Attenuation of Photosynthetically Available Radiation (PAR) in

Florida Bay: Potential for Light Limitation of Primary Producers

CHRISTOPHER R. KELBLE1,2,*, PETER B. ORTNER2, GARY L. HITCHCOCK1, and JOSEPH N. BOYER3

1 Rosenstiel School of Marine and Atmospheric Sciences, University of Miami, 4600 Rickenbacker

Causeway, Miami, Florida 33149

2 Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric

Administration, 4301 Rickenbacker Causeway, Miami, Florida 33149

3 Southeast Environmental Research Center, Florida International University, Miami, Florida 33199

ABSTRACT: Light attenuation in marine ecosystems can limit primary production and determine the species composition

and abundance of primary producers. In Florida Bay, the importance of understanding the present light environment has

heightened as major upstream water management restoration projects have been proposed and some are already being

implemented. We analyzed a 2-yr (2001–2003) data set of the light attenuation coefficient (Kt) and its principal components

(water, chromophoric dissolved organic matter [CDOM], tripton, phytoplankton) obtained at 40 stations within Florida Bay,

calibrated synoptic underway data to produce high spatial resolution maps, examined the potential for light limitation, and

quantified the individual effect of each component upon light attenuation. Tripton was the dominant component controlling

light attenuation throughout Florida Bay, whereas the contribution of chlorophyll a and CDOM to Kt was much smaller in all

regions of Florida Bay. It was possible to accurately estimate the light attenuation coefficient from component

concentrations, using either a mechanistic or a statistical model with root mean square errors of 0.252 or 0.193 m21

,

respectively. Compared to other estuaries, Florida Bay had the lowest overall Kt and the greatest relative contribution from

tripton. Comparing the recent data to a study of Florida Bay’s light environment conducted in 1993–1994, we found that

overall water clarity in the Bay increased significantly, indicated by a nearly 3-fold decrease in Kt, as a result of lower tripton

concentrations, although the percent contribution of each of the components to Kt is unchanged. Only the northwest corner

of Florida Bay, an area comprised of approximately 8% of the Bay’s total area, was found on average to have sufficient light

attenuation to limit the growth of seagrasses. This is much less extensive than in 1993–1994, when seagrass growth was

potentially limited by light at over 50% of the stations sampled.

Introduction

Light attenuation within the water column is of

critical importance to both benthic and pelagic

primary producers in marine ecosystems. The light

available for primary production (photosynthetically

available radiation [PAR]) is defined as the light

reaching the depth of primary production at all the

wavelengths of the visible spectrum. Attenuation of

light in marine ecosystems is the result of inherent

optical properties that include the absorption

coefficient, scattering coefficient, and volume scattering function. The attenuation of PAR in marine

ecosystems is expressed by the light attenuation

coefficient, Kt, an apparent optical property, which

can be calculated from a depth profile of PAR

intensity. It has been shown in numerous studies

that light attenuation can limit photosynthesis and

the growth of primary producers (Cloern 1987;

Duarte 1991; Kenworthy and Fonseca 1996). Traditionally, light limitation of pelagic phytoplankton

growth is thought to occur when the euphotic

depth, the depth at which light intensity is 1% of

the surface irradiance, is less than the mixing depth

(Sverdrup et al. 1954). Seagrasses (higher plants)

require considerably more light energy than unicellular phytoplankton, about 10–15% of the surface

irradiance (Kenworthy and Fonseca 1996).

In most of Florida Bay, seagrass dominates overall

system primary production because of the shallow

depth (ca. 1 m on average) and suitability of

sediments (Zieman et al. 1989; Fourqurean et al.

2001). Florida Bay underwent a massive seagrass dieoff beginning in 1987 (Robblee et al. 1991) that

attracted a great deal of attention, because of the

Bay’s importance as a nursery ground for many of

the regional commercial and recreational fisheries

including pink shrimp (Farfantepenaeus duorarum),

spiny lobster (Panulirus argus), and numerous

saltwater fish (Tilmant 1989). Since the initial dieoff, Florida Bay has been the subject of intense study

and has been reported to have undergone additional ecological changes (Fourqurean and Robblee

1999; Hunt in press).

The exact cause of the seagrass die-off was never

determined, although it is known that it was

preceded by an extended period of higher than

normal temperatures and salinities (Fourqurean * Corresponding author: tele: 305/361-4330; fax: 305/361-

4392; e-mail: Chris.Kelble@noaa.gov

Estuaries Vol. 28, No. 4, p. 560–571 August 2005

2005 Estuarine Research Federation 560
and Robblee 1999). Among other components, the

light available for photosynthesis can determine the

rate at which seagrass beds recover from a die-off,

and in some cases, which seagrass species will

recolonize. A study of light attenuation in Florida

Bay conducted after the die-off in 1993–1994

concluded that in a large area of Florida Bay

seagrass growth might be inhibited due to light

limitation (Phlips et al. 1995). That study was

conducted during a large, persistent cyanobacterial

bloom in north-central Florida Bay that was more

intense and persisted longer than any observed in

the ensuing decade (Boyer et al. 1999).

Ecological threats to the Everglades ecosystem

(including Florida Bay) resulted in the passage of

legislation by the United States Congress (WRDA

2000) and the State of Florida (EFA 1994; FFA

2000) to implement a Comprehensive Everglades

Restoration Plan (CERP) with the goal of restoring

the Everglades ecosystem to a sustainable condition.

The 68 projects of CERP are intended to restore the

quantity, timing, distribution, and quality of freshwater flow throughout the Everglades and delivery

to adjacent marine ecosystems. This alteration of

freshwater runoff to Florida Bay could influence

water quality and primary production in the Bay.

Increased freshwater deliveries may change the

salinity distribution favoring (at least locally) particular seagrass or phytoplankton species. Particulates

and dissolved organics introduced by the freshwater

runoff may increase light attenuation, limiting the

growth of primary producers.

Given the possible negative effects of increased

light attenuation on diverse healthy seagrass beds,

one of the ecological restoration targets or end

points for Florida Bay, it will be necessary to

monitor and assess light attenuation as restoration

projects are implemented. Light attenuation in this

system needs to be quantitatively understood in

order to evaluate various restoration alternatives

prior to construction. This information is a precondition for adaptive management of CERP with

respect to Florida Bay.

Four major components contribute to light

attenuation in the water column: seawater, phytoplankton, chromophoric dissolved organic matter

(CDOM), and tripton (nonphytoplanktonic particulate matter). Several classification schemes have

been proposed to define the light environment of

aquatic ecosystems based on the dominant component(s) influencing light attenuation (Kirk 1980;

Gordon and Morel 1983). Others have proposed

a more quantitative approach determining the

effect of each individual component upon light

attenuation. The simplest of these quantitative

partitioning approaches assumes that the effects of

these 4 constituents are additive in nature (McPherson and Miller 1987; Vant 1990). In this case, the

light attenuation coefficient, Kt, is partitioned as

follows:

Kt~KswzKCDOMzKchlzKtripton ð1Þ

where Ksw is the partial attenuation coefficient for

seawater, KCDOM is the partial attenuation coefficient for CDOM, Kchl is the partial attenuation

coefficient for chlorophyll a (chl a; a measure of

phytoplankton abundance), and Ktripton is the

partial attenuation coefficient for tripton.

The applicability of this model to management

issues (prediction of total attenuation under different scenarios and conditions) would be further

enhanced if it were possible to quantitatively

characterize each partial attenuation coefficient as

a function of the specific attenuation coefficient, k*,

and concentration of that constituent. The simplest

form of this application would be a simple linear

relationship such as:

Kx~(k

x)(CONCx) ð2Þ

where Kx is the partial attenuation coefficient for

component x, k*x is its specific attenuation coefficient, and CONCx is the concentration. Such an

approach would be particularly useful in water

quality modeling, to the degree that it could enable

a model to accurately estimate light attenuation

from the concentrations of CDOM, chl a, and

tripton. This approach enables various restoration

alternatives to be evaluated in regard to the acceptable range and combination of the various components with respect to reaching and maintaining

overall water clarity goals.

In this study, 2 yr of approximately monthly

measurements at 40 fixed stations in Florida Bay

are summarized with respect to light attenuation,

tripton, chl a, and CDOM. These data are used in

conjunction with hydrographic data to calibrate an

underway water sampling system in order to obtain

high spatial resolution maps of the individual

components regulating light attenuation. Continuous high resolution data is essential because of

Florida Bay’s complex morphology and the high

degree of isolation between basins (Fig. 1). The

effect of each of the components on light attenuation is quantified and fit to models estimating Kt

and the accuracy of these models is assessed. The

high resolution Kt data is then superimposed upon

a high resolution grid of the bathymetry of Florida

Bay to determine if there are specific subregions of

light limitation on primary producers.

Our prior inability to estimate Kt from component concentrations had been cited as a significant

gap in the knowledge required for accurate modeling of Florida Bay water quality (Dortch et al. 1997).

Attenuation of PAR in Florida Bay 561
As a result of this study, these relationships between

the component concentrations and Kt can now be

used by water quality modelers and restoration

managers to predict what effects the implementation of CERP may have on light attenuation and in

turn upon the primary producers of Florida Bay.

Methods

Data were collected using the R/V Virginia K, a

23-foot shallow water catamaran, operated by the

National Oceanic and Atmospheric Administration’s Atlantic Oceanographic and Meteorological

Laboratory. Cruises were run at approximately

monthly intervals from July 2001 until June 2003.

Each cruise sampled 40 discrete stations along an

underway sampling track (Fig. 1). The underway

water sampling system consisted of a Seabird model

21 thermosalinograph, Japan Radio Corporation

Global Position System receiver model DGPS 200,

WetLabs C-Star transmissometer, Seapoint Chlorophyll a fluorometer, and a Seapoint CDOM fluorometer. The system was controlled by Seabird’s

SeaSoft software and recorded time, latitude,

longitude, temperature, salinity, CDOM fluorescence (excitation l 5 370 nm; emission l 5

440 nm), chl a fluorescence (excitation l 5

470 nm; emission l 5 685 nm), and beam transmission (l 5 660 nm) at 7-s intervals. The underway

data was regressed against measured concentrations

of chl a, CDOM, and tripton at the 40 discrete

stations. These regressions were then applied to all

the underway data to yield high resolution fields of

estimated chl a biomass, CDOM concentration, and

tripton concentration throughout Florida Bay.

Using a statistical model discussed in detail below,

these estimated concentration fields were used to

generate high resolution estimates of Kt in Florida

Bay. These high resolution estimates were then

standardized via the Surfer software program

(Golden Software, Inc., Golden, Colorado) using

the Kriging gridding process to produce estimates

every hundredth of degree in latitude and longitude. This standardization allowed the data from

the different cruises to be compared, in that it

removed any variation between cruise tracks. It also

allowed for the use of data from a high resolution

survey of bathymetry in Florida Bay to estimate light

limitation on primary production via the same

standardization procedure.

At every discrete station, Kt was determined and

samples were collected for chl a biomass and total

suspended solids (TSS) concentration. CDOM

measurements were made directly from the CDOM

fluorometer calibrated to quinine sulfate units

(QSU). Light penetration was measured using two

4-p spherical quantum sensors, Li-Cor model LI-193

SA. The two sensors were placed 0.5 m apart on an

L-shaped pole and measurements were taken every

0.25 m below the surface until the bottom was

contacted or a depth of 2.5 meters was achieved

with the bottom sensor. The light attenuation

coefficient was then calculated from each set of

light intensity measurements as follows:

Kt~ln(Ez1=Ez2)=(z2{z1) ð3Þ

where z1 and z2 are the depths of the light sensors

and Ez1 and Ez2 are the light intensities at the two

respective depths (Kirk 1994). Calculating Kt from

concurrent light intensity measurements, removes

errors introduced by collecting intensities in succession, caused for example by changes in cloud

cover. Calculating numerous Kt values at varying

depths for each station enables rigorous estimation

of the mean and standard deviation of the light

attenuation coefficient at each station for each

survey.

Light limitation for phytoplankton was estimated

from the light attenuation coefficient as the ratio of

the euphotic depth, Zeu, the depth at which light

intensity is 1% of the surface irradiance, to the

mixed layer depth, Zm (in this study Zm was assumed

to be the same as the overall depth, because there is

minimal stratification in Florida Bay and its average

Fig. 1. A map of Florida Bay portraying the complex system of

shallow mudbanks located within the Bay. The gray areas indicate

the location of shallow (,1 m depth) mudbanks. A plot of the 40

discrete station locations in Florida Bay and the regional

breakdown of Florida Bay into 4 distinct subregions as proposed

by Nuttle et al. (2000).

562 C. R. Kelble et al.
depth is quite shallow). When this ratio is below 1.5,

light attenuation is thought to play a minimal role

in limiting phytoplankton growth; when the ratio is

below 1.0, light attenuation plays a major role in

limiting phytoplankton growth; and when the ratio

is between 0.1–0.5, net production by phytoplankton drops below zero due to light limitation of

photosynthesis (Cloern 1987). To calculate the

euphotic depth, Zeu, Eq. 3 was rearranged to yield

the following equation:

Zeu~4:61=Kt ð4Þ

The ratio of the maximum seagrass colonization

depth to the overall depth, Z, was used to determine

the likelihood of light limitation upon seagrass

growth. Where this ratio is consistently below 1.0 it

is thought that seagrass growth is inhibited due to

insufficient light reaching the benthos (Phlips et al.

1995). To generate first order estimates, and

because this factor is both variable and was not

measured in this study, the relative height of

seagrass leaves above the bottom was not considered. Two different light intensities were used to

estimate the maximum seagrass colonization depth.

Maximum seagrass colonization depth, Zcol, was

based on a review of worldwide data of seagrass

maximum colonization depth along with corresponding light attenuation coefficients yielding the

following relationship (Duarte 1991):

Zcol~1:87=Kt ð5Þ

The second estimate is specific to turtle grass,

Thalassia testudinum, which dominates the seagrass

community over most of Florida Bay. It has been

found that T. testudinum requires a minimum of

10% of the surface irradiance to reach the benthos

(Zieman et al. 1999). Rearranging Eq. 3 to calculate

the maximum seagrass colonization depth based on

the depth of the 10% light level, Zloc, yields the

following equation:

Zloc~2:30=Kt ð6Þ

Chl a concentration was determined via a standardized filtration-extraction method using a 60:40

mixture of acetone and dimethyl sulfoxide (Shoaf

and Lium 1976). The fluorescence of each sample

was measured before and after acidification in order

to correct for phaeophytin on a Turner Designs

model TD-700 fluorometer. These fluorescence

values were calibrated using known concentrations

of chl a to yield chl a concentration in mg m23

.

TSS concentration was determined gravimetrically at each station following Young et al. (1981). As

large a volume of the sample as possible, with

a minimum of 200 ml, was filtered onto preweighed

filters that were then dried and reweighed to

calculate TSS via the following equation:

TSS~(Wpost{Wpre)



Vfilt ð7Þ

where Wpre is the prefiltration filter weight, Wpost is

the postfiltration filter weight, and Vfilt is the

volume filtered.

In order to calculate tripton the dry weight of the

phytoplankton was subtracted from TSS. The dry

weight of phytoplankton was determined based on

the chl a biomass multiplied by the inverse of the

percent of the dry weight of a phytoplankton cell that

is chl a, equal to approximately 0.84% (Parsons 1961):

Phytoplankton (dw)~(chl a|119)=1,000 ð8Þ

The percent weight of chl a per phytoplankton cell

was assumed to be the mean of literature values

corresponding to the families of phytoplankton

found in Florida Bay, primarily cyanobacteria, diatom, and dinoflagellate (Parsons 1961; Phlips and

Badylak 1996).

The CDOM fluorometer was calibrated to measure CDOM in QSU following Moore et al. (1993).

This calibration is necessary to standardize fluorescence measurements from different instruments.

The fluorescence signal voltage of standard concentrations of quinine sulfate was regressed against

the concentration in QSU.

Results

DISCRETE STATION DATA

The results from the discrete station measurements were as follows. Measured light attenuation

coefficients ranged from 0.13 to 2.82 m21

, with the

lowest value in the northeast Bay at station 40 in

September 2002 and the highest value in the

northwest Bay at station 10 in October 2001. The

mean Kt values for each station ranged from

0.33 m21 in the northeast (both stations 38 and

39) to 1.42 m21 in the northwest (station 10). Chl

a biomass had a minimum of 0.04 mg m23 in the

north-central region at station 4 in April 2002 and

a maximum of 20.4 mg m23 also in the northcentral region at station 7 in July 2002. The mean

chl a values varied from 0.26 mg m23 at station 1 in

the northeast to 4.03 mg m23 at station 12 in the

northwest. The CDOM values ranged from

3.67 QSU in the northeast at station 20 in July

2002 to 80.7 QSU in the north-central region at

station 7 in September 2002. The mean CDOM

varied between 10.4 QSU in the northeast at station

18 to 46.9 QSU in the north-central region at

station 7. Tripton concentrations ranged from

0.01 g m23 in the northeast and south at stations

26, 28, 29, 34, 37, and 40 to 47.0 g m23 in the northAttenuation of PAR in Florida Bay 563
central region at station 7 in July 2002. The mean

tripton concentrations varied from 1.39 g m23 in

the north-central region at station 25 to 15.0 g m23

in the northwest at station 10.

Mean values of Kt, tripton, chl a, and CDOM over

the 2-yr period of record are summarized in the

contour maps of Fig. 2. The distribution of Kt and

tripton are similar with peak values in northwest

Florida Bay and the lowest values in the southcentral Bay. Tripton and Kt also exhibit smaller

secondary peaks in northeast Florida Bay corresponding to the area of the largest direct freshwater

influence. Chl a has its maximum mean biomass in

the northwestern Bay, as well, but it displays

a secondary peak in the north-central Bay only

slightly below the maximum biomass and has its

lowest values in south and northeast Florida Bay.

The CDOM distribution has a single maximum in

the north-central Bay, close to shore, with values

decreasing progressively with distance away from the

peak concentration.

The coefficient of variation was used to estimate

the variability at each station with respect to each of

the four parameters. The spatial distribution of

these coefficients of variation is depicted in Fig. 2.

The coefficient of variation for Kt has a maximum

in the northeast region, although the value of that

coefficient is low. The relatively high variation in the

northeast region was most likely a result of the

direct localized runoff in this area that would alter

the concentration of all three of the parameters that

contribute to light attenuation (Nuttle et al. 2000).

Tripton and chl a were the most variable parameters, with maximum coefficients of variation, in

excess of 200, located in the center of the Bay for

both variables. The variability of tripton in the

central Bay most likely results from the physical

isolation of this area due to shallow mudbanks that

restrict exchange with adjacent areas to the northeast and the west (Fig. 1) in combination with

seasonal wind mixing (Lawrence et al. 2004), also

the seagrass die-off was most pronounced in this

Fig. 2. The spatial distribution of the mean light attenuation coefficient (Kt; m21), tripton (inanimate particulate matter)

concentration (g m23

), chlorophyll a biomass (mg m23), and chromophoric dissolved organic matter (CDOM) concentration (quinine

sulfate units) in Florida Bay from July 2001 to June 2003. Spatial distribution of the coefficients of variation for Kt, tripton, chlorophyll a,

and CDOM in Florida Bay from July 2001 to June 2003.

564 C. R. Kelble et al.
region (Robblee et al. 1991) causing the sediments

to destabilize. High variability in chl a is a product

of seasonal cyanobacteria blooms in the northcentral Bay that resulted in both the highest and

lowest measurement of chl a biomass occurring in

the north-central region (Phlips et al. 1999). The

maximum coefficient of variation for CDOM along

the western boundary is explained by seasonal

discharge from Shark River that increases CDOM

in this region (Clark et al. 2002).

ESTIMATES OF COMPONENT CONCENTRATIONS

To examine light attenuation and its component

concentrations at spatial scales with higher resolution than that yielded by the discrete station data

(presumably better reflecting the bathymetric complexity of the Bay), it is necessary to estimate the

component contributions from the underway instrumentation. For CDOM this is straightforward by

simply calibrating CDOM fluorescence to QSU. It is

more complicated for chl a and TSS.

A multiple regression of the underway variables

(chl a fluorescence, beam attenuation, temperature,

salinity, CDOM) on measured chl a from the entire

data set was statistically significant (F4,705 5 158, p ,

0.001), yet unsatisfactory at estimating chl a biomass

(R2 5 0.473, root mean square error [RMS] 5

1.32 mg m23

), no doubt because the Bay has

significant spatial and temporal differences in

phytoplankton species composition and standing

stock (Phlips and Badylak 1996). To account for

these changes in phytoplankton community structure the ratio of chl a in situ fluorescence versus chl

a extracted concentration (f:chl) was calculated.

This ratio has been shown to differ significantly

between different species of phytoplankton (McKee

et al. 1999), and it was used here to divide the data

into four nominal categories: cyanobacterial bloom

(f:chl # 0.11), diatom and cyanobacterial mixed

bloom (0.11 , f:chl # 0.25), diatom bloom (0.25 ,

f:chl # 0.55), and no bloom (f:chl . 0.55). The

calculation of four separate regressions on data

sets sorted based on these categories provided

a much improved estimation of chl a concentration

from the underway data (R2 5 0.833 to 0.925, RMS

5 0.422 mg m23

, which is 39% of the mean and 2%

of the maximum chl a biomass). To make estimates

between discrete stations the weighted average

ratios of the nearest stations were used.

The regression of the underway variables on TSS

was also statistically significant, yet inadequate to

reliably estimate TSS from the underway data (F4,733

5 329, p , 0.001, R2 5 0.642, RMS 5 3.63 g m23).

The poor predictive power of this model is probably

due to heterogeneity of the contributing sediments

to TSS (Wanless and Tagett 1989), as well as

seasonal differences in wind forcing events in

Florida Bay (Wang et al. 1994). A model similar to

the one used for chl a was derived by dividing the

data set into TSS types based on the ratio of beam

transmission (from the transmissometer) to TSS

concentration. This ratio has been shown by other

researchers to vary with TSS type (McKee et al.

1999). The model of separate multiple regressions

for each TSS type produced far superior results,

with an RMS of 1.85 g m23 or 44% of mean TSS and

3% of maximum TSS and coefficients of determination ranging from 0.666 to 0.902. The underway

TSS estimations between stations used the weighted

average ratios of the nearest stations to determine

which equation to apply.

ESTIMATION OF KT

To examine the contribution each of the four main

components makes to Kt and to determine the utility

of estimating Kt from the component concentrations

two different approaches were taken. Both approaches are based on partitioning Eq. 1 into specific

attenuation coefficients for each component (with

the exception of seawater, which was taken to be

a constant), yielding the following equation:

Kt~Kswz(k

CDOM CONCCDOM)z

(k

trip CONCtrip)z(kchl CONCchl) ð9Þ

The first method is mechanistic in nature,

whereby Ksw and the specific attenuation coefficients, k*, for chl a and CDOM are based on

experimental observations. Ktripton is then calculated by subtraction of Ksw, Kchl, and KCDOM from Kt

and is regressed on tripton concentration in

attempts to calculate k*trip. The seawater constant,

Ksw, was assumed to be 0.0384 m21 (Lorenzen

1972). The k* for chl a has been reported between

0.012 and 0.016 m2 mg21 by numerous researchers

(Lorenzen 1972; Bannister 1974), and in a comparison of species specific k*chl among different

families of phytoplankton, little variability was

observed with a range of 0.012 to 0.015 m2 mg21

and a mean value of 0.014 m2 mg21 (Atlas and

Bannister 1980). This mean value of 0.014 m2 mg21

was used for the mechanistic model.

Unfortunately, there are no relevant available

values for k*CDOM. Previous light attenuation studies

have estimated CDOM using dissolved organic carbon

(Gallegos 2001), apparent color in platinum-cobalt

units (McPherson and Miller 1987; Phlips et al. 1995),

and absorption at 440 nm (g440; Vant 1990). None of

these parameters is directly related to CDOM in QSU

fluorescence units so none of the k*s for these

variables is applicable herein. It is well known that

the type and number of chromophores, as well as the

Attenuation of PAR in Florida Bay 565
chemical composition of CDOM, varies in different

locations (Stedmon et al. 2000).

A regionally relevant k*CDOM was calculated based

upon a series of measurements of CDOM (QSU)

and absorption at l 5 300 nm from the Southwest

Florida Shelf to the west Florida Bay transition

zone (Clark et al. 2002). To extrapolate absorption

by CDOM, aCDOM, over the PAR spectrum the

following equation from Hoge et al. (1993) was

integrated from l 5 400–700 nm:

aCDOM(l)~aCDOM(lo)exp1⁄2 ð {Sð Þ l{lo 10Þ

where lo is the reference wavelength (300 nm) and

S is the spectral slope coefficient for CDOM. The

spectral slope was calculated from a series of

measurements of CDOM absorption spectra in

Florida Bay by the Southeast Environmental Research Center (Boyer unpublished data). The

measurements of PAR absorption by CDOM were

divided by the corresponding CDOM concentration

to calculate absorption per QSU. These values

were then averaged, yielding a mean k*CDOM of

0.000424 m21 QSU21

.

To calculate the k*trip it is first necessary to

calculate Ktripton by subtracting the partial attenuation coefficients for seawater, chl a, and CDOM

from overall Kt. The estimated Ktripton values can

then be regressed on tripton concentration yielding

the following equation:

Ktripton~0:0492|CONCtripz0:296 ð11Þ

This regression is statistically significant (F1,841 5

863, p , 0.001) and relatively efficient (R2 5 0.506,

RMS 5 0.280, which is 56% of the mean and 11% of

the maximum Ktripton). The nonzero intercept is

most likely the result of different sizes of suspended

sediments in different areas of the Bay, as well as at

different times of the year. It has been shown by

previous studies that the sediments of Florida Bay

are heterogeneous (Wanless and Tagett 1989); the

same would be expected of the suspended sediments and, as a result, tripton. Not only are the

wind patterns of Florida Bay known to be relatively

variable but their effect upon wave generation (and

mixing) is still more variable due to the complex

bank topography (Wang et al. 1994; Lawrence et al.

2004). This heterogeneity in both time and space

would affect both the type and size of tripton in

Florida Bay and the k*trip, resulting in the appreciable intercept of Eq. 11.

Combining these estimates the equation for the

mechanistic model is as follows:

Kt~0:0384z0:014|CONCchlz0:000424|

CONCCDOMz(0:296z0:0492|CONCtrip) ð12Þ

Figure 3 shows the plot of predicted Kt from the

mechanistic model (Eq. 12) versus observed Kt. The

mechanistic model predicted Kt values from component concentrations with an RMS of 0.246 m21

,

which is 41% of the mean K t value of 0.594 m21 and

9% of the maximum Kt of 2.82 m21

. This model

efficiency is similar to that of a numerical water

quality model already being used in another Florida

estuary, Indian River (Christian and Sheng 2003).

We conclude that it is reasonable to use the

mechanistic model to estimate Kt in Florida Bay

from component concentrations.

A second approach to modeling Kt uses multiple

regression analysis of tripton, CDOM, and chl

a concentration on Kt. While this approach lacks

the physical constraints of the mechanistic model, it

does (at least mathematically) take into account

unmeasured influences that may covary with

the three main components. The multiple linear

regression model for the entire data set was

statistically significant (F3,649 5 375, p , 0.001)

Fig. 3. Scatter-plot of predicted light attenuation coefficient,

Kt, on observed Kt from the two different modeling strategies.

The mechanistic model uses experimentally measured specific

attenuation coefficients, k*, to estimate Kt from the concentrations of tripton, CDOM, and chlorophyll a. The most efficient

model uses four separate multiple linear regression models

applied to the data after it was partitioned based on the ratio of

beam attenuation to TSS. The gray line in each plot represents

a perfect model.

566 C. R. Kelble et al.
and essentially similar in predictive efficiency to

the mechanistic model (RMS 5 0.246 m21

, R2 5

0.634). A model analogous to the chl a model

based on phytoplankton type and using the same

three break points provides a still more accurate

model with an RMS of 0.197 m21 and R2 5 0.161,

0.642, 0.811, and 0.819. A multiple regression

model estimating Kt using the TSS-type data set

partitioning approach model proved to be the

most accurate method (Fig. 3), with an RMS of

0.193 (32% of the mean Kt and 7% of maximum

Kt) and coefficients of determination from 0.425 to

0.816.

Because of its superior accuracy, the method of

separate multiple regression models for each of the

four different TSS-types was used to estimate Kt

from the underway estimates of chl a, CDOM, and

tripton (Fig. 4). The use of underway component

concentration estimates instead of the actual measurements resulted in a minimal loss of model

accuracy (RMS 5 0.206, R2 5 0.338–0.830).

Using the underway data yielded over 6,000 Kt

estimates per survey, providing a high resolution

image of Kt throughout Florida Bay. For each survey

a grid was made that produced a Kt estimate every

0.01 degree of latitude and longitude. This was

done to make the data sets between surveys

comparable, since the survey track varies slightly

between cruises. These high resolution grids of Kt

were then averaged and contoured to produce the

high resolution contour map of mean Kt from July

2001 to June 2003 shown in Fig. 5.

Unfortunately, none of the models were accurate

at predicting Kt near the low end of the range of the

data set, less than approximately 1.0 m21 (Fig. 4).

This is a significant weakness, because this low end

of the range is characteristic of much of Florida Bay

on many occasions. On the other hand, the

significance of this systematic deficiency is minimal

with respect to estimating the effect of Kt on

primary producers, because Kt must be relatively

high in order to affect the growth of primary

producers in Florida Bay. For example, a mean

depth of 1 m is assumed for Florida Bay, Kt would

have to be greater than 1.87 to negatively affect

seagrass growth based on the most restrictive

calculation.

Discussion

LIGHT LIMITATION OF PRIMARY PRODUCERS

To examine light limitation of pelagic phytoplankton, the underway Kt values were used to

calculate the depth of the euphotic zone, Zeu,

according to Eq. 4. The mean Zeu values were then

compared to the mean water depth from a high

resolution bathymetry study conducted in 1990

(Hansen and DeWitt 2000). Only in the extreme

northwest corner of Florida Bay was the ratio of

Zeu:Zm less than 1.5, indicating that light limitation

may play some role in limiting phytoplankton

growth. This area with a ratio less than 1.5 may be

subject to some seasonal light limitation of phytoplankton, but even there it appears that light

availability is not the dominant component controlling phytoplankton growth throughout the year.

Nowhere in Florida Bay was the mean Zeu:Zm ratio

less than 1.0 during this 2-yr period.

To examine the potential for light limitation of

seagrass growth, the maximum seagrass colonization

depth was calculated from underway Kt according

to Eqs. 5 and 6. The mean Zcol and Zloc values were

then divided by the water depth to calculate the

ratios Zcol:Zm and Zloc:Zm. The resulting contour

maps are shown in Fig. 6.

With either approach, there is a large area in the

northwest corner of the Bay where the ratio is less

than 1.0, and seagrass growth would be expected to

Fig. 4. Scatter-plot of the underway estimates of the light

attenuation coefficient, Kt , versus the observed values. There is

a minimal loss in accuracy when the underway estimates of

component concentrations are used instead of the directly

measured component concentrations.

Fig. 5. Contour map of the mean light attenuation coefficient,

Kt , from July 2001 to June 2003 as estimated from the underway

data. The solid line depicts a typical survey track.

Attenuation of PAR in Florida Bay 567
be inhibited due to insufficient light levels. It is

estimated that seagrass growth could be inhibited in

approximately 8% of Florida Bay due to light

limitation. When ratios less than 1.5 are included

to examine where light limitation may play some

role, even if it is not the dominant component

limiting seagrass growth, the area of Florida Bay

affected nearly doubles to 14%, still centered in the

northwestern corner of Florida Bay.

COMPONENT CONTRIBUTIONS TO Kt

To understand and predict the effects of individual components upon Kt the mechanistic

model is preferred. While its accuracy was less than

the statistical model, the specific attenuation coefficients, k*, for each variable are based on controlled

experiments, whereas the pseudo-k*s produced by

linear regression include the effects of other

components that covary with the component of

interest. The experimentally based k*s for chl a and

seawater have been proven to be highly accurate in

various studies (Lorenzen 1972; Atlas and Bannister

1980). The percent contribution of each component was calculated using the mechanistic model, by

estimating Kt as well as the partial attenuation

coefficients for seawater, chl a, CDOM, and tripton

for each underway data point.

Mean underway Kt and the mean percent

contribution by each of the four main components

are shown in Table 1 for the entire Bay and for each

of the four regions shown in Fig. 1. Florida Bay has

been separated into distinct subregions because the

isolation between basins, resulting from the mudbanks, has resulted in considerable heterogeneity

with respect to nearly all ecological variables including, but not limited to seagrass (Zieman et al.

1989), sediments (Wanless and Tagett 1989),

freshwater influence (Nuttle et al. 2000), fisheries

(Tilmant 1989), benthic mollusks (Turney and

Perkins 1972), water quality (Boyer et al. 1997),

and light attenuation (Phlips et al. 1995). The four

subregions used in this study are similar to those

used by Phlips et al. (1995) and Nuttle et al. (2000),

and are based on physical factors: the geomorphology of the mudbanks (Fig. 1), freshwater inflow

influence, and advective connections.

Differences between the four subregions of

Florida Bay with respect to the contributions of

various components to attenuation were remarkably

small, although there was a considerable variation in

mean Kt values that ranged between 0.468 m21 in

the south region to 0.734 m21 in the west region.

The relative contribution of each of the components to Kt (Table 2) was nearly identical in every

region of Florida Bay. Tripton was the dominant

contributor to light attenuation in all four subregions and accounted for 88% to 89% of Kt in

every region. Tripton dominance is further evidence

by the fact that the tripton distribution and Kt

distribution (Fig. 2), are nearly indistinguishable. Kt

increases with tripton and the tripton contribution

to Kt is nearly constant over the four subregions.

The contribution of seawater to Kt is the second

most influential component to light attenuation,

varying from 7% in the west and north-central to 9%

in the south and northeast. Since the contribution

of seawater to Kt is a constant, its percent

contribution is an inverse function of the overall

Kt distribution (Fig. 2). The contribution of chl

a and CDOM to Kt in Florida Bay is negligible in all

regions. The contribution of chl a to Kt varied from

3% in the west and north-central regions where chl

TABLE 1. Mean Kt (m21) for all of Florida Bay and each of the

four subregions shown in Fig. 1. Mean percent contribution to Kt

from seawater, chlorophyll a, tripton, CDOM, and unknown for

the same areas. All data was calculated from the underway

instrumentation.

Kt (m21) Ksw Kchl Ktripton KCDOM

Average 0.588 8 2 89 1

North-central 0.633 7 3 88 2

Northeast 0.473 9 1 89 1

South 0.468 9 2 88 1

West 0.734 7 3 89 1

Fig. 6. Contour map of the ratio of the mean seagrass

colonization depth to the water depth from July 2001 to June

2003. The top method is based on global data and the seagrass

colonization depth is more restrictive, requiring just over 15% of

the surface irradiance to reach the benthos, whereas the method

specific for Thalassia testudinum requires only 10% of the surface

irradiance to reach the bottom.

568 C. R. Kelble et al.
a biomass is at a maximum to 1% in the northeast

where chl a concentration is lowest. The contribution of CDOM to Kt was even less, varying from 2%

in the north-central region where CDOM values are

highest to 1% in the other three regions. The small

amount of variation in the light environment of

Florida Bay is all the more remarkable, because so

many other water quality and ecological parameters

show large systematic differences between the

subregions of the Bay (Boyer et al. 1997; Nuttle

et al. 2000).

The percent contribution of each component to

Kt, and the Kt value itself, are compared to three

other Florida estuaries in Table 2. The overall light

attenuation in Florida Bay was significantly less than

that reported for other estuarine systems in Florida.

Tripton dominated the light attenuation in all four

estuaries, with Florida Bay receiving the greatest

contribution at 89%, followed by Charlotte Harbor

at 72%, Indian River at 61%, and Tampa Bay at

55%. With respect to the other components, the

light environments of the four estuaries were

significantly different. Florida Bay did not receive

a significant contribution to Kt from either CDOM

or chl a, whereas Charlotte Harbor and the Indian

River Lagoon both had CDOM as the second

leading contributor to Kt. Although Charlotte

Harbor had no significant contribution from chl a,

in Indian River chl a accounted for 10% of Kt. In

Tampa Bay chl a was the second leading contributor

to Kt at 27%, while CDOM accounted for 13% of

light attenuation.

The lack of a significant relative contribution from

chl a and CDOM to Kt in Florida Bay results from

lower concentrations of these constituents, and not

from having drastically higher tripton concentration,

as indicated by Florida Bay having the lowest mean Kt

of the four Florida estuaries. This is further evidenced

by the fact that mean tripton concentration for this

study (4.05 g m23

) was lower than Indian River

(7.83 g m23

) and Charlotte Harbor (10.0 g m23),

the other two estuaries for which absolute tripton

concentrations were reported.

These relatively lower component concentrations

are a result of the physical regime in Florida Bay.

The shallow mudbanks and secluded basins act to

dampen physical forcing, limiting the extent of

sediment resuspension despite the shallow water

depth (Wang et al. 1994). This, in turn, decreases

nutrient resuspension (Lawrence et al. 2004) and

along with the separation between nitrogen and

phosphorous sources (to the east and west, respectively; Boyer et al. 1997) helps keep the chl

a biomass low. The relatively low amount of

freshwater runoff into Florida Bay (Nuttle et al.

2000), and its containment due to the aforementioned isolation, results in significantly lower average CDOM values. It is the diminished concentration of tripton, CDOM, and chl a that results in

lower light attenuation than that measured in other

Florida estuarine systems. This might be an oversimplification, as it does not account for regional

differences in phytoplankton species composition,

CDOM chromophores, and perhaps most importantly for the fact that Florida Bay sediments are

primarily composed of calcium carbonate and have

a high reflectance (Wanless and Tagett 1989).

COMPARISON TO 1993–1994

The recent data are compared to the only

previous systematic study of the light environment

of Florida Bay (Phlips et al. 1995; Table 3). The

most significant difference was that the chl a biomass

was approximately 5-fold greater in 1993–1994. In

the north-central region the difference was the

largest, with about 10-fold higher chl a in 1993–

1994. This high chl a biomass was primarily

associated with a large, persistent cyanobacterial

TABLE 3. Mean Kt, tripton, and chlorophyll a concentrations

for all subregions of Florida Bay from this study and the prior

study of light attenuation in Florida Bay (Phlips et al. 1995).

Kt (m21)

Tripton

(g m23)

Chlorophyll a

(mg m23)

Entire Bay 2001–2003 0.588 4.05 1.08

1993–1994 1.47 16.0 5.04

North-central 2001–2003 0.633 3.82 1.33

1993–1994 2.08 17 11.7

Northeast 2001–2003 0.473 2.74 0.384

1993–1994 0.95 11.2 1.31

South 2001–2003 0.468 2.07 0.635

1993–1994 0.97 11.3 3.14

West 2001–2003 0.734 6.74 1.81

1993–1994 2.01 26.2 4.26

TABLE 2. The mean light attenuation coefficient, Kt, and the mean percent contribution of each component to Kt for 5 different light

attenuation studies in Florida estuaries.

K t (m21) Ktrip KDOM Kchl Ksw

Florida Bay (2001–2003) 0.3–1.4 89 1 2 8

Florida Bay—reported (Phlips et al. 1995) 0.8–2.8 75 7 14 4

Florida Bay—recalculated (Phlips et al. 1995) 0.8–2.8 85 7 4 4

Indian River Lagoon (Christian and Sheng 2003) 0.5–2.5 61 25 10 4

Charlotte Harbor (McPherson and Miller 1987) 0.5–6.0 72 21 4 3

Tampa Bay (McPherson and Miller 1994) 0.5–1.4 54 13 27 6

Attenuation of PAR in Florida Bay 569
bloom in 1993–1994 that was initiated and centered

in the north-central Bay (Butler et al. 1995). While

the other regions also showed elevated chl a biomass

in 1993–1994, the differences were much less

dramatic than in the north-central region.

There was also a difference in tripton concentration between the two studies, although the difference was less dramatic than it was for chl a. Tripton

concentration was almost 4-fold greater in 1993–

1994, and this difference was fairly evenly distributed throughout the Bay. It is purely speculative but

not unreasonable that tripton concentrations were

higher in 1993–1994 for two reasons. The 1993–

1994 cyanobacterial bloom was resulting in various

ecological changes including a die-off of sponges in

the Bay (Butler et al. 1995). In shallow water

environments like Florida Bay, filtration by benthic

sponges can substantially affect overall water column particulate distributions. The 1993–1994 study

was also conducted soon after the massive seagrass

die-off of the late 1980s. Seagrass root structures

stabilize sediments (Fonseca 1989), and such a dieoff could cause the sediments throughout Florida

Bay to be significantly less consolidated and more

susceptible to resuspension events. On the other

hand, the overall distribution of tripton was similar

for the two studies and reflected not the distribution of seagrass die-off (Robblee et al. 1991) but the

distribution of the forcing events, i.e., winds, waves

and tides, that govern resuspension (Wang et al.

1994; Smith 1997). No comparison is possible

between CDOM concentrations, because the prior

study measured color in platinum-cobalt units that

are not convertible to CDOM as measured herein.

Decreases in chl a and tripton concentration

resulted in the substantial decrease in total light

attenuation shown in Table 3. The north-central

region had the largest difference in Kt values

between the two studies, with mean Kt almost 4-

fold higher in 1993–1994. The other regions all had

Kt values approximately one half of those measured

in 1993–1994. Despite this dramatic change in the

overall light environment, the percent contribution

of the various components to Kt in Florida Bay has

not changed much from 1993–1994 to 2001–2003 as

shown in Table 2. The percent contribution values

from the first study had to be recalculated to permit

direct comparison, because Phlips et al. (1995) used

a k* for chl a of 0.058 m2 mg21

. Recalculating

overall values with a k*chl of 0.014 m2 mg21 yielded

relatively similar effects of each component upon

PAR attenuation as in 2001–2003 (Table 2). The

percent contribution of CDOM to Kt was higher in

1993–1994, but this may well be an artifact of the

difference in measurement methods.

These differences in light attenuation imply

substantial differences in the potential for light

limitation of primary producers. The potential for

light attenuation to play a minor role in controlling

phytoplankton growth in 2001–2003 manifested

itself in only 1% of the Bay, whereas in 1993–1994

almost 12% of the stations displayed this potential.

In 1993–1994, 6% of the stations showed the

potential for light limitation to be the dominant

component controlling phytoplankton growth, but

nowhere in the Bay in 2001–2003 was light

limitation observed to be the dominant component

controlling phytoplankton growth.

The potential for light limitation on seagrasses

was significantly more pronounced in 1993–1994

with 11 of the 17 stations having mean Zcol:Zm ratios

less than 1.0 and all station’s mean Zcol:Zm ratio was

less than 1.5 (Phlips et al. 1995). In 2001–2003, only

8% of the Bay had a Zcol:Zm ratio less than one and

14% less than 1.5. If the period 2001–2003 is

representative, since 1993–1994 the water clarity in

Florida Bay has improved dramatically, reducing the

risk of light limitation inhibiting seagrass growth in

Florida Bay. Some areas in the northwest Bay may

still be at risk, but this is a small portion of the Bay

compared to 1993–1994 when the entire Bay was at

risk.

ACKNOWLEDGMENTS

The authors wish to acknowledge everyone who helped in the

data collection including Lloyd Moore, Grant Rawson, Charlie

Fischer, Jeff Absten, Chris Humphries, Traci Kiesling, and Jeff

Judas. We would also like to thank Libby Johns and two

anonymous reviewers for their help in revising this manuscript.

Support for this project was provided by the National Oceanic and

Atmospheric Administration’s Coastal Ocean Program. This is

contribution #268 of the Southeast Environmental Research

Center at Florida International University.

LITERATURE CITED

ATLAS, D. AND T. T. BANNISTER. 1980. Dependence of mean

spectral extinction coefficient of phytoplankton on depth, water

color, and species. Limnology and Oceanography 25:157–159.

BANNISTER, T. T. 1974. A general theory of steady state

phytoplankton growth in a nutrient saturated mixed layer.

Limnology and Oceanography 19:13–30.

BOYER, J. N., J. W. FOURQUREAN, AND R. D. JONES. 1997. Spatial

characterization of water quality in Florida Bay and Whitewater

Bay by multivariate analyses: Zones of similar influence.

Estuaries 20:743–758.

BOYER, J. N., J. W. FOURQUREAN, AND R. D. JONES. 1999. Seasonal

and long-term trends in the water quality of Florida Bay (1989-

1997). Estuaries 22:417–430.

BUTLER, M. J., J. H. HUNT, W. F. HERNKIND, M. J. CHILDRESS, R.

BERTELSEN, W. SHARP, T. MATTHEWS, J. M. FIELD, AND M. G.

MARSHALL. 1995. Cascading disturbances in Florida Bay, USA:

Cyanobacteria blooms, sponge mortality, and implications for

juvenile spiny lobsters, Panulirus argus. Marine Ecology Progress

Series 129:119–125.

CHRISTIAN, D. AND Y. P. SHENG. 2003. Relative influence of various

water quality parameters on light attenuation in Indian River

Lagoon. Estuarine Coastal and Shelf Science 57:961–971.

CLARK, C. D., J. JIMENEZ-MORAIS, G. JONES, E. ZANARDI-LAMARDO,

C. A. MOORE, AND R. G. ZIKA. 2002. A time-resolved fluorescence

570 C. R. Kelble et al.
study of dissolved organic matter in a riverine to marine

transition zone. Marine Chemistry 78:121–135.

CLOERN, J. E. 1987. Turbidity as a control on phytoplankton

biomass and productivity in estuaries. Continental Shelf Research

7:1367–1381.

DORTCH, M. S., C. F. CERCO, A. M. TEETER, AND R. T. MCADORY. 1997.

Work Plan for a Water Quality Model of Florida Bay. Army Corps

of Engineers, U.S. Army Engineer District Jacksonville, Florida

DUARTE, C. M. 1991. Seagrass depth limits. Aquatic Botany

40:363–377.

EVERGLADES FOREVER ACT (EFA). 1994. Florida Statute 373.4592.

Florida State Legislature, Tallahassee, Florida.

FLORIDA FOREVER ACT (FFA). 2000. Florida Statute 259.105.

Florida State Legislature, Tallahassee, Florida.

FONSECA, M. S. 1989. Sediment stabilization by Halophila decipiens

in comparison to other seagrasses. Estuarine Coastal and Shelf

Science 29:501–507.

FOURQUREAN, J. W. AND M. B. ROBBLEE. 1999. Florida Bay: A history

of recent ecological changes. Estuaries 22:345–357.

FOURQUREAN, J. W., A. WILLSIE, C. D. ROSE, AND L. M. RUTTEN.

2001. Spatial and temporal pattern in seagrass community

composition and productivity in south Florida. Marine Biology

138:341–354.

GALLEGOS, C. L. 2001. Calculating optical water quality targets to

restore and protect submersed aquatic vegetation: Overcoming

problems in partitioning the diffuse attenuation coefficient for

photosynthetically active radiation. Estuaries 24:381–397.

GORDON, H. R. AND A. Y. MOREL. 1983. Remote Assessment of

Ocean Colour for Interpretation of Satellite Visible Imagery, A

Review. Springer, New York.

HANSEN, M. AND N. T. DEWITT. 2000. 1890 and 1990 Bathymetry of

Florida Bay. United States Geological Survey open-file report

#00-374. St. Petersburg, Florida.

HOGE, F. E., A. VODACEK, AND N. V. BLOUGH. 1993. Inherent

optical properties of the ocean: Retrieval of the absorption

coefficient of chromophoric dissolved organic matter from

fluorescence measurements. Limnology and Oceanography

38:1394–1402.

HUNT, J. H. In Press. A Synthesis of Research on Florida Bay.

Technical Report ###. Florida Fish and Wildlife Research

Institute, St. Petersburg, Florida.

KENWORTHY, W. J. AND M. S. FONSECA. 1996. Light requirements of

seagrasses Halodule wrightii and Syringodium filiforme derived

from the relationship between diffuse light attenuation and

maximum depth distribution. Estuaries 19:740–750.

KIRK, J. T. O. 1980. Spectral absorption properties of natural

waters: Contribution of soluble and particulate fractions to light

absorption in some inland waters of southeastern Australia.

Australian Journal of Marine and Freshwater Research 31:287–296.

KIRK, J. T. O. 1994. Light and Photosynthesis in Aquatic

Ecosystems, 2nd edition. Cambridge University Press, Cambridge, U.K

LAWRENCE, D., M. J. DAGG, H. LIU, S. R. CUMMINGS, P. B. ORTNER,

AND C. R. KELBLE. 2004. Wind events and benthic-pelagic

coupling in a shallow subtropical bay in Florida. Marine Ecology

Progress Series 266:1–13.

LORENZEN, C. J. 1972. Extinction of light in the ocean by

phytoplankton. Journal du Conseil 34:262–267.

MCKEE, D., A. CUNNINGHAM, AND K. JONES. 1999. Simultaneous

measurements of fluorescence and beam attenuation: Instrument characterization and interpretation of signals from

stratified coastal waters. Estuarine Coastal and Shelf Science

48:51–58.

MCPHERSON, B. F. AND R. L. MILLER. 1987. The vertical attenuation

of light in Charlotte Harbor, a shallow, subtropical estuary,

south-western Florida. Estuarine Coastal and Shelf Science

25:721–737.

MCPHERSON, B. F. AND R. L. MILLER. 1994. Causes of light

attenuation in Tampa Bay and Charlotte Harbor, Southwestern

Florida. Water Resources Bulletin 30:43–53.

MOORE, C. A., C. T. FARMER, AND R. G. ZIKA. 1993. Influence of the

Orinoco River on hydrogen peroxide distribution and production in the eastern Caribbean. Journal of Geophysical Research

98:2289–2299.

NUTTLE, W. K., J. W. FOURQUREAN, B. J. COSBY, J. C. ZIEMAN, AND

M. B. ROBBLEE. 2000. The influence of net freshwater supply on

salinity in Florida Bay. Water Resources Research 36:1805–1822.

PARSONS, T. R. 1961. On the pigment composition of eleven

species of marine phytoplankters. Journal of the Fisheries Research

Board of Canada 18:1017–1025.

PHLIPS, E. J. AND S. BADYLAK. 1996. Spatial variability in

phytoplankton standing stock and composition in a shallow

inner-shelf lagoon, Florida Bay, Florida. Bulletin of Marine Science

58:203–216.

PHLIPS, E. J., S. BADYLAK, AND T. C. LYNCH. 1999. Blooms of the

picoplanktonic cyanobacterium synechococcus in Florida Bay,

a subtropical inner-shelf lagoon. Limnology and Oceanography

44:1166–1175.

PHLIPS, E. J., T. C. LYNCH, AND S. BADYLAK. 1995. Chlorophyll a,

tripton, color, and light availability in a shallow tropical innershelf lagoon, Florida Bay, USA. Marine Ecology Progress Series

127:223–234.

ROBBLEE, M. B., T. R. BARBER, P. R. CARLSON, M. J. DURAKO, J. W.

FOURQUEREAN, L. K. MUEHLSTEIN, D. PORTER, L. A. YARBRO, R. T.

ZIEMAN, AND J. C. ZIEMAN. 1991. Mass mortality of the tropical

seagrass Thalassia testudinum in Florida Bay (USA). Marine

Ecology Progress Series 71:297–299.

SHOAF, W. T. AND B. W. LIUM. 1976. Improved extraction of

chlorophyll a and b from algae using dimethyl sulfoxide.

Limnology and Oceanography 21:926–928.

SMITH, N. P. 1997. An introduction to the tides of Florida Bay.

Florida Scientist 60:53–67.

STEDMON, C. A., S. MARKAGER, AND H. KAAS. 2000. Optical

properties and signatures of chromophoric dissolved organic

matter (CDOM) in Danish coastal waters. Estuarine Coastal and

Shelf Science 51:267–278.

SVERDRUP, H. U., M. W. JOHNSON, AND R. H. FLEMING. 1954. The

Oceans: Their Physics, Chemistry, and General Biology.

Prentice Hall, Englewood Hills, New Jersey.

TILMANT, J. T. 1989. A history and an overview of recent trends in

the fisheries of Florida Bay. Bulletin of Marine Science 44:3–22.

TURNEY, W. J. AND B. F. PERKINS. 1972. Molluscan distribution in

Florida Bay. Sedimenta III. Report; University of Miami, Miami,

Florida.

VANT, W. N. 1990. Causes of light attenuation in nine New

Zealand estuaries. Estuarine Coastal and Shelf Science 31:125–137.

WANG, J. D., J. VAN DE KREEKE, N. KRISHNAN, AND D. SMITH. 1994.

Wind and tide response in Florida Bay. Bulletin of Marine Science

54:579–601.

WANLESS, H. R. AND M. G. TAGETT. 1989. Origin, growth, and

evolution of carbonate mud-banks in Florida Bay. Bulletin of

Marine Science 44:454–489.

WATER RESOURCES DEVELOPMENT ACT (WRDA). 2000. Public Law

106-541 Title VI, section 601. Government Publishing Office,

Washington, D.C.

YOUNG, R. A., T. L. CLARKE, R. MANN, AND D. J. P. SWIFT. 1981.

Temporal variability of suspended particulate concentrations in

New York Bight. Journal of Sedimentology and Petrology 51:293–306.

ZIEMAN, J. C., J. W. FOURQUREAN, AND T. A. FRANKOVICH. 1999.

Seagrass die-off in Florida Bay: Long-term trends in abundance

and growth of turtle grass, Thalassia testudinum. Estuaries

22:460–470.

ZIEMAN, J. C., J. W. FOURQUREAN, AND R. L. IVERSON. 1989.

Distribution, abundance, and productivity of seagrasses and

macroalgae in Florida Bay. Bulletin of Marine Science 44:292–311.

Received, August 18, 2004

Revised, March 7, 2005

Accepted, April 20, 2005

Attenuation of PAR in Florida Bay 571