Text

Renewal of the Crystal River Units 1, 2 and 3 - Industrial Wastewater Permit FL0000159, Seagrass Quantification Report for the Area Adjacent to the Crystal River Power Generation Facility, Florida
	ML100210787
Person / Time
Site:	Crystal River
Issue date:	12/05/2009
From:	Holt J; Progress Energy Florida, ReMetrix
To:	; Office of Nuclear Reactor Regulation, Progress Energy Florida
References
	3F1209-10
	Download: ML100210787 (52)
	v • d • e

Seagrass Quantification Report for the Area Adjacent to the Crystal River Power Generation Facility, Florida Data collected. Nov-Dec, 2007 Report: Apr 24, 2008 Prepared for:

Progress Energy Florida, Inc.

515 Independence Highway Inverness, FL 34453 Prepared by:

11550 N. Meridian, Suite 600 Carmel, IN 46032 317-428-4591 Setlgrtlss Quantification Report for the Area Adjacent to the Crystal River Power Gent!rQtion F tlcility,. Florida Data collected: Nov-Dec, 2007 Report: Apr 24, 2008 Prepared for:

Progress Energy Florida, Inc.

515 Independence Highway Inverness, FL 34453 Prepared by:

[IJ ReNletrix-11550 N. Meridian, Suite 600 Carmel, IN 46032 317 -428-4591

Table of Contents A. Introduction/Project Goals............................................................................................

1 B.

Study Area Description............................................................................................

1 C. W ater Quality Sampling..........................................................................................

2 D. Hydroacoustic M ethodology (Background)............................................................

3 E.

Species Sampling M ethodology.............................................................................

4 Rake Sampling M ethodology.........................................................................

4 Video Sampling M ethodology.........................................................................

6 SCUBA D iver Survey M ethodology................................................................

7 F.

M ethodology Discussion........................................................................................

8 G. Data Analysis...........................................................................................................

9 Continuous and Dot-Density Representations................................................

9 Endpoints of Noise Threshold Settings.........................................................

12 H. Accuracy Assessm ent of the M odel.....................................................................

14 I.

Vegetation Area Determ ination.............................................................................

15 J.

Comparison to Previous W ork...............................................................................

18 References Cited..........................................................................................................

22 Appendix.....................................................

23 Table of Contents A. IntroductionlProject Goals............................................................................................ 1 B.

Study Area Description................................................................................................. 1 C. Water Quality Sampling............................................................................................... 2 D. Hydroacoustic Methodology (Background)................................................................. 3 E.

Species Sampling Methodology............................................................. :.....................4 Rake Sampling Methodology............................................................................... 4 Video Sampling Methodology............................................................................. 6 SCUBA Diver Survey Methodology.................................................................... 7 F.

Methodology Discussion.............................................................................................. 8 G. Data Analysis................................................................................................................ 9 Continuous and Dot-Density Representations.................................................... 9 Endpoints of Noise Threshold Settings.............................................................. 12 H. Accuracy Assessment of the Model........................................................................... 14 I.

Vegetation Area Determination.................................................................................. 15 J.

Comparison to Previous Work................................................................................... 18 References Cited............................................................................................................... 22 Appendix............................................................................................................................ 23

A. Introduction/Project Goals Progress Energy is a power generating facility that discharges coolant water into a marine costal area containing submerged aquatic vegetation (SAV). The purpose of this study was to estimate the area covered by various species of seagrass, various species of macro algae, and areas with no plant cover, and to compare these results, if possible, to the conclusions of previous studies done in the same area from previous years.

To address these goals, ReMetrix employed several methods of data collection including hydroacoustic transect sampling, point-intercept rake sampling, SCUBA diver random point surveys, and several underwater video random samples. Each method had unique advantages and limitations, but each contributed to an accurate overall estimation of SAV.

B. Study Area Description The study area encompassed 3,522 acres although 688 acres were inaccessible due to oyster beds, shoals, or very shallow water. A total of 2,842 acres was analyzed for SAV cover. The area had many challenging navigational obstacles such as, sensitive vegetation and corals, shoals, oyster beds, shallow water areas, and manatee. Other challenges of this study area included tide fluctuations greater than three feet, areas with high winds, and water with low visibility.

During data collection, there were several manatee, dolphin and stingray sightings. The majority of these sightings occurred in the area labeled on the map.

1 Inaccessible Areas Analysis Area Figure 1. The area surrounded by the teal line represents the study area for this project.

I A. Introduction/Project Goals Progress Energy is a power generating facility that discharges coolant water into a marine costal area containing submerged aquatic vegetation (SA V). The purpose of this study was to estimate the area covered by various species of seagrass, various species of macro algae, and areas with no plant cover, and to compare these results, if possible, to the conclusions of previous studies done in the same area from previous years.

To address these goals, ReMetrix employed several methods of data collection including hydroacoustic transect sampling, point-intercept rake sampling, SCUBA diver random point surveys, and several underwater video random samples. Each method had unique advantages and limitations, but each contributed to an accurate overall estimation of SA V.

B. Study Area Description The study area encompassed 3,522 acres although 688 acres were inaccessible due to oyster beds, shoals, or very shallow water. A total of 2,842 acres was analyzed for SA V cover. The area had many challenging navigational obstacles such as, sensitive vegetation and corals, shoals, oyster beds, shallow water areas, and manatee. Other challenges of this study area included tide fluctuations greater than three feet, areas with high winds, and water with low visibility.

During data collection, there were several manatee, dolphin and stingray sightings. The majority of these sightings occurred in the area labeled on the map.

Figure I. The area surrounded by the teal line represents the study area for this project.

C. Water Quality Sampling Water quality information was collected at five of the ten diver sites at the same time the diver was in the water. Two sites representative of the average depths found throughout the study area were monitored every other day for the remainder of the study period. Five parameters were collected : water temperature, salinity, turbidity, light transmittance, and water depth.

Water temperature and salinity were measured using a YSI 556 multi-probe system (www.ysilifesciences.com, Figure 2a), turbidity was measured using a LaMotte 2020e portable turbidity meter (www.lamotte.com, Figure 2b); all three measurements were taken 1 foot below the water surface. Light transmittance was measured using a Secchi disk (Figure 2c) and water depth was measured by using a graduated lead line (Figure 2d). Table 1 below shows the breakout of water quality monitoring sites by depth. The full dataset of water quality information can be found in the Appendix.

Table 1. Water Quality Monitorin Sites 0.5-1.5 1

1*

1.5-2 1*

2-3 1*

3-4 1*

4-5 1*

Total 5*

Sites were sampled every other day throughout the data collection period.

Figure 2a. YSI 556 multi-probe system.

Figure 2b. LaMotte 2020c turbidity meter.

Figure 2d. Graduated lead line Figure 2c. Secchi disk 2

C. Water Quality Sampling Water quality information was collected at five of the ten diver sites at the same time the diver was in the water. Two sites representative of the average depths found throughout the study area were monitored every other day for the remainder of the study period. Five parameters were collected: water temperature, salinity, turbidity, light transmittance, and water depth.

Water temperature and salinity were measured using a YSI 556 multi-probe system (www.ysilifesciences.com. Figure 2a), turbidity was measured using a LaMotte 2020e portable turbidity meter (www.lamotte.com. Figure 2b); all three measurements were taken 1 foot below the water surface. Light transmittance was measured using a Secchi disk (Figure 2c) and water depth was measured by using a graduated lead line (Figure 2d). Table 1 below shows the breakout of water quality monitoring sites by depth. The full dataset of water quality information can be found in the Appendix.

Sites were sampled every other day throughout the data collection period.

Figure 2a. YSI 556 multi-probe system.

Figure 2b. LaMotte 2020c turbidity meter.

Figure 2c. Secchi disk Figure 2d. Graduated lead line 2

D. Hydroacoustic Methodology (Background)

Hydroacoustic data is collected using a digital 420kH BioSonics (www.biosonicsinc.com) transducer mounted on a boat actively linked to DGPS. Transects are driven across the study area while the transducer pings the water column approximately five-to-ten times per second.

The data from each ping are linked to a geographic coordinate via the DGPS beacon. Figure 3a depicts this process.

Figure 3 a.

rowl Figure 3b.

Figure 3c Figures 3a-c. General depiction of the hydroacoustic mapping process. See text for explanations.

The data from each ping contains submerged plant cover and height information as well as the depth to the sediment layer. BioSonics Inc, testing indicates that the hydroacoustic system returns digital samples with greater than 0.013% accuracy every 1.8 centimeters. Figure 3b (above) shows an example of raw acoustic data collected along a sample transect.

Raw acoustic data are processed to filter out noise and calculate statistics, and then exported for viewing in a geographic information system (GIS). Data from all transects is combined in GIS and modeled using a geostatistical GIS extension to produce a vegetative cover estimate, (biocover) maps for the entire study area. Biocover is an estimate of the percentage of the bottom covered with plants. Figure 3c above shows a whole-site biocover model.

ReMetrix collected data from crossing transects oriented WSW to ENE spaced 400-meters apart and SSE to NNW spaced 60-meters apart. This totaled approximately 140 miles of transects collected over the 2,842-acre site. Figure 4 represents the proposed crossing transects used for hydroacoustic sampling of this site.

3 D. Hydroacoustic Methodology (Background)

Hyclroacoustic data is collected using a digital 420kH BioSonics (www.biosonicsinc.com) transducer mounted on a boat actively linked to DGPS. Transects are driven across the study area while the transducer pings the water column approximately five-to-ten times per second.

The data from each ping are linked to a geographic coordinate via the DGPS beacon. Figure 3a depicts this process.

1.... r~

1 1 1

Figure 3b.

Figure 3c.

Figures 3a-c. General depiction of the hydroacoustic mapping process. See text for explanations.

The data from each ping contains submerged plant cover and height information as well as the depth to the sediment layer. BioSonics Inc, testing indicates that the hydroacoustic system returns digital samples with greater than 0.013% accuracy every 1.8 centimeters. Figure 3b (above) shows an example of raw acoustic data collected along a sample transect.

Raw acoustic data are processed to filter out noise and calculate statistics, and then exported for viewing in a geographic information system (GIS). Data from all transects is combined in GIS and modeled using a geostatistical GIS extension to produce a vegetative cover estimate, (biocover) maps for the entire study area. Biocover is an estimate of the percentage of the bottom covered with plants. Figure 3c above shows a whole-site biocover model.

ReMetrix collected data from crossing transects oriented WSW to ENE spaced 400-meters apart and SSE to NNW spaced 60-meters apart. This totaled approximately 140 miles of transects collected over the 2,842-acre site. Figure 4 represents the proposed crossing transects used for hydroacoustic sampling of this site.

3

Figure 4. Crossing transects planned for hydroacoustic data collection totaled approximately 140-miles within the 2,842-acre study area. Closely spaced transects (oriented roughly north-south) were 60-meters apart, and widely spaced transects (oriented roughly east-west) were 400-meters apart.

E. Species Sampling Methodology Hydroacoustic vegetation sampling alone cannot currently explicitly determine species by their acoustic signatures. For this reason, supplemental physical sampling must be used in order to determine species. ReMetrix used three methods for collecting physical samples: rake samples, underwater video and SCUBA diver surveys.

Rake Sampling Methodology In areas deeper than three feet, a physical plant sample was collected by throwing a double-sided thatch rake toward the shoreline at each sampling site. A rake tethered to a 25-foot rope was tossed into the water and allowed to sink until it made contact with the bottom. The rake was then slowly dragged along the bottom back toward the boat, (Figure 5a).

In areas shallower than three feet, a rake with a handle was dipped into the water until it made contact with the bottom. Steady pressure was put on the rake handle as it was scraped along the bottom (Figure 5b,c).

Figure 5a.

Figure 5b.

Figure 5c.

Figures 5a-c. A double-sided thatch rake was used to sample submerged vegetation at 109 sample points.

4 Figure 4. Crossing transects planned for hydroacoustic data collection totaled approximately 140-miles within the 2,842-acre study area. Closely spaced transects (oriented roughly north-south) were 60-meters apart, and widely spaced transects (oriented roughly east-west) were 400-meters apart.

E. Species Sampling Methodology Hydroacoustic vegetation sampling alone cannot currently explicitly detennine species by their acoustic signatures. For this reason, supplemental physical sampling must be used in order to detennine species. ReMetrix used three methods for collecting physical samples: rake samples, underwater video and SCUBA diver surveys.

Rake Sampling Methodology In areas deeper than three feet, a physical plant sample was collected by throwing a double-sided thatch rake toward the shoreline at each sampling site. A rake tethered to a 25-foot rope was tossed into the water and allowed to sink until it made contact with the bottom. The rake was then slowly dragged along the bottom back toward the boat, (Figure Sa).

In areas shallower than three feet, a rake with a handle was dipped into the water until it made contact with the bottom. Steady pressure was put on the rake handle as it was scraped along the bottom (Figure 5b,c).

Figure Sa.

Figure Sb.

Figure Sc.

Figures Sa-c. A double-sided thatch rake was used to sample submerged vegetation at 109 sample points.

4

At least two rake samples were taken at each of 109 sample points (Figure 6). Ninety-one point-intercept sites were located at hydroacoustic transect crossings and 18 off-transect sites were selected randomly to facilitate biocover model accuracy assessment. The data recorded about each sample included species name, relative abundance, density, and latitude and longitude (Table 2). If no plant was found, then "no plant" was recorded as the species name. Photos were taken at most sampling sites where vegetation was found.

Figure 6. Rake samples were taken at 109 locations (blue points); 91 points were collected at hydroacoustic transect crossings and 18 points were collected off-transects. Point numbers can be found on the Monitoring Sites map in the Appendix.

Relative abundance Relative abundance is a visual estimation of the proportion of the two rake samples combined for a site that each species represents. For example, if two species were found during a rake sample, one may have represented 75% of the sample and the other may have only represented 25% of the sample. In order to make this estimation quickly in the field, each species' relative abundance was assigned a score placing them in one of five easily discernable ranges. The ranges used in this study are listed in Table 2.

Table 2. Relative abundance scores from two rake samples at each of 109 sample sites were paeint ve visually discernaDle rangies or cover.

I I AAV I/3I0IO 4

1 IVUYO resent as -

o samp e 2

75%

Present as -75% of samplet 3

50%

Present as -50% of samplet 4

25%

Present as -25% of samplet 5

5%

Present as -5% of samplet or less sample in this context refers to an aggregate of both samples per physical sample site 5

At least two rake samples were taken at each of 109 sample points (Figure 6). Ninety-one point-intercept sites were located at hydroacoustic transect crossings and 18 off-transect sites were selected randomly to facilitate biocover model accuracy assessment. The data recorded about each sample included species name, relative abundance, density, and latitude and longitude (Table 2). If no plant was found, then "no plant" was recorded as the species name. Photos were taken at most sampling sites where vegetation was found.

Figure 6. Rake samples were taken at 109 locations (blue points); 91 points were collected at hydroacoustic transect crossings and 18 points were collected off-transects. Point numbers can be found on the Monitoring Sites map in the Appendix.

Relative abundance Relative abundance is a visual estimation of the proportion of the two rake samples combined for a site that each species represents. For example, if two species were found during a rake sample, one may have represented 75% of the sample and the other may have only represented 25% of the sample. In order to make this estimation quickly in the field, each species' relative abundance was assigned a score placing them in one of five easily discernable ranges. The ranges used in this study are listed in Table 2.

sample in this context refers to an aggregate of both samples per physical sample site 5

Density Density is the percent of the immediate sample area represented by each species. For example, if only a few stems of a plant were pulled up by the rake, the density would be considered sparse.

This estimation was made by gently compressing the combined vegetation sample and placing each species onto a one sided garden rake with graduated tines (Figure 7). The relative density of each species was estimated using four categories representative of the percent of the tines each species covered. Table 3 lists the categories and scale used for this estimation.

Figure 7. Species density was estimated by gently compressing the sample onto a one-sided garden rake with graduated tines.

The white stripes on the tines mark 20% and 60% of the total tine length.

Table 3. Density scale for species found during rake sampling at each of the 109 sample sites estimated from the percent of the rake tines each species covered.

D Dense

>60% of rake tines C

Moderate 20%-60% of rake tines B

Minor Up to 20% of rake tines A

Sparse 1-5 stems Video Sampling Methodology A video camera specifically designed for underwater use was affixed to a 12-foot long pole and carefully lowered into the water until it was just above the sediment layer. It was then panned around to find vegetation. When vegetation was observed, the camera was maneuvered to a range where the plants could be identified and held stationary for several seconds (Figure 8a).

Thirty-one videos where taken at seventeen different random sampling locations (Figure 8b).

ReMetrix encountered adverse environmental conditions that yielded mixed results when attempting to use video sampling as a reliable physical sampling method at some sample site locations.

6 Density Density is the percent of the immediate sample area represented by each species. For example, if only a few stems of a plant were pulled up by the rake, the density would be considered sparse.

This estimation was made by gently compressing the combined vegetation sample and placing each species onto a one sided garden rake with graduated tines (Figure 7). The relative density of each species was estimated using four categories representative of the percent of the tines each species covered. Table 3 lists the categories and scale used for this estimation.

Figure 7. Species density was estimated by gently compressing the sample onto a one-sided garden rake with graduated tines.

The white stripes on the tines mark 20% and 60% of the total tine length.

Table 3. Density scale for species found during rake sampling at each of the 109 sample sites estimated from the of the rake tines each covered.

Video Sampling Methodology A video camera specifically designed for underwater use was affixed to a 12-foot long pole and carefully lowered into the water until it was just above the sediment layer. It was then panned around to find vegetation. When vegetation was observed, the camera was maneuvered to a range where the plants could be identified and held stationary for several seconds (Figure 8a).

Thirty-one videos where taken at seventeen different random sampling locations (Figure 8b).

ReMetrix encountered adverse environmental conditions that yielded mixed results when attempting to use video sampling as a reliable physical sampling method at some sample site locations.

6

tVigure za. When vegetation was touncl, the video rigure so.

nirty-one vioeo ctips were maae from camera was maneuvered to a range where plant seventeen random sampling locations (black identification was possible.

videocamera symbols), all located north of the discharge canal. Site numbers can be found on the Monitoring Sites map in the Appendix.

SCUBA Diver Survey Methodology To verify the plant type and growing conditions, a SCUBA diver survey was used. Prior to the diver entering the water, a hydroacoustic pass was made over the site, a DGPS point was taken over the specific diver entry site and a water quality sample was taken. Divers then entered the water to locate submerged plant beds, identify vegetative species present, measure plant heights, estimate percent bottom cover, and characterize overall bed density. Ten diver sites were surveyed (Figure 9).

AP Figure 9. Ten randomly selected SCUBA diver survey points (blue symbols) were sampled between 11/15/2007 and 11/16/2007. Site numbers can be found on the Monitoring Sites map in the Appendix.

7 Figure 8a. When vegetation was found, the video camera was maneuvered to a range where plant identification was possible.

SCUBA Diver Survey Methodology Figure 8b. Thirty-one video clips were made from seventeen random sampling locations (black videocamera symbols), all located north of the discharge canal. Site numbers can be found on the Monitoring Sites map in the Appendix.

To verify the plant type and growing conditions, a SCUBA diver survey was used. Prior to the diver entering the water, a hydroacoustic pass was made over the site, a DGPS point was taken over the specific diver entry site and a water quality sample was taken. Divers then entered the water to locate submerged plant beds, identify vegetative species present, measure plant heights, estimate percent bottom cover, and characterize overall bed density. Ten diver sites were surveyed (Figure 9).

Figure 9. Ten randomly selected SCUBA diver survey points (blue symbols) were sampled between 11/1 5/2007 and 1111612007. Site numbers can be found on the Monitoring Sites map in the Appendix.

7

Density Bed density was visually estimated as sparse, low, medium, or high density.

Cover Percent bottom cover and species composition was measured using the quadrat-cell methodology described by Estevez and Marshal (1995). Once a plant bed was found, a 1-m2 quadrat subdivided into one hundred 100-cm 2 cells was positioned two to three meters inside the bed's edge (Figure 10). Species name and number of 100 cm 2 cells each species occupied was recorded. A cell was considered populated by a species if at least one rooted stem was found within a cell. The number of populated cells out of 100 is the percent bottom cover for the species. An example of a diver site cover table can be found in Table 4.

Table 4. Genus and number of populated 100 cm 2 cells data from a sample diver site.

I I Halodule Thalassia Caulerpa spp.

total seagrass total rooted SAV ITotal count 1 30 42 27 51 72 rigure 1U. A suo-uivaueU i-in quaarat assisted divers in estimating species cover.

F. Methodology Discussion The goal for each of these methods was to help determine species type and cover. Although each successfully accomplished the goal of determining species presence/absence, they each had unique strengths and challenges.

The most time effective method to determine vegetation presence/absence was hydroacoustics.

The challenge to using hydroacoustics is that it does not provide species information.

Diver sites were an excellent way to obtain accurate cover and species type without disturbing the vegetation. The drawback to diver sites was time. Diver surveys were too time consuming to sample the entire study area.

Video sample methods were an excellent way to determine if vegetation was growing on the bottom. It had the advantage of providing species identification and the exact latitude and longitude on screen. It was not as time consuming as a diver site, yet seagrass presence/absence could still be confirmed. The primary challenge with this method was determining the exact species due to cloudy or obscured water conditions. Furthermore, since the area the camera could view was small, there were times when the bottom was scanned for several minutes before any plants were detected.

8 Density Bed density was visually estimated as sparse, low, medium, or high density.

Cover Percent bottom cover and species composition was measured using the quadrat-cell methodology described by Estevez and Marshal (1995). Once a plant bed was found, a l_m2 quadrat subdivided into one hundred 100-cm2 cells was positioned two to three meters inside the bed's edge (Figure 10). Species name and number of 100 cm2 cells each species occupied was recorded. A cell was considered populated by a species if at least one rooted stem was found within a cell. The num ber of populated cells out of 100 is the percent bottom cover for the species. An example of a diver site cover table can be found in Table 4.

Total count Figure 10. A sub-divided 1 quadrat assisted divers in estimating species cover.

F. Methodology Discussion The goal for each of these methods was to help determine species type and cover. Although each successfully accomplished the goal of determining species presence/absence, they each had unique strengths and challenges.

The most time effective method to determine vegetation presence/absence was hydroacoustics.

The challenge to using hydroacoustics is that it does not provide species information.

Diver sites were an excellent way to obtain accurate cover and species type without disturbing the vegetation. The drawback to diver sites was time. Diver surveys were too time consuming to sample the entire study area.

Video sample methods were an excellent way to determine if vegetation was growing on the bottom. It had the advantage of providing species identification and the exact latitude and longitude on screen. It was not as time consuming as a diver site, yet seagrass presence/absence could still be confirmed. The primary challenge with this method was determining the exact species due to cloudy or obscured water conditions. Furthermore, since the area the camera could view was small, there were times when the bottom was scanned for several minutes before any plants were detected.

8

The rake sample method could successfully capture the species type, relative density, and estimate relative abundance. Additionally, this method could be employed while collecting the hydroacoustics making this the least time consuming of all the methods. Another advantage was photos could be taken to document the species and abundance, which could be linked back to a precise spatial location. The primary challenge involved while sampling with the rake method was retrieving a plant sample from the sediment. The only way to verify if the rake sample was missing vegetation was to check the hydroacoustics. If the hydroacoustics indicated plant while rake samples showed no plant, additional rake samples were attempted. Certain seagrass species were missed by rake sampling simply due to plant physiology. Long narrow leaf blades, dense root mats and un-branched structure allowed the rake to "comb" through sparsely populated seagrass stands rather than hooking or snagging the vegetation. For sites where this was true, vegetation was typically pulled up by the anchor, which dug into the soil like a shovel (Figure 11). Anchor samples were recorded as rake samples when these situations arose.

Figure 11. The anchor would occasionally capture vegetation samples in seagrass beds when rake sampling did not.

G. Data Analysis In order to calculate the area of the project and define an extent for all the data, a study area polygon was created by tracing the water-land interface. This interface was based on digital ortho-rectified quarter-quadrangle (DOQQ) imagery dated 2004 and obtained from the USGS seamless data website (http://seamless.usgs.gov). Islands and obstructions were also isolated from the analysis area in a similar manor. The hydroacoustic data were processed though software that analyzes the return signature to determine the percent biocover.

Continuous and Dot-Density Representations After processing the hydroacoustic data, spatial data models were made to estimate biocover by interpolating between measured hydroacoustic samples and unsampled areas (Figures 12a and 12b). Both figures communicate slightly different informational contexts about estimated biocover, so both figures are included for discussion. Figure 12a shows the biocover model as a continuous surface, with color gradations indicating the percent biocover at each given location.

A continuous biocover surface is the typical map output because the model estimates biocover 9

The rake sample method could successfully capture the species type, relative density, and estimate relative abundance. Additionally, this method could be employed while collecting the hydroacoustics making this the least time consuming of all the methods. Another advantage was photos could be taken to document the species and abundance, which could be linked back to a precise spatial location. The primary challenge involved while sampling with the rake method was retrieving a plant sample from the sediment. The only way to verifY if the rake sample was missing vegetation was to check the hydroacoustics. If the hydroacoustics indicated plant while rake samples showed no plant, additional rake samples were attempted. Certain seagrass species were missed by rake sampling simply due to plant physiology. Long narrow leaf blades, dense root mats and un-branched structure allowed the rake to "comb" through sparsely populated seagrass stands rather than hooking or snagging the vegetation. For sites where this was true, vegetation was typically pulled up by the anchor, which dug into the soil like a shovel (Figure 11). Anchor samples were recorded as rake samples when these situations arose.

G. Data Analysis Figure II. The anchor would occasionally capture vegetation samples in seagrass beds when rake sampling did not.

In order to calculate the area of the project and defme an extent for all the data, a study area polygon was created by tracing the water-land interface. This interface was based on digital ortho-rectified quarter-quadrangle (DOQQ) imagery dated 2004 and obtained from the USGS seamless data website (http://seamless.usgs.gov). Islands and obstructions were also isolated from the analysis area in a similar manor. The hydroacoustic data were processed though software that analyzes the return signature to determine the percent biocover.

Continuous and Dot-Density Representations After processing the hydroacoustic data, spatial data models were made to estimate biocover by interpolating between measured hydroacoustic samples and unsampled areas (Figures 12a and 12b). Both figures communicate slightly different informational contexts about estimated biocover, so both figures are included for discussion. Figure 12a shows the biocover model as a continuous surface, with color gradations indicating the percent biocover at each given location.

A continuous biocover surface is the typical map output because the model estimates biocover 9

values for all geographic space between data transects. However, the seagrass and macroalgae beds within this study area typically occur as patchy cover, not large contiguous beds. For that reason, Figure 12b was created to more intuitively communicate the patchy nature of the beds.

Figure 12b shows the exact same biocover model as seen in Figure 12a, but shows it as a gradational dot-density surface instead. Areas of high percentage biocover (reds and oranges on the map) have dots (a.k.a., "beds") spaced very closely together, as one might expect to naturally observe in a high biocover area. Areas of lower percentage biocover (yellows and greens) have dots (beds) spaced further apart, as one might expect to naturally observe in a low biocover area.

It is important to note that the coverage statistics for both types of maps are the same; only the display techniques are different. Other figures using the dot-density technique are included in the Appendix.

After the model was completed, assessments for model accuracy were conducted by checking the model against rake samples, diver surveys, and video samples to calculate errors of omission and commission (see Section H).

10 values for all geographic space between data transects. However, the seagrass and macroalgae beds within this study area typically occur as patchy cover, not large contiguous beds. For that reason, Figure 12b was created to more intuitively communicate the patchy nature of the beds.

Figure 12b shows the exact same biocover model as seen in Figure 12a, but shows it as a gradational dot-density surface instead. Areas of high percentage biocover (reds and oranges on the map) have dots (a.k.a., "beds") spaced very closely together, as one might expectto naturally observe in a high biocover area. Areas oflower percentage biocover (yellows and greens) have dots (beds) spaced further apart, as one might expect to naturally observe in a low biocover area.

It is important to note that the coverage statistics for both types of maps are the same; only the display techniques are different. Other figures using the dot-density technique are included in the Appendix.

After the model was completed, assessments for model accuracy were conducted by checking the model against rake samples, diver surveys, and video samples to calculate errors of omission and commission (see Section H).

10

Figure 12a. BioCover model derived from hydroacoustic measures of vegetative cover, displayed as a gradational continuous surface (the legend beside the figure indicates percent biocover at a given location).

00-5 M 5.01 - 10 010.01 -20 020.01 -30 030.01 -40 040.01-50 050.01 -60

60.01 - 70 70.01 -80 0 80.01 -90 U 90.01 - 100 Figure 12b. BioCover model derived from hydroacoustic measures of vegetative cover, displayed as a gradational dot-density surface (the legend beside the figure indicates percent biocover at a given location).

00-5

5.01 - 10 0 10.01 -20 020.01 -30 030.01 -40 0r40.01 -so 0 50.01 - 60 M 60.01 - 70 M 70.01 - 80 M 80.01 - 90 90.01 - 100 II Figure 12a. BioCover model derived from hydroacoustic measures of vegetative cover, displayed as a gradational continuous surface (the legend beside the figure indicates percent biocover at a given location).

Figure l2b. BioCover model derived from hydroacoustic measures of vegetative cover, displayed as a gradational dot-density surface (the legend beside the figure indicates percent biocover at a given location).

11

Endpoints of Noise Threshold Settings A patented software algorithm is used to interpret the amount of submerged vegetation along each hydroacoustic transect. Examples of this process can be seen in the figures labeled "Transect Line 2007x" found in Appendix (these show the raw transect data with corresponding interpretations). Noise threshold settings influence how conservatively the algorithm filters noise within the hydroacoustic signal responses. The noise threshold settings are based on established ranges and can be adjusted by the data analyst during data processing. As processing proceeds, the data analyst compares the amount of submerged vegetation interpreted by the algorithm with visual inspection of raw transect data and other field data types. Noise threshold settings are considered acceptable when the data types are in agreement.

For any project, noise threshold settings can fall within an acceptable range based on a variety of environmental and physical factors related to the data collection (e.g., surface noise during data collection, water depth, physical structure and density of the target vegetation, etc.). The acceptable noise threshold settings in this project fell within a small range primarily due to the short, spindly nature of the seagrass blades. The endpoints of the acceptable range are termed

'conservative' settings and 'less conservative' settings. The data models obtained using results within the acceptable range are considered by ReMetrix to be realistic models of the actual submerged vegetation cover in the project area. For that reason, cover models produced from each endpoint of the acceptable range are provided for comparison in Figures 13a ('conservative' thresholds) and 13b ('less conservative' thresholds).

The total biocover for the conservative noise threshold settings is 7.6%. The total biocover for the less conservative noise threshold settings is 10.4%. Table 7 in Section I provides greater detail of specific biocover types for the threshold endpoints.

The total biocover results obtained by the conservative noise threshold settings are used in the statistical calculations discussed in Section H and elsewhere in this report, unless noted otherwise.

12 Endpoints o/Noise Threshold Settings A patented software algorithm is used to interpret the amount of submerged vegetation along each hydroacoustic transect. Examples of this process can be seen in the figures labeled "Transect Line 2007x" found in Appendix (these show the raw transect data with corresponding interpretations). Noise threshold settings influence how conservatively the algorithm filters noise within the hydroacoustic signal responses. The noise threshold settings are based on established ranges and can be adjusted by the data analyst during data processing. As processing proceeds, the data analyst compares the amount of submerged vegetation interpreted by the algorithm with visual inspection of raw transect data and other field data types. Noise threshold settings are considered acceptable when the data types are in agreement.

For any project, noise threshold settings can fall within an acceptable range based on a variety of environmental and physical factors related to the data collection (e.g., surface noise during data collection, water depth, physical structure and density of the target vegetation, etc.). The acceptable noise threshold settings in this project fell within a small range primarily due to the short, spindly nature of the seagrass blades. The endpoints of the acceptable range are termed

'conservative' settings and 'less conservative' settings. The data models obtained using results within the acceptable range are considered by ReMetrix to be realistic models of the actual submerged vegetation cover in the project area. For that reason, cover models produced from each endpoint of the acceptable range are provided for comparison in Figures 13a ('conservative' thresholds) and 13b ('less conservative' thresholds).

The total biocover for the conservative noise threshold settings is 7.6%. The total biocover for the less conservative noise threshold settings is 10.4%. Table 7 in Section I provides greater detail of specific biocover types for the threshold endpoints.

The total biocover results obtained by the conservative noise threshold settings are used in the statistical calculations discussed in Section H and elsewhere in this report, unless noted otherwise.

12

Figure 13a. Map showing the 'conservative' interpretation of total biocover (7.6%) within the project area. (See above section for explanation.)

Figure 13b. Map showing the 'less conservative' interpretation of total biocover (10.4%) within the project area. (See above section for explanation.)

Figure 13a. Map showing the 'conservative' interpretation of total biocover (7.6%) within the project area. (See above section for explanation. )

Figure 13b. Map showing the 'less conservative' interpretation of total biocover (10.4%) within the project area. (See above section for explanation.)

H. Accuracy Assessment of the Model Typical measures for error in models are omission and commission error. These measures estimate how well a model correlates with actual sample data at the same location. For this analysis, ReMetrix compared all three types of physical sampling results (both as a whole and individually) to the biocover model derived from hydroacoustic transect data as a means for determining model correlation.

We used two 'classes' to develop the error estimate: 'plant', for where a rake sample or biocover model indicated plant was present, or 'no plant', where a rake sample or biocover model indicated no plants were present. As a means for explaining a particularly difficult concept we will follow just one comparison through the description, however error was calculated for both

'classes' and both types of error. In the following example, we will use 'plant' rake samples and

'no plant' areas in the model.

Calculating omission error: Of all the physical sampling points indicating plant was found, what proportion of these points lie within a 'no plant' area in the model? In this scenario, a high omission error suggests that the model could be underestimating the amount of plant that is truly present at that location.

Calculating commission error: Of all physical sampling points ('plant' or 'no plant') that lie within a 'no plant' area in the model, what proportion are 'plant' physical sample points? In this scenario, a high commission error suggests that the model could be overestimating the amount of

'no plant' that is truly present at that location.

Table 5 shows omission and commission errors of the model compared to all physical sampling methods combined. The higher 'no plant' omission error would suggest the model may not account for all the non-plant areas that were actually present, however some factors should be taken into consideration. Rake samples were taken from the bow of the boat while the hydroacoustic equipment and GPS antenna were located near the stem of the boat (approximately 18-feet of separation). The typical rake sample was made approximately 20-feet away from the boat. Combining these two distances results in a margin of error up to 38-feet between the nearest hydroacoustic point and the site of rake collection (depending upon the orientation of the boat and the actual rake sample distance at each site). Additionally, the boat may have drifted with currents while video of the bottom was taken so the actual position of the GPS antenna may have not coincided precisely with the location of the video sample or the hydroacoustic sample. Similarly, divers did not necessarily remain directly under the boat (or GPS antenna) while counting plants and therefore diver reference points may not directly relate to hydroacoustic estimates. These positional errors can account for a majority of the error when evaluating the omission and commission statistics (Table 6).

14 H. Accuracy Assessment of the Model Typical measures for error in models are omission and commission error. These measures estimate how well a model correlates with actual sample data at the same location. For this analysis, ReMetrix compared all three types of physical sampling results (both as a whole and individually) to the biocover model derived from hydroacoustic transect data as a means for determining model correlation.

We used two 'classes' to develop the error estimate: 'plant', for where a rake sample or biocover model indicated plant was present, or 'no plant', where a rake sample or biocover model indicated no plants were present. As a means for explaining a particularly difficult concept we will follow just one comparison through the description, however error was calculated for both

'classes' and both types of error. In the following example, we will use 'plant' rake samples and

'no plant' areas in the model.

Calculating omission error: Of all the physical sampling points indicating plant was found, what proportion of these points lie within a 'no plant' area in the model? In this scenario, a high omission error suggests that the model could be underestimating the amount of plant that is truly present at that location.

Calculating commission error: Of all physical sampling points ('plant' or 'no plant') that lie within a 'no plant' area in the model, what proportion are 'plant' physical sample points? In this scenario, a high commission error suggests that the model could be overestimating the amount of

'no plant' that is truly present at that location.

Table 5 shows omission and commission errors of the model compared to all physical sampling methods combined. The higher 'no plant' omission error would suggest the model may not account for all the non-plant areas that were actually present, however some factors should be taken into consideration. Rake samples were taken from the bow of the boat while the hydroacoustic equipment and GPS antenna were located near the stem of the boat (approximately 18-feet of separation). The typical rake sample was made approximately 20-feet away from the boat. Combining these two distances results in a margin of error up to 38-feet between the nearest hydroacoustic point and the site of rake collection (depending upon the orientation of the boat and the actual rake sample distance at each site). Additionally, the boat may have drifted with currents while video of the bottom was taken so the actual position of the GPS antenna may have not coincided precisely with the location of the video sample or the hydroacoustic sample. Similarly, divers did not necessarily remain directly under the boat (or GPS antenna) while counting plants and therefore diver reference points may not directly relate to hydroacoustic estimates. These positional errors can account for a majority of the error when evaluating the omission and commission statistics (Table 6).

14

Table 5. Study area-wide BioCover model accuracy estimate without consideration of positional error (38-feet) due to GPS antenna location on the boat relative to the physical sampling location.

Raster Classification omission error

plant no plant All physical samples plant 17%

62 13 no plant 62%

36 22 commission error -

37%

Table 6. Study area-wide BioCover model accuracy estimate after consideration of positional error (38 feet) due to GPS antenna location on the boat relative to the physical sampling location.

Raster Classification omission error I plant no plant All physical samples plant 0%

75 0

no plant 62%

36 22 commission error -

32%

0%

The patchiness or randomness of aquatic vegetation beds, and the characteristics of very low-density vegetation might explain the remaining error. A majority of the areas where the model indicated there was "plant" but physical sampling indicated "no plant" occurred in areas of very low-density vegetation (69% in < 5% cover, 86% in < 10%

cover), where the probability of a physical sampling method contacting vegetation was low. No adjustments were made to the model for these areas since the number of hydroacoustic samples (1,116,900) vastly out-numbers the number of physical samples (139 total). After reviewing the hydroacoustic data for many of these areas, ReMetrix confirmed that these zones have low-density plant populations where a limited number of physical samples may have easily missed patchy or sparsely populated plant beds.

Results of additional error estimates comparing each physical sampling method individually can be found in the Appendix.

I. Vegetation Area Determination The overarching goal of this project was to determine the number of acres of seagrass.

Using the physical samples as a guide, ReMetrix separated vegetated areas in the study area into four classes: seagrass, other, mixed and no plant. Sample sites where Halodule spp., Syringodiumfiliforme, Thalassia testudinum, or Halophila engelmannii were found exclusively were placed in the 'seagrass' class. Sample sites where vegetation other than seagrass, e.g. Caulerpa or Udotea, was found exclusively were classed as 'other'. Sites where both seagrass and other species were found together were classified as 'mixed',

and sites where no plants were collected during the rake sample, diver survey, or video sample, were placed into the 'no plant' class.

The second step in this process was to divide the study area into zones which could be labeled one of the four predefined classes. Zone boundaries were made using a method called Thiessen polygons. Thiessen polygons are mathematically defined by the intersections of perpendicular bisectors of the lines between all the sampling sites (Figure 14). Each zone was assigned the class of its corresponding sample site's classification, and the area of vegetation within that zone was calculated.

15 Table 5. Study area-wide BioCover model accuracy estimate without consideration of positional error 38-feet) due to GPS antenna location on the boat relative to the physical sampling location.

Raster Classification omission error ~

plant no plant All physical samples I plant I

no plant 17%

62 13 36 22 62%

commission error->

37%

Table 6. Study area-wide BioCover model accuracy estimate after consideration of positional error 38 feet) due to GPS antenna location on the boat relative to the physical sampling location.

All physical samples I plant I no plant omission error ~

0%

62%

commission error->

Raster Classification plant no plant 75 0

36 22 32%

0%

The patchiness or randomness of aquatic vegetation beds, and the characteristics of very low-density vegetation might explain the remaining error. A majority of the areas where the model indicated there was "plant" but physical sampling indicated "no plant" occurred in areas of very low-density vegetation (69% in < 5% cover, 86% in < 10%

cover), where the probability of a physical sampling method contacting vegetation was low. No adjustments were made to the model for these areas since the number of hydroacoustic samples (1,116,900) vastly out-numbers the number of physical samples (l39 total). After reviewing the hydroacoustic data for many of these areas, ReMetrix conftrmed that these zones have low-density plant populations where a limited number of physical samples may have easily missed patchy or sparsely populated plant beds.

Results of additional error estimates comparing each physical sampling method individually can be found in the Appendix.

I. Vegetation Area Determination The overarching goal of this project was to determine the number of acres of seagrass.

Using the physical samples as a guide, ReMetrix separated vegetated areas in the study area into four classes: seagrass, other, mixed and no plant. Sample sites where Halodule spp., Syringodium filiforme, Thalassia testudinum, or Halophila engelmannii were found exclusively were placed in the 'seagrass' class. Sample sites where vegetation other than seagrass, e.g. Caulerpa or Udotea, was found exclusively were classed as 'other'. Sites where both seagrass and other species were found together were classifted as 'mixed',

and sites where no plants were collected during the rake sample, diver survey, or video sample, were placed into the 'no plant' class.

The second step in this process was to divide the study area into zones which could be labeled one of the four predefmed classes. Zone boundaries were made using a method called Thiessen polygons. Thiessen polygons are mathematically deftned by the intersections of perpendicular bisectors of the lines between all the sampling sites (Figure 14). Each zone was assigned the class of its corresponding sample site's classiftcation, and the area of vegetation within that zone was calculated.

15

Figure 14. The study area was divided into Thiessen-polygon-defined zones based upon the spatial location of the sampling sites.

7.

I..

10(0 ~

S S

The percent cover within each zone was calculated from the biocover map derived from the hydroacoustic sampling method. The product of the zone area and the mean percent cover within that zone returns the number of acres of vegetation in that zone. Figure 15 shows an example of one zone with tabulated results.

Mean % Cover Acres in Class Figure 15. Acres of vegetation in a class were calculated from the area of the zone and the mean percent biocover from the hydroacoustic model.

Acres of each vegetation class by zone were summed to determine the number of acres of seagrass, other, mixed, and no plant classes (Table 7).

16 Figure 14. The study area was divided into Thiessen-polygon-defined zones based upon the spatial location of the sampling sites.

I

~

The percent cover within each zone was calculated from the biocover map derived from the hydroacoustic sampling method. The product of the zone area and the mean percent cover within that zone returns the number of acres of vegetation in that zone. Figure 15 shows an example of one zone with tabulated results.

Acres in Zone Class Mean % Cover Acres in Class Figure 15. Acres of vegetation in a class were calculated from the area of the zone and the mean percent biocover from the hydroacoustic model.

19.77 Mixed 16.6%

3.28 Acres of each vegetation class by zone were summed to determine the number of acres of seagrass, other, mixed, and no plant classes (Table 7).

16

Table 7. Vegetation class areas were summed from the acres in class calculated in each zone and percent of the total project acreage was calculated.

Conservative Noise Threshold seagrass 16 0.56%

mixed 81 2.85%

seagrass 46 1.62%

other 35 1.23%

other 65 2.29%

unclassified 58 2.04%

No plant 2622 92.26%

Total Area 2842 Less Conservative Noise Threshold seagrass 27 0.95%

mixed 101 3.55%

seagrass 58 2.04%

other 43 1.51%

other 85 2.99%

unclassified 80 2.81%

no plant 2549 89.70%

Total Area 2842 Total Area 2842 It was possible to subdivide the 'mixed' class acres into percent 'seagrass' and 'other' since relative abundance of individual species was recorded. The product of the area of a mixed zone and the corresponding relative abundance for each species yielded the acres of each class (seagrass and other). The model indicated plants were present in a number of 'no plant' zones. Acres of vegetation found within a no plant zone were assigned to a new class named 'unclassified'. The unclassified acreage represented 29% of the total vegetated area so it is important to understand where these unclassified zones occurred.

Fifty percent of the unclassified vegetation occurred in just 10% of the no plant classified zones. This means the bulk of the unclassified data occurred in a relatively small number of zones. All six of these zones were surrounded by zones of a defined vegetation type.

Based on the classification of adjoining zones, many were likely mixed stands of seagrass (Figure 16). Most likely, the rake sampling was not representative of the whole zone.

17 Table 7. Vegetation class areas were summed from the acres in class calculated in each zone and percent of the total project acreage was calculated.

Conservative Noise Threshold Category Acres Percent Total Area seagrass 16 0.56%

mixed 81 2.85%

seagrass 46 1.62%

other 35 1.23%

other 65 2.29%

unclassified 58 2.04%

No plant 2622 92.26%

Total Area 2842 Less Conservative Noise Threshold Category Acres Percent Total Area seagrass 27 0.95%

mixed 101 3.55%

seagrass 58 2.04%

other 43 1.51%

other 85 2.99%

unclassified 80 2.81%

no plant 2549 89.70%

Total Area 2842 It was possible to subdivide the 'mixed' class acres into percent 'seagrass' and 'other' since relative abundance of individual species was recorded. The product of the area of a mixed zone and the corresponding relative abundance for each species yielded the acres of each class (seagrass and other). The model indicated plants were present ina number of 'no plant' zones. Acres of vegetation found within a no plant zone were assigned to a new class named 'unclassified'. The unclassified acreage represented 29% of the total vegetated area so it is important to understand where these unclassified zones occurred.

Fifty percent of the unclassified vegetation occurred in just 10% of the no plant classified zones. This means the bulk of the unclassified data occurred in a relatively small number of zones. All six of these zones were surrounded by zones of a defined vegetation type.

Based on the classification of adjoining zones, many were likely mixed stands of seagrass (Figure 16). Most likely, the rake sampling was not representative of the whole zone.

17

I irUlL IV.

I

-lI. OIA IHV ]IlhhlL U

wI; l

g tn i

illl Vv;LatIvI LUVviI W ICK; llluOt likely 'mixed' zones where a physical sampling method was unable to locate vegetation.

J. Comparison to Previous Work Broad comparisons were made between 2007 data and the transect data reported in Marshall (2001). The data from 2001 was loaded into a GIS and transects were drawn between the sampling points. Average biocover was calculated from the current model along the 2001 transects in an attempt to compare the same areas. Average cover was tabulated for both 2001 and 2007 (Table 8). There could be several reasons the 2007 results were lower than the 2001 results. First, 2007 data were not sampled along the exact same transects, rather they were based on a segment laid over a model of hydroacoustic data. Both transects 2a and 3w each had two data points that were more than 50 meters from any 2007 sampling locations.

Table 8. Comparisons were made for average cover between 2001 and 2007 along similar transect lines.

1N 32.09 6.01 1W 46 1.70 2a 20.25 0.15 2W 39.19 4.90 3W 34.52 4.83 4W 5.28 3.04 5W 0.25 1.66 18 Figure 16. The six 'no plant' zones showing high vegetative cover were most likely 'mixed' zones where a physical sampling method was unable to locate vegetation.

J. Comparison to Previous Work Broad comparisons were made between 2007 data and the transect data reported in Marshall (2001). The data from 2001 was loaded into a GIS and transects were drawn between the sampling points. Average biocover was calculated from the current model along the 2001 transects in an attempt to compare the same areas. Average cover was tabulated for both 2001 and 2007 (Table 8). There could be several reasons the 2007 results were lower than the 2001 results. First, 2007 data were not sampled along the exact same transects, rather they were based on a segment laid over a model of hydroacoustic data. Both transects 2a and 3w each had two data points that were more than 50 meters from any 2007 sampling locations.

Table 8. Comparisons were made for average cover between 2001 and 2007 along similar transect lines.

Name 2001 Mean 2007 Mean 1N 32.09 6.01 1W 46 1.70 2a 20.25 0.15 2W 39.19 4.90 3W 34.52 4.83 4W 5.28 3.04 5W 0.25 1.66 18

Another concern when comparing these two sample methods is simply the difference in the sampling methodology used to calculate cover. Comparing quadrats sampled along a transect to a model derived from hydroacoustic transect sampling should be done with careful consideration of how each method calculates percent cover. The 2001 quadrat method estimated plant cover as 1% per 100 cm2, even if it was very sparsely distributed and repeated every 100 meters along the transect. A transect's average biocover was then calculated by averaging over all cover estimates for that transect. Hydroacoustic sampling records 10 pings per second of plant or no plant and computes an average across 10 pings to make one sample estimate of biocover. This equals one sample per second or roughly one sample per 2.5 meters. These samples are then used to create a model, thereby interpolating a 5-meter grid between samples in all directions. As a example, we investigated video point 9992 located less than 300 ft from a 2001 reported sampling location along transect 4w (Figure 17). The 2001 sample listed Halodule at 86% cover, while the 2007 model estimated it at 11% cover.

Figure 17. Screen capture of digital underwater video sample (left) showing sparse vegetative cover, with corresponding sample location (right).

/~ ~1 ~

19 Another concern when comparing these two sample methods is simply the difference in the sampling methodology used to calculate cover. Comparing quadrats sampled along a transect to a model derived from hydroacoustic transect sampling should be done with careful consideration of how each method calculates percent cover. The 2001 quadrat method estimated plant cover as 1 % per 100 cm2, even if it was very sparsely distributed and repeated every 100 meters along the transect. A transect's average biocover was then calculated by averaging over all cover estimates for that transect. Hydroacoustic samp ling records 10 pings per second of plant or no plant and computes an average across 10 pings to make one sample estimate ofbiocover. This equals one sample per second or roughly one sample per 2.5 meters. These samples are then used to create a model, thereby interpolating a 5-meter grid between samples in all directions. As a example, we investigated video point 9992 located less than 300 ft from a 2001 reported sampling location along transect 4w (Figure 17). The 2001 sample listed Halodule at 86% cover, while the 2007 model estimated it at 11 % cover.

Figure 17. Screen capture of digital underwater video sample (left) showing sparse vegetative cover, with corresponding sample location (right).

< conrinued on the /lext page....

19

The following illustration (Figure 18) may describe why the average cover comparison from 2001 to 2007 differs so greatly. In the following diagram, a green cell represents a

'plant' cell.

I 1

A 1

A-

"7 2 0

in 1

2 3

4 5

6 7

8 9

10 Quadrat Sample:

86 of 100 cells = 86% cover.

12/

7.9;

/n 2.5 5

7.5 10 12.5 5

10 15 20 25 Hydroacoustic pings on 2.5m scale (plant versus no plant):

2 of 5 pings show plant = 40% cover Hydroacoustic Sample = 40%

Final percentage calculation is done at a 5m scale.

Average over area = 11%

Figure 18 (whole page). Comparison of scales for different data collection methods.

20 The following illustration (Figure 18) may describe why the average cover comparison from 2001 to 2007 differs so greatly. In the following diagram, a green cell represents a

'plant' cell.

1 2

3 4

5 6

7 8

9 10 5

7.5 10 25 2

3 4

5 6

7 8

9 10 o

o o

Quadrat Sample:

86 of 100 cells = 86% cover.

o Hydroacoustic pings on 2.5m scale (plant versus no plant):

2 of 5 pings show plant = 40% cover Hydroacoustic Sample = 40%

Final percentage calculation is done at a 5m scale.

Average over area = 11 %

Figure 18 (whole page). Comparison of scales for different data collection methods.

20

Furthermore, transects 1W, IN, 3W, and 4W don't appear to be sampled on 100-meter intervals. This indicates there may have been some post-directed sampling used for the 2001 data, which may have greatly influenced the average cover for the transect.

21 Furthermore, transects 1 W, IN, 3W, and 4W don't appear to be sampled on 100-meter intervals. This indicates there may have been some post-directed sampling used for the 2001 data, which may have greatly influenced the average cover for the transect.

21

REFERENCES CITED Estevez, E.D., and Marshal, M. J., 1995. 1995 Summary Report for: Crystal River 3 Year NPDES Monitoring Project, Mote Marine Laboratory, Sarasota, FL, 131 p.

Marshall, M.J., 2001. Seagrass Survey: November 2001 Resurvey at the Florida Power Crystal River Generating Facility, Coastal Seas Consortium, Inc., Bradenton, FL, 19 p.

22 REFERENCES CITED Estevez, E.D., and Marshal, M. J., 1995. 1995 Summary Report for: Crystal River 3 Year NPDES Monitoring Project, Mote Marine Laboratory, Sarasota, FL, 131 p.

Marshall, M.J., 2001. Seagrass Survey: November 2001 Resurvey at the Florida Power Crystal River Generating Facility, Coastal Seas Consortium, Inc., Bradenton, FL, 19 p.

22

Appendix Appendix

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L22b SAV Area = 218 Acres Seagrass Area = 62 Acres Non-Seagrass Area = 100 Acres Unclassified SAV = 58 Acres Projection:

Datum:

Units:

State Plane Florida West NAD 83 Feet Er Inaccessible Area werldw Hydroacoustic Transects 0% - 100% Cover w

ý BioCover Estimates 1 inch equals 0.42 miles 11

- Miles 0.84 0

0.21 0.42 Estimated BioCover=7.6%

Total Area =2,842 Acres SAV Area = 218 Acres Seagrass Area = 62 Acres Non-Seagrass Area = 100 Acres Unclassified SAV :::: 58 Acres

-- Hydroacoustic Transects 0% - 100% Cover BioCover Estimates Feet 1 inch equals 0.42 miles

_-==-.:= _____ Miles o

0.21 0.42 0.84

I 4 i

/ ; * *,i ***i

.2

~

lotal Area =,t542 Acres SAV Area = 295 Acres Seagrass Area = 85 Acres Non-Seagrass Area = 128 Acres Unclassified SAV = 80 Acres Projection:

Datum:

Units:

State Plane Florida West NAD 83 Feet irI Inaccessible Area Hydroacoustic Transects 0% - 100% Cover L

BloCover Estimates 1 inch equals 0.42 miles ii 0

MIMN Miles 0

0.21 0.42 0.84 I

I Estimated BioCover=10.4%

Total Area =2,842 Acres SAV Area = 295 Acres Seagrass Area = 85 Acres Non-Seagrass Area = 128 Acres Unclassified SAV = 80 Acres

-- Hydroacoustic Transects 0% - 100% Cover BioCover Estimates Feet 1 inch equals 0.42 miles

_-==-..:== _____ Miles o

0.21 0.42 0.84

I.

Transect 20071206115224_par LInsct2071204121833

-'.7 Transect 20071202_145701

00, 100 300 0

200 1100 1300 1500 1700 1 00 27100 2300 2500 2700 29,00 3100 3300 3500 3700 3900 4100 1000 1 200 14-00 to 130 1800 2000 2200 2400 "S00 2800 3000 3200 3400 3400 3800

,4000 400 All1 oil I

41

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Video Sites Projection:

Datum:

Units:

State Plane Florida West NAD 83 Feet Is 0.42 miles V

Diver Site Location O

Rake Toss 0% - 100% Cover w

BioCover Estimates 1 inch equa Miles 0

0.21 0A2 0.84

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NAD 83 Units:

Feet V

Diver Site location Rake Toss 1 inch equals 0.42 miles 0% - 100% Cover BioCover Estimates o

_1I::::::JI_c::=-_____ Miles 0,84 0.21 0.42

mffimb mom

-.. HW§

~

000 0o 0 0

0 0

0

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0 ogo 0

0 0

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0 00 0

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%0000 000 Water Quality Inoatn MAME 050001 A'ater Tamp (dog C) 229 s'ample Date 11115/200.7 Sample lime 12:25PM rtubidity (ntu) 5.09 Saliniy (W)25.9 Secchi Depth (It)

___5 Phyical Depth (it) 5 Tide Level L2:25PM Nator Depth (in) 1.5-2m V

Diver Site Location Rake Toss 0% - 100% Cover a

m*a

&Hvdroacousitc Points Bed Characterstics SNot defined; scattered lent MHeht 1'6 Inche

'ior Cove 23%

d D t

I Sparsel art Sample Melhod cover I

Species Present Diver Sample

-I ----

3 calrGaý fd hae Diver Sample 20 McellslCauler pe r"a Rake Toss

>OD%

of rake tIneslCaulae prlfetra Hydroacoustc Model 5%1 a

(.000..0 0

0 0

0 0

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0 0

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0

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0 0

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0 0

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0 COoo. ooo Bed Characteristics Not defined; scattered 6 inches 23%

S ars ecies hart Sam Ie Method Cover S ecies Present DiverSam Ie 3 cells Graci/aria 6kvhlae DiverSam Ie 20 cells Caule a rollfera Rake Toss

>60% of rake tines Caulerpa prollfera Hydroacoustic Model 5%

~

V Diver Site Location Rake Toss 0% - 100% Cover Hydroacousitc Points Projection: State Plane Florida West Datum:

NAD 83 Units:

Feet 1 inch equals 59 feet II

-===--== __

iIIiIIIFeet o

29.5 59 118

0 00 00 0

0 0

0 0 0 0

Ogg@ý 0

0 0

00 0

0 0

0 0 0

0 0

0 Water Quality Information NAME DSMO00 Nater Temp (deg C) 22*1 Semple Date 11/15I'O07 Sample Time 1:40PIV turbidity (ntu) 3.94 salinity (ppt) 29.1 Becchi Depth (ft) 3.A

'hysicl Depth (ft)

M___

ride Level L2:25PIV Nater Depth (m) 1-1.5r Legend V

Diver Site Location Rake Toss 0% - 100% Cover 9* Hydroacousitc Points Bed Characteristlcs size Not defined; scafteredl Neant He"ht 1Iwto oftom Covere 22%

3ed DensU SI !rse Projection:

Datum:

Units:

1 inch equa State Plane Florida West NAD 83 Feet

¶l Is 59 feet L

Sample Meod I

Cover specds Present sIver Samp Is 22 ceowcoautes erteulsres 5e Tos O%[No Plant KqM "a-Feet 0

29.5 59 118

-lydroawoustle Model I 0%1 I

I o

o 0

0

~..

o o

0 0

o o

o 0

0 0

0 0

0 0000 o

0 0

o 0

0 0

0 o

o o

0 0

o o

00 o

o 00 o

o o 0 o 0 22.9 11/15/2007 1:40PM 3.94 29.1 3.6 3.9 L2:25PM 1-1.5m o

o Bed Characteristics Size Not defined; scattered Plant Height 1 foo Bottom Covera e 22%

Bed Densi S ars S ecles Chart o 0 o

Sample Method Cover Species Present OiverSam Ie 22 cells Caule sertu/aroides Rake Toss 0% NoP/ant Hydroacoustic Model 0%

Legend V Diver Site Location Rake Toss 0% - 100% Cover Hydroacousitc Points Projection: State Plane Florida West Datum:

NAD83 Units:

Feet -tl 1 inch equals 59 feet Feet 0

29.5 59 118

4b

e 0o 0

0 0

00 0

0 0

0 e

0 0

0 0, "

I 0

00 0

0 I

f Coverage 100%

D*esI H

Legend V

Diver Site Location 0

Rake Toss 0% - 100% Cover Is x e

.a*

Hydroacousitc Points ap~e hart Sample Method C

har species Present Sape 68 clls Sngodlum rfr llbme Diver Sape 51 ceLls Grachda fi*kahlao Toss

>60% of rake fines Grac#iada tfikvhle Rake Toss

>60% of rake Ines Caulerpa serfularoides Toss

>60% of rake tlines Udotea c

,/ulaina Rake Toss 20%-60% of rake tinesl Sy filormn ke Toss

>60% of k tnes Cn ssdularold rToss

>60% of rake ties S*essum natans Hydroacousuic Model 37%

Projection:

Datum:

Units:

State Plane Florida West NAD 83 Feet 11 on M

0 19 us 1 inch equals 59 feet i-eet 0

29.5 59 118

.1 0

~

0 0

0 0 * **

0

* * *

0

0 0

0 Hydroacoustic Model 0

0

0 0

0

o 0 0 0 *

0 0

Lar 1 foo 100%

H' h

>60% of rake tines Sa 37%

0 0

0 0.*

Legend V

Diver Site Location Rake Toss 0% - 100% Cover Hydroacousitc Points Projection: State Plane Florida West Datum:

NAD83 Feet Units:

11 1 inch equals 59 feet II

-=::::=--==::i ___ iIIiiIIII Feet o

29,5 59 118

1Iq,o000 cb 0

4 L

0 S

S 0

0

0 196 0 a 0 0 0 0 @ so 0

S

0 0

0 0*

0 0

qAME INater Temp (deg C) 3ample Date 3ample Time rurbidity (ntu)

Becchi Depth (ft)

SF;;ical Depth (ift ride Level Naer Depth (m 23.2 11115/2007 4:38PM 9.37 29.9 1.5 1.8 L2:25PM 0.5-1m 0

nt Heght Ifool tmCoverae 100%

Denst Hih Spde' Chart T. SpcesPes ivrSampl 24 celit Gauerp mexlcnan w Sample 10 cabslGracftift tlvahise ilvff Sampl 7 cellsjH8Jhida ingcuSta IvrSam Fe4 celt SwiMessum Ruit.,,

roacouslic Model 2N ______

Legend V

Diver Site Location Rake Toss 0% - 100% Cover 6

4! 0I Hydroacousitc Points Projection: State Plane Florida West Datum:

NAD 83 Units:

Feet 1 inch equals 59 feet Feet on M

0 29.5 59 118 000 o * *

0 *

o o

o 0 0 0 * *

0 0 080004 23.2 11/15/2007 4:38PM 9.37 29.9 1.5 1.8 L2:25PM 0.5-1m Lar 1foo 100%

H* h S ecies Chart Cover S

cies Present 86 cells S fin odium filiforme 24 cells Cau/e a mexicana 10 cells Graci/aria tikvahiae 7 cells Halimeda incrassata 4 cells Swa assum f1uitans 23%

o *

o o

o Legend V

Diver Site Location Rake Toss 0% - 100% Cover

... ~

Hydroacousitc Projection:

Datum:

Units:

State Plane Florida West NAD83 Feet 1 inch equals 59 feet

~

_1I:::::JI_== _____ Feet o

29.5 59 118

~w--

3310 0 0 0

r]

0 0

O 0 a

S 0

0 S

S S

0 N

E DSOO0 ater Temp (deg C) 16.

=pIeDate 11/16/2007 leTime 12:05PM urbl (ntu) 3.62 elinit (ppt) 27.6 chiD epth ft) 0.9 P

ical ift) 1 0.9 ide Level L3:20P ater D m

0.5-im WBed Charcteistics S*.

/ 'Not defined; scattered4 nt Heght II fo Scoer I

95l Dens' Medium Legend V

Diver Site Location 0

Rake Toss 0% - 100% Cover

£ e 0I Hydroacousitc Points Projection: State Plane Florida West Datum:

NAD 83 Units:

Feet 1 inch equals 59 feet Feet 0*

sampeMethod I

RCoe I

Species Presot ier san 61 calbsSrnodium fforme ir Sam 34 cells Gracilatfa Ncvhisae H'd0ro Woust Mod6e 0

5.00%2 7072-Q708 0

29.5 59 118 NAME DSOOOS Water Temp (dell C) 16.2 Sample Date 11/16/2007 Sample Time 12:0SPM urbidity (ntu) 3.62 Salinity (ppt) 27.6 Secchi Depth (ft) 0.9 Physical Depth (ft) 0.9 ide Level L3:20PM Water Depth (m) 0.S-1m Bed Characteristics Size Not defined; scattered Plant Height 1 foo Bottom Coverage 9S%

Bed Density Medium cies hart Sample Method Cover Species Present Diver Sam e 61 cells S rin odium filiforme Diver Sam Ie 34 cells Graci/aria tikvahiae Hydroacoustic Model 5.00%

Legend V

Diver Site location Rake Toss 0% - 100% Cover Hydroacousitc Points Projection: State Plane Florida West Datum:

NAD 83 Units:

Feet 1 inch equals 59 feet

_-==--=::::J _____ Feet o

29.5 59 118

11 0 I'll 030 0

0 0

0 w0 0

0 0

'AME Nater Temp (deg C) 3ampte Date Sample Time ru *bkdty (ntu) 3alini~ty (ppt) 5ecchi Depth (ft) 2hysical Depth (ft) ride Level Nater Depth Lm)

DSOO0 16.9 11/16/2007 1:22PM 2.55 30.9 3.6 L3:20PM 1-1.5m Bed CharaIcteistics itze Not defined: scattered

'lent Height I too 3ottom Coverage 100%

3ed Density,,Medium Legend V

Diver Site Location Rake Toss 0% - 100% Cover 0,

,a Hydroacousitc Points art Sample Method Cover species Present iver Sample 2cells Dkcpo ap.

ver Sam 7 cats Helimeda Incrassata Sample 6 cals Udotea coutineta Sam 45 cells Sa asum natans iver sampe a caUs lAP ay Wrulata Sample 47 cel Cal mexicani iver Same 7 cels Ca sertularoides am c Moda 8%

Projection:

Datum:

Units:

State Plane Florida West NAD 83 Feet 1 inch equals 59 feet

'ii mFeet VTm MMIMMM 0

29.5 59 118

- - 0

0 *

0 0

0 0

0 0

0 0

~

0 OS0006 Legend Bed Characteristics Not defined' scattered V

Diver Site Location Rake Toss 1 foo 0% - 100% Cover 100%

Hydroacousitc Poin Medium Projection:

Datum:

Units:

State Plane Florida West NAD83

~

_-==--=:::::::. _____ Feet Feet ecies hart Sam Ie Method Cover S Bcies Present DiverSam Ie 2 cells Dlct ota s.

DiverSam Ie 7 cells Halimeda incrassata Diver Sample 6 cells Udotaa conglutinata DiverSam Ie 45 cells Sa assum natans DiverSam Ie 8 cells Le t 0 ia vi ulata DlverSam Ie 47 cells Caula maxicana

\\!lb!d#l"i~

DiverSam Ie 7 cells Caule a sertularoides 1 inch equals 59 feet H roacoustic Model 8%

o 29.5 59 118

MMrObNAM fl- 03D Lr-0 0

0 0

0 0 0 I

0 0

.000 0

0

'SAME Nater Temp (deg C) rample Date Sample Time rufbidt (ntu)

Saliity(pptQ Smechl Depth (ft) 3hysical DPth (ft)_

ride Level Nater Depth jm)

DS0007 19.3 11116/2007 2:55PM 4.09 29.8 IS 5.5 L3:20PM 1.5-2m IBed Chia ctedstics I

~ize No defined; scattered i*nt Height 6 Inch Goe=

covew 65S~

DeMedim Sam

~

C Method Cho r Present 6wr Sample 62 cells Cea mexicana 2ls Sample

, 2 cefls Leaogora wdM D;iver 9,mpie 2 cels Setassurm natanw Rake Toss 0% No Plant

-,ydroacoustlc Model I8 Legend V

Diver Site Location Rake Toss 0% - 100% Cover AL ý Hydroacousitc Points Projection: State Plane Florida West Datum:

NAD 83 Units:

Feet 1 inch equals 59 feet I Feet w

MUM OOMMUM m

0 29.5 59 118

~~~

~_\\YADO~"_

0 **

0 0

0

o.

DS0007 19.3 11/16/2007 2:55PM 4.09 29.8 3.9 5.5 L3:20PM 1.5-2m Bed Characteristics No defined; scattered 6 inches 65%

Sam Ie Method Diver Sam Ie Diver Sam Ie Diver Sample Rake Toss Hydroacoustlc Model 0

0 * * *

0 0 0

0 0

0 *

0 0 0

Legend V

Diver Site Location Rake Toss 0% - 100% Cover Hydroacousitc Point Projection: State Plane Florida West Datum:

NAD 83 Units:

Feet

~

1 inch equals 59 feet

_-==--=:::JI _____ Feet o

29.5 59 118

LI 000 0

0 0 0

00 0 0 0 0

0 0o 0

Legend V

Diver Site Location Rake Toss 0% - 100% Cover 41 a#** Hydroacousitc Pointh

--'--'--I

ýatHeight

-,-Raw M

Projection:

Datum:

Units:

State Plane Florida West NAD 83 Feet Rnfn*tml t.nVArnnA

]

3ed Density, Sample Methc>d

,ha" gRan-42 cells Malod4ft ýýýpmý 1i 1 inch equals 59 feet maw Rake Toss 0% No Plant 1)ra sie model 16%-

0 29.5 59 118 I-eet 00 e

0

~

0 0

0

e '

0 0 *

e

0

~,

0 0

e

0 0

e 0

V Diver Site Location Rake Toss 0% - 100% Cover Hydroacousitc Poi State Plane Florida West NAD 83 Feet 1 inch equals 59 feet

~

_-==--=::::11 _____ Feet o

29.5 59 118

~-

@~

0 00Oft o~9r

,1,9 0

0 0

0 S

0 DS0009 26.9 11116/2007 4:19PM 7.62 31.8 1.8 2

L3:20PM 0.5-1m Legend V

Diver Site Location Rake Toss 0% - 100% Cover

÷a N* Hydroacousitc Points Bed Chaieti I6ami I1000%1 Projection:

Datum:

Units:

State Plane Florida West NAD 83 Feet W~Es Cho"I sit tw II WWINO 1Ufll 5 sernms-20%I 1n~du ffihibme 1 inch equals 59 feet I!

......2M-4fl%lRvtnanIu fj~ffn-m

, ieet 1 - 5 stemsi Caubpa seuiar/ddes L

Inil 19%1 0

29.5 59 118 I_

I

~~

.~

~.~O~;gtlDO~

~_O~;g_

o~

o 0

0 II' 0

Legend V

Diver Site Location Rake Toss 0% - 100% Cover Hydroacousitc Projection: State Plane Florida West Datum:

NAD 83 Units:

Feet

~

1 inch equals 59 feet

_-==-II::::JI _____ Feet o

29.5 59 118

ozq-0, 0

0 0

0 000 0

0 0

ý-ý 0 0

0 0

0 0

oc, 40 0

0 0

00 0

0 0

0 26.5 11/16/2007 5:00PM 13.6 31.9 1.7 1.7 L3:20PM 0.5-Im 0

Legend V

Diver Site Location 0

Rake Toss 0% - 100% Cover

. a 00 Hydroacousitc Points qg

.1 L

am Projection:

Datum:

Units:

State Plane Florida West NAD 83 Feet Mar mSwis Chart Sam I Method Cover s

" Present Sam 98 cel.falodule wrght#i I~vrsample 2 cbISresmfu'~~

R~ TOss,

5 ratms 20%WHaodue g

y Tosa 5 atems - 20%

7I asaum natans droacousti

.2 7

1 inch equals 32 feet L

MONEW OMMIN 0

16 32 64

~~~

~_)%O~~~

e.

o

0 ~o S ecies Chart o

o Sam Ie Method Cover S ecies Present DiverSam Ie 98 cells Ha/odule w. htii DiverSam Ie 2 cells Sa essum fluitens Rake Toss 5 stems* 20% Halodule wrightii Rake Toss 5 stems - 20% Sar assum natans Hydroacoustic Model 7%

0 0

o 0 0 0 o *

O.

o o

o 00 0 0

0 O

o o

o O.

0 o

o o

o Legend 0

V Diver Site Location e

Rake Toss 0% - 100% Cover

< Hydroacousitc Points Projection: State Plane Florida West Datum:

NAD 83 Units:

Feet

~

1 inch equals 32 feet

_11::::=--=:::::11 _____ Feet o

16 32 64

APPENDIX - Calculations of Biocover Model Accuracy BioCover model error estimates for combined physical sampling points and comparisons of the three different physical sampling methods individually.

The total physical sample point count does not match the sum of the individual sampling methods points since there were a number of cases where two or more methods were used for sampling a single location and the results did not match, (one indicated 'plant' the other indicated 'no plant'). In these instances only the sample where 'plant' was found was used in the 'all' analysis since 'plant' was indeed found at the location. See Section H of the reportfor a discussion of interpreting these tables.

All types without 38 foot margin of error Raster no omission error

plant plant all plant 17.3%

62 13 no plantI 62.1%

36 22 commission error -

36.7%

37.1%

Rake only without 38 foot margin of error Raster no omission error $

plant plant Rake plant 14.8%

46 8

no plant 61.0%

36 23 commission error --

43.9%

25.8%

Rake only with 38 foot margin of error Raster no omission error $

plant plant Rake plant 0.0%

54 0

no plant 61.0%

36 23 commission error --

40.0%

0.0%

All types with 38 foot margin of error all Raster no Dlant Dlant omission error ý plant [

no plant 0.0%

62.1%

75 36 0

22 commission error -,

32.4%

0.0%

APPENDIX - Calculations of Biocover Model Accuracy BioCover model error estimates for combined physical sampling points and comparisons of the three different physical sampling methods individually.

The total physical sample point count does not match the sum of the individual sampling methods points since there were a number of cases where two or more methods were used for sampling a single location and the results did not match, (one indicated 'plant' the other indicated 'no plant'). In these instances only the sample where 'plant' was found was used in the 'all' analysis since 'plant' was indeed found at the location. See Section H of the report for a discussion of interpreting these tables.

All types without 38 foot margin of error all omission error l plant I 17.3%

no plant 62.1 %

plant Raster 62 36 no plant 13 22 Rake only without 38 foot margin of error Rake omission error l plant I 14.8%

no plant 61.0%

plant Raster 46 36 no plant 8

23 commission error -+

36.7%

37.1%

commission error -+

43.9%

25.8%

All types with 38 foot margin of error all omission error l plant I 0.0%

no plant 62.1 %

commission error -+

Raster plant 75 36 32.4%

no plant o

22 0.0%

Rake only with 38 foot margin of error Rake omission error ~

plant I 0.0%

no plant 61.0%

commission error -+

Raster plant 54 36 40.0%

no plant o

23 0.0%

APPENDIX - Calculations of Biocover Model Accuracy (continued)

Diver only without 38 foot margin of error Raster no omission error $

plant plant Diver plant 0.0%

9 0

no plant 0

0 commission error --

0.0%

Video only without 38 foot margin of error Raster no olant olant omission error I Video I

I plant no plant 41.7%

80.0%

commission error --

7 4

5 1

36.4%

83.3%

Diver only with 38 foot margin of error Diver Video only with 38 foot margin of error Raster no plant plant Raster no olant olant omission error,

omission error I plant no plant 0.0%

9 0

0 0

0.0%

Video I

l plant no plant 0.0%

80.0%

12 0

4 1

commission error,

commission error -

25.0%

0.0%

APPENDIX - Calculations ofBiocover Model Accuracy (continued)

Diver only without 38 foot margin of error Diver Diver only with 38 foot margin of error Diver omission error ~

plant I 0.0%

no plant commission error ->

omission error ~

plant I 0.0%

no plant commission error ->

Raster no plant plant 9

0 o

0 0.0%

Raster no plant plant 9

0 o

0 0.0%

Video only without 38 foot margin of error Video Video only with 38 foot margin of error Video omission error ~

plant I 41.7%

no plant 80.0%

commission error ->

omission error t plant I 0.0%

no plant 80.0%

commission error ->

plant Raster 7

4 no plant 5

1 36.4%

83.3%

Raster 12 4

25.0%

no plant o

1 0.0%

APPENDIX - Calculations of Biocover Model Accuracy (continued) without 38 foot margin of error Raster no omission error t plant plant off-transect only plant 16.7%

10 2

no plant 60.0%

6 4

commission error -

37.5%

33.3%

with 38 foot margin of error off-transect only plant no plant Raster no plant plant omission error I 0.0%

12 60.0%

6 commission error -

33.3%

0 4

0.0%

APPENDIX - Calculations of B iocover Model Accuracy (continued) margin of error off-transect only I off-transect onIY-1 with 38 foot margin of error off-transect only I plant no plant omission error ~

16.7%

60.0%

commission error -+

omission error ~

plant I 0.0%

no plant 60.0%

commission error -+

Raster no lant 2

4 37.5%

33.3%

Raster plant 12 6

33.3%

no plant o

4 0.0%

Site Name DS0001 DS0002 DS0003 DS0004 DS0005 DS0006 DS0007 DS0008 DS0009 DS0010 DS0002 DS0008 DS0002 DS0008 DS0002 DS0008 DS0002 DS0008 DS0002 DS0008 LAT

+28.9754524

+28.9661273

+28.9569691

+28.9584628

+28.9453151

+28.9445241

+28.9500012

+28.9597790

+28.9619191

+28.9658914

+28.9661273

+28.9597790

+28.9661273

+28.9597790

+28.9661273

+28.9597790

+28.9661273

+28.9597790

+28.9661273

+28.9597790 LON WaterTemp (C)

-82.7532661 22.9

-82.7455230 22.9

-82.7355315 22.4

-82.7283993 23.2

-82.7293885 16.2

-82.7487268 16.9

-82.7514686 19.3

-82.7380978 18.3

-82.7292325 26.9

-82.7278804 26.5

-82.7455230 22.1

-82.7380978 23.9

-82.7455230 23.8

-82.7380978 25.3

-82.7455230 23.8

-82.7380978 25.7

-82.7455230 19.0

-82.7380978 21.7

-82.7455230 21.9

-82.7380978 23.2 Sample Date Sample Time Turbidity (ntu) Salinity ppn Secchi Depth (It) Physical Depth (ft) 11152007 12:25PM 5.09 25.9 5

5 11152007 1:40PM 3.94 29.1 3.6 3.9 11152007 3:43PM 9.44 29.9 2.7 3.8 11152007 4:38PM 9.37 29.9 1.5 1.8 11162007 12:05PM 3.62 27.6 0.9 0.9 11162007 1:22PM 2.55 30.9 3.6 3.6 11162007 2:55PM 4.09 29.8 3.9 5.5 11162007 3:38PM 5.42 27.6 3.9 11.7 11162007 4:19PM 7.62 31.8 1.8 2

11162007 5:00PM 13.6 31.9 1.7 1.7 11282007 3:47PM 2.40 31.6 3.2 4.9 11282007 4:55PM 3.11 33.8 4.1 10.4 11302007 5:18PM 3.03 31.4 3.2 5

11302007 5:11PM 2.34 31.4 3.6 5.5 12022007 4:48PM 2.65 32.4 3.8 4.5 12022007 4:40PM 2.22 32.4 3.5 6.5 12042007 4:38PM 2.10 27.9 3.4 4.1 12042007 5:03PM 2.89 34.1 3.2 3.6 12062007 4:53PM 2.22 33.8 4.0 4.2 12062007 3:14pm 3.58 33.9 3.2 5.1 Tide Level Water Depth L2:25PM L2:25PM L2:25PM L2:25PM L3:20PM L3:20PM L3:20PM L3:20PM L3:20PM L3:20PM L1:46PM L1:46PM L3:27PM L3:27PM L5:12PM L5:12PM L6:53PM L6:53PM L8:04PM L8:04PM 1.5-2m 1-1.5m 1-1.5m 0.5-1m 0.5-1m 1-1.5m 1.5-2m 3-4m 0.5-1m 0.5-1m 1-1.5m 3-4m 1-1.5m 3-4m 1-1.5m 3-4m 1-1.5m 3-4m 1-1.5m 3-4m Site Name LAT LON WaterTemp (C) Sample Oate Sample Time Turbidity (ntu) Salinity ppn Secchi Oepth (tt) Physical Oepth (tt) Tide Level Water Oepth 050001

+28.9754524 -82.7532661 22.9 11152007 12:25PM 5.09 25.9 5

5 L2:25PM 1.S-2m 050002

+28.9661273 -82.7455230 22.9 11152007 1:40PM 3.94 29.1 3.6 3.9 L2:25PM 1-1.5m 050003

+28.9569691 -82.7355315 22.4 11152007 3:43PM 9.44 29.9 2.7 3.8 L2:25PM 1-1.5m 050004

+28.9584628 -82.7283993 23.2 11152007 4:38PM 9.37 29.9 1.5 1.8 L2:25PM 0.5-1m 050005

+28.9453151 -82.7293885 16.2 11162007 12:05PM 3.62 27.6 0.9 0.9 L3:20PM O.S-lm 050006

+28.9445241 -82.7487268 16.9 11162007 1:22PM 2.55 30.9 3.6 3.6 L3:20PM 1-1.5m 050007

+28.9500012 -82.7514686 19.3 11162007 2:55PM 4.09 29.8 3.9 5.5 L3:20PM 1.S-2m 050008

+28.9597790 -82.7380978 18.3 11162007 3:38PM 5.42 27.6 3.9 11.7 L3:20PM 34m 050009

+28.9619191 -82.7292325 26.9 11162007 4:19PM 7.62 31.8 1.8 2

L3:20PM O.S-lm 050010

+28.9658914 -82.7278804 26.5 11162007 5:00PM 13.6 31.9 1.7 1.7 L3:20PM O.S-lm 050002

+28.9661273 -82.7455230 22.1 11282007 3:47PM 2.40 31.6 3.2 4.9 Ll:46PM 1-1.5m 050008

+28.9597790 -82.7380978 23.9 11282007 4:55PM 3.11 33.8 4.1 10.4 Ll:46PM 34m 050002

+28.9661273 -82.7455230 23.8 11302007 5:18PM 3.03 31.4 3.2 5

L3:27PM 1-1.5m 050008

+28.9597790 -82.7380978 25.3 11302007 5:11PM 2.34 31.4 3.6 5.5 L3:27PM 34m 050002

+28.9661273 -82.7455230 23.8 12022007 4:48PM 2.65 32.4 3.8 4.5 L5:12PM 1-1.5m 050008

+28.9597790 -82.7380978 25.7 12022007 4:40PM 2.22 32.4 3.5 6.5 L5:12PM 34m 050002

+28.9661273 -82.7455230 19.0 12042007 4:38PM 2.10 27.9 3.4 4.1 L6:53PM 1-1.5m 050008

+28.9597790 -82.7380978 21.7 12042007 5:03PM 2.89 34.1 3.2 3.6 L6:53PM 34in 050002

+28.9661273 -82.7455230 21.9 12062007 4:53PM 2.22 33.8 4.0 4.2 L8:04PM 1-1.5m 050008

+28.9597790 -82.7380978 23.2 12062007 3:14pm 3.58 33.9 3.2 5.1 L8:04PM 34m

Site Scientific Name Common Name Date Abundanc Injury Density I Notes Latitude ULngitude]

2 no plant no plant 12/4/2007 28.975850 -82.738910 12 no plant no plant 12/5/2007 na 28.944482 -82.72463 13 no plant no plant 12/6/2007 na 28.944981 -82.72223 14 Gracilaria tikvahiae edible drift alga 12/5/2007 2

1 2

na 28.945496 -82.71984 14 Sargassum natans gulfweed drift alga 12/5/2007 2

1 2

na 28.945496 -82.71984 14 Halophila engelmannii stargrass 12/5/2007 5

1 3

na 28.945496 -82.71984 15 Gracilaria tikvahiae edible drift alga 12/5/2007 1

1 4

na 28.945980 -82.71743 16 no plant no plant 12/5/2007 na 28.946479 -82.71503 17 Gracilaria tikvahiae edible drift alga 12/5/2007 5

1 1

na 28.946978 -82.712638 25 no plant no plant 11/29/2007 28.944540 -82.742460 26 no plant no plant 11/29/2007 1

28.945060 -82.740040 27 Caulerpa sertularoides feather caulerpa 12/2/2007 3

2 28.945530 -82.737650 27 Sargassum natans gulfweed drift alga 12/2/2007 2

2 28.945530 -82.737650 27 Sargassum fluitans gulfweed drift alga 12/2/2007 4

2 28.945530 -82.737650 27 Caulerpa prolifera grass caulerpa 12/2/2007 5

0 28.945530 -82.737650 27 Syringodium filiforme manatee grass 12/2/2007 1

4 28.945530 -82.737650 28 Gracilana tikvahiae edible drift alga 12/2/2007 4

1 28.945910 -82.735130 28 Syringodium filiforme manatee grass 12/2/2007 1

1 28.945910 -82.735130 28 Syringodium filiforme manatee grass 12/2/2007 3

0 28.945910 -82.735130 28 Sargassum fluitans gulfweed drift alga 12/2/2007 5

1 28.945910 -82.735130 29 Gracilaria tikvahiae edible drift alga 12/2/2007 2

2 28.946510 -82.732750 29 Syringodium filiforme manatee grass 12/2/2007 3

3 28.946510 -82.732750 30 Caulerpa mexicana feather calulerpa 12/2/2007 1

2 28.946970 -82.730450 31 Syringodium filiforme manatee grass 12/2/2007 1

1 3

28.947490 -82.727970 32 Gracilaria tikvahiae edible drift alga 12/5/2007 1

1 1

na 28.947986 -82.725581 32 Thalassia testudinum turtle grass 12/5/2007 4

1 5

na 28.947986 -82.725584 33 Sargassum natans gulfweed drift alga 12/5/2007 2

1 2

na 28.948486 -82.72318 33 Gracilaria tikvahiae edible drift alga 12/5/2007 2

1 2

na 28.948486 -82.72318 33 Sargassum fluitans gulfweed drift alga 12/5/2007 2

1 2

na 28.948486 -82.72318 34 Gracilaria tikvahiae edible drift alga 12/5/2007 1

1 4

na 28.948985 -82.72078 35 Syringodium filiforme manatee grass 12/5/2007 2

1 4

na 28.949484 -82.71838 35 Thalassia testudinum turtle grass 12/5/2007 2

1 4

na 28.949484 -82.71838 36 Caulerpa prolifera grass caulerpa 12/5/2007 na 28.949984 -82.715988 36 Gracilaria tikvahiae edible drift alga 12/5/2007 na 28.949984 -82.715988 62 no plant no plant 11/28/2007 28.950140 -82.751500 63 no plant no plant 11/28/2007 28.950530 -82.749110 64 Caulerpa sertularoides feather caulerpa 11/28/2007 3

2 28.951030 -82.746770 64 Sargassum natans gulfweed drift alga 11/28/2007 3

2 28.951030 -82.746770 64 Sargassum fluitans gulfweed drift alga 11/28/2007 3

0 28.951030 -82.746770 65 Sargassum fluitans gulfweed drift alga 11/28/2007 2

2 28.951540 -82.744190 66 Caulerpa sertularoides feather caulerpa 11/28/2007 3

2 28.952040 -82.741770 66 Penicillus sp. fragments shaving brush plant 11/28/2007 3

4 28.952040 -82.741770 67 Sargassum natans gulfweed drift alga 11/28/2007 3

2 28.952530 -82.739410 67 Sargassum fluitans gulfweed drift alga 11/28/2007 2

1 28.952530 -82.739410 67 Gracilaria tikvahiae edible drift alga 11/28/2007 4

1 28.952530 -82.739410 67 Caulerpa mexicana feather calulerpa 11/28/2007 28.952530 -82.739410 69 Penicillus sp. fragments shaving brush plant 12/4/2007 1

2 1

28.953540 -82.734740 70 Caulerpa sertularoides feather caulerpa 12/4/2007 3

1 28.953930 -82.732290 70 Penicillus sp. fragments shaving brush plant 12/4/2007 2

2 28.953930 -82.732290 71 no plant no plant 12/4/2007 128.954500 -82.729920 72 no plant no plant 12/4/2007 28.954960 -82.727570 Site I Scientific Name II Common Name II Date II Abundanc~1 Injury II Density II Notes II Latitude II Longitude I 2

no plant no plant 12/4/2007 28.975850 -82.738910 12 no plant no plant 12/5/2007 na 28.944482 -82.72463 13 no plant no plant 12/6/2007 na 28.944981 -82.72223 14 Graci/aria tikvahiae edible drift alga 12/5/2007 2

1 2

na 28.945496 -82.71984~

14 Sargassum natans gulfweed drift alga 12/5/2007 2

1 2

na 28.945496 -82.71984 14 Halophila engelmannii stargrass 12/5/2007 5

1 3

na 28.945496 -82.71984~

15 Graci/aria tikvahiae edible drift alga 12/5/2007 1

1 4

na 28.945980 -82.71743 16 no plant no plant 12/5/2007 na 28.946479 -82.71503E 17 Graci/aria tikvahiae edible drift alga 12/5/2007 5

1 1

na 28.946978 -82.71263E 25 no plant no plant 11/29/2007 28.944540 -82.742460 26 no plant no plant 11/29/2007 28.945060 -82.740040 27 Caulerpa sertularoides feather caul erpa 12/212007 3

2 28.945530 -82.737650 27 Sargassum natans gulfweed drift alga 12/2/2007 2

2 28.945530 -82.737650 27 Sargassum f/uitans gulfweed drift alga 12/2/2007 4

2 28.945530 -82.737650 27 Caulerpa prolifera grass caulerpa 12/2/2007 5

0 28.945530 -82.737650 27 Syringodium filiforme manatee grass 121212007 1

4 28.945530 -82.737650 28 Graci/aria tikvahiae edible drift alga 121212007 4

1 28.945910 -82.735130 28 Syringodium filiforme manatee grass 121212007 1

1 28.945910 -82.735130 28 Syringodium filiforme manatee grass 12/2/2007 3

0 28.945910 -82.735130 28 Sargassum f/uitans gulfweed drift alga 12/2/2007 5

1 28.945910 -82.735130 29 Graci/aria tikvahiae edible drift alga 121212007 2

2 28.946510 -82.732750 29 Syringodium filiforme manatee grass 12/2/2007 3

3 28.946510 -82.732750 30 Caulerpa mexicana feather calulerpa 12/2/2007 1

2 28.946970 -82.730450 31 Syringodium filiforme manatee grass 12/2/2007 1

1 3

28.947490 -82.727970 32 Gracilaria tikvahiae edible drift alga 12/5/2007 1

1 1

na 28.947986 -82.72558 32 Thalassia testudinum turtle grass 12/5/2007 4

1 5

na 28.947986 -82.72558 33 Sargassum natans gulfweed drift alga 12/5/2007 2

1 2

na 28.948486 -82.72318 33 Graci/aria tikvahiae edible drift alga 12/5/2007 2

1 2

na 28.948486 -82.72318 33 Sargassum f/uitans gulfweed drift alga 1215/2007 2

1 2

na 28.948486 -82.72318 34 Graci/aria tikvahiae edible drift alga 12/5/2007 1

1 4

na 28.948985 -82.72078 35 Syringodium filiforme manatee grass 12/5/2007 2

1 4

na 28.949484 -82.71838E 35 Tha/assia testudinum turtle grass 12/5/2007 2

1 4

na 28.949484 -82.71838E 36 Caulerpa prolifera grass caulerpa 12/5/2007 na 28.949984 -82.71598E 36 Graci/aria tikvahiae edible drift alga 12/5/2007 na 28.949984 -82.71598E 62 no plant no plant 11/28/2007 28.950140 -82.751500 63 no plant no plant 11/28/2007 28.950530 -82.749110 64 Caulerpa sertularoides feather caulerpa 11/28/2007 3

2 28.951030 -82.746770 64 Sargassum natans gulfweed drift alga 11/28/2007 3

2 28.951030 -82.746770 64 Sargassum f/uitans gulfweed drift alga 11/28/2007 3

0 28.951030 -82.746770 65 Sargassum f/uitans gulfweed drift alga 11/28/2007 2

2 28.951540 -82.744190 66 Caulerpa sertularoides feather caulerpa 11/28/2007 3

2 28.952040 -82.741770 66 Penicillus sp. fragments shaving brush plan 11/28/2007 3

4 28.952040 -82.741770 67 Sargassum natans gulfweed drift alga 11/28/2007 3

2 28.952530 -82.739410 67 Sargassum f/uitans gulfweed drift alga 11/28/2007 2

1 28.952530 -82.739410 67 Graci/aria tikvahiae edible drift alga 11/28/2007 4

1 28.952530 -82.739410 67 Caulerpa mexicana feather calulerpa 11/28/2007 28.952530 -82.739410 69 Penicillus sp. fragments shaving brush plan 1214/2007 1

2 28.953540 -82.734740 70 Caulerpa sertularoides feather caulerpa 12/4/2007 3

1 28.953930 -82.732290 70 Penicillus sp. fragments shaving brush plan 1214/2007 2

2 28.953930 -82.732290 71 no plant no plant 12/4/2007 28.954500 -82.729920 72 no plant no plant 12/4/2007 28.954960 -82.727570

Site I Scientific Name ICommon Name I Date Abundancl Injury I Density FI Notes Latitude Longitude 72 Caulerpa sertularoides feather caulerpa 12/4/2007 3

2 28.954960 -82.727570 73 Gracilaria tikvahiae edible drift alga 12/4/2007 4

1 28.955580 -82.725150 73 Syringodium filiforme manatee grass 12/4/2007 5

1 28.955580 -82.725150 73 Sargassum natans gulfweed drift alga 12/4/2007 2

1 28.955580 -82.725150 73 Gracilania tikvahiae edible drift alga 12/4/2007 4

1 28.955580 -82.725150 82 no plant no plant 11/28/2007 28.953610 -82.752540 83 no plant no plant 11/29/2007 28.953950 -82.749940 84 no plant no plant 11/29/2007 28.954440 -82.747680 85 no plant no plant 11/29/2007 28.954940 -82.745200 86 no plant no plant 11/28/2007 28.955560 -82.742890 87 Caulerpa sertularoides feather caulerpa 11/29/2007 2

2 28.955990 -82.740440 87 Sargassum natans gulfweed drift alga 11/29/2007 5

2 28.955990 -82.740440 87 Caulerpa mexicana feather calulerpa 11/29/2007 4

2 28.955990 -82.740440 89 Gracilaria tikvahiae edible drift alga 12/4/2007 2

1 28.956960 -82.735630 89 Caulerpa sertularoides feather caulerpa 12/4/2007 2

1 28.956960 -82.735630 89 Udotea conglutinata Udotea spp 12/4/2007 5

1 28.956960 -82.735630 89 Syringodium filiforme manatee grass 12/4/2007 5

2 28.956960 -82.735630 89 Caulerpa sertularoides feather caulerpa 12/4/2007 5

1 28.956960 -82.735630 89 Sargassum natans gulfweed drift alga 12/4/2007 5

1 28.956960 -82.735630 90 Syringodium filiforme manatee grass 12/4/2007 3

2 28.957460 -82.733210 90 Caulerpa sertularoides feather caulerpa 12/4/2007 3

3 28.957460 -82.733210 91 Syringodium filiforme manatee grass 12/4/2007 5

1 28.957920 -82.730880 91 Gracilaria tikvahiae edible drift alga 12/4/2007 3

2 28.957920 -82.730880 91 Caulerpa mexicana feather calulerpa 12/4/2007 3

2 28.957920 -82.730880 93 no plant no plant 12/4/2007 28.958770 -82.726200 103 no plant no plant 11/29/2007 28.957480 -82.750690 103 no plant no plant 12/4/2007 28.957430 -82.750990 104 no plant no plant 11/29/2007 28.957950 -82.748600 105 no plant no plant 11/29/2007 28.958460 -82.746260 106 Sargassum natans gulfweed drift alga 11/29/2007 4

28.959010 -82.743640 107 Sargassum natans gulfweed drift alga 11/29/2007 1

4 28.958970 -82.741270 108 no plant no plant 11/29/2007 28.960000 -82.738950 but I couldn't really see bottom to 108 no plant no plant 12/6/2007 verify 28.960083 -82.739004 109 no plant no plant 11/29/2007 28.960600 -82.736490 lot here, but enough to see on 109 Caulerpa prolifera grass caulerpa 12/6/2007 1

1 4

video 28.960583 -82.736583 110 Halodule wrightii shoal grass 11/29/2007 1

3 28.960990 -82.734290 110 Sargassurn natans gulfweed drift alga 11/29/2007 4

4 28.960990 -82.734290 110 Halodule wrightii shoal grass 11/29/2007 1

1 1

28.961000 -82.734400 111 Halodule wrightii shoal grass 11/29/2007 1

4 28.961460 -82.731800 111 Halodule wrightii shoal grass 12/6/2007 2

3 28.961478 -82.731900 111 Sargassum natans gulfweed drift alga 12/6/2007 4

8 3

28.961478 -82.731900 111 Gracilarna tikvahiae edible drift alga 12/6/2007 4

1 4

28.961478 -82.731900 112 Syringodium filiforme manatee grass 11/29/2007 1

1 3

28.962060 -82.729410 Site I Scientific Name II Common Name II Date II Abundanc~1 Injury II Density II Notes II Latitude II Longitude I 72 Caulerpa serlularoides feather caulerpa 12/4/2007 3

2 28.954960 -82.727570 73 Graci/aria tikvahiae edible drift alga 12/4/2007 4

1 28.955580 -82.725150 73 Syringodium filiforme manatee grass 12/4/2007 5

1 28.955580 -82.725150 73 Sargassum natans gulfweed drift alga 12/4/2007 2

1 28.955580 -82.725150 73 Graci/aria tikvahiae edible drift alga 12/4/2007 4

1 28.955580 -82.725150 82 no plant no plant 11/28/2007 28.953610 -82.752540 83 no plant no plant 11/29/2007 28.953950 -82.749940 84 no plant no plant 11/29/2007 28.954440 -82.747680 85 no plant no plant 11/29/2007 28.954940 -82.745200 86 no plant no plant 11/28/2007 28.955560 -82.742890 87 Caulerpa serlularoides feather caulerpa 11/29/2007 2

2 28.955990 -82.740440 87 Sargassum natans gulfweed drift alga 11/29/2007 5

2 28.955990 -82.740440 87 Caulerpa mexicana feather calulerpa 11/29/2007 4

2 28.955990 -82.740440 89 Gracilaria tikvahiae edible drift alga 12/4/2007 2

1 28.956960 -82.735630 89 Caulerpa serlularoides feather caulerpa 12/4/2007 2

1 28.956960 -82.735630 89 Udotea conglutinata Udotea spp 12/4/2007 5

1 28.956960 -82.735630 89 Syringodium filiforme manatee grass 12/4/2007 5

2 28.956960 -82.735630 89 Caulerpa serlularoides feather caulerpa 12/4/2007 5

1 28.956960 -82.735630 89 Sargassum natans gulfweed drift alga 12/4/2007 5

1 28.956960 -82.735630 90 Syringodium filiforme manatee grass 12/4/2007 3

2 28.957460 -82.733210 90 Caulerpa serlularoides feather caulerpa 12/4/2007 3

3 28.957460 -82.733210 91 Syringodium filiforme manatee grass 12/4/2007 5

1 28.957920 -82.730880 91 Graci/aria tikvahiae edible drift alga 12/4/2007 3

2 28.957920 -82.730880 91 Caulerpa mexicana feather calu Ie rpa 12/4/2007 3

2 28.957920 -82.730880 93 no plant no plant 12/4/2007 28.958770 -82.726200 103 no plant no plant 11129/2007 28.957480 -82.750690 103 no plant no plant 12/4/2007 28.957430 -82.750990 104 no plant no plant 11/29/2007 28.957950 -82.748600 105 no plant no plant 11/29/2007 28.958460 -82.746260 106 Sargassum natans gulfweed drift alga 11/29/2007 4

28.959010 -82.743640 107 Sargassum natans gulfweed drift alga 11/29/2007 1

4 28.958970 -82.741270 108 no plant no plant 11/29/2007 28.960000 -82.738950 but I couldn't really see bottom to 108 no plant no plant 12/6/2007 verify 28.960083 -82.73900C 109 no plant no plant 11129/2007 28.960600 -82.736490

'v<" "V,,,

lot here, but enough to see on 109 Caulerpa profifera grass caulerpa 12/6/2007 1

1 4

video 28.960583 -82.73658 110 Hafodufe wrightii shoal grass 11/29/2007 1

3 28.960990 -82.734290 110 Sargassum natans gulfweed drift alga 11/29/2007 4

4 28.960990 -82.734290 110 Hafodufe wrightii shoal grass 11/29/2007 1

1 1

28.961000 -82.734400 111 Hafodufe wrightii shoal grass 11/29/2007 1

4 28.961460 -82.731800 111 Halodufe wrightii shoal grass 12/6/2007 2

3 28.961478 -82.731900 111 Sargassum natans gulfweed drift alga 12/6/2007 4

8 3

28.961478 -82.731900 111 Graci/aria tikvahiae edible drift alga 12/6/2007 4

4 28.961478 -82.731900 112 Syringodium fififorme manatee grass 11/29/2007 1

3 28.962060 -82.729410

Site I Scientific Name 1I Common Name Date Abundancj Injury IDensity Notes Latitude ILongitude mosily manatee or 112 Syringodium filiforme manatee grass 12/6/2007 2

2 shoal grass 28.962000 -82.729483 112 Caulerpa sertularoides feather caulerpa 12/6/2007 5

4 28.962000 -82.729483 113 Syringodium filiforme manatee grass 12/2/2007 1

4 28.962520 -82.727030 114 Syringodium filiforme manatee grass 12/3/2007 1

4 28.962980 -82.724600 123 no plant no plant 11/29/2007 28.960860 -82.751770 123 Caulerpa sertularoides feather caulerpa 12/4/2007 2

2 28.961010 -82.751990 123 Sargassurn natans gulfweed drift alga 12/4/2007 5

2 28.961010 -82.751990 124 Sargassum natans gulfweed drift alga 11/29/2007 2

2 28.961512 -82.749540 124 Cladophora spp filamentous algae 11/29/2007 4

1 28.961512 -82.749540 124 Caulerpa prolifera grass caulerpa 11/29/2007 4

1 28.961512 -82.749540 125 no plant no plant 11/29/2007 28.961950 -82.747110 126 no plant no plant 11/28/2007 28.962490 -82.744790 127 no plant no plant 11/30/2007 28.963030 -82.742460 128 no plant no plant 11/29/2007 28.963560 -82.739900 128 no plant no plant 12/6/2007 1

28.963550 -82.739967 129 Udotea conglutinata Udotea spp 11/29/2007 2

1 28.964040 -82.737530 129 Udotea conglutinata Udotea spp 12/6/2007 2

0 0

28.963950 -82.737500 129 Halodule wrightii shoal grass 12/6/2007 2

0 0

28.963950 -82.737500 129 Sargassurn fluitans gulfweed drift alga 12/6/2007 2

0 0

28.963950 -82.737500 131 Halodule wrightii shoal grass 11/29/2007 2

1 28.965040 -82.732800 131 Sargassum fluitans gulfweed drift alga 11/29/2007 2

4 28.965040 -82.732800 site looked to be mostly dominated by shoal grass on 131 Halodule wright/i shoal grass 12/6/2007 1

1 1

video 28.965100 -82.732817 blades of what looked like turtle grass on 131 Thalassia testudinumr turtle grass 12/6/2007 4

1 3

video 28.965100 -82.732817 132 Syringodium filiforme manatee grass 11/29/2007 1

3 1

28.965330 -82.730380 133 Halodule wrightii shoal grass 12/2/2007 3

3 28.966040 -82.727930 133 Sargassurn natans gulfweed drift alga 12/2/2007 3

4 28.966040 -82.727930 143 Caulerpa sertularoides feather caulerpa 11/29/2007 4

1 28.964550 -82.752890 143 Udotea conglutinata Udotea spp 11/29/2007 4

1 28.964550 -82.752890 143 Caulerpa prolifera grass caulerpa 11/29/2007 4

0 28.964550 -82.752890 143 Sargassurn natans gulfweed drift alga 11/29/2007 3

1 28.964550 -82.752890 143 Gracilaria tikvahiae edible drift alga 11/29/2007 3

1 28.964550 -82.752890 143 Caulerpa mexicana feather calulerpa 11/29/2007 3

1 28.964550 -82.752890 143 Caulerpa prolifera grass caulerpa 11/29/2007 4

1 28.964550 -82.752890 144 no plant no plant 11/29/2007 28.964960 -82.750460 145 Sargassum natans gulfweed drift alga 11/29/2007 1

0 28.965050 -82.747990 146 no plant no plant 11/28/2007 28.966180 -82.745590 147 no plant no plant 11/29/2007 28.966410 -82.743210 148 no plant no plant 11/29/2007 28.967030 -82.740820 149 no plant no plant 11/29/2007 28.967490 -82.738530 150 Syringodium filiforme manatee grass 12/2/2007 1

3 28.968020 -82.7361001 Site I Scientific Name II Common Name II Date II Abundanc~1 Injury II Density II Notes II Latitude II Longitude I T1U>;UY manatee or 112 Syringodium filiforme manatee grass 12/6/2007 2

2 shoal grass 28.962000 -82.72948 112 Caulerpa sertularoides feather caulerpa 12/6/2007 5

4 28.962000 -82.729483 113 Syringodium filiforme manatee grass 121212007 1

4 28.962520 -82.727030 114 Syringodium filiforme manatee grass 12/3/2007 1

4 28.962980 -82.724600 123 no plant no plant 11/29/2007 28.960860 -82.751770 123 Caulerpa sertularoides feather caulerpa 12/4/2007 2

2 28.961010 -82.751990 123 Sargassum natans gulfweed drift alga 12/4/2007 5

2 28.961010 -82.751990 124 Sargassum natans gulfweed drift alga 11/29/2007 2

2 28.961512 -82.749540 124 Cladophora spp filamentous algae 11/29/2007 4

1 28.961512 -82.749540 124 Caulerpa prolifera grass caulerpa 11/29/2007 4

1 28.961512 -82.749540 125 no plant no plant 11/29/2007 28.961950 -82.747110 126 no plant no plant 11128/2007 28.962490 -82.744790 127 no plant no plant 11/30/2007 28.963030 -82.742460 128 no plant no plant 11/29/2007 28.963560 -82.739900 128 no plant no plant 12/6/2007 28.963550 -82.739967 129 Udotea conglutinata Udotea spp 11/29/2007 2

1 28.964040 -82.737530 129 Udotea conglutinata Udotea spp 12/6/2007 2

0 0

28.963950 -82.737500 129 Halodule wrightii shoal grass 12/6/2007 2

0 0

28.963950 -82.737500 129 Sargassum f/uitans gulfweed drift alga 12/6/2007 2

0 0

28.963950 -82.737500 131 Halodule wrightii shoal grass 11/29/2007 2

1 28.965040 -82.732800 131 Sargassum f/uitans gulfweed drift alga 11/29/2007 2

4 28.965040 -82.732800 site looked to be mostly dominated by shoal grass on 131 Halodule wrightii shoal grass 12/6/2007 1

1 1

video 28.965100 -82.73281 blades of what looked like turtle grass on 131 Tha/assia testudinum turtle grass 12/6/2007 4

1 3

video 28.965100 -82.73281 132 Syringodium filiforme manatee grass 11/29/2007 1

3 28.965330 -82.730380 133 Halodule wrightii shoal grass 12/2/2007 3

3 28.966040 -82.727930 133 Sargassum natans gulfweed drift alga 12/2/2007 3

4 28.966040 -82.727930 143 Caulerpa sertularoides feather caulerpa 11/29/2007 4

1 28.964550 -82.752890 143 Udotea conglutinata Udotea spp 11/29/2007 4

1 28.964550 -82.752890 143 Caulerpa prolifera grass caulerpa 11/29/2007 4

0 28.964550 -82.752890 143 Sargassum natans gu Ifweed d rift alga 11/29/2007 3

1 28.964550 -82.752890 143 Graci/aria tikvahiae edible drift alga 11/29/2007 3

1 28.964550 -82.752890 143 Caulerpa mexicana feather calulerpa 11/29/2007 3

1 28.964550 -82.752890 143 Caulerpa prolifera grass caulerpa 11/29/2007 4

1 28.964550 -82.752890 144 no plant no plant 11/29/2007 28.964960 -82.750460 145 Sargassum natans gulfweed drift alga 11/29/2007 1

0 28.965050 -82.747990 146 no plant no plant 11/28/2007 28.966180 -82.745590 147 no plant no plant 11/29/2007 28.966410 -82.743210 148 no plant no plant 11/29/2007 28.967030 -82.740820 149 no plant no plant 11/29/2007 28.967490 -82.738530 150 Syringodium filiforme manatee grass 12/2/2007 1

3 28.968020 -82.736100

FSite I!

Scientific Name Common Name]

Date IIAbundanl Injury Density Notea LatitudeI Longitude llO'u'y d;'y plant only one small sprig. Video 152 Caulerpa spp caulerpa 12/6/2007 1

4 point 28.969000 -82.731367 152 no plant no plant 12/6/2007 28.969000 -82.731367 164 Caulerpa sertularoides feather caulerpa 11/29/2007 2

1 28.968560 -82.751350 164 Sargassum natans gulfweed drift alga 11/29/2007 3

1 28.968560 -82.751350 164 Gracilaria tikvahiae edible drift alga 11/29/2007 3

1 28.968560 -82.751350 164 Gracilaria tikvahiae edible drift alga 11/29/2007 4

1 28.968560 -82.751350 165 no plant no plant 11/29/2007 28.968880 -82.749020 166 no plant no plant 11/28/2007 28.969780 -82.746740 166 no plant no plant 11/29/2007 1

28.969570 -82.746370 167 no plant no plant 11/29/2007 28.970010 -82.744210 170 no plant no plant 12/2/2007 28.971320 -82.736980 no plant 172 no plant no plant 12/6/2007 rake toss 28.972518 -82.73224C video sample 172 no plant no plant 12/6/2007 point 28.972483 -82.73220(

184 Halophila enge/mannii stargrass 11/30/2007 2

3 28.972010 -82.752440 184 Caulerpa prolifera grass caulerpa 11/30/2007 1

3 28.972010 -82.752440 stargrass maybe from 184 Halophila engelmannii stargrass 11/20/2007 4

4 video 28.971950 -82.752483 184 Cladophora spp filamentous algae 11/20/2007 4

4 hairy plant 28.971950 -82.752483 184 Sargassum fluitans gulfweed drift alga 11/20/2007 4

4 28.971950 -82.752483 184 Caulerpa prolifera grass caulerpa 11/20/2007 4

4 28.971950 -82.752483 185 no plant no plant 11/29/2007 28.972280 -82.749930 Sample Point 185 Hardly any 185 Udotea conglutinata Udotea spp 12/6/2007 1

1 4

veg at all 28.972250 -82.749967 186 Penicillus sp. fragments shaving brush plant 11/28/2007 1

4 28.973050 -82.747590 189 no plant no plant 12/2/2007 28.974560 -82.740450 191 no plant no plant 12/2/2007 28.975510 -82.735520 204 Caulerpa pro/ifera grass caulerpa 11/30/2007 1

1 28.975420 -82.753330 205 no plant no plant 11/29/2007 28.975820 -82.750970 207 no plant no plant 12/4/2007 28.977040 -82.746160 210 Cladophora spp filamentous algae 12/2/2007 0

28.978460 -82.738910 211 Cladophora spp filamentous algae 12/2/2007 28.979040 -82.736500 212 no plant no plant 12/2/2007 28.979530 -82.734150 225 no plant no plant 11/29/2007 28.979400 -82.751850 226 no plant no plant 11/28/2007 28.979930 -82.749520 227 no plant no plant 11/29/2007 28.980510 -82.747100 228 no plant no plant 11/29/2007 28.981030 -82.744730 229 no plant no plant 11/29/2007 28.981480 -82.742320 9910 no plant no plant 12/6/2007 na 28.951983 -82.749051 9911 Sargassum fluitans gulfweed drift alga 12/6/2007 na 28.966620 -82.734857 9912 Halodule wrightii shoal grass 12/6/2007 1

1 Ina 28.966114 -82.731204 very sparse 9912 Halodule wnght//

shoal grass 12/6/2007 1

1 3

vegetation 28.966133 -82.731217 9913 no plant no plant 12/6/2007 1

1 I na 28.961489 -82.729164 Site I Scientific Name II Common Name II Date II Abundan~1 Injury II Density II Notes II Latitude II Longitude I "Q'U'Y Q"Y plant only one small sprig. Videc 152 Caulerpa spp caulerpa 12/6/2007 1

4 point 28.969000 -82.731361 152 no plant no plant 12/6/2007 28.969000 -82.731367 164 Caulerpa sertularoides feather caulerpa 11/29/2007 2

1 28.968560 -82.751350 164 Sargassum natans gulfweed drift alga 11/29/2007 3

1 28.968560 -82.751350 164 Gracilaria tikvahiae edible drift alga 11/29/2007 3

1 28.968560 -82.751350 164 Gracilaria tikvahiae edible drift alga 11/29/2007 4

1 28.968560 -82.751350 165 no plant no plant 11/29/2007 28.968880 -82.749020 166 no plant no plant 11/28/2007 28.969780 -82.746740 166 no plant no plant 11/29/2007 28.969570 -82.746370 167 no plant no plant 11/29/2007 28.970010 -82.744210 170 no plant no plant 12/2/2007 28.971320 -82.736980 no pan 172 no plant no plant 12/6/2007 rake toss 28.972518 -82.73224C IV foOl<" "

video sample 172 no plant no plant 12/6/2007 point 28.972483 -82.73220C 184 Ha/ophila engelmannii stargrass 11/30/2007 2

3 28.972010 -82.752440 184 Caulerpa prO/itera grass caulerpa 11/30/2007 1

3 28.972010 -82.752440 Slargrass maybe from 184 Ha/ophila engelmannii stargrass 11120/2007 4

4 video 28.971950 -82.75248 184 Cladophora spp filamentous algae 11/20/2007 4

4 hairy plant 28.971950 -82.75248 184 Sargassum f/uitans gulfweed drift alga 11/20/2007 4

4 28.971950 -82.752483 184 Caulerpa prolifera grass caulerpa 11/20/2007 4

4 28.971950 -82.752483 185 no plant no plant 11/29/2007 28.972280 -82.749930

'UvV Sample Point 185 Hardly any 185 Udotea conglutinata Udotea spp 12/6/2007 1

1 4

veg at all 28.972250 -82.74996 186 Penicillus sp. fragments shaving brush plan 11/28/2007 1

4 28.973050 -82.747590 189 no plant no plant 12/2/2007 28.974560 -82.740450 191 no plant no plant 12/2/2007 28.975510 -82.735520 204 Caulerpa prO/ifera grass caulerpa 11/30/2007 1

1 28.975420 -82.753330 205 no plant no plant 11/29/2007 28.975820 -82.750970 207 no plant no plant 12/4/2007 28.977040 -82.746160 210 Cladophora spp filamentous algae 12/2/2007 0

28.978460 -82.738910 211 Cladophora spp filamentous algae 12/2/2007 28.979040 -82.736500 212 no plant no plant 12/2/2007 28.979530 -82.734150 225 no plant no plant 11/29/2007 28.979400 -82.751850 226 no plant no plant 11/28/2007 28.979930 -82.749520 227 no plant no plant 11/29/2007 28.980510 -82.747100 228 no plant no plant 11/29/2007 28.981030 -82.744730 229 no plant no plant 11/29/2007 28.981480 -82.742320 9910 no plant no plant 12/6/2007 na 28.951983 -82.749051 9911 Sargassum f/uitans gulfweed drift alga 12/6/2007 na 28.966620 -82.73485 9912 Halodule wrightii shoal grass 12/6/2007 na 28.966114 -82.73120~

very sparse 9912 Halodule wrightii shoal grass 12/6/2007 1

1 3

vegetation 28.966133 -82.73121 9913 no plant no plant 12/6/2007 na 28.961489 -82.72916E

Site II Scientific Name UCommon Name IIDate IAbundancI Injury II Density IINotes IILatitude IILongitude]

9914 Sargassum fluitans gulfweed drift alga 12/6/2007 na 28.961709 -82.73274 9915 Penicillus sp. fragments shaving brush plant 12/6/2007 na 28.961050 -82.74093 9915 no plant no plant 12/6/2007 28.961050 -82.7409331 9916 Cladophora spp filamentous algae 12/6/2007 na 28.971991 -82.74715 9917 no plant no plant 12/6/2007 na 28.976953 -82.75124 9918 no plant no plant 12/6/2007 na 28.979026 -82.75010ý 9919 no plant no plant 12/6/2007 na 28.981367 -82.74951E no piant 9992 no plant no plant 12/6/2007 video site 28.972970 -82.73848 plant toss 9992 no plant no plant 12/6/2007 0

no plant 28.972970 -82.73848 9993 Caulerpa prolifera grass caulerpa 12/6/2007 28.975756 -82.742440 sample no plant on 9993 Caulerpa prolifera grass caulerpa 12/6/2007 rake 28.956170 -82.742717 9994 Penicillus sp. fragments shaving brush plant 12/6/2007 na 28.966532 -82.749896 9994 Penicillus sp. fragments shaving brush plant 12/6/2007 1

44 28.966567 -82.749850 9995 no plant no plant 12/6/2007 noplant 28.961437 -82.745404 9996 no plant no plant 12/6/2007 na 28.959769 -82.738068 9997 Sargassum natans gulfweed drift alga 12/6/2007 na 28.957730 -82.739777 9997 Udotea conglutinata Udotea spp 12/6/2007 na 28.957730 -82.739777 9997 Caulerpa prolifera grass caulerpa 12/6/2007 na 28.957730 -82.739777 9998 no plant no plant 12/6/2007 na 28.951697 -82.738041 9999 Caulerpa prolifera grass caulerpa 12/6/2007 Ina 28.951717 -82.740340 dsOO01 Caulerpa prolifera grass caulerpa 11/15/2007 20%

28.975452 -82.75326 dsOO01 Gracilaria tikvahiae edible drift alga 11/15/2007 3%

28.975452 -82.75326 dsOO02 Caulerpa sertularoides feather caulerpa 11/15/2007 22%

0.000000 0.000000 dsOO03 Syringodium fliforme manatee grass 11/15/2007 68%

28.956969 -82.73553 dsOO03 Gracilaria tikvahiae edible drift alga 11/15/2007 51%

28.956969 -82.73553 dsOO04 Syringodium filiforme manatee grass 11/15/2007 186%

28.958463 -82.72839 dsOO04 Caulerpa mexicana feather caulerpa 11/15/2007 24%

28.958463 -82.72839 dsOO04 Gracilaria tikvahiae edible drift alga 11/1512007 10%

28.958463 -82.72839 dsOO04 Halimeda incrassata Halimeda spp 11/15/2007 7%

28.958463 -82.72839 dsOO04 Sargassum fluitans gulfweed drift alga 11/15/2007 4%

28.958463 -82.72839 dsOO05 Syringodium fliforme manatee grass 11/16/2007 34%

28.945315 -82.72938 dsOO07 Caulerpa mexicana feather calulerpa 11/16/2007 165%

28.950001 -82.75146 dsOO07 Leptogorgia virgulata sea whip 11/16/2007 2

28.950001 -82.75146 dsOO07 Sargassum natans gulfweed drift alga 11/16/2007 2

28.950001 -82.75146 dsO008 Halodule wnighti/

shoal grass 11/16/2007 42%

28.959779 -82.73809 dsOO09 Halodule wnight/i shoal grass 11/16/2007 100%

28.961919 -82.72923 dsOO10 Halodule wright/i shoal grass 11/16/2007 100%

28.965891 -82.72788 dsOO10 Sargassum fluitans gulfweed drift alga 11/16/2007 12%

28.965891 -82.72788 dsOO05 Gracilaria tikvahiae edible drift alga 11/16/2007 61%

28.945315 -82.729389 dsOO06 Dictyota sp.

11/16/2007 2cells 0.000000 0.000000 dsOO06 Halimeda incrassata Halimeda spp 11/16/2007 7 cells 0.000000 0.000000 dsOO06 Udotea conglutinata Udotea spp 11/16/2007 6 cells 0.000000 0.000000 dsOO06 Sargassum natans gulfweed drift alga 11/16/2007 45 cells 0.000000 0.000000 dsOO06 Caulerpa mexicana feather caulerpa 11/16/2007 1

147 cells 0.000000 0.000000 dsOO06 Caulerpa sertularoides feather caulerpa 11/16/2007 1

17 cells 0.000000 0.000000 dsOO06 Leptogorgia virgulata sea whip 11/16/2007 1

18 cells 0.000000 0.000000 Site I Scientific Name II Common Name II Date IIAbundanc~1 Injury II Density II Notes II Latitude II Longitude I 9914 Sargassum f/uitans gulfweed drift alga 1216/2007 na 28.961709 -82.73274E 9915 Penicillus sp. fragments shaving brush plant 12/6/2007 na 28.961050 -82.74093 9915 no plant no plant 12/6/2007 28.961050 -82.740933 9916 Cladophora spp filamentous algae 12/6/2007 na 28.971991 -82.74715~

9917 no plant no plant 12/6/2007 na 28.976953 -82.75124~

9918 no plant no plant 12/6/2007 na 28.979026 -82.75010~

9919 no plant no plant 12/6/2007 na 28.981367 -82.74951~

no plant 9992 no plant no plant 12/6/2007 video site 28.972970 -82.73848 plant toss 9992 no plant no plant 12/6/2007 0

no plant 28.972970 -82.73848 9993 Caulerpa prolifera grass caulerpa 12/6/2007 28.975756 -82.742440 "u<:v sample no plant on 9993 Caulerpa prolifera grass caulerpa 12/6/2007 rake 28.956170 -82.74271 9994 Penicillus sp. fragments shaving brush plant 12/6/2007 na 28.966532 -82.74989E 9994 Penicillus sp. fragments shaving brush plan 12/6/2007 1

44 28.966567 -82.749850 9995 no plant no plant 12/6/2007 noplant 28.961437 -82.74540~

9996 no plant no plant 12/6/2007 na 28.959769 -82.73806~

9997 Sargassum natans gulfweed drift alga 12/6/2007 na 28.957730 -82.73977 9997 Udotea conglutinata Udotea spp 12/6/2007 na 28.957730 -82.73977 9997 Caulerpa prolifera grass caulerpa 12/6/2007 na 28.957730 -82.73977 9998 no plant no plant 12/6/2007 na 28.951697 -82.738041 9999 Caulerpa prolifera grass caulerpa 12/6/2007 na 28.951717 -82.74034C dsOO01 Caulerpa prO/ifera grass caulerpa 11/15/2007 20%

28.975452 -82.75326E dsOO01 Graci/aria tikvahiae edible drift alga 11/15/2007 3%

28.975452 -82.75326E dsOO02 Caulerpa sertularoides feather caulerpa 11/15/2007 22%

0.000000 0.000000 dsOO03 Syringodium fiiliforme manatee grass 11/15/2007 68%

28.956969 -82.73553 dsOO03 Gracilaria tikvahiae edible drift alga 11/15/2007 51%

28.956969 -82.73553 dsOO04 Syringodium fiiliforme manatee grass 11/15/2007 86%

28.958463 -82.72839~

dsOO04 Caulerpa mexicana feather caulerpa 11/15/2007 24%

28.958463 -82.72839~

dsOO04 Graci/aria tikvahiae edible drift alga 11/15/2007 10%

28.958463 -82.72839~

dsOO04 Halimeda incrassata Halimeda spp 11/15/2007 7%

28.958463 -82.72839~

dsOO04 Sargassum f/uitans gulfweed drift alga 11/15/2007 4%

28.958463 -82.72839~

dsOO05 Syringodium fiiliforme manatee grass 11/16/2007 34%

28.945315 -82.72938~

dsOO07 Caulerpa mexicana feather cal ule rpa 11/16/2007 65%

28.950001 -82.75146~

dsOO07 Leptogorgia virgulata sea whip 11/16/2007 2

28.950001 -82.75146~

dsOO07 Sargassum natans gulfweed drift alga 11/16/2007 2

28.950001 -82.75146~

dsOO08 Halodule wrightii shoal grass 11/1612007 42%

28.959779 -82.73809~

dsOO09 Halodule wrightii shoal grass 11/16/2007 100%

28.961919 -82.72923 ds0010 Halodule wrightii shoal grass 11/16/2007 100%

28.965891 -82.72788C ds0010 Sargassum f/uitans gulfweed drift alga 11/16/2007 2%

28.965891 -82.72788C dsOO05 Graci/aria tikvahiae edible drift alga 11/16/2007 61%

28.945315 -82.72938~

dsOO06 Dictyota sp.

11/16/2007 2cells 0.000000 0.000000 dsOO06 Halimeda incrassata Halimeda spp 11/16/2007 7 cells 0.000000 0.000000 dsOO06 Udotea conglutinata Udotea spp 11/16/2007 6 cells 0.000000 0.000000 dsOO06 Sargassum natans gulfweed drift alga 11/16/2007 45 cells 0.000000 0.000000 dsOO06 Caulerpa mexicana feather caulerpa 11/16/2007 47 cells 0.000000 0.000000 dsOO06 Caulerpa sertularoides feather caulerpa 11/16/2007 7 cells 0.000000 0.000000 dsOO06 Leptogorgia virgulata sea whip 11/16/2007 8 cells 0.000000 0.000000

3F1209-10, Renewal of the Crystal River Units 1, 2 and 3 - Industrial Wastewater Permit FL0000159, Seagrass Quantification Report for the Area Adjacent to the Crystal River Power Generation Facility, Florida

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3F1209-10, Renewal of the Crystal River Units 1, 2 and 3 - Industrial Wastewater Permit FL0000159, Seagrass Quantification Report for the Area Adjacent to the Crystal River Power Generation Facility, Florida

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