Causes of Light Attenuation 

with Respect to Seagrasses 

in Upper and Lower 

Charlotte Harbor

Final Report 

Submitted to: Southwest Florida Water Management District

Surface Water Improvement and Management Program

7601 U.S. Highway 301 North

Tampa, FL 33637

(813) 985-7481 

Charlotte Harbor National Estuary Program

4980 Bayline Drive 4th Floor

North Fort Myers, Florida 33917

(941) 995-1777 

Submitted by: L. Kellie Dixon and Gary J. Kirkpatrick

Mote Marine Laboratory 

1600 Ken Thompson Parkway

Sarasota, FL 34236

(941) 388-4441 

September 20, 1999

Mote Marine Laboratory Technical Report No. 650.
Please note that pages do not always correspond to those listed in the table of contents. This was caused when the document was converted to a PDF file.
i

Acknowledgments

Thanks are due to many, but especially Linda Franklin, Jay Leverone, Tiffany Lutterman, Ari 

Nissanka, Jon Perry, Joie Simoneaux, Roberta Starke, and David Tomasko.
ii

Table of Contents

Acknowledgments.............................................................................................................................i

Table of Contents.............................................................................................................................ii

List of Tables...................................................................................................................................iv

List of Figures...................................................................................................................................v

Executive Summary.........................................................................................................................ix

Introduction .....................................................................................................................................1 

Materials and Methods.....................................................................................................................4 

Study Sites.............................................................................................................................4 

Instrumentation and Tides...................................................................................................7 

Biotic Measurements............................................................................................................8 

Epiphytic Attenuation ..........................................................................................................8 

Continuous PAR Data Reduction......................................................................................10 

Bottom Reflectance ............................................................................................................11 

Manual PAR Data Reduction.............................................................................................12 

Adjustment for Sun Angle .................................................................................................13 

Verification of Continuous Readings.................................................................................15 

Water Quality Sampling .....................................................................................................15 

Suspended and Dissolved Spectral Absorption...................................................................17 

Optical Modeling................................................................................................................18 

Statistical Testing................................................................................................................21 

Results and Discussion....................................................................................................................22 

Climatic conditions.............................................................................................................22 

Long-term Water Clarity....................................................................................................22 

Temperature and Salinity...................................................................................................25 

Maximum Depths of Grassbeds..........................................................................................29 

Seagrass Response...............................................................................................................30 

Epiphytic Attenuation ........................................................................................................40 

Attenuation Coefficients - Continuous Data......................................................................47 

%PARw and %PARp - Continuous Data ...........................................................................47 

Weekly and Monthly Data .................................................................................................52 

PAR Annual and Daily Totals - Continuous Data..............................................................56 

Water Quality Data ............................................................................................................67 
iii

Optical Model.....................................................................................................................67 

Literature Cited..............................................................................................................................81 

Appendix A

Water Quality Data 
iv

List of Tables

Table 1. Sites and sampling levels for the light requirements of seagrasses in 

Charlotte Harbor, April 1997 to April 1998. ............................................................... 7 

Table 2. Laboratory methodologies for water quality parameters. ............................................ 17 

Table 3. Annual average water quality parameters from the SWFWMD-SWIM 

Charlotte Harbor monthly monitoring program; all stations pooled. Values 

from 1998 represent six months of data..................................................................... 24 

Table 4. Mean surface salinity (PSU) by station, for June through November, and 

for the project year. ....................................................................................................29 

Table 5. Maximum depth limits of grassbeds at study sites in Charlotte Harbor and 

Pine Island Sound..................................................................................................... 30 

Table 6. Mean shoot density (# m-1) for June, August, and October for all stations. .................. 32 

Table 7. Mean annual average shoot density (# m-1) for the stations sampled over a 

12 month period. ...................................................................................................... 33 

Table 8. Change in shoot density (# m-1) between May 1997 and April 1998........................... 33 

Table 9. Change in blade area per shoot between May 1997 and April 1998........................... 39 

Table 10. Summary of biotic changes at seagrass stations between May 1997 and 

April 1998................................................................................................................ 39 

Table 11. Epiphytic attenuation (as % reduction of PAR) for all sites, June through 

November, and for the eight sites sampled over a 12 month period. ........................... 44 

Table 12. Monthly and annualized water clarity parameters from continuous 

instrumentation, 1000-1400 hours............................................................................. 50 

Table 13. Monthly and annualized adjusted attenuation coefficients (K0 Adj; m-1) from 

weekly observations. Except as noted, n~50 for each station. .................................. 55 

Table 14. Monthly and annualized %PARp at seagrass depth limits. Except as noted, 

n~50 for water column observations and n=12 for epiphytic attenuation. .................... 58 
v

Table 15. The effect of tidally varying water column depths on mean daily PARw and 

annual PARw received at depth. Unless specified, units are Moles m-2 day-1...................... 

 59 

Table 16. Estimation of PARw and PARp (including bottom reflectance) at seagrass 

canopy heights for comparison with previous work. Unless specified, units 

are Moles m-2 day-1. .................................................................................................. 62 

List of Figures

Figure 1. Charlotte Harbor study area........................................................................................ 2 

Figure 2. Northern Charlotte Harbor seagrass stations. Level I, II, and III indicate 

sampling regime over the course of the project year. ..................................................... 5 

Figure 3. Southern Charlotte Harbor seagrass stations. Sites MAT, PAS, SAN, 

and BIG sampled for a six month period...................................................................... 6 

Figure 4. Apparatus for determination of epiphytic attenuation. ................................................... 9 

Figure 5. Geometry of longer path length for PAR under conditions of low solar 

elevation. ....................................................................................................................14 

Figure 6. Example of raw and adjusted attenuation coefficients (K0 and K0 Adj) at Site 

DEV, December 1, 1997.......................................................................................... 16 

Figure 7. Combined flows of the Peace and Myakka Rivers. Historical averages and 

flows during the project year. .................................................................................... 23 

Figure 8. Annual means of water clarity from the SWFWMD-SWIM monthly 

monitoring program. Station CH-009 is near the COT site for this project................. 26 

Figure 9. Monthly means and standard deviations of temperature (A) and salinity (B) 

at the HOG site during the project year...................................................................... 27 

Figure 10. Standard deviation (s.d.) of salinity as a function of mean annual salinity at 

the eight stations sampled for the entire project year................................................... 28 
vi

Figure 11. Maximum depths of seagrass beds as a function of mean annual salinity at 

the eight stations sampled for the entire project year................................................... 31 

Figure 12. Shoot density (# m-2; mean and standard deviation) observed at the 

maximum depths of Thalassia testudinum beds over the project year. ...................... 35 

Figure 13. Shoot density (# m-2; mean and standard deviation) observed at the 

maximum depths of Halodule wrightii beds over the project year. ............................ 36 

Figure 14. Biotic response (blade widths, blade areas per shoot, leaf area index, and 

shoot density) observed at the maximum depths of Thalassia testudinum

beds over the project year. Means and standard deviations of individual 

station means............................................................................................................ 37 

Figure 15. Biotic response (blade widths, blade areas per shoot, leaf area index, and 

shoot density) observed at the maximum depths of Halodule wrightii beds 

over the project year. Means and standard deviations of individual station 

means....................................................................................................................... 38 

Figure 16. Epiphytic attenuation of PAR at the maximum depths of seagrass beds over 

the project year. Means and standard deviations of individual station 

means....................................................................................................................... 41 

Figure 17. Epiphytic attenuation of PAR as a function of monthly mean salinity during 

August and September, 1998, for both northern and southern stations........................ 42 

Figure 18. Annual station means and standard deviations of the epiphytic attenuation of 

PAR for the project year........................................................................................... 43 

Figure 19. Annual station means and standard deviations of the epiphytic loads on 

seagrasses. Means and standard deviations from four samplings, May, 

August, and October of 1997, February 1998........................................................... 45 

Figure 20. Epiphytic attenuation of PAR as a function of epiphytic loading................................... 46 

Figure 21. Monthly means and standard deviations of adjusted attenuation coefficients 

(K0 Adj) measured with continuous instrumentation....................................................... 48 

Figure 22. Annualized %PARw and %PARp for the stations with continuous 

instrumentation.......................................................................................................... 49 
vii

Figure 23. Mean monthly adjusted attenuation coefficients (K0 Adj) as a function of 

mean monthly salinity values for all stations using weekly or monthly data.................... 53 

Figure 24. Maximum seagrass depths as a function of adjusted attenuation coefficients 

(K0 Adj; A) and as a function of total attenuation coefficients (Ktotal)which 

incorporate epiphytic attenuation (B). Based on weekly or monthly data......................54 

Figure 25. Annualized %PARp by site, segregated by seagrass species. T = Thalassia 

testudinum, H = Halodule wrightii. Based on weekly data..................................... 57 

Figure 26. Annualized PARp (at canopy height) from continuous instrumentation in 

Charlotte Harbor and Tampa Bay, Florida................................................................. 61 

Figure 27. Mean daily PARp (at canopy height) from continuous data in Tampa Bay 

(A) and Charlotte Harbor (B).................................................................................... 65 

Figure 28. PARp (at canopy height) during the growing season (April through July) 

from continuous instrumentation in Charlotte Harbor and Tampa Bay, 

Florida...................................................................................................................... 66 

Figure 29. Mean and standard deviations of water quality parameters over the project 

year for northern Charlotte Harbor stations................................................................ 68 

Figure 30. Mean and standard deviations of water quality data for the southern 

Charlotte Harbor stations.......................................................................................... 69 

Figure 31. Scalar attenuation coefficient (K0) ad a function of secchi depths when 

secchi depths were less than or equal to overall water column depth........................... 70 

Figure 32. Mean spatial distribution of salinity between June and November. Based 

on weekly data for the northern stations and monthly data for the southern 

stations..................................................................................................................... 71 

Figure 33. Mean spatial distribution of color between June and November. Based on 

weekly data for the northern stations and monthly data for the southern 

stations..................................................................................................................... 72 

Figure 34. Mean spatial distribution of chlorophyll between June and November. 

Based on weekly data for the northern stations and monthly data for the 

southern stations. ...................................................................................................... 73 
viii

Figure 35. Agreement between modeled diffuse attenuation coefficient (KdPAR) and 

adjusted attenuation coefficients (Kd Adj) measured in the field. .................................... 74 

Figure 36. Annual proportions of water column attenuation contributed by chlorophyll, 

color, and turbidity for the northern Charlotte Harbor stations. Diameter is 

proportional to the annual adjusted attenuation coefficients (Kd Adj).............................. 76 

Figure 37. Annual proportions of water column attenuation contributed by chlorophyll, 

color, and turbidity for the southern Charlotte Harbor stations. Diameter is 

proportional to the annual adjusted attenuation coefficients (Kd Adj).............................. 77 

Figure 38. Long term (1993-1998) proportions of water column attenuation 

contributed by chlorophyll, color, and turbidity for the SWFWMD-SWIM 

Charlotte Harbor monitoring stations. Diameter is proportional to 

approximate mean adjusted attenuation coefficients (Kd Adj)......................................... 80 

 
ix

Executive Summary

Little direct destruction of grasses has occurred in the Charlotte Harbor system, but although 

anthropogenic impacts appear minor at this time in comparison to Tampa Bay, nutrient loadings 

are expected to increase. The Charlotte Harbor system is also very different in hydrological and 

water quality characteristics from Tampa Bay, predominantly due to higher freshwater inflows. In order 

to evaluate the susceptibility of seagrasses in Charlotte Harbor to impacts from increased nutrients, the 

Southwest Florida Water Management District - Surface Water Improvement and Management 

Program funded a study of grasses in the Harbor with the following goals; 1) to determine the amount 

of light present at the deep edge of seagrass meadows, 2) to determine the amount of light 

reduction associated with epiphyte coverage of seagrass blades, and 3) to determine the relative 

importance of various light attenuators on water column clarity.

Studying both Thalassia testudinum and Halodule wrightii seagrasses, photosynthetically active 

radiation (PAR) and attenuation were continuously monitored for one year at the deep edges of 

four grass beds along a gradient of salinity (15 - 28 PSU) from the upper to lower Harbor. The 

morphological behavior of the grass bed (shoot density, blade area) was determined bimonthly 

and was used to evaluate the sufficiency of PAR received. PAR data are presented both as Moles 

m

-2

 yr

-1

 and as a percentage of the immediately subsurface radiation remaining in the water 

column at the maximum seagrass depth (%PARw). In addition to the continuous stations, four 

other stations were visited weekly for determination of water column attenuation. Monthly, 

measurements of water color, turbidity, phytoplankton (as chlorophyll), and epiphytic attenuation 

were determined for all stations. With both theoretical, literature-derived and empirically 

determined values, a spectrally sensitive optical model of water column attenuation was prepared 

to determine the relative contributions of the attenuating substances. Geographic patterns 

allowed an assessment of areas which may be susceptible to degradation of water clarity under 

increased nutrient loading scenarios. With epiphytic attenuation, PAR data could also be 

presented as the %PAR available to the plant (%PARp). Subsequent to initiation of the project, 

the Charlotte Harbor National Estuary Program provided additional funding for another four 

stations in the Pine Island Sound, Matlacha Pass, San Carlos Bay areas. The components of water 

column and epiphytic attenuation were determined monthly at these four stations.

Rainfall and flows during the project year were extraordinary, with winter and springtime flows as 

much as 20 times the long term averages and with resulting decreases in both salinity and water 

clarity measured at almost all stations. The biotic changes observed during the project should be 

interpreted with the consideration that the project year represented higher levels of attenuation 

following two years of comparatively better water clarity. Shoot density and blade area were used 
x

to determine significant biotic increases in seagrasses at one of the most saline stations and 

significant declines at the four stations furthest up the Harbor (least saline). Annual averages of 

epiphytic attenuation ranged from 21% to 44% of additional reduction in PAR reaching the 

plant, with monthly values ranging between 6% and 89%. Highest epiphytic attenuations were in 

the late summer and fall.

For the stations measured continuously, annualized means of light received at the maximum 

depth extent of seagrasses produced %PARw values of 29%, 20%, 16%, and 13% for stations along 

a gradient of increasing salinity. Annual %PARp for the same stations were 16%, 13%, 11%, and 

11%. Biotic increases were observed at the station which received the lowest %PARp. In view of 

the net biotic changes, annual %PARp requirements either exhibit an inverse salinity-related 

gradient or salinity stress produced biotic declines in T. testudinum and H. wrightii, regardless of 

PAR received. The lowest salinity site experienced 16% of subsurface irradiance as %PARp, and 

still experienced biotic declines in above ground biomass. At 11% PARp, sites either exhibited a 

mix of responses (T. testudinum) or increased in biomass (H. wrightii). With one exception, stations 

with a mean annual salinity of below 18 PSU experienced biotic declines.

After correction for epiphytic attenuation (PARp), annual totals available to the plant at the 

canopy height (0.2 m) ranged between about 2,000 and 3,100 Moles m

-2

 yr

-1

. Declines in biomass 

were observed at the lower salinity stations which received between 3,100 and 2,600 Moles m

-2

 yr

-

1

. The higher salinity sites which received near 2,000 and 2,300 Moles m

-2

 yr

-1

 of PARp, (T. 

testudinum and H. wrightii, respectively) experienced either a mixed suite of biotic changes 

(increased shoot density, and decreased blade area per shoot) or a significant increase in density 

and blade area. The annual light requirements for T. testudinum in lower Charlotte Harbor 

appear to be at least as great or greater than the PAR recorded and the light requirements for 

H. wrightii are somewhat less than that for T. testudinum. Seasonal distribution of PARp also 

appears critical. Under comparable salinity, sites with at least three months of the growing season 

in which PARp averaged 10 Moles m-2 day-1 maintained biomass. Low salinity stress at some 

stations apparently resulted in biomass decline despite having received higher PARp.

The results of the optical model were very good, with mean percentage agreement between 

modeled and observed Kd of 110%. Using water quality data from all stations as inputs, the 

model determined that chlorophyll, color, and turbidity accounted for 4%, 66%, and 31% of 

water column attenuation, respectively. The maximum annual average chlorophyll contribution 

was 6%, with an individual maximum of 18% during a phytoplankton bloom (143 ug l-1

chlorophyll). Color dominated water column attenuation, ranging from 40% to 78% to total 

water column attenuation. Color was still the dominant attenuator even after an arbitrary 
xi

doubling of modeled chlorophyll concentrations. Stations in the lower Harbor and southern sites 

showed increased attenuation due to turbidity, compared to upper Harbor sites. Long term data 

produced similar allocations of attenuation, indicating that even under more >typical= rainfall 

patterns, water column attenuation in Charlotte Harbor is dominated by color. 

The results of light requirements for seagrasses, while summarized above as annual totals of PAR 

or %PARw, cannot be applied indiscriminately for management efforts. Levels of light in the 

water column can be used as targets only if epiphytic attenuations are similar. Levels of PAR 

available to the plant (PARp, %PARp) are more robust and biologically meaningful targets, but 

require that epiphytic attenuation be monitored in addition to water column parameters. The 

probable response of epiphytes to increased nutrients in the Charlotte Harbor system is unknown 

at this time. In addition, seasonal signals are strong both in physical water quality, water clarity, 

epiphytic growth, and seagrass growth. Light requirements that vary by season are likely and 

changes in water quality which disproportionately impact clarity during the growing season will 

have an enhanced impact on seagrass growth and survival.
1

Introduction

Seagrasses have been identified as keystone species that offer crucial estuarine habitat and water 

quality benefits. Unfortunately, substantial declines in seagrasses have been documented 

worldwide. Other than direct destruction, declines are most often linked to anthropogenic 

nutrient enrichment (Cambridge and McComb, 1984, Orth and Moore, 1983, 1984), and 

consequent increases in phytoplankton and epiphytic algae (Silberstein et al., 1986; Neverauskas, 

1987; Tomasko and LaPointe, 1991) and available light at depth. Similarly, substantial losses of 

seagrasses have also been identified at depth on the west coast of Florida; in Tampa (Lewis et al., 

1985) and Sarasota Bays (Culter and Leverone, 1993), as well as in Charlotte Harbor (Harris et al., 

1983; Duke and Kruczynski, 1991). Light limitation has been postulated and demonstrated as the 

major limiting factor and demonstrated for grassbeds in Tampa Bay (Hall et al., 1990).

For Charlotte Harbor (Figure 1), however, the situation is somewhat more complex. Little direct 

destruction of grasses has occurred in this system and anthropogenic effects in the comparatively 

undeveloped watershed of Charlotte Harbor have been minor in comparison to Tampa Bay. 

Nutrient enrichment from point sources appears restricted to localized areas (Ardaman et al., 

1995; Coastal, 1995), although the far field effects of these inputs are presently unknown. 

Nutrients are predicted to increase (Hammett, 1988; Coastal, 1995), however, and could 

ultimately be expected to impact seagrasses in ways analogous to worldwide examples of nutrient 

enrichment. The Southwest Florida Water Management District (SWFWMD) is in the process of 

evaluating allowable nutrient loading scenarios for the purpose of protecting the spatial extent of 

grasses and other natural resources within the Harbor.

There are other differences between the Tampa Bay and Charlotte Harbor systems. The 

Charlotte Harbor watershed is substantially larger in relation to the receiving water area than is 

Tampa Bay. The watershed to bay area ratio is approximately 12:1, compared to a ratio of near 

6:1 for Tampa Bay. As a result, freshwater inflows to Charlotte Harbor are large, and during 

the wet season, salinity values across the Harbor can be severely depressed. Many regions of the 

Harbor may at times be at the extreme low end of an acceptable salinity range for seagrasses. 

High color values (and light attenuation) are associated with the freshwater discharges, which 

generally display a rapid onset and a persistent presence throughout the summer wet season. In 

particular, grasses near the mouth of the Myakka and Peace Rivers experience the combined 

stresses of both low light and low salinity. The duration as well as the magnitude of both stresses 

undoubtedly affect seagrass persistence. While the anthropogenic development of a watershed 

can change the timing and flow characteristics of freshwater entering an estuary, climatological 
2

variations in annual rainfall or extreme rainfall events complicate the feasibility of watershed 

management for flows. 

4

The goal of the Charlotte Harbor seagrass light requirement project was threefold; 

1) to determine the amount of light present at the deep edge of seagrass meadows,

2) to determine the amount of light reduction associated with epiphyte coverage of seagrass 

blades,

3) and to determine the relative importance of various light attenuators on water column 

clarity.

Light requirements of seagrasses in Charlotte Harbor were determined through a year-long empirical 

study, using continuous measurement of PAR at the deep edges of selected grassbeds within the 

Harbor. Site selection was critical for interpretation of results, i.e., that the grassbeds were truly light 

limited. Robustness of results was dependent on the relative absence of any extreme climatological 

events during the year. In order to interpret resulting data in the event of an unusual year, behavior of 

the grassbed (measured as shoot density) was determined periodically over the course of study. The 

empirical approach was carried out across a variety of stations that spanned the water quality conditions 

experienced within the estuary. The determination of the relative contributions of water color, turbidity, 

phytoplankton, and epiphytic growth to light attenuation was to allow an assessment of areas which may 

be susceptible to degradation of water clarity under increased nutrient loading conditions.

The study represents a cooperative funding effort between the Southwest Florida Water Management 

District - Surface Water Improvement and Management Section (SWFWMD-SWIM) for work within 

the mainstem of the Harbor, and the Charlotte Harbor National Estuary Program (CHNEP) for work in 

the lower Harbor and Pine Island Sound. The project was carried out under a Research Quality 

Assurance Plan submitted (Nissanka and Dixon, 1997a) through the District to the Florida Department 

of Environmental Protection and under a complementary Quality Assurance Project Plan (Nissanka and 

Dixon, 1997b) submitted through the CHNEP to the U.S. Environmental Protection Agency. 
5

Materials and Methods

Study Sites

Study sites were located along known gradients of salinity, nutrients, chlorophyll, color, and water 

clarity (SWFWMD-SWIM, unpublished data), from the Boca Grande to upper Charlotte Harbor, 

and in Pine Island Sound to San Carlos Bay. Near Boca Grande and Captiva Pass, the influence 

of coastal waters is high. Near the mouth of the Myakka, Peace and Caloosahatchee Rivers, 

dissolved humics in freshwater frequently result in highly colored waters, particularly in the wet 

season. After review of SWIM=s GIS change analysis of seagrass coverage and visual inspection of 

1:24,000 scale aerial photography, sites were established in areas with generally persistent 

seagrasses, although year to year variations in coverage appears high. Less was known of trends in 

seagrass coverage in the southern Harbor. There were no bathymetric controls (i.e., no offshore 

sandbars) at the selected sites. In some instances, sensors had to be installed a distance from the 

deep edge (50 m or less) of the grass beds in order to have sufficient water depth to cover the 

sensors. Deep edges were fast marked for reproducible sampling.

Three levels of sites were established. Four Level I sites were each instrumented with two 

continuous PAR sensors for a year. Sites were also visited weekly for cleaning and monthly for 

collection of water quality and seagrass samples. Four Level II sites were not instrumented, but 

were visited weekly for a year for the determination of attenuation coefficients, and monthly for 

water quality and seagrass samples. Four Level III sites were visited monthly for 6 months to 

determine water column attenuation coefficients and to collect water quality samples and 

seagrasses. All sites were located at the deep edges of seagrass beds. Dominant seagrasses varied 

by site between Thalassia testudinum and Halodule wrightii. Site designations, locations (Figures 2 

and 3), and dominant grasses are listed in Table 1.

Not all grassbeds were monospecific. H. wrightii occurred as a non-dominant seagrass at COT and 

PUN sites, and Syringodium filiforme was present as a minor component at the PAS site near the 

mouth of Captiva Pass.

Instrumentation and Tides

For instrumented sites, two scalar quantum irradiance sensors (4ð; LI-COR 193SA) and a data 

logger were installed immediately outside the deep edge of the grassbed at each site and 

programmed to record 15 minute averages of photo flux. The lower sensor was located at canopy 

height (0.2 m above the bottom); the upper sensor was located 0.5 m above the lower. The lower 
6

sensor was shielded from bottom reflectance with a 38 cm diameter non-reflecting collar, so that 

attenuation coefficients determined would be representative of the water column. Data loggers 

were recovered monthly for data download.


9

 

Table 1. Sites and sampling levels for the light requirements of seagrasses in Charlotte 

Harbor, April 1997 to April 1998.

Level I

PUN - Punta Gorda 26° 53.946' 82° 05.830' T. testudinum

COT - Cotton Key 26° 48.780' 82° 04.386' T. testudinum

BOK - Bokeelia 26° 43.316' 82° 09.129' T. testudinum

DEV - Devilfish Key 26° 45.453' 82° 13.962' H. wrightii

Level II

HOG - Hog Island 26° 55.203' 82° 09.317' H. wrightii

RAD - Radio Tower 26° 51.841' 82° 09.224' H. wrightii

CAP - Cape Haze 26° 47.703' 82° 08.561' T. testudinum

PAT - Patricio Island 26° 42.234' 82° 12.452' T. testudinum

Level III

MAT - Matlacha Pass 26° 39.239' 82° 05.247' H. wrightii

PAS - Captiva Pass 26° 36.804' 82° 11.980' T. testudinum

SAN - Sanibel 26° 29.543' 82° 09.158' H. wrightii

BIG - Big Island 26° 30.023' 82° 01.407' H. wrightii

 

Sensors were covered with clear plastic food wrap, to permit removal of fouling. Instrument 

arrays were operated from April 1997 through April 1998. Depths and vertical PAR profiles were 

recorded on each site visit with PAR sensors (LI-COR 193S [4ð] and 190SA [air, 2ð]). The profiles 

included additional upward and downward facing sensors (LI-COR 192SA [2ð]) to determine incolumn and bottom reflectance. Salinity (Jaeger, 1973) and secchi depths were determined on 

each visit. 

Tidal variations in water depths were estimated from tides recorded at Boca Grande and at Punta 

Gorda, applying the tidal variation in water depth to the mean depths observed on station (Tide 

Tables; U.S. Department of Commerce, 1994).

Biotic Measurements

 
10

Bimonthly, quadrat counts (n~40 per site) of the defined and fast-marked deep edge were 

performed. Due to the manner of station location (at the extreme deep edges of the grassbed) 

shoot densities were expected to be substantially lower than obtained from other regional studies. 

 Quadrats used for T. testudinum-dominated stations were 0.0625 m2 (25 cm X 25 cm), while 

counts at H. wrightii-dominated stations employed 0.0156 m2 quadrats (12.5 cm X 12.5 cm). Blade 

widths and blade areas per shoots were also determined monthly in conjunction with epiphytic 

attenuation measurements.

Epiphytic Attenuation

The attenuation of PAR due to epiphytic materials (here referring to attached epiphytes, 

epifauna, and associated sediment or detritus) was determined monthly on 5 shoots per site, each 

collected from within 1.0 m of the deep edge. Necrotic portions were removed and the 

remaining blades of an entire shoot were scraped with a razor to remove all epiphytic material. 

The area scraped was determined as twice blade length times blade width. 

Epiphytic attenuation was determined by the method of Dixon and Kirkpatrick (1995), after work 

by following Sand-Jensen and Sondergaard (1981), with the apparatus illustrated in Figure 4. The 

suspension of scraped material was placed into a transparent (acrylic) dish. The dish area was 

matched to a typical shoot area to the extent possible, using a 71.3 cm2 dish for epiphytes scraped 

from T. testudinum samples and a 20.3 cm2 dish for H. wrightii epiphytes. The walls of the 

transparent dish were lined with reflective mylar, and the dish illuminated with a quartz-halogen 

light source (Dyna-Lume 240-350) and a white acrylic diffusing slide. A cosine PAR sensor 

(LI-COR 190SB) was placed 12 cm below the sample. The sensor tube was also lined with 

reflective mylar. For each sample, a blank reading of the irradiance passing through deionized 

water ( EBlank)was followed by readings of the irradiance penetrating the epiphyte suspension ( 

ESample). The inverse logarithmic relationship between the epiphyte suspension density and 

transmitted irradiance was used to correct for inequalities between dish area and blade area 

scraped. 

 ln ( E Sample ) - ln ( EBlank ) 1 

y = ! + ln ( EBlank )

 ( Dish Area-1 ) 2 * Blade Area

 Eblank - e 

y

Epiphyte Attenuation (%) = ! 100%
11

 Eblank

Epiphytic attenuations so determined represent area-averaged conditions, i.e., as if the entire 

epiphyte burden of the productive blades was evenly distributed across both sides of the blade 

surfaces. Epiphytic communities have been demonstrated to have a broad absorption spectrum 

(Neckles, 1991, 1993; Marrs et al. 1999). As a result, PAR attenuation of an epiphytic suspension 

is an appropriate approximation to calculate the PAR which is available to the seagrass blades, in 

the absence of more rigorous spectral attenuation information for water column and epiphytes, 

and photosynthetic action spectra for the individual grass species. Dry weights and ash free dry 

weights were determined on a subset of the epiphyte samples.

13

Continuous PAR Data Reduction

The attenuation produced by fouling communities on each continuous PAR sensor was directly 

measured during the monthly retrieval and download of the data loggers. Fouling removed 

during the remaining weekly maintenance visits was calculated by registering the times of the postand pre-cleaning PAR readings (generally separated by 45 minutes) to the theoretical incident 

irradiance curve for the day. An unfouled reading at the precleaning time was calculated by the 

ratio of the theoretical irradiances, and compared to the actual fouled reading to obtain the 

degree of fouling. Fouling rates were computed as the total percentage of PAR reduction by the 

epiphytic community (measured or estimated) per number of days of deployment. 

The attenuation due to sensor fouling was linearly interpolated for all PAR readings between 

endpoints of 0% at the time of cleaning to the percentage measured or estimated immediately 

before the time of the next cleaning. All sensor data were subsequently corrected for the 

interpolated fouling. Water column scalar attenuation coefficients (K0) were calculated from 

corrected sensor data and were additionally examined for consistency before and after cleaning to 

support the validity of fouling estimates. Corrected bottom sensor readings, attenuation 

coefficients, depths at the bottom sensor, and depths at the deepest extent of the grassbeds were 

used to calculate light readings at the deep edge. Since the bottom sensors were shielded, light 

levels presented do not include the PAR received by seagrasses from bottom reflectance, but do 

include the irradiance reflectance due to materials in the water column.

Absolute levels of light received at the deep edge were computed by summation of the individual 

15 minute averages of PAR at the deep edge over the various time periods of interest. 

Calculations of absolute light levels received by seagrasses made use of all available data points 

from 0500 hours until 2100 hours. 

The PAR remaining at the bottom of the water column (% PARw), as a percentage of the 

immediately subsurface scalar irradiance, was computed from each attenuation coefficient (K0) and 

the station water level (z) at the maximum depths of grasses. 

% PARw = e

-K0@z

! 100% 

The percentage of PAR so calculated represents water column processes only, and does not 

include reductions of incident solar irradiance due to air-water surface reflection. As wind and 

wave conditions and solar elevation will alter the percentage of reflection, and as these properties 

are not subject to management actions, the percentage reduction by the water column alone was 
14

the appropriate goal of the study. Annual or seasonal light percentages were computed as the 

mean of percentages received, rather than from annual mean attenuation coefficients. 

The effects of epiphytic attenuation were also incorporated in calculations. Absolute and 

percentages of PAR in the water column (%PARw) at seagrass depth limits, already corrected for 

sensor fouling, were further reduced by the amount of PAR reduction effected by epiphytes to 

estimate the PAR actually available to the plant (%PARp).

% PARp = % PARw ! ( 1 - Epiphyte Attenuation% ! 100-1 )

To maintain correspondence with other seagrass monitoring programs, seasonal and annual 

averages of K0, %PARw, and %PARp are based on data only between 1000 and 1400 hours. Siteand time-specific interpolated water depths obtained from tidal data were also used to compute 

both absolute and percentages of light remaining at the bottom of the water column under 

simulated tidal conditions. 

Bottom Reflectance

Bottom reflectance decreases with a dark bottom type, vegetation, and when the light field 

reaching the bottom of the water column is diffuse. Bottom reflectance must be distinguished 

from upwardly scattered light (upwelling light), produced by the interaction of downwelling light 

and water molecules or, to a lesser extent, particulates. Upwelling light is present at all depths in 

the water column and is not an exclusive function of bottom reflectance. Upwelling light is 

attenuated similarly to downwelling light, and typically consists of between 4-10% of downwelling 

light (Kirk, 1994). 

Bottom reflectance was of concern in this project due to the use of 4ð sensors deployed near the 

bottom of the water column for the determination of attenuation coefficients. In the event of 

substantial bottom reflectance, an attenuation coefficient determined on measurements from 

near the bottom of the water column would be lower than if determined for the entire water 

column, thus resulting in an artificially high %PARw. The degree of the bias would increase with 

higher bottom reflectance and if attenuation coefficients are determined from a relatively small 

fraction of the water column near the bottom. 

The lower continuous sensors were shielded from bottom reflectance, but manual profiles made 

during field visits used no shielding on any sensors. Estimates of bottom reflectance were 

obtained from the irradiance reflectance (ratio of upwelling:downwelling PAR) computed at all 
15

measurements depths from upward and downward facing cosine (2ð) sensors, and difference 

between the surface irradiance reflectance (expected to be 4-5%; Kirk, 1994) and the irradiance 

reflectance measured at bottom. Bottom reflectance was used compute the PAR amounts 

reflected, and added back to the water column values computed from attenuation coefficients, to 

estimate the total PAR received by seagrasses at depths 0.1 m and 0.2 m above the bottom.

Manual PAR Data Reduction

Data reduction of field measurements included calculation of the vertical diffuse attenuation 

coefficient (Kd) between individual depths as:

 - ln ( Ed(z2)/Ed(z1) )

 Kd (z1,z2) = 

 z2 - z1

where Ed is the downwelling irradiance as measured by the cosine sensor at depths z1 and z2. The 

incident irradiance readings (in air) were used to normalize Ed(z1) and Ed(z2) for changing 

atmospheric conditions. Additionally, water column Kd values were computed as the slope of the 

regression of the natural log of the downwelling irradiance [Ed(z)] against measurement depths, 

ignoring the slight non-linearity produced by differential absorption of various wavelengths. 

Scalar attenuation coefficients (K0) were computed similarly to Kd above, and used as a verification 

of continuous sensors readings and attenuation coefficients. 

Downwelling irradiance, upwelling irradiance, and scalar irradiance were used to compute the 

average cosine of photon zenith angles for total light [_ ̄(z) - scattering dependent], the vertical 

attenuation coefficient for net downward irradiance [KE(z)] and the absorption coefficient [a(z)] 

(Kirk, 1992). These parameters provide an empirical means to compare the relative importance 

of scattering and absorbing material between sites, independent of water quality parameters, and 

were calculated as follows, again normalizing for changing incident conditions based on 

measurements in air:

 Ed(z) - Eu(z)

_ ̄(z) = 

 

 Eo(z)
16

- ln ((Ed(z2) - Eu(z2))/(Ed(z1) - Eu(z1)))

KE(z) = 

 

z2 - z1

 

a(z) = KE(z) ! _ ̄(z)

For regression analyses, _ ̄ and a were calculated as the arithmetic average of all values obtained at 

a station, while KE was computed as an average of the slopes of the linear regression of the natural 

log of the difference between downwelling and upwelling irradiance, as a function of depth 

between replicate profiles.

Adjustment for Sun Angle

The size of the study area and time of travel between sites dictated that manual water column 

profiles were made over a substantial period of the day. Solar elevation, therefore, varied 

considerably between sites on a given day, as well as varying between seasons. 

To allow for station intercomparisons and a more direct evaluation of attenuation coefficients as a 

function of water column attenuators, it was necessary to account for the longer effective path 

length required for light to reach a sensor at specific depth when solar elevations are lower (Miller 

and McPherson, 1993). Solar elevations were calculated from time (EST) and date, using the 

latitude of each site (Kirk, 1994). Solar zenith angle (Èa) and the refractive index of water were 

used to compute the cosine of the zenith angle in water (Èw) to correct Kd measurements to Kd adj

(Figure 5) by:

 - ln (Ed(z2) / Ed(z1))

Kd adj(z1,z2) = 

 

 zadj

 z2 - z1 

= Kd (z1,z2) !

 

 zadj

= Kd ! Cos (Èw)
17

The correction uses a longer path length (the effective light path length) to compute attenuation 

coefficients, rather than the measured vertical distances. By accounting for varying path length 

19

over the day, it can be seen that diurnal variation in K0 is substantially minimized (Figure 6), a 

result more consistent with relatively stable water quality conditions at a given site. During 

wintertime at low maximum solar elevations, the effect of the adjustment is also visible between 

1000 and 1400 hours. Seasonally, the longer effective paths in winter (minimal solar elevation) 

can decrease calculated attenuation coefficients by up to 20%.

The adjustment for solar elevations below vertical was used to compare stations sampled at 

differing times of day and to determine the relationship of water quality parameters to 

attenuation. The adjustment, however, does not account for increased scattering effects 

associated with decreasing sun angle and the increasingly diffuse nature of the light field. This 

and other factors controlling the degree of diffuseness (cloud cover, wave state, turbidity) remain 

as a source of variation in relating water column attenuators to the measured attenuation 

coefficient.

Verification of Continuous Readings 

For independent verification of in situ, continuous sensor readings, the attenuation coefficients 

determined during the weekly manual PAR profiles were compared to those calculated from 

corrected top and bottom sensor readings. The comparisons of attenuation coefficients should be 

relatively robust to the differences in integration times between continuous (15 minute) and 

manual (5 sec) readings. Such differences may include the result of bottom reflectance, as manual 

readings were not shielded. Additionally, absolute light levels recorded with continuous sensors 

were confirmed using manually determined attenuation coefficients, the manual PAR readings at 

the deepest depth, and the sensor depths to calculate the PAR levels that would have been 

obtained from a manual profile at the continuous sensor depths. Differences in recorded and 

estimated PAR, however, in addition to being sensitive to bottom reflectance, may also disagree if 

scattered clouds produce varying readings over the 15 minute integration period.

Water Quality Sampling

Water quality samples were collected monthly as deep surface grab samples, preserved 

appropriately, and processed for color, turbidity, chlorophyll a. Laboratory parameters and 

references used in the project appear in Table 2. Conventional water quality parameters were all 

analyzed with EPA-approved methodologies. Methods with no EPA-approved equivalent included 

those for the determination of spectral absorption of the suspended and dissolved components, 

and the light attenuation attributable to epiphytic material, described herein.

21

 

Table 2. Laboratory methodologies for water quality parameters.

Parameter Method Description

Color 2120B1 Visual, nessler tube

Chlorophyll 'a' 10200H1 Acidification and trichromatic

Turbidity 2130B1 Nephelometric

Specific Conductance 120.12 Electrical resistivity

Salinity by calculation3

Spectral Absorption See Text

Epiphytic Attenuation See Text

 

1

 APHA. 1989. Standard Methods for the Examination of Water and Wastewater, APHA, 

AWWA, WPCF, 17th Edition. Washington, D.C.

2

 U.S. EPA. 1983. Methods for the Chemical Analysis of Water and Wastes, EPA-600/4-79-

020.

3

Jaeger. 1973.

 

Suspended and Dissolved Spectral Absorption

On a monthly schedule, the raw water sample from each station was filtered through a glass fiber 

filter (Whatman, GF/F) and the optical density (OD) spectrum (between 400 and 700 nm) of all 

particulate matter (detrital, mineral and chlorophyll) on the filter pad was measured relative to a 

blank, wetted filter in a scanning, dual-beam spectrophotometer (Varian, DMS-80). A blank 

baseline was measured with no filters in the light paths before and after each set of samples and 

the mean of these baselines was subtracted from all scans for that sample set. The calibration of 

the spectrophotometer was checked against calibration standards (Milton Roy) before and after 

running each set of samples.

After the OD spectrum was measured for total particulate matter, each filter was placed in 

methanol (Kishino et al., 1985) for at least 50 minutes to extract the phytoplankton pigments. 

Following the methanol extraction, the OD of the particulate (now only detrital and mineral) was 

measured again. These filter pad OD (ODf) spectra were converted to suspension optical density 

(ODs) spectra by correcting for pathlength amplification (ß; Butler, 1962) caused by multiple 
22

reflectance associated with the concentration of particles on the filters (Cleveland and 

Weidemann, 1993). The ODs spectra were converted to absorption spectra using the following 

equation:

 ai(ë) = 2.3 ! (ODs,i(ë)/pl)

where ai(ë) is the spectral absorption coefficient (m-1) for each component (raw particulate and 

detrital) and pl is the pathlength equivalent to the volume of water filtered divided by the 

clearance area of the filter. Phytoplankton pigment absorption spectra (ap) were obtained by 

subtracting the detrital absorption (ad) spectra (after methanol extraction) from the raw particulate 

(arp) spectra (before methanol extraction).

The dissolved color absorption spectra were obtained using the water filtrates from the above 

filtrations. The optical density spectrum of this filtered water was measured against double 

distilled water in 10 cm cuvettes using the same spectrophotometer described above. The 

dissolved color optical density spectra were converted to absorption spectra (adc) using the above 

equation with ODs replaced by the dissolved color optical density (Oddc) and pl=0.1 m.

Optical Modeling

The optical model originated with equations presented in Kirk (1994) and Gallegos (1993) in 

which vertical attenuation (Kd) is a function of solar zenith angle ( ì0), scattering (b) and total 

absorption (at) with g1=0.473 and g2=0.218 ( determined for the midpoint of the euphotic zone; 

Kirk, 1994). 

Kd = 1 [ at

2

 + G(ì0) * at * b] 2

 ì0

and G(ì0) = g1 * ì0 - g2

As both scattering and absorption are wavelength dependent, the optical model developed was 

spectrally sensitive (1 nm increments). Analytical data (color, turbidity and chlorophyll) were used 

to empirically develop general relationships of absorption and scattering spectra of individual 

attenuators (color, detrital particles, chlorophyll). Wavelength-specific Kd values computed were 

integrated over 400-700 nm and compared against attenuation coefficients measured in the field 

to test model efficacy. 
23

Scattering is reported to be best described as a function of turbidity (Morel and Gentili, 1991) 

rather than of suspended solids (Gallegos, 1994), with: 

b(ë)= (550 / ë) * [Turbidity]. 

Total absorption (at) was partitioned into that attributable to water (aw), chlorophyll or 

phytoplankton (aph), dissolved color (adc), and detritus (ad). 

at = aw +aph + adc + ad

Water absorption profiles [ aw(ë )]were drawn from literature values (Smith and Baker, 1981) as 

reported in Gallegos (1993), with 1 nm values linearly interpolated from the 5 nm values 

provided. Initial attempts to use literature values for chlorophyll specific absorption resulted in 

distributed residuals with the best agreement for chlorophyll concentrations less than 5 ug/l. 

Calculated spectra for samples with higher analytical concentrations were significantly higher than 

observed and may represent a packaging effect of more efficient absorption under lower 

concentrations or smaller cell size. As a result, individual spectra were normalized to the 

maximum absorption (~ 437-440 nm), and averaged for all samples for an overall normalized 

spectra. Chlorophyll-specific absorption [apl(ë)] was calculated for the model from the linear 

relationship between maximum absorption MApl and analytical chlorophyll concentrations as;

apl(ë) = MApl * Normalized Spectra (ë), where

MApl= 0.0209 * (Chlorophyll a, corrected) + 0.0761, n= 129, r2= 0.7647 

Absorption due to dissolved color appears as a negative exponential function (Bricaud et al., 1981) 

of absorption at 440 nm (g440) and spectral slope (sdc), with;

adc(ë) = g440 * e [-sdc* (ë - 440)]

Empirically, absorption at 440 nm was determined as a function of color (in PCU) to be;

g440 = 0.0667 * [Color], n=129, r2=0.9329 

While the mean spectral slope determined during the project agreed well with literature values 

(0.015 compared to a range of between 0.013 to 0.018) there was a remaining dependence of 
24

slope with analytical color concentrations. Accordingly, spectral slope was also computed as a 

function of color (PCU) in the form;

sdc = 0.00003 * [Color] - 0.0178, n= 129, r2 = 0.5111 

Absorption due to detritus and other particulate material is also represented as an exponential 

function of turbidity (NTU) and the absorption cross section of turbidity (ód) in the form;

ad(ë) = ód(ë) * [Turbidity]

In turn, the turbidity absorption cross section is a function of the longwave absorption cross 

section, óbl (i.e., particulate absorption at 720 nm, normalized to turbidity), scaled to absorption at 

400 nm (ó400, or particulate absorption at 400 nm, also normalized to turbidity), and slope 

(empirically determined, sd = 0.01125) as;

ód(ë) = óbl + ó400 * e [-sd

* (ë - 440)]

In addition, ó400 displayed a further dependence on color, that may be attributed to the type of 

particulates present. The dependence of ó400 on color was incorporated as follows;

ó400 = 0.0014 * [Color] + 0.0731, n=129, r2= 0.4676 

Incident irradiance spectra to initiate wavelength-specific attenuation were provided by Li-COR 

(1982). Intensity was seasonally adjusted using a sin function empirically fit to maximum E0

readings (in air) collected throughout the project, and assuming no seasonal or diurnal spectral 

shifts. Cloudless conditions were therefore implicit in the model, and spectral shifts and loss in 

intensity due to surface reflectance at the air-water interface were assumed to be minimal.

The PAR spectrum at a selected depth was calculated by using the incident sunlight spectra to 

"illuminate" a water column of specified conventional properties (color, chlorophyll and turbidity), 

calculating wavelength specific absorption as described above, and integrating the both the 

incident and resulting spectra at depth over 400-700 nm. There were no absorption or scattering 

parameters computed by difference and so overall error terms were evaluated directly, by 

agreements with in situ values of attenuation coefficients, adjusted for sun angle. 

Individual stations were assessed through the optical model for relative contributions of 

attenuating substances, referenced to the attenuation of water alone at the given depth. Using 

the individual sampling data, analytical concentrations of color, chlorophyll, and turbidity were 
25

successively set to zero and supplied as input to the model. Other existing data which included 

attenuation coefficients, turbidity, color, and chlorophyll were used to evaluate model robustness 

under combinations of water quality conditions that may not be represented under initiating 

conditions used to develop the model.

During model calibration, relationships of average cosine measurements (collected using in situ

instrumentation and defined as downwelling irradiance divided by downward scalar irradiance, ìo

= Ed / Eo ) with turbidity were examined in an attempt to improve predictive capability. In 

general, the diffuseness of the underwater light field is expected to increase with turbidity, 

increasing the apparent cosine of the zenith angle, which may improve the slight bias observed in 

prior applications of model results (Gallegos, 1993). Based on measurement conditions, varying 

incident sunlight spectra (cloudy or clear sky) was also examined to improve model accuracy. 

Statistical Testing

Statistical analyses of the data were performed using SYSTAT 7.0 (SPSS, 1997) statistical analysis 

software with significance levels set at p<0.05. Analyses included t-test, and one-way ANOVA; 

with non-normally distributed data, Mann-Whitney, Friedman's two way ANOVA, Kruskal-Wallis, 

and Wilcoxon signed rank tests were used. 
26

Results and Discussion

Climatic conditions

Rainfall during the project year was extraordinary. Examining averages from the Northern Peace, 

Southern Peace, and Manasota Basins (SWFWMD, 1997, 1998), cumulative rainfalls during April 

and May 1997 were 3 to 4 inches above historical means. For June through October, cumulative 

rainfalls during the project year were comparable to or slightly below historical levels. Beginning 

in November, however, cumulative monthly rainfall exceeded historical means, and by March 

1998, rainfall received during the project year was 21 inches greater than the average amounts 

received during a comparable period. In addition, much of the rainfall was received in a relatively 

small number of storms, thus increasing runoff potential.

As expected, recorded flows mirrored rainfall. Combined flows of the Peace River at Arcadia and 

the Myakka River near Sarasota during the project year were plotted against the historical 

averages at the same stations (Figure 7). Deviations from typical conditions were slight during the 

initial months (April through October 1997) of the project. In November, however, combined 

flows rose to many times greater than historical monthly averages and by March 1998, combined 

flows were over 9000 cfs, in contrast to an expected 450 cfs (SWFWMD, 1998). The increased 

flows were evident not only in salinity regimes within the Harbor (below) but also in the water 

clarity measured at almost all stations. 

Long-term Water Clarity

Since 1993, the SWFWMD-SWIM department has fielded a monthly water quality program in 

Charlotte Harbor. Parameters include attenuation coefficients and water column attenuators, as 

well as nutrients and other physical measurements. Stations are all in Charlotte County, ranging 

from the riverine portions of the Myakka and Peace Rivers to outside of Boca Grande Pass. At 

various times, data have been collected from up to 15 stations of which Station CH-09B is near 

Site RAD and Station CH-009 is near COT. Annual means of selected water quality parameters 

appears in Table 3. Depth of 1% surface irradiance cannot be directly converted to Kd or Kd Adj 

as used in this project as the depths reported are those to which 1% of the incident (in air) 

irradiance reaches, incorporating the loss of PAR at the air-water interface.

These data indicate that water clarity and parameters which contribute to attenuation are highly 

variable, not only seasonally, but between years. Attenuation coefficients, secchi depths, color, 

turbidity, total suspended solids, and chlorophyll a (corrected for pheophytin) all exhibit significant 
27

differences by year (Kruskal-Wallis). Salinity also varied significantly by year, while temperatures 

during 1993 through 1997 (the only years with complete records) displayed no significant 

variations. Overall salinity was depressed during 1995 and 1998, while the 


30

remaining years (1993, 1994, 1996, and 1997) were comparable to one another. As freshwater 

inflow is thought to be one of the major contributors (through highly colored humic substances) 

to attenuation in the Harbor, it was not surprising to see a comparable elevation of overall 

attenuation coefficients during 1995 and 1998 as well. (Salinity and color values were strongly 

correlated, and significant linear models of attenuation could be constructed with either 

parameter.) Annual means of attenuation coefficients at SWIM Site CH-009, adjusted for sun 

angle, appear in Figure 8 for 1993 through the partial year of 1998. The biotic changes observed 

during the seagrass project and the net gain or loss of grasses at the deep edge should be 

interpreted with the consideration that the project year represented higher levels of attenuation 

following two years of comparatively better water clarity.

Temperature and Salinity

Temperature data, gathered weekly, indicated no unusual events and displayed a typical cyclical 

annual pattern. Temperatures ranged from 14°C in January and February 1998 to 34°C in July 

and August 1997. Temperatures in April 1998 were comparable to April 1997 data, and so 

changes in seagrass densities between the beginning and end of the project are more likely to be 

attributable to light or salinity forcing functions. Differences between stations were not 

significant. Temperatures are illustrated for the HOG site (Figure 9A).

Salinity on the other hand responded to the record freshwater inflows to the Harbor and 

displayed no cyclical pattern during the project year. A gradual decline in salinity from the 

beginning of the project became more precipitous in late July and early August, recovered 

somewhat, but continued to decline from late August until the end of the study. Trends in 

salinity are illustrated for the HOG site, the least saline station (Figure 9B). The very wet winter 

produced minimum salinities during February or March 1998 for the 8 Harbor sites, with a 

minimum value of 2.0 PSU at HOG. Annual averages of salinity (Table 4) ranged between 14.7 

(HOG) and 27.5 PSU (PAT) with the amount of variation in salinity (as either the range or 

standard deviation) strongly and inversely correlated with mean annual salinity values (Figure 10). 

 In the shorter period of record available for the southern sites, the lowest salinities were generally 

observed in September. Table 4 also provides mean salinity values during concurrent periods of 

record (June through November) for all twelve sites and indicates that the HOG site again 

received the most freshwater, while the PAS site was under the most marine influence during this 

period.



34

 

Table 4. Mean surface salinity (PSU) by station, for June through November, and for the 

project year.

 June 1997 - November 1997 April 1997 - April 1998

Site Mean s.d. n Site Mean s.d. n

HOG 17.3 5.5 25 HOG 14.8 7.5 52 

RAD 17.8 6.4 23 RAD 16.2 8.1 50 

CAP 19.7 6.2 25 CAP 18.0 7.5 52 

PUN 20.8 4.4 26 PUN 18.9 6.3 52 

MAT 21.3 3.9 6 

COT 23.3 4.5 26 COT 21.4 6.5 53 

BOK 26.3 4.2 26 BOK 24.6 5.8 53 

BIG 26.6 1.2 +6

DEV 27.0 4.9 25 DEV 25.9 5.6 52 

PAT 28.2 4.2 26 PAT 27.5 4.5 52 

SAN 29.2 2.9 6 

PAS 30.3 6.0 6 

 

Maximum Depths of Grassbeds

The maximum depth limits of sites visited during this project appear in Table 5. Values are the 

project means of water depths recorded at the deep edges and are assumed equal to depth below 

mean tide level (MTL) as samplings were random with respect to tides and as the number of 

station visits was large (n~50) for the eight Harbor sites. The number of visits to the southern 

sites in Pine Island Sound, San Carlos Bay, and Matlacha Pass were much smaller (n=6) and the 

depths at these stations must be considered approximate.
35

 

Table 5. Maximum depth limits of grassbeds at study sites in Charlotte Harbor and Pine Island 

Sound. Depths are in meters below mean tide level. *- Stations with only six 

observations should be considered approximations.

Site Depth (m) n=

HOG 0.93 53 

PUN 0.95 51 

RAD 1.47 51 

CAP 1.69 53 

COT 1.70 51 

DEV 2.43 52 

BOK 2.63 53 

PAT 2.72 52 

MAT 1.48* 6 

PAS 2.32* 6 

SAN 2.34* 6 

BIG 2.43

*

 6 

 

Maximum depths ranged from 0.9 to 2.7 m below MTL. Maximum depths, in general, increased 

with distance from the mouths of the Myakka and Peace Rivers and with increasing salinity 

(Figure 11). (Salinity in the Charlotte Harbor system, as discussed below, has a highly significant 

inverse relationship with light attenuation.) Of the eight sites with 12 months of data, the PUN 

site was shallower than would be expected based on salinity alone. Shoot density at the maximum 

depth was also higher than the remaining T. testudinum-dominated sites, indicating that this 

station may not have been light limited. Additionally, the higher epiphytic loading at this site 

may have contributed to the shallower depths at this station (see below). The deepest sites were 

on the southern side of the Harbor near Bokeelia (BOK) and Patricio Key (PAT). The southern, 

less frequented stations appeared to increase in maximum depth from north to south (from 1.5 to 

2.4 m) with the deepest site (BIG) near the mouth of the Caloosahatchee River.

Seagrass Response
36

Species-specific shoot densities were substantially lower than for other work on T. testudinum and 

H. wrightii (Hall et al., 1991; Czerny and Dunton, 1995; Tomasko and Dunton, 1995). Dixon and 

Leverone (1995) have demonstrated the sensitivity of shoot density to proximity to the deepest 

edge in a light limited bed, as counts at 1 m from the edge were substantially greater than at the 

defined deepest edge. The design of this project, working at the deepest edge of the various 

stations, would be expected to have minimal values of shoot 

38

density. The variation in density between stations with similar grasses may represent a true 

difference in shoot density at the deep edge, or may be a result of marking the deepest edge with a 

series of straight segments. The linearity of the edge of the grassbed determined the number of 

quadrats of bare sand counted and would contribute to variations in apparent density. It should 

be emphasized, however, that the same deep edge at each station was consistently sampled, and so 

observed changes in density at a given station represent meaningful change. For comparison of all 

12 sites, Table 6 provides mean shoot densities by species for June, August, and October, 

combined, while Table 7 lists minimum, maximum, and annualized shoot densities for the sites 

sampled over a 12 month period.

 

Table 6. Mean shoot density (# m-1) for June, August, and October for all stations. H = 

Halodule wrightii; T = Thalassia testudinum.

Site Species Mean s.d.

BIG H 49 24 

SAN H 75 41 

MAT H 86 20 

HOG H 112 49 

RAD H 164 77 

CAP H 294 164 

DEV H 383 162 

PAT T 21 6 

BOK T 29 10 

PAS T 39 6 

COT T 50 14 

PUN T 109 35 

 

 
39

 

Table 7. Mean annual average shoot density (# m-1) for the stations sampled over a 12 month 

period. H = Halodule wrightii; T = Thalassia testudinum.

Site Species Mean s.d.

RAD H 112 67 

HOG H 116 44 

CAP H 198 133 

DEV H 314 128 

PAT T 21 6 

BOK T 29 14 

COT T 52 13 

PUN T 122 30 

 

Shoot density along the marked deep edge over the course of the project was used as one of the 

primary biotic indicator of seagrass bed persistence. Non-parametric tests (Kruskal-Wallis one way 

analysis of variance) of initial (early May 1997) and final (late April 1998) shoot densities by site 

indicated that of the instrumented sites, PUN and COT experienced no significant change, while 

DEV and BOK both recorded significant increases (Table 8). Of the remaining non-instrumented 

sites sampled over a 12 month period, only the PAT site displayed a significant increase, while 

HOG, RAD, and CAP were statistically unchanged. 

 

Table 8. Change in shoot density (# m-1) between May 1997, and April 1998. (H=Halodule 

wrightii, T=Thalassia testudinum.)

 May 1997 April 1998 Signif.

Site Species Mean s.d. Mean s.d.Change p=

HOG H 182 364 88 124 - >0.05

RAD H 75 130 67 80 - >0.05

CAP H 131 171 71 92 - >0.05

DEV H 142 150 269 152 Incr 0.000
40

PUN T 114 88 112 116 - >0.05

COT T 40 42 46 66 - >0.05

BOK T 7 16 36 27 Incr 0.000

PAT T 11 17 26 27 Incr 0.002

 

During the project year, there were statistically significant changes in shoot density by month for 

the T. testudinum and the H. wrightii sites. Seasonal signals in shoot density were suppressed at the 

T. testudinum stations (Figure 12), PUN, COT, BOK, and PAT, compared to H. wrightii sites and 

compared to non-light limited sites studied elsewhere (Hall et al., 1991; Dixon and Leverone, 

1995). The comparative lack of seasonal signal was consistent with other work on T. testudinum at 

maximum depth limits (Dixon and Leverone, 1995). Lowest T. testudinum shoot densities were, 

in general, observed in October, approximately 2 months following substantive increases in 

attenuation coefficient, rather than during the coldest months as noted in other work (Dixon and 

Leverone, 1995). 

Sites composed predominantly of H. wrightii (RAD, CAP, and DEV) generally displayed more 

seasonal variation in shoot density (Figure 13) than T. testudinum sites. Maximum H. wrightii

densities were recorded in either June or August. Again, density appeared to decline following the 

onset of decreased water clarity rather than in response to temperature declines. In contrast, 

H. wrightii densities at the HOG site were lower overall, and there were no significant differences 

in density by month. The HOG site was the site with the lowest overall salinity, highest salinity 

variation (either absolute or as a percentage of the mean), and the highest annual attenuation 

coefficient.

Blade area per shoot also changed significantly over the project (Table 9). Of the eight upper 

Harbor stations, all but CAP and DEV exhibited significant net declines between April 1997 and 

March 1998. After an initial increase in blade area between May and June, areas at both T. 

testudinum and H. wrightii sites declined steadily. Over the course of the study, blade areas at T. 

testudinum stations were reduced by approximately 38%, while leaf areas at H. wrightii-dominated 

stations declined by 17%. Shoot density and blade area per shoot were combined into leaf area 

index (LAI; cm2 m

-2

). While not testable, LAI increased at sites BOK, PAT, and DEV between 

April 1997 and March 1998, and decreased at the remaining sites. Of the continuously 

instrumented sites, only DEV exhibited significant increases in both shoot density and blade area 

per shoot. Both the PUN and COT sites had decreased blade area and unchanged shoot density 

for a presumed net decline in above ground biomass. The net change is unclear at the BOK site,
41

which experienced decreased blade area together with increased shoot density. Biotic changes are 

summarized by species for all sites in Figures 14 and 15. Overall biotic change assessment used to 

interpret measured light levels appears in Table 10. Stations in the upper Harbor experienced 

declines in comparison to the lower Harbor stations, with decline typically produced by loss of 

blade area per shoot.




46

 

Table 9. Change in blade area per shoot (cm2 shoot-1) between May 1997 and April 1998. 

(H=Halodule wrightii, T=Thalassia testudinum.)

 May 1997 April 1998 Signif.

Site Species Mean s.d. Mean s.d.Change p=

HOG H 12.5 0.9 10.7 1.2 Decr 0.028

RAD H 11.0 2.2 7.6 2.1 Decr 0.028

CAP H 10.4 1.6 11.6 2.4 - >0.05

DEV H 11.7 2.8 8.8 1.3 Decr 0.028

 

PUN T 34.8 2.3 26.2 5.6 Decr 0.016

COT T 41.7 10.0 29.6 4.0 Decr 0.028

BOK T 43.8 9.1 28.3 3.3 Decr 0.009

PAT T 40.6 5.4 27.1 8.3 Decr 0.047

 

Table 10. Summary of biotic changes at seagrass stations between May 1997 and April 1998. 

Site SpeciesLAI Blade Area Shoot Net 

 (m2 m

-2

) Per Shoot Density Change

 (cm2 shoot-1) (# m-2)

HOG H D D - D

PUN T D D - D

RAD H D D - D

COT T D D - D

CAP H D - - -

BOK T I D I ?

PAT T I D I ?

DEV H I - I I

 
47

Changes, or lack thereof, at the southern sites sampled for a 6 month period are more difficult to 

interpret. Of the three sites recording a net increase over a 12 month period, one was a 

H. wrightii site and the other two dominated by T. testudinum. Each exhibited declines in shoot 

density between June and October that may have been associated with declines in water clarity. 

Of the four stations sampled for 6 months, three exhibited no change in shoot density between 

June and October (the last sampling common to all 12 stations), while at the SAN site, shoot 

density declined. As the SAN site was not the most affected by decreasing water clarity during this 

period, and as the decline in shoot density did not appear deleterious based on the 12 month 

stations, the four 6 month stations were assumed to represent stable grassbeds.

Epiphytic Attenuation

Epiphytic attenuation varied widely over the course of the project with maximum attenuation 

observed in August and September 1997 (Figure 16). Seasonal patterns were most pronounced at 

the upper Harbor sites, HOG, PUN, and RAD, while lower Harbor and southern sites were 

relatively consistent over the time periods sampled. There were significant differences in epiphytic 

attenuation both among stations, between months, and by species, with attenuations for 

T. testudinum samples slightly higher than H. wrightii samples. During the months with highest 

epiphytic attenuation (August and September 1997), higher attenuations were associated with 

lower salinity values (Figure 17). 

Among the sites sampled for a 12 month period (Figure 18), PUN experienced the highest annual 

mean attenuation due to epiphytes, at 44%. Of the H. wrightii sites, RAD recorded the highest 

annual epiphytic attenuation, at 40%. The minimum fouling for either species was observed at 

the DEV site, with an annual mean of 21%. Individual monthly values of epiphytic attenuation 

ranged from 6% to 71% for H. wrightii sites, and 6% to 89% for T. testudinum sites. There was no 

overall trend of annual epiphytic attenuation against mean annual salinity. In examining monthly 

epiphytic attenuation values as a function of other monthly water quality parameters, higher 

epiphytic attenuations were generally present under moderate color values (50-150 PCU), 

moderate salinity values (10-20 PSU), shallower depths (1-2 m), and mid-range water column 

attenuation (1-2 m-1). 

Of the southern stations, the SAN site recorded the lowest epiphytic attenuation for the six 

month period (13%), while the PAS site, under the most marine influence, recorded 29%. 

Summary data for the eight sites with 12 months of data and for all sites during the summer 

period appear in Table 11. Linear interpolation between monthly, site-specific epiphytic 

attenuation were used to generate plant-available PAR levels from continuous sensor data.



51

 

Table 11. Epiphytic attenuation (as % reduction of PAR) for all sites, June through November, 

and for the eight sites sampled over a 12 month period.

 June 1997 -November 1997 April 1997 - April 1998

Site Species Mean s.d. Site Species Mean s.d.

RAD H 46.0 19.1 RAD H 40.4 20.7

HOG H 44.8 24.3 HOG H 34.3 21.0

CAP H 27.4 14.2 CAP H 25.9 15.4

MAT H 27.5 10.9 DEV H 20.8 9.7 

DEV H 22.7 12.7 

BIG H 21.6 7.3 

SAN H 13.5 3.5 

PUN T 58.8 22.6 PUN T 44.3 23.9

COT T 35.1 16.0 COT T 35.3 16.9

BOK T 28.3 8.2 BOK T 33.9 17.4

PAT T 21.4 13.4 PAT T 25.1 12.4

PAS T 29.2 7.8 

 

Fouling rates on continuous sensors displayed seasonal patterns consistent with the monthly 

epiphytic attenuation values. Implied is that blade turnover was relatively consistent over time, a 

result supported by the low seasonal signals observed in shoot density and leaf area indices.

 

Four of the monthly sets of epiphytic attenuation samples were processed for dry weights with 

loads (as mg cm-1) computed from seagrass blade areas. While epiphytic loads did not vary 

significantly by species, there were significant seasonal and station differences that were even more 

pronounced than the differences in attenuation (Figure 19). This result is due to the composition 

of the epiphytes, which in the case of HOG, RAD, and PUN, consisted of a large proportion of 

calcareous material (i.e., limpets, barnacles, etc.).

The general relationship of epiphytic attenuation to loading was similar to that observed 

previously for Tampa Bay (Dixon and Leverone, 1995) although the range in the optical 

properties of the various epiphytic types produced a large amount of scatter in the results. The 
52

regression between attenuation and the natural log of loadings was significant (Figure 20), 

although the curve produced differed from that for Tampa Bay (Dixon and Leverone, 1995), and 

for that derived for other species and regions (Sand-Jensen and Borum, 1983; Odum, 1985; Kemp 

et al., 1988; Burt et al., 1995; Neckles, 1993; Fong and Harwell, 1994). In general, the largest 

amount of scatter in the relationship was associated with those stations where the calcareous 

epiphytes were prevalent.

Fig 19


54

Attenuation Coefficients - Continuous Data

Accuracy of attenuation coefficients determined with continuous instrumentation was assessed by 

comparison with the weekly in situ profiles. Differences between the two quantities could result 

from changing water clarity over the 15 minute integration period of the continuous recorder, 

non-homogeneity of the water column, bias in the correction of continuous data for fouling, and 

individual biases of both the upper and lower continuous sensors and the field profiling sensor. 

Despite the multiple potential sources for error, the slope between continuous attenuation 

coefficients and manual profiles was 0.95, with the 95% confidence interval ranging between 0.90 

and 1.00. A slope of 1.00 indicates perfect agreement. Differences did not appear attributable to 

the shielding of the continuous sensors.

As salinity declined over the project year, scalar attenuation coefficients (K0 Adj) collected with 

continuous sensors increased for the region as a whole. Figure 21 illustrates the monthly means of 

mean daily adjusted attenuation coefficients between 1000 and 1400 hours, with standard 

deviations indicating the variation between the four continuous stations. Corrected for sun angle, 

the coefficients are smaller than typical coefficients (K0) during the winter months. Depressions in 

salinity in August were matched by increased attenuation during this month. From October 1997 

through March 1998, water clarity steadily declined, with only a slight improvement in April 

1998. Monthly values of adjusted and unadjusted attenuation coefficients appear in Table 12 for 

the four continuous stations.

%PARw and %PARp - Continuous Data

The attenuation coefficients measured at 15 minute intervals and mean station depths were used 

to compute the percentage of immediately subsurface PAR reaching the maximum depth limits of 

seagrasses (%PARw) and after attenuation by epiphytic loads (%PARp; Table 12). Again data were 

limited to 1000-1400 hours, and monthly means are the calculated from daily means to reduce the 

impacts of missing data. Monthly values of %PARw ranged between 56% at PUN during June 

1997 to less than 1% at DEV during March 1998. Values of %PARp ranged between 38% and 

0.3%for the same stations and time periods. Annualized means produced %PARw values of 

29.0%, 19.8%, 16.2%, and 13.2% for PUN, COT, BOK, and DEV, respectively, while annual 

%PARp for the same stations were 15.9%, 13.2%, 11.0%, and 10.6%. The PUN site received 

substantially more PAR at depth than the remaining stations (Figure 22). Even with the PUN site 

data included, however, agreement between stations, measured as %RSD, was better for %PARp 

than for %PARw, emphasizing the utility of incorporating epiphytic attenuation in determination 

of light requirements. 
55

In view of the net biotic changes, annual %PARp requirements for T. testudinum appear to exhibit 

a salinity-related gradient. The PUN site, the lowest salinity site, experienced 16% of subsurface 

irradiance as PARp, and still experienced biotic declines in above ground biomass. Similarly, the 

COT site received even less %PARp (13%), with similar biotic declines. At 




60

11% PARp, the BOK site exhibited a mix of responses, although with a net increase in LAI. The 

only instrumented H. wrightii site, DEV, experienced increased biomass with only 11% PARp.

Weekly and Monthly Data

Monthly mean adjusted attenuation coefficients computed from weekly visits and from continuous 

data were compared (paired t-tests) to assess the robustness of conclusions using weekly data. For 

the four sites as a whole, there were no significant differences between the two methods, and the 

monthly attenuations determined with the continuous data averaged only 0.04 m-1 higher that the 

monthly attenuations determined from weekly visits. By station, the COT site was the only one 

with a significant difference; monthly attenuation computed from continuous data averaged 0.29 

m

-1

 lower than the mean weekly profile. In using the annualized attenuation to compute %PAR 

at depth, the COT site could be in error by as much as much as 12% (i.e., 16% PAR annualized 

value based on weekly visits, versus 28% PAR based on continuous data). The remaining stations 

would be in error by only between 0.6% and 6% PAR. It is obvious that some degree of caution is 

needed, but in general, the weekly samplings appear to be a relatively robust approach to sampling 

a large number of stations. 

Monthly and annual mean adjusted attenuation coefficients based on weekly profiles appear in 

Table 13 for all sites. Adjusted attenuations were used since not all sampling could take place 

between 1000 and 1400 hours. Annual attenuation coefficients ranged from 0.80 to 1.79 m-1. 

Temporal patterns of attenuation were comparable at all of the northern stations with a steady 

decline in water clarity over the course of the project. The southern sites were sampled for a 

shorter period of time but did not exhibit the same degree of attenuation increase over time as did 

the northern sites and so the mean attenuation obtained was used as a surrogate for annual values 

in the subsequent analyses.

Viewing all sites and data together, a simple yet highly significant model of mean monthly 

adjusted attenuation coefficient can be produced with mean monthly salinity alone (Figure 23), 

perhaps in part due to the range in salinities experienced in this atypical year. Varying slopes 

between the northern and southern regions also highlight the effects of the differing sources, 

amounts, and color of freshwater to the respective areas. Surprisingly, color was not as significant 

a predictor of attenuation overall as was salinity.

Maximum depth limits of all sites sampled are illustrated as a function of annual mean adjusted 

attenuations (Table 13) in Figure 24A. The generalized logarithmic relationship illustrated 

supports water column light limitation as the controlling physical factor on maximum depth, but 
61

the increased scatter in the relationship at high attenuation (and low salinity) could indicate other 

controlling factors as well. The mean %PARw for all sites was 17.6% of the immediately 

subsurface PAR, ranging from 10.1% (BIG) to 26.2% (PUN).

Fig 23



64

Monthly mean adjusted attenuation, monthly epiphytic attenuation, and mean maximum depths 

were used to compute mean monthly %PARp (Table 14) and an annual %PARp at each site. A 

Atotal attenuation@ coefficient (Ktotal) was then computed for each station from annualized %PARp 

values and station depths. The total attenuation coefficient incorporates the effects of epiphytic 

attenuation, but only pertains to the depth for which is was calculated. It is presented in 

Figure 24B as a device to illustrate the necessity of including epiphytic attenuation, as the scatter 

between stations is substantially reduced. 

The logarithmic fit illustrated in . 24B represents an annualized plant requirement of 13% of the 

immediately subsurface PAR, with individual stations ranging between 16.8% (MAT) and 7.3% 

(RAD). The mean %PARp at all 12 sites from weekly data (13%), was very comparable to the 

mean annual %PARp computed from continuous data at four sites (12.7%) and consistent with 

earlier work in Tampa Bay (Dixon and Leverone, 1995) which reported annual mean %PARp

values between 13.0% and 14.2%.

Annualized values of %PARp appear in Figure 25. T. testudinum stations were closely grouped, 

receiving roughly between 13.9% and 15.7%PARp annually. A trend of increasing %PARp with 

decreasing salinity was observed and declines in biomass noted even for the station receiving the 

highest %PARp (PUN). The range in %PARp at H. wrightii stations was broader and somewhat 

lower, between 7.3 and 13.1%PARp. Biomass declines were noted for stations with up to 

15.7%PARp (PUN). With the exception of CAP, stations with mean annual salinities below 18 

PSU experienced biotic declines.

 

PAR Annual and Daily Totals - Continuous Data

Individual PAR readings (15 minute averages) and attenuation coefficients were used to compute 

the PAR at maximum depths and available to the seagrasses after epiphyte attenuation. The 

values represent PAR calculated at the deep edge of each grassbed and do not include any PAR 

received by the grassbed after reflectance off of the bottom. Values were summed over a day at 

each of the four instrumented sites and daily totals averaged by month. Monthly means were 

further averaged and multiplied by 365 to compute annual totals of PAR and to minimize impacts 

of any missing data. Monthly and annual values by site appear in Table 15, together with the 

effects of tidally varying water depths. Applying tidal variation during the study year produced 

mixed results in that some stations received lowered PARw and PARp (2-4%) while at the PUN site, 

annual PARp was increased by 5%. As there was no clear bias, the remaining data analyses were 

performed using fixed water depths.
65

In an effort to report biologically meaningful PARp levels, PARw was computed for depths 0.1 m 

and 0.2 m above the bottom (canopy heights), including estimates of the PAR reflected from the 

bottom. Bottom reflectance was obtained from field measurements of irradiance reflectance at 

surface and at bottom, and used to calculate the absolute quantities of PAR reflected. Bottom 

reflectance was typically less than 10%, and while there were station to 



69

station differences, there were no relationships observed to either cloud cover, time, sun angle, 

attenuation coefficient, color, chlorophyll, turbidity, incident irradiance, or measured cosine angle 

of irradiance. Accordingly, monthly mean values of bottom reflectance were calculated for each 

site and used to compute PARp at the canopy height.

The reflected PAR was attenuated through 0.2 m using measured attenuation coefficients (K0) and 

the resulting quantity added to the downward propagating PARw. The resulting PARw at 0.2 m 

shallower ranged between 20% and 30% higher than the values at the maximum depth limits, 

with the most obvious increases noted at the shallow PUN station. Subsequently, epiphytic 

attenuation was used to reduce PAR to compute the PARp at the canopy height (Table 16). These 

data reductions permitted comparisons with previous work and other investigators in which 

sensors were unshielded and at canopy heights.

Annual totals received at the canopy depth (PARw) ranged from 5,287 Moles m

-2

 yr

-1

 at the PUN 

site to 2,871 Moles m

-2

 yr

-1

 at DEV. Sites DEV (H. wrightii-dominated) and BOK (T. testudinumdominated) were very similar in PARw, while the COT site received 3,770 Moles m

-2

 yr

-1

. After 

correction for epiphytic attenuation (PARp), annual totals available to the plant at the canopy 

height (0.2 m) ranged between 1,982 and 3,051 Moles m

-2

 yr

-1

. Declines in biomass (Table 10, 

above) were presumed at PUN and COT sites, which received 3,051 and 2,609 Moles m

-2

 yr

-1

, 

respectively. The BOK site, with 1,982 Moles m

-2

 yr

-1

 of PARp, experienced a mixed suite of biotic 

changes (increased shoot density, and decreased blade area per shoot) although with an increase in 

LAI over the study. The annual light requirements for T. testudinum in Charlotte Harbor appear 

to be at least as great or greater than the PAR recorded and again light requirements appear to 

increase with decreased salinity.

Annual PARp at canopy height (Figure 26) was 2,334 Moles m

-2

 yr

-1

 at the DEV site, which, 

accompanied by an increase in density and blade area, indicate that H. wrightii light requirements 

are somewhat less than that for T. testudinum. The PARw at the DEV site was 2,871 Moles m

-2

 yr

-1

and H. wrightii at this site exhibited biotic increases over the project, primarily through shoot 

density increases. The PARw of the site was greater than the 2,200 to 2,400 Moles m

-2

 yr

-1

identified by Dunton (1994) as insufficient to maintain a positive carbon balance, and also greater 

than the 1,600-2,000 Moles m

-2

 yr

-1

 measured when H. wrightii died within 9 months (Czerny and 

Dunton, 1995). The range between the PARw and PARp is a further examples of the importance 

of including epiphytic attenuation, as the levels of Texas epiphytes and PARp are unknown.

Czerny and Dunton (1995) also defined 1,900 Moles m

-2

 yr

-1

 as a necessary annual PARw level for 

T. testudinum in Texas. Previous work in Tampa Bay on T. testudinum sites (Dixon and Leverone, 
70

1995) recorded PARw levels between 3,600 and 4,900 Moles m

-2

 yr

-1

, and PARp levels between 

2,200 and 3,100 Moles m

-2

 yr

-1

 with shading effects noted for the one station which received less 

than 3000 Moles m

-2

 yr

-1

. Stations in Tampa Bay which received near 3,100 Moles m

-2

 yr

-1

experienced biotic changes that were consistent with no net change and the seasons sampled. The 

annual PARp at the BOK T. testudinum site, however, was 1,982 



74

Moles m

-2

 yr

-1

, less than that observed at the shaded site in Tampa Bay. The lack of a definitive 

decline in biomass at BOK indicates that there may be specific seasonal light requirements which 

were satisfied during the project year or that there were confounding effects of additional stressors 

(i.e., salinity).

Additional support for the concept of a growing season has been developed for seagrasses in 

Charlotte Harbor (Tomasko and Hall, 1999). In that work, regardless of water clarity and/or 

salinity, substantial growth of seagrasses did not occur once temperatures were below 20 °C. 

Other confounding seasonal signals include epiphytic attenuation which, in other systems 

(Sarasota Bay, Tampa Bay), has reached maximum values during periods of low growth 

(wintertime). When considering light requirements for grasses, therefore, it is likely that annual 

totals of PAR or annual percentages of PAR have critical seasonal distributions - a wintertime 

minima needed to maintain oxygenated conditions and support little additional growth and a 

growing season minima to replace lost tissues and build to the seasonal biomass peak. It is critical 

to keep in mind the underlying plant physiology determining light requirements and recognize 

that the annual totals determined in this and other projects represent an integrated value. It is 

conceivable that under some conditions, annual totals could be maintained, but if particular 

seasonal totals were insufficient, might still permit an overall decline of grasses. 

To investigate seasonal light levels, the average daily values of PARp at canopy height were 

examined by month for both Tampa Bay and Charlotte Harbor (Figures 27A and 27B). The two 

studies exhibited a very different temporal pattern of water clarity and PAR received at depth, 

some of which can be attributed to the unusually high rainfall received in 1997-1998. In Tampa 

Bay, maximum water clarity was between March and May. For the Tampa Bay site with shading 

effects (Site 2), there were no months in which the mean daily PAR exceeded 10 Moles m

-2

 day-1. 

The remaining sites of Tampa Bay each experienced at least three months in which mean daily 

PARp exceeded 10 Moles m

-2

 day-1. 

April (1997) through July in Charlotte Harbor was the time of maximum water clarity, with all 

sites experiencing three months in which mean daily PARp exceeded 10 Moles m

-2

 day-1 and PUN 

and COT receiving more than 20 Moles m

-2

 day-1 on average during two months. All sites also 

experienced eight consecutive months (August through March) in which mean daily PARp was 

generally less than 5 Moles m

-2

 day-1. It is evident in Figure 27B that the BOK site consistently 

received lower daily PARp totals than did the other T. testudinum sites, and yet, unlike the BOK 

site, both PUN and COT exhibited biotic declines.
75

Examination of a seasonal mean daily PARp (Figure 28) further illustrates the differences between 

the Tampa Bay and Charlotte Harbor stations. For Tampa Bay, differences in PARp were 

maximized between the shaded and stable sites in Tampa Bay for a four month period (April 

through July). The defined Agrowing season@ also coincided with the period when shading effects 

were first noted for Tampa Bay. Mean daily PARp during the growing season exceeded 

10 Moles m

-2

 day-1 for stable Tampa Bay sites and was less than 8 Moles m

-2

 day-1 for


78

 the site with shading effects. The PARp received by all of the Charlotte Harbor T. testudinum

sites, however, was at least as great as the mean growing season PARp received by stable Tampa 

Bay sites, despite having clear declines in biomass at both PUN and COT.

Biotic declines over the course of the study at the PUN and COT sites were controlled by a decline 

in blade area per shoot. Monthly values of blade area per shoot were examined as a function of 

mean monthly salinity and mean monthly %PARp to examine the results of salinity stress. Due to 

the correspondence of salinity and water column attenuation, both salinity and %PARp

individually accounted for a significant amount of variation in blade area, but in multivariate 

regression, salinity was not significant once the variation attributable to %PARp was removed. 

The same was true for the DEV site, in that %PARp was a stronger predictor of blade area for 

H. wrightii than was salinity, although blade area did not decline between the beginning and end 

of the study. 

Despite the relationship of blade area with %PARp, the differences between PARp observed in 

Tampa Bay and Charlotte Harbor for both seasonal and annual totals deserve consideration. 

Sudden low salinity stress has resulted in etiolation of T. testudinum in other studies (Dixon and 

Leverone, 1995). It is possible that the depressed salinities observed in the Harbor reduced the 

above-ground biomass and that the resulting ambient light levels and/or below-ground reserves 

were insufficient to restore Alost@ biomass. This hypothesis is consistent with the elevated seasonal 

PARp values observed in Charlotte Harbor and may also account for the net above-ground 

biomass declines at PUN which received comparable annual amounts of PARp as the stable Tampa 

Bay stations. The annual PARp received by the stable BOK site, roughly 2,000 Moles m

-2

 yr

-1

, is 

also very different from the Tampa Bay values, but it is possible that the continued above-ground 

biomass at this site was supported by underground reserves.

Water Quality Data

Water quality data collected during this project appear in Appendix A and are summarized in 

Figures 29 and 30. Turbidity values were low overall and ranged between 0.4 and 16.9 NTU. 

Two values of chlorophyll a were unusual (143.3 ug/l at PUN and 49.2 ug/l at HOG) with the 

remainder typically below 20 ug/l and a median value of 6 ug/l. Color values were quite high at 

times in the upper Harbor, with a maximum value of 200 PCU at HOG. The relationship of 

scalar attenuation coefficient (K0) to secchi depth (where secchi was less than overall water depth) 

was strong enough (Figure 31) to provide an alternative approach to estimating water column 

attenuation from historical data sets. Spatial distributions of selected parameters for the six 

month period when all 12 stations were sampled appears in Figures 32 through 34 and illustrate 
79

the persistence of freshwater, high color, and higher chlorophyll values along the western side of 

upper Charlotte Harbor.






86

Optical Model

The spectrally sensitive attenuation coefficients (Kd(PAR)) computed by the optical model from water 

depths, color, chlorophyll, and turbidity agreed well with the individual Kd values measured in situ

(Figure 35) with an overall slope near unity. Mean percentage agreement between modeled and 

observed Kd was 110%, with the 95% confidence interval ranging from 104% to 115%. Paired ttests indicate an overall positive bias of 0.08 m-1

 in the modeled values, direction and degree of 

bias comparable to previous application of similar techniques (Gallegos, 1993).

The largest bias was typically restricted to data sets in which color was greater than 100 PCU (15 of 

120 samples). The residuals from color-dependent relationships incorporated into the model do 

not explain the bias of these samples. A factor which may have contributed to this result is that 

samples were collected from just below the water surface, while field measurements of attenuation 

coefficients extended throughout the water column. It is possible that salinity stratification 

occurred under high flow (and high color) conditions, with the result that color samples collected 

were representative only of the upper water column. This is supported by several of the in situ

profiles in which depth integrated attenuation deviated from modeled attenuation. As a result of 

the correlation of color with model bias, stations with some of the highest color values (HOG, 

RAD, and CAP) reported the some of the largest differences between modeled and measured 

attenuation. There were, however, no significant distributions of bias by station, indicating that 

site-specific relationships of the components of light attenuation (Pierce, et al., 1986; Gallegos, 

et al., 1990; Gallegos, 1994) have been adequately represented by the model.

Observed bias between modeled and observed Kd values was also correlated with sun angle. Some 

portion of this dependence can be attributed to varying spectral distribution with solar elevation, a 

factor which was not incorporated in the model. Spectral variations with sun angle can include 

increasing atmospheric effects with decreasing solar elevation, such as increased pathlength, 

increasing contributions of short wavelength skylight to total irradiance with decreasing solar 

elevation, and the decreased proportion of short wavelength light in direct solar beams with 

decreasing elevations (Kirk, 1984). Additionally, bias may be produced by the literature constants 

employed in the cosine dependent G(ì0) variable. Constants available were for the depths to 

which 10% radiation penetrates rather than 20% which would be more appropriate for seagrasses. 

 Biases are uncorrelated with chlorophyll or turbidity, indicating that the model approach has 

successfully incorporated these values. Remaining sources of non-agreement between modeled 

and measured Kd values could also include the Li-COR sensor used for field measurements as the 

sensor departs from a quantum response at the extremes of the 400-700 nm spectrum.
87

The modeled attenuation was repeated with the analytical parameters of chlorophyll, color, and 

turbidity successively set to zero to compute the individual contributions to total attenuation. The 

effects of depth and water column attenuation are held constant and are not included in this

89

apportionment. Annual averages by station of modeled contributions to attenuation appear in 

Table 17 and Figures 36 and 37 with the diameter of the station symbol proportional to the water 

column attenuation coefficient. Overall, chlorophyll, color, and turbidity accounted for 4%, 

66%, and 31% of attenuation, respectively. The PUN site recorded the maximum annual average 

chlorophyll contribution of 6%, followed by the COT site with 5%. The individual maximum 

chlorophyll contribution of 18% was also at PUN, during a phytoplankton bloom with a 

chlorophyll concentration of 143 ìg l-1. Color generally dominated water column attenuation, 

with attenuation due to chlorophyll becoming substantial only under phytoplankton bloom 

conditions. Stations in the lower Harbor (PAT and DEV) showed increased attenuation due to 

turbidity, compared to upper Harbor sites. For the southern sites, a much larger portion of 

attenuation was produced by turbidity during the six months sampled, up to 55% for the PAS site. 

 Due in part to lesser influence of freshwater and the time period sampled, attenuations were also 

lower overall for the southern sites. Even in the southern sites, however, the chlorophyll 

component of attenuation was small, between 3% and 6%. The observed spatial distribution of 

attenuation and it=s dependence on color is consistent with the general findings of McPherson and 

Miller (1987; 1994) for Charlotte Harbor in which attenuation coefficients were partitioned via 

other methods. Contributions of chlorophyll to attenuation, however, were much greater in 

McPherson and Miller (1994) at over 16%, than in either their earlier work or in this project.

 

 

Table 17. Percentage contribution to water column attenuation. Data collected during this 

project (1997-1998) and by the SWIM water quality monitoring project (1993-

1998).

Site Chlorophyll Color Turbidity Site Chlorophyll Color Turbidity

HOG 3 78 19 CH001 8 85 14 

PUN 6 72 22 CH002 3 76 20 

RAD 3 77 21 CH02B 6 67 27 

COT 5 71 24 CH029 1 84 15 

CAP 3 74 24 CH004 5 71 24 

BOK 3 72 25 CH005 5 68 27 

PAT 4 63 33 CH05B 4 67 28 

DEV 3 59 38 CH006 5 71 24 

MAT 5 73 23 CH09B 3 73 24 

PAS 6 40 55 CH007 4 71 25 

SAN 4 48 48 CH009 5 70 24 
90

BIG 3 60 37 CH011 4 56 40 

CH013 4 35 61 

CH014 7 40 54 

 


93

To provide a rough estimate of attenuation under increased nutrient loading levels, chlorophyll 

concentrations in the model were arbitrarily doubled for all monthly data. As a result, the 

percentage of attenuation attributable to chlorophyll doubles, with a concomitant reduction in the 

proportion of attenuation due to color. This is somewhat of an underestimate of total water 

column attenuation under increased chlorophyll regimes, as there would undoubtedly also be an 

increase in turbidity due to increased phytoplankton. The impact of increased chlorophyll alone, 

however, should be fairly represented. Under the arbitrary chlorophyll projections, the 

contribution to water column attenuation now is as great as 12% annually at PUN and 11% at the 

COT site. Modeled Kd at the PUN site decreases from 1.78 to 1.66 m-1, or %PARp is reduced 

form 21.4% to 19.1%. What is unknown, however, is the net impact of the increased nutrient 

loads on epiphytic loading of seagrasses. 

Using the SWIM water quality data from other stations in the Harbor as input to the model, long 

term patterns in attenuation were examined to determine if the unusual rainfall of the project 

year altered the partitioning of attenuation. Recognizing that not all stations were sampled in all 

months and years, monthly averages were prepared from available data on chlorophyll, turbidity, 

and color concentrations. Attenuation was partitioned by month, and the distribution averaged 

across the twelve monthly periods (Figure 38). Surprisingly, while water column attenuation was 

much greater during the project year, the partitioning of attenuation between chlorophyll and the 

remaining attenuators was very similar. There are portions of the upper Myakka and Peace Rivers 

(CH-001 and CH-004) where the largest relative influence of chlorophyll appears. For co-located 

stations in the upper Harbor, color accounted for somewhat more attenuation during the project 

year, but was still the dominant attenuator in the long-term SWIM data set. This result would 

indicate that, even during year with lower rainfall, color should persist as the dominant attenuator 

within Charlotte Harbor 

95

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Appendix A