,1 = 1,....b, the v
*C (II = area.ectorThis is aofnested random effects due or heirarchical term, to nested stations located within an within areas, 1 = 1,... c.
Dn = the effect of the seasonal factor on the level of dependent variables , o = 1, . . . ,d, E, = the vector of effects of size class on the dependent variables. l 0 = 1,....e. BD,BE_,D_E. E = the vectors of first order interaction factors, BDE = the vectors of the second order interaction factor, E = the error term, p = 1,. .. .s IV-336
The list of dependent variables for the 202u and 64p samples (zooplankton categories) has been presented earlier. The two categories of covariates are presented below. The form in which the data were coded for analysis and the rationale for inclusion in the analysis is given for each of the variables contained in these covariate sets. Nonplant-driven Variables This set includes those variables whose fluctuations are thought to be driven by phenomena that are either not influenced by power plant operations or are only influenced to a minor degree by the plant. These might be considered the natural environment. (See Figures 11 to 14 ). Salinity and Temperature These have obvious importance as two of the more critical factors in the survival of estuarine plankters and also as indicstors of the spatial and temporal source of the water mass sampled.
. Dissolved Oxygen This is used primarily as a further aid in water mass indication because of its high correlation with salinity and temperature ( .51 with salinity and .79 with temperature).
Turbidity This is measured as depth in feet of a secchi disk and is used as an estimata of water clarity and light transmittance. Wind Speu The wind is extremely important in determining the surface conditions of the water. Station Oepth This is a measure of the overall depth of the water at the sampling point. Sea Conditions, Cloud Cover, and Precipitation These are qualitative estimates of the general weather conditions measured on a scale of 1 to 4 at the time of sampling. Daytime, Moontime Previously (Crystal River Report #3), we reported the significance of time of day to the zooplankton abundance. In this model we have made several transformations in order to express the time variable in a manner significant to the biology of the plankton community. The transfonnations were as follows: O IV-337
L FIGURE 11 SALINITY BY SEASONS 30 - 29 - AREA 3 AREA 3 28 - 27 - AREA 2 AREA 2 AREA 5 AREA 6 3 _ AREA 4 AREA 5 AREA 1 AREA 4 25 - AREA 6 S 24 - A f 23 - - N o/ AREA 1,- 22 - T Y 21 - 20 - 19 - 18 - 17 _, e I I t i FALL WINTER SPRING SUMMER SEASONS I'V-338 l l
-my.- ,- - ,
~
FIGURE 12 TEMPERATURE BY SEASONS 36 - AREA 5 34 - 32 - 30 - AREA 6 AREA 1 AREA 4 AREA 2 28 - AREA 3 E - M P 26 - e R OC A T 24 - U R E 22 - AREA 5 20 - I AREA 6 18 - AREA 2 / AREA 3 , m AREA 1 16 -' AREA 4 e t FALL WINTER SPRING SUMMER SEASONS IV-339
w sA ~p , y-ws-me. Ab*h .4 FIGURE 13 DO BY SEASONS 8.4 - AREA 1 - 8.2 - 8.0 - ARE A 2 7.8 - AREA 6 IREA 4 7.6 - AREA 3[ S 8 7.4 - mg/ml E 7.2 - D ggg g O 7 .0_ - X G 6.8 - AREA 1 E N
- 6. 6 - REA 4 6.4 - .
AREA 6 6.2 - AREA 2 REA 5 6.0 - REA 3 f FALL WINTER SPRING SUMMER SEASONS IV-340
FIGURE 14
, PYROHELIOMETER BY. SEASONS .
206 - 205 - 200 - 195 - 190 - 185 - 180 - P fO 17 5 - 170 - E N L I I T 165 - O S
'M E 160 -
T E R 155 - 150 - 145 - 140 - 135 - 130 - 125_ FALL WINTER SPRING SUMMER SEASONS IV-341
,. n w. ~ . . . - .- . -
2 Daytime = Time of day (sunrise + (sunset - sunrise)/2) Moontime = Time of day (moonrise + (moonset - moonrise)/2) Sunrise = 1 if sunrise < time of day < sunset 0 otherwise Moonrise = 1 if moonrise < time of day < moon set 0 otherwise Then, we now have time centered around the noon of the two cycles of probable biological importance. In addition, we have coded whether the presence of the sun or moon overhead is important. The phase of the moon was co#d as follows: Lunar Phase = 2 'if full 1 if quarter or three-quarter , 0 if new. Tide and Tide Stage This term includes the height of the tide at the time of the sample and the direction of the tide cycle (-l out-going, O slack, and +1 incoming). This adds what we considered the other time cycle of probable biological importance. These were initially thought to be among the most important variables. The above group of variables represent those that are related to the sample at the particular instant of collection. These serve to characterize the sample with reference to conditions during that day of sampling. The nex_t seven variables are either averaged values for the two weeks preceding the day of collection or are thought to represent such values. Incident light The pyroheliometer tracings supplied by Florida Power Corporation were digitized and integrated over a diurnal cycle. The integrated values were then averaged for the two-week period including the day of sample. This resulted in an average daily incident light value. Phosphate, Nitrate, Ammomium, Silicate, Nitrite, and Total Organic Carbon These were all compiled from data supplied by Mr. R. Gibson, MSI. Although the nutrient and phytoplankton data represent only a point source estimate, our personal experience with measuring total organic carbon over i a diurnal cycle suggests that these values are relatively stable over short l periods of time. This supports our use of the data to represent general conditions during the biweekly period of collections. 1 Plant-Driven Variables These represent variables that were included to get an estimate of l l IV-342 l l
i the . variability present in 'the power plaint'.s activity l in. the estuary. These variables are utilized to see if the plant's' activity over the year is significantly related to the fluctuations in the zooplankton comunity numbers. These variables were also averaged over the biweekly period. Delta T This was compiled from biweekly intake and discharge temperature values supplied by Florida Power Corporation. Delta T = Discharge temperature - Intake temperature This variable shows some small scale variations about its mean, but it is primarily an indication of plant "down" time. The delta temperature is of prime importance to the organisms being entrained. Discharge Temperature In many instances the maximum temperature experienced is of greater biological importance than the delta temperature. This is especially true when the temperature approaches the upper maximum of the thermal tolerance zone. Gross Megawatt Load This is used to moniter the daily and seasonal fluctuations of the power plant activity. In a further extension of the power plant as a predator analogy, this is a measure of the " metabolic rate" of the power l plant. Flow Rate The average amount of water entrained by the plant was used to examine any possible relationships between volume and standing crop fluctuations. In addition to these continuous or quasi-continuous variables, we have several analyses of variance factors in our statis-tical model. Area Our stations were grouped into six areas. The areas were determined by hydrographic considerations and biological implications. The areas and the rationale for their selection are discussed above. Table 24 i refers to the areas and the stations which they contain. Table 24 Areas of Study Area Stations Description 1 A,Z Inshore, intake 2 B Intermediate, intake
.3 C,J Offshore, intake 5 F,G.H Discharge canal and thermal plume 6 I Intermediate, Discharge l 4 D,E Intake canal i l
l IV-343 '
Stations This is a term that is nested within the areas. This pennits us to estimate the various components associated with each area as well as the change in mean standing crop due to areas. Seasons
, Partitioning the year into seasonal, periods is somewhat difficult in a June that is approaching the tropic. The standard calendar seasons may apply in concept, not in their traditional onset or duration. We decided to utilize the water temperature in conjunction with judgement based on practical field experience to determine the seasons. The temperature data utilized were daily averages of hourly intake temperature provided by Florida Power Corporation. The temperature data were platted in a temperature versus date manner and visually inspected for trends and inflection points. The general trend was for a long summer, spring, and fall, and short winter. The seasons were delimited as follows:
Fall - November and December. Our data do not begin until November, although fall undoubtedly begins some time in late September or early October. Winter - January and February. Winter is a short period of time most easily distinguished by its weather variability. Spring-March, April,May(Firsttwoweeks) Summer - May (last two weeks), June, July. Sumer also extends past July but our last processed sample is in July. Size Class This investigates the effect of size class designation on the standing crop of the zooplankton community. Depth This term examines the effect on the zooplankton coninunity of whether the sample was collected at the surface or at subsurface. The following interactions were selected as important: Area x Season Area x Size Class Size Class x Season Area x Season x Size Class The biweekly model for 64u size class samples is the same as for the 202u with one exception. In the 64u sample there is only one size class. Therefore, all references to size class terms are to be omitted. IV-344 l I l
Diuinal Models . The specific models for the diurnal analyses are listed below: A) Stations E and G surface samples in the fall, winter, spring, and sumer diurnals; and Stations E, B, I, and G for the fall and winter diurnals. kimo "YII5 lii z
)(klm)o+(EEj j)41m)o+A +B; +(+$,+BE),+ABCk ),+c(klm)o k-B) Stations E and G surface and depth samples for the spring and summer diurnals:
Y kimo"Y(I5 1*i)(W)o+IElj j)(klm)o+A Z
+B_)+yggj+km+B_E),+ABCkj,+c(klmn)o k
where, Y = the dependent variable vector, p_ = the overall mean levels of the dependent vector, (Ib_,x_))= the vector of nonplant-influenced covariates, (rij j)= Z the vector of plant-influenced covariates, A = the vector of diurnal / season difference effects, k = 1,... a. B_ = the vector of station contrast effects,1 = 1,...,b. C = the vector effects related to size class differences, m=1,...,c. D_ = the vector of effects related to surface or depth differences, n=1, ....d, A_B,,AC,8C_= the vectors of first order interaction effects, ABC = the vector of effects due to the record order interaction. c = the error term, o = 1,...,s. The multivariate test statistics, rationale for such, and the covariate transformation have been discussed extensively earliiw in the report. The dependent categories for the diurnal models are the same as for the biweekly 202u model. Because of the different sampling schemes we employed for the various diurnals, three basic models for hypothesis testing are proposed: (1) Stations E and G surface samples for all four seasons, (2) Stations E, B, I, and C surface samples for fall and winter, and (3) E and G surface and depth in the spring and summer seasons. The two types of covariants used in the diurnal model are slightly different from ones used in the biweekly model. IV-345
m- , u - _ - Nonplant-Driven Variables Many of the same variables used in the biweekly model are used in the diurnal models. Salinity, temperature, dissolved oxygen, current speed, wind speed, station depth, precipitation, sunrise, moonrise, daytime and moon time are all measured in the same manner as described above for the biweekly model. The major aim of the diurnal model was to investiage the types of small scale fluctuations present at different seasons for selected stations at Crystal River. Our sampling regime (1 sample every half an hour) allows us to examine fluctuations with a periodicity range of one to twelve hours. Variables that change only on a seasonal or monthly basis will be confounded with the diurnal and are not considered. This type of variable includes the moon phases and most nutrients levels. Other variables that experience both seasonal and diel fluctuations, which were entered in an average or integrated form in the biweekly, are entered into the diurnal model on an hourly basis. These variables include: tide direction, tide height, and pyroheliometer readings. Plant-Driven Variables These are entered as hourly readings. On relatively small number of observations, the data were missing. In those cases we entered either our best estimate of what the actual value was or the near value of the variable. This technique was followed because the number of times it was necessary was small and we decided this course of action was better than deleting the entire observation. The variables included in this grouping are: delta T, discharge temperature, gross megawatt level, and flow rate. The analysis of variance for diurnal variables included a depth factor, station factor, diurnal factor, size class, and the interactions: diurnal by station, size class by station, diurnal by size class, and diurnal by size class by station. RESULTS Biweekly 202u Analysis Program The model was run on each dependent variable as a univariate analysis to obtain some estimate of how well the model fit the observed data. This estimate was obtained by examining the multiple correlation coefficient, R2 statistic (see Table 25), which expresses the percent of the variation in the dependent variable that is explained by the statistical model. A 2 number of the R 's are over 70% (elg. Acartia tonsa R2=.923),theaverage is close to 50%, R2 = 47%, and all are significant. The best R2 variables are for the most comon categories, such as Acartia tonsa and Paracalanus crassirostris. (hese are the categories that one would expect to be least influenced by errors introduced via patchiness and overall rarity of the organisms. In a qualitative way, these results suggest that the proposed model supplies an adequate fit for the important components-of the.202p to 3000u zooplankton community. IV-346
h Ta'ble125. Multipie Correlation Coefftcents- for the- ' ' 202u Univariate Analyses Category R2 Significance A. tonsa .923 .001 Labidocera sp. .597 .001 P. quasimodo .462 .001 P. crassirostris .885 .001 T. turbinata .576 .001 P. coronatus .622 .001 , T. setacaudatus .323 .001 C. furcatus .229 .001 M. holothusiae .220 .001 Other calanoids .101 .001 Oithona sp. .693 .001 Other cyclopoids .156 .001 E. acutifrons .723 .001 L. heloolandica .416 .001 Other harpacticoids .299 .001 Gastropod veligers .787 .001 Bivalve veligers .681 .001 Barnacle larvae .637 .001 Penaeid shrimp .146 .001 Lucifer sp. .376 .001 Other shrimp .689 .001 Crab larvae .718 .001 Other crustaceans .319 .001 Polychaete larvae .347 .001 Chaetognaths .562 .001 Tunicates .293 .001 Medusae .167 .001
!;iscellaneous .167 .001 Fish eggs .265 .001 Fish larvae __
.275 .001 R2 = .469 IV-347,
._ ~ _ . . _
. - _ , . ..~,_ - __ . .
The results of the MANCOVA are sumarized in Table 26 These results show that nearly all terms in the rrodel were significant. One of the goals of our analysis was to detemine the significance of power plant operation on the fluctuation of zooplankton in the light of natural fluctuations that occur. This was accomplished by performing a Canonical Correlation between the zooplankton variables and the variables relating to power plant activity. The zooplankton variables were adjusted so as to remove the effects of the other terms in the independent portion of the model. The results of this analysis are displayed below: Canonical Canonical Variable 2 Correlation x DF Sig 1 .36 898 120 .0001 2 .30 519 87 .0001 3 .23 254 56 .0001 4 .20 109 27 .0001 The Canonical Correlation significance levels show four orthogonal statistically significant relationships between the fluctuations of zooplankton category standing crops and the avrilable measures of power plst activity. The correlation between the original variables and their respective canonical variable provides a vehicle for applying an interpretation to the canonical correlations (Table 27). The interpretation of these results will follow the presentation of the 64u sample biweekly results. Biweekly 64u Analysis Program As in the 202p sampling program, we calculated univariate R2 on each depenuent. variooie to assess the goodness of fit of our statistical model (Table 28). The 64u sample results are decidedly different from the results obtained in the 202u program. The RZ values are generally lower, averaging cnly 23%, with the maximum observed being 55%. In fact, seven of the sixteen cateogries have no significant regression on the model proposed. These results suggest several explanantions for the lack of adequate statistical fit to the date: (1) The parameters selected, while adequately modelling the larger size classes, were not the proper ones for explaining the observed variatioa in the 64u data. (2) The data on the dependent variable set had a large amount of random variation (white noise) which observed the underlying tends sufficiently to render the regression ineffective. This noise could be the result of: increased patchiness of the 64u samples, artificiality of the category assignments, or imprecise standing crop estimates because of the low volume of water filtered by the tows which resulted from the high incidence of net clogging encountered. The results of the 64p MANC0VA are sumarized in Table 29 These results show that nearly all'of the covariate terms offer no significant explanation for the variation observed in the dependent variables. However, almost all of the ANOVA variables are significant. Thus, as a result of - the' extreme noisiness of the data, .we are only able to detect significant cffects in long tem or large scale terms, such as season or area. The IV-248
j
, . . , 4 ,.- - , ..',,.. - ! Table 26. Summary Results ef MANC0VA for 202u Biweekly Sampling Program o
i Nonplant-influenced Covariates Wilks' A Sionificance i' Daytime . .963 .0001 Moontime .931 .0001 Sunrise .988 .3281 Moonrise .958 .0001 Phase of the moon .957 .0001 Tide height .968 .0001 Tide direction .982 .0121 Pyroheliometer .950 .0001 Phosphate .962 .0001 Nitrate .983 .0275 , Ammonium .934 .0001 Silicate .983 .0237 Nitrite .953 .0001 Total organic carbon .968 .0001 Salinity .947 .0001 . Temperature .934 .0001 L Dissolved oxygen .981 .0091 Turbidity .971 .0001 Wind speed .973 .0001 Station depth .986 .1765 Sea condition .984 .0521 Cloud cover .962 .0001 Precipitation .987 .2176 Plant-ir'luenced Covt.rf ate s Delta T .9 40 .0001 Discharge temperature .910 .0001 Gross megawatt load .932 .0001 Water flow through .898 .0001 plant ANOVA Variables Depth ~ .947 .0001 Area .802 .0001 Stations within area .838 .0001 Seasons .788 .00C1 Area by season .605 .0001 Size class .012 .0001 Area by size class .443 .0001 : Season by size class .165 .0001 j Area by season by .283 .0001 1 size class IV-349 l
\
~
* *}}