assessment of present and future nitrogen loads, water ......q 2001 estuarine research federation...

926Q 2001 Estuarine Research Federation

Estuaries Vol. 24, No. 6A, p. 926–938 December 2001

Assessment of Present and Future Nitrogen Loads, Water Quality,

and Seagrass (Thalassia testudinum) Depth Distribution in Lemon

Bay, Florida

DAVID A. TOMASKO1,*, DENISE L. BRISTOL1, and JUDITH A. OTT2

1 Southwest Florida Water Management District, 7601 Highway 301 North, Tampa, Florida 336372 Florida Department of Environmental Protection, 12301 Burnt Store Road,

Punta Gorda, Florida 33955

ABSTRACT: Nitrogen loads into Lemon Bay, Florida were modeled to have increased ca. 59% between pre-develop-ment (i.e., 1850) estimates (5.3 kg TN ha21 yr21) and estimates for the year 1995 (8.4 kg TN ha21 yr21). By the year2010, nitrogen loads are predicted to increase an additional 45% or 58%, depending upon progress being made towardreplacing older septic tank systems with centralized sewerage (nitrogen loads of 12.2 and 13.3 kg TN ha21 yr21, respec-tively). Using 1995 estimates, nonpoint sources (stormwater runoff) are thought to be responsible for ca. 76% of theannual nitrogen load, followed by septic tank systems (14%), rainfall (10%), and an insignificant load from baseflow.Based on an empirically-derived nitrogen load:chlorophyll a relationship developed for a portion of nearby Tampa Bay,a 45% increase in nitrogen loads into Lemon Bay could result in a 29% increase in annual average chlorophyll a con-centrations. Using the estimate of a 29% increase in future chlorophyll a concentrations, an empirically-derived opticalmodel for Lemon Bay suggests that light attenuation coefficients in the bay would increase ca. 9%, and the average depthlimit of Thalassia testudinum in Lemon Bay would decrease by ca. 24%.

IntroductionIn Tampa Bay, Florida, recent increases in sea-

grass coverage are thought to be linked to im-proved water quality, which in turn is related tosubstantial reductions in anthropogenic nitrogenloads ( Johansson 1991; Avery 1997; Johansson andRies 1997; Johansson and Greening 1999; Kurz etal. 1999). In Sarasota Bay, Florida, the biomass andproductivity of seagrass meadows were shown to bebetter bioindicators of pollutant loads than tradi-tional water quality variables (Tomasko et al.1996). In response to reductions in point-sourcenitrogen loads, both water quality and seagrass cov-erage have improved in Sarasota Bay in recentyears (Kurz et al. 1999). Spatial and temporal var-iation in the productivity and biomass of seagrassmeadows in Charlotte Harbor, also in Florida, donot appear to be related to anthropogenic influ-ences (Tomasko and Hall 1999).

Lemon Bay, located south of Sarasota Bay andwest of Charlotte Harbor, is experiencing rapidpopulation growth in its watershed (Florida De-partment of Natural Resources [FDNR] 1991;Charlotte Harbor National Estuary Program[CHNEP] 1997). In contrast to Tampa and Sara-sota Bays and Charlotte Harbor, little is known

* Corresponding author; e-mail: [email protected].

about the susceptibility of Lemon Bay to the in-creased nitrogen loads that are expected to accom-pany the ongoing urbanization of its watershed.Unlike Tampa and Sarasota Bays and CharlotteHarbor, Lemon Bay’s seagrass coverage appears tohave decreased during the past decade (Kurz et al.1999).

The funding and resources used to develop pol-lutant loading models, water quality monitoringprograms, and seagrass mapping efforts for Tampaand Sarasota Bays and Charlotte Harbor have notbeen previously available for Lemon Bay. As Lem-on Bay may be poised to experience significantlosses of seagrass coverage if future nitrogen loadsincrease as expected, it is imperative that resource-management oriented research efforts be under-taken to aid in the development of strategies tominimize or reverse potential losses of fisherieshabitats in Lemon Bay.

In response to the need to provide timely andcost-effective information to ongoing resourcemanagement efforts, this study was designed to de-velop a nitrogen loading model for Lemon Bay,estimate potential changes in phytoplankton pop-ulations that might occur under future nitrogenloading scenarios, determine the relationship be-tween phytoplankton populations and water clarityin Lemon Bay, estimate potential changes in waterclarity that would occur as a result of changes in

Lemon Bay Seagrasses 927

Fig. 1. Location of Lemon Bay in southwestern Florida withstudy sites. GPBI 5 Gasparilla Pass Barrier Island, GPML 5 Gas-parilla Pass Mainland, DPBI 5 Don Pedro Barrier Island, DPML5 Don Pedro Mainland, SPBI 5 Stump Pass Barrier Island,SPML 5 Stump Pass Mainland, IMBI 5 Indian Mound BarrierIsland, and IMML 5 Indian Mound Mainland. Lemon Bay isapproximately centered at 268569N, 828219W.

phytoplankton populations, predict the changes (ifany) in seagrass depth distributions that would oc-cur with changes in water clarity, and predict thechanges (if any) in seagrass productivity that wouldoccur with changes in water clarity.

Materials and MethodsFIELD SITES

Lemon Bay is located in Southwest Florida (Fig.1). The bay is 21 km long and 0.2 to 1.9 km wide(FDNR 1991). The open water of the bay is 31 km2

in size, with an average depth of 2 m (FDNR 1991).Tidal exchange occurs via two inlets, Stump Passand Gasparilla Pass. The contributing watershed toLemon Bay is 154 km2 (CHNEP 1999), which givesLemon Bay a watershed to open water ratio of 5to 1, a value similar to those for Tampa Bay (6:1;Squires et al. 1998) and Sarasota Bay (3:1; Heyl1992) but considerably less than for nearby Char-lotte Harbor (12:1; Tomasko and Hall 1999).

Eight locations were chosen within Lemon Bayto represent a variety of conditions. Four pairs ofstations were selected, with two pairs being locatednear passes and two pairs located in anticipatednull zones for flushing (Sheng and Peene 1992).For each of the four pairs of stations, individualstations were located either on the barrier islandside or on the mainland side of the bay. Stationswere named to reflect both their relative degree ofinfluence from flushing passes and their barrierisland or mainland location: Gasparilla Pass Barrier

Island (GPBI), Gasparilla Pass Mainland (GPML),Don Pedro Barrier Island (DPBI), Don PedroMainland (DPML), Stump Pass Barrier Island(SPBI), Stump Pass Mainland (SPML), IndianMound Barrier Island (IMBI), and Indian MoundMainland (IMML; see Fig. 1). Water quality, sea-grass growth, and biomass data were collected ap-proximately every month between June 1998 andJuly 1999.

WATER QUALITY

At each location, water temperature and salinitywere measured using a YSI water quality meter.Light attenuation coefficients were determined fordownwelling irradiance using a planar quantumsensor (LI-COR 192SA) attached to a loweringframe that had depths marked in 20-cm incre-ments. Incident irradiance was simultaneously re-corded with an on-deck sensor (LI-COR 190SA) tocorrect for changing cloud cover. Two light pro-files were recorded at each station, with measure-ments made at depths of 40, 60, and 80 cm belowthe surface. All light measurements were made asclose to solar noon as possible (i.e., between 1000and 1400, local time).

Water samples were transported back to the lab-oratory for quantifying chlorophyll a (chl a) con-centrations (mg l21), as well as color (Platinum-Co-balt units) and turbidity (Nephelometric TurbidityUnits).

For chl a, samples were collected in 1 literopaque containers and brought back to the labo-ratory on ice. After filtration through 1 mm glassfiber filters, the filters were mechanically groundin 90% acetone, then centrifuged for 20 min at 500G. After acidification with 0.1 N HCl, absorbanceof the supernate was measured at 750 and 665 nmusing a Perkin-Elmer UV/VIS Spectrophotometer.

For color, samples were collected in 250 ml con-tainers and brought back to the laboratory on ice.After filtration through 1 mm glass fiber filters, thefiltered samples were placed in a Lovibond Color-meter and compared to readings from an adjacentrotating color disc.

Turbidity samples were collected in 250 ml con-tainers and brought back to the laboratory on ice.Unfiltered and unacidified samples were shakenand then measured using a Hach 18900 Ratio Tur-bidimeter.

SEAGRASS SAMPLING

At each location, marker buoys were installed inareas of continuous seagrass (Thalassia testudinumBanks ex Konig) coverage. Water depths rangedfrom 50 to 70 cm (MTL). Sites were visited ap-proximately every month between June 1998 andJuly 1999.

928 D. A. Tomasko et al.

Short-shoot density, aboveground standing crop,and blade productivity were determined usingstandard procedures (i.e., Tomasko and Lapointe1991). At each station, short shoots were enumer-ated in ten replicate quadrats (25 3 25 cm). Tenhaphazardly chosen short shoots within 2 m of aPVC post were tagged and their blades punched atthe blade-sheath junction of the oldest intact bladewith a 25-gage hypodermic needle, and after 7–14d (depending on season) the marked short shootswere collected. After collection of at least five pre-viously marked shoots, and their transport to thelaboratory in coolers, blade epiphytes were re-moved by lightly scraping the leaves with a razorblade. Newly formed blade material was then sep-arated from old material, and epiphytes, new bladematerial, and old blade material were dried sepa-rately for at least 24 h at 658C. Mean values forbiomass and productivity per short shoot at eachstation were multiplied by average short shoot den-sities m22 to determine areal blade biomass (g dwm22) and areal blade productivity (g dw m22 d21).On occasion, either vandalism or wave energy re-sulted in the loss of marker buoys. These eventsdid not prevent us from acquiring data on density,biomass, or epiphyte abundance, but did precludeus from determining productivity using standardprocedures. On these occasions (9 of 48 occur-rences or 19%), blade biomass data were multi-plied by the average blade turnover rate calculatedfrom the other study sites, allowing productivity es-timates for locations where productivity experi-ments were disturbed. One-way analysis of varianceshowed no significant differences in blade turn-over rates among stations on dates when data wereavailable from all locations (p . 0.10, 7 df). Bladeturnover rates of T. testudinum did not exhibit evi-dence of spatial variation in either Sarasota Bay(Tomasko et al. 1996) or Charlotte Harbor (To-masko and Hall 1999), providing additional sup-port for the use of this method to estimate arealproductivity at disturbed sites.

Maximum depth distributions were measured bytowing a diver along the sea bed into increasingwater depths until no further seagrass shoots wereencountered. Local tide tables were used to cor-rect measured water depths for tidal stage to nor-malize all depth measurements to mean tide level(MTL).

NITROGEN LOADING MODEL DEVELOPMENT

Traditionally, nitrogen is thought to be the pri-mary nutrient limiting phytoplankton productivityin estuarine ecosystems (Ryther and Dunstan1971). Previous monitoring efforts in Lemon Bayhave shown that nitrogen to phosphorus ratios(molar) of water samples average less than 12

(Southwest Florida Water Management District[SWFWMD] 1999), suggesting strong nitrogen lim-itation of phytoplankton biomass (Florida Depart-ment of Environmental Protection 1994). Resultsof manipulative experiments in the adjacent estu-aries of Sarasota Bay and Charlotte Harbor haveshown nitrogen to be the limiting factor for phy-toplankton productivity (Dixon and Kirkpatrick1999a and Montgomery et al. 1991, respectively).The pollutant loading model for Lemon Bay con-structed here was focused on nitrogen as the nu-trient of concern.

Estimates were made of the amount of nitrogenloaded into Lemon Bay from nonpoint sources(i.e., stormwater runoff), baseflow (uncontaminat-ed groundwater), direct rainfall onto the waters ofLemon Bay, and septic tank systems. A previous as-sessment did not identify any significant pointsource loads in the Lemon Bay watershed (CHNEP1997). Nitrogen loads were estimated for four dif-ferent scenarios: expected nitrogen loads from apre-development time period (i.e., 1850), nitrogenloads in 1995, nitrogen loads in the year 2010 withseptic tank loads mostly eliminated, and nitrogenloads in the year 2010 with septic tank loads noteliminated.

The development of a nitrogen loading modelfor Lemon Bay is based on previous efforts for Sar-asota Bay (Heyl 1992) and Charlotte Harbor(Squires et al. 1998), which are themselves similar-ly constructed as most estuarine nutrient loadingmodels (see reviews by Stanley 2001; Turner et al.2001). The nonpoint source loading estimates forthese models attempt to determine the overall ef-fect of different land use types on stormwater loadsof various nutrients. Implicit in these and othernutrient loading models is the concept that non-point source nutrient loads associated with partic-ular land use categories (e.g., agriculture, residen-tial, forested, etc.) will be similar throughout theentirety of the watershed. In this manner, land usemaps can be used, with locally derived nonpointsource data, to estimate nonpoint source loads ona watershed level. This approach has been usedpreviously to estimate nonpoint source loads fornumerous estuaries throughout the U.S. (Stanley2001; Turner et al. 2001). Also, nutrient fluxes intoLemon Bay from the Gulf of Mexico were not ac-counted for, as these waters were assumed to be aboundary condition where nutrient-induced waterquality degradation was minor or nonexistent.Most nutrient loading models for U.S. estuariesalso exclude loads from oceanic sources (Stanley2001; Turner et al. 2001), as does the U.S. Geolog-ical Survey’s watershed nitrogen loading model,SPARROW (Spatially Referenced Regression onWatershed attributes; Alexander et al. 2001).


TABLE 1. Parameters used to develop a pollutant loading es-timate for non-point source total nitrogen loads.

Land-Use Type

Run-off Coefficient

DrySeason1

WetSeason1 Heyl2

Event MeanConcentration

(mg 121)

Residential3

CommercialIndustrialMining

0.370.820.800.50

0.530.910.900.50

0.450.870.87nd

2.051.061.641.18

Agricultural4

Forested/Uplands5

Wetlands6

Other7

0.220.190.730.52

0.300.240.800.60

0.160.160.850.54

1.240.441.441.29

1 Data from Squires et al. (1998).2 Data derived from Heyl (1992).3 Average of all residential land use types.4 Average of all agricultural land use types.5 Uplands includes non-forested rangeland.6 Average of all freshwater wetland types.7 Average of all land use types.nd 5 no data available.

As organic nitrogen has been shown to be bio-logically available for phytoplankton assimilation(Anita et al. 1991; Peierls and Paerl 1997; Seitzin-ger and Sanders 1997), loads for all sources aretreated without distinguishing between inorganicand organic forms of nitrogen.

Stormwater nitrogen loads were modeled by firstestimating land use acreage in the Lemon Bay wa-tershed. The SWFWMD maps land use throughoutits jurisdictional area on a five-year schedule. Themost recent completed effort was for photographyshot in 1995. The SWFWMD’s Mapping and Geo-graphic Information Systems program was used toestimate the acreage of land use in the followingcategories: residential, commercial, industrial,mining, agricultural, forested/uplands (uplandsincludes non-forested rangeland), wetlands, andother land uses. For estimating land use patternsin 2010, information on expected populationgrowth and land use changes were extracted fromCHNEP (1997). For estimating land use patternsin pre-development times (i.e., 1850), it was as-sumed that the only categories of land use wereforested/uplands and wetlands. This is thought tobe a safe assumption, as the first documented set-tlers in the Lemon Bay region did not arrive until1878 (FDNR 1991). It was assumed that the 50%decline in wetlands coverage that had occurredthroughout Florida between 1850 and 1990 (Estev-ez 1992) had also occurred in Lemon Bay, with theresult that Lemon Bay would have had twice asmuch wetlands acreage in 1850 as in 1995.

For each land use category, runoff coefficientswere estimated using values from nitrogen loadingmodels developed for the adjacent estuary Char-lotte Harbor (Squires et al. 1998). These valueswere verified by comparison with values derivedfrom Heyl (1992) in a study from adjacent SarasotaBay. Runoff coefficient estimates were used forboth the dry season (October 1 to June 30) andthe wet season ( July 1 to September 30). In theLemon Bay watershed, 0.51 and 0.78 m of rainfalloccur in typical dry and wet seasons, respectively(FDNR 1991). By multiplying rainfall amounts byrunoff coefficients and summing up for the acre-age of land use in each category, estimates weremade for the total amount of runoff generated byeach land use type for each scenario. Event meanconcentrations of nitrogen (i.e., the concentrationrequired to account for a measured load) associ-ated with runoff from each land use type were de-rived from Squires et al. (1998) to determine thenitrogen load associated with stormwater runofffor each land use type (Table 1). The event meanconcentration data used for nutrient loading mod-els for Charlotte Harbor and Sarasota Bay arebased on multiple storm event sampling efforts

originally conducted by Delwiche and Haith(1983) and Harper (1991).

Baseflow (groundwater flow to Lemon Bay) ni-trogen contributions from the entire watershedwere estimated using a formula modified from thenutrient loading model for Charlotte Harbor(Squires et al. 1998). When modified, the algo-rithm for estimating groundwater flow on a water-shed level is:

Q 5 (1,000) 3 T I L

Where, Q 5 flow of groundwater into Lemon Bay(l d21), 1,000 liters 5 one cubic meter of water, T5 transmissivity of the surficial aquifer (m2 d21), I5 the hydraulic gradient of the watershed (mkm21), and L 5 the flow zone (km of shoreline).

For this effort, T was estimated to be 74 m2 d21

(Squires et al. 1998), I was estimated to be 0.94 mkm21 (FDNR 1991), and L was estimated to be 12.9km (FDNR 1991). The resulting watershed levelgroundwater inflow estimate was then multipliedby a locally-derived estimate of surficial aquifer ni-trate concentrations from the Charlotte Harborwatershed (0.014 mg l21; Squires et al. 1998). Ni-trate, rather than total nitrogen, was used for cal-culating groundwater nitrogen loads, as nitrate isthe predominant form of nitrogen in local ground-water, and is the only form of nitrogen with anability to migrate horizontally over any appreciabledistance (Florida Department of Health and Re-habilitative Services 1994). Daily loads were thensummed for the dry season (273 d) and the wetseason (92 d).

The nitrogen load associated with rainfall di-rectly onto the surface of Lemon Bay was calculat-ed by multiplying rainfall amounts by locally-de-rived rainfall nitrogen concentrations and sum-


ming for the area of open water. In the averagedry season, 0.51 m of rain falls on the 31 km2 ofopen water of Lemon Bay (FDNR 1991). This valuewas then converted to liters and multiplied by theaverage dry season total nitrogen concentrationmeasured from adjacent Sarasota Bay (Dixon andKirkpatrick 1999a). The average wet season rainfallamount of 0.79 m was used to calculate the volumeof rain falling on the open waters of Lemon Bay,and this amount was multiplied by the average wetseason total nitrogen concentration measured forSarasota Bay (Dixon and Kirkpatrick 1999a) to cal-culate a wet season nitrogen load.

For septic tank system nitrogen loads, method-ology from Squires et al. (1998) was used. In 1995,about 10,000 septic tank systems were located inthe Lemon Bay watershed (Englewood Water Dis-trict personal communication). A public worksprogram is now in place to remove approximately90% of these systems by the year 2010, resulting ina planned future septic tank system population ofonly 1,000 (Englewood Water District personalcommunication). Septic tank system nitrogenloads were estimated by assuming an average of 2.3people per household, producing a wastewaterstream of approximately 285 liters per person perday (Heyl 1992; Squires et al. 1998). This flow ratewas then multiplied by an estimated total nitrogenconcentration of septic tank effluent of 39 mg TNl21 (Heyl 1992; Squires et al. 1998). This potentialnitrogen load was estimated to be attenuated byapproximately 80% as effluent filters through theunsaturated soil layer between the bottom of theseptic tank drainfield and the water table, and thento nearby surface water features. While nitrate hasbeen shown to travel significant distances in somegeologic settings (Walker et al. 1973; Lapointe etal. 1990), previous work in Southwest Florida hasshown that the majority of nitrogen loaded intothe surficial aquifer in the Lemon Bay watershedwould most probably be denitrified prior to its ap-pearance in the creeks and shoreline emptyinginto Lemon Bay (Florida Department of Healthand Rehabilitative Services 1994). The 80% atten-uation value used for septic tank nitrogen loadswould result in a reduction in groundwater nitro-gen concentrations beyond that due to dilutionalone, and is meant to approximate the fate of theaverage plume of septic tank effluent over the 154km2 watershed.

DEVELOPMENT OF AN OPTICAL MODEL FOR LEMONBAY WATER QUALITY

An empirically-derived optical model for LemonBay was constructed using techniques modifiedfrom McPherson and Miller (1987) in their workon Charlotte Harbor. Light attenuation coeffi-

cients from each location and date combination (n5 96) were used with concurrently collected dataon color, chl a, and turbidity to develop a statisticalrelationship through the use of forward stepwisemultiple regression (Statgraphics). Light attenua-tion coefficients were separated into partial atten-uation coefficients as follows:

k 5 constant 1 (k 3 color)total color

1 (k 3 chla) 1 (k 3 turb)chla turb

Where, ktotal 5 total light attenuation coefficient,constant 5 value derived from multiple regression,kcolor 5 partial attenuation coefficient of color(from multiple regression), color 5 dissolved or-ganic matter (in Platinum-Cobalt units), kchla 5partial attenuation coefficient of chl a (from mul-tiple regression), chla 5 chl a concentration (mgl21), kturb 5 partial attenuation coefficient of tur-bidity (from multiple regression), and turb 5 tur-bidity (in nephelometric turbidity units; NTU).

The regression equation developed for all siteand date combinations was then used with waterquality data from individual locations to determinesite-specific estimates of the amount of light atten-uation associated with phytoplankton biomass, asin McPherson and Miller (1987).

NITROGEN LOADING:CHL A RELATIONSHIPS

A problem encountered with the developmentof a linked pollutant load-water quality model forLemon Bay is the scarcity of historical water qualitydata. In contrast to Tampa and Sarasota Bays andCharlotte Harbor, where water quality data sets ex-tend back into the 1970s, there has been a con-spicuous absence of organized water quality mon-itoring efforts in Lemon Bay. There is too little in-formation to test for trends (if any) in water qualityin Lemon Bay (CHNEP 1997).

To estimate future chl a concentrations in Lem-on Bay, based on future nitrogen load estimates,Lemon Bay’s nitrogen load estimates and chl aconcentrations were compared to an empirically-derived relationship developed for HillsboroughBay, in the northeastern region of Tampa Bay.While no residence time estimates are available forLemon Bay, estimates are available for three simi-lar lagoonal systems located just north of LemonBay, in the Sarasota Bay region. Based on Eulerianplots of residence times, with residence time de-fined as the period required for initial particle con-centrations to be reduced to 1 e21 times the initialconcentration, these three systems, Roberts Bay,Little Sarasota Bay, and Blackburn Bay, have resi-dence times between 5 and 10 d (Sheng and Peene1992). In Hillsborough Bay, much of the bay has asimilarly defined residence time (based on Euleri-


TABLE 2. Light attenuation coefficients (k m21), percent of sub-surface irradiance at the deep edge of seagrass meadows, chlorophylla concentrations (mg 121), turbidity (NTU), and color (platinum-cobalt values) for all sites. Values are means (SD) of n 5 12. Notethat the minimum detectable level for color is 5.

AttenuationCoefficient

Percent SubsurfaceIrradiance Chl a Turbidity Color

Gasparilla Pass—Barrier IslandGasparilla Pass—MainlandDon Pedro—Barrier IslandDon Pedro—Mainland

1.20 (0.59)1.52 (0.74)1.47 (0.43)1.51 (0.32)

45.38 (21.88)15.23 (6.60)20.16 (7.14)18.73 (6.02)

3.66 (1.28)10.05 (4.31)11.31 (4.95)10.76 (3.94)

4.70 (3.69)6.87 (7.25)6.35 (1.87)6.43 (1.66)

5.42 (1.44)6.25 (4.33)5.00 (0.00)5.00 (0.00)

Stump Pass—Barrier IslandStump Pass—MainlandIndian Mound—Barrier IslandIndian Mound—Mainland

1.29 (0.28)1.46 (0.42)1.67 (0.68)1.76 (0.65)

17.20 (4.93)31.82 (12.01)21.10 (14.34)17.00 (8.68)

8.32 (4.26)9.55 (4.15)

15.84 (9.13)16.24 (8.75)

7.58 (3.33)8.23 (6.04)6.96 (3.34)6.87 (3.94)

5.00 (0.00)5.00 (0.00)6.25 (2.26)5.00 (0.00)

an plots) of less than 18 d (Burwell et al. 2000).As residence time estimates were not entirely dis-similar between lagoonal systems adjacent to Lem-on Bay and in Hillsborough Bay, the empirical re-lationship between watershed nitrogen yields andwater column chl a concentrations derived forHillsborough Bay was used to predict future chl aconcentrations in Lemon Bay that might be ex-pected with future nitrogen loading scenarios.

The Hillsborough Bay empirical model com-pares annual nitrogen loads to annual average chla concentrations for the years 1964 to 1990 ( Jo-hansson 1991). By normalizing watershed level ni-trogen loads to kg TN ha21 yr21, Lemon Bay nitro-gen load estimates could be directly compared tothose from Hillsborough Bay. In Lemon Bay, phy-toplankton abundances at sites GPBI and SPBI, thetwo sites closest to passes, are probably kept lowthrough the flushing action of their respectivepasses; data from these two locations were not in-cluded in this analysis.

STATISTICAL ANALYSES

Statistical analyses were used to examine spatialand/or temporal differences in seagrass growth pa-rameters and water quality data. Data were testedto ensure compliance with assumptions regardingnormality and homogeneity of variance using Stat-graphics. If data failed either test, comparisonswere made using the non-parametric Kruskal-Wal-lis test. If significant differences were found (p ,0.05) for normally distributed data, Least Signifi-cant Differences multiple comparison tests wereused. If significant differences were found (p ,0.05) for non-normally distributed data, mediannotch Box and Whisker plots were used to deter-mine where differences occurred between datasets.

Forward stepwise multiple linear regression(Statgraphics) was used to develop an optical mod-el relating light attenuation coefficients (the de-pendent variable) against potentially significant in-dependent variables (color, chl a, turbidity). The

F-value to add a variable was 4.00 (p 5 0.050), andthe F-value to remove a variable was 3.90 (p 50.053).

Results

WATER QUALITY

Water temperature varied between 20.38C and33.78C. Values varied among dates (p , 0.01; Krus-kal-Wallis) but not locations (p . 0.05; one-wayANOVA). Salinity ranged between 26.0‰ and37.6‰, with significant variation between dates (p, 0.01; Kruskal-Wallis) but not locations (p . 0.05;Kruskal-Wallis). Salinities were lowest in the wetseason ( July to September) of 1998 and highest inthe dry season (October to June) of 1998 to 1999.

Light attenuation coefficients (Table 2) variedbetween dates (p , 0.01; Kruskal-Wallis) but notlocations (p . 0.10; Kruskal-Wallis). Although notsignificantly different, mean attenuation coeffi-cients were lowest at sites located closest to flush-ing passes (GPBI and SPBI).

The mean percent of subsurface irradiance atthe deep edges of the sampled seagrass meadows(Table 2) varied between locations (p , 0.01; Krus-kal-Wallis) but not dates (p . 0.05; Kruskal-Wallis).Mean values ranged from 15.2% to 21.1% of sub-surface irradiance for sites GPML, DPBI, DPML,SPBI, IMBI, and IMML. Mean values for SPML andGPBI were significantly higher: 31.8% and 45.4%of subsurface irradiance, respectively.

Mean chl a concentrations ranged between 3.66and 16.24 mg l21 (Table 2). Concentrations variedbetween both dates and locations (p , 0.01; Krus-kal-Wallis). Mean concentrations were lowest at thetwo stations located closest to flushing passes(GPBI and SPBI) and highest at the four sites lo-cated in null zones for circulation (IMBI, IMML,DPBI, and DPML). Mean turbidity values rangedbetween 4.70 and 8.23 NTU (Table 2). Values var-ied between dates (p , 0.01; Kruskal-Wallis) butnot locations (p . 0.05; Kruskal-Wallis). Mean val-ues for color ranged between 5.00 and 6.25 plati-


Fig. 2. Percent of Lemon Bay watershed in uplands, wet-lands, and developed land use categories (see text for expla-nation).

TABLE 3. Nitrogen load estimates (kg TN) for nonpointsource loads, baseflow, direct rainfall, septic tank systems, andtotal loads for the dry season (October 1 to June 30), the wetseason ( July 1 to September 30), and total annual loads. 18505 pre-development estimates, 1995 5 estimates for the year1995, 2010 A 5 estimates for the year 2010 with 90% of septictank system loads removed, 2010 B 5 estimates for the year 2010with no septic tank system loads removed.

1850 1995 2010 A 2010 B

Dry SeasonNonpoint SourceBaseflowRainfallSeptic Tank Systems

25,1444

3,3170

33,0954

3,31713,903

56,8384

3,3171,390

56,8384

3,31713,903

Total LoadsWet Season

Nonpoint SourceBaseflowRainfall

28,465

43,8501

9,029

50,319

65,6791

9,029

61,548

117,6491

9,029

74,062

117,6491

9,029Septic Tank SystemsTotal Loads

AnnualNonpoint Source

052,880

68,994

4,68579,394

98,774

469127,148

174,487

4,685131,365

174,487BaseflowRainfallSeptic Tank SystemsTotal Loads

512,346

081,345

512,34618,588

129,713

512,3461,859

188,696

512,34618,588

205,426

num-cobalt units, with many locations having novalue recorded other than 5.00 (Table 2). Valuesdid not vary between either dates or locations (p. 0.05; one-way ANOVA).

SEAGRASS PARAMETERS

Shoot density values ranged between 56 and 450shoots m22. Densities varied between locations (p, 0.01; Kruskal-Wallis) but not dates (p . 0.05;one-way ANOVA). Highest mean values were atIMBI and GPML, 347 and 337 shoots m22, respec-tively. Lowest mean values were at DPBI and SPML,55 and 119 shoots m22, respectively.

Aboveground standing crop estimates rangedbetween 3.7 and 115.1 gdw m22. Values varied be-tween locations (p , 0.01; Kruskal-Wallis) but notdates (p . 0.05; Kruskal-Wallis). Highest meanstanding crop values were at IMBI and GPML, 68.3and 58.9 gdw m22, respectively. Lowest mean valueswere at DPBI and SPML, 11.1 and 17.7 gdw m22,respectively.

Areal blade productivity values ranged between0.08 and 2.55 gdw m22 d21. Values varied both be-tween locations (p , 0.01; Kruskal-Wallis) anddates (p , 0.05; Kruskal-Wallis). Highest mean ar-eal blade productivity values were at IMBI andGPML, 1.42 and 1.37 gdw m22 d21, respectively.Lowest mean values were at DPBI and SPML, 0.26and 0.38 gdw m22 d21, respectively.

Epiphyte abundance ranged between 5.5% and197.1% of blade weight. Values varied betweendates (p , 0.01; Kruskal-Wallis) but not locations(p . 0.05; Kruskal-Wallis). Highest mean epiphyteabundance values were at SPBI and DPBI, 82.4%and 70.9% of blade weight, respectively. Lowestmean values were at IMBI and GPML, 30.6% and32.9% of blade weight, respectively.

NITROGEN LOADING MODEL

Land use mapping estimates using 1995 photog-raphy indicate that residential land use accountedfor ca. 41% of the watershed, followed by forested/uplands (32%) and wetlands (14%). By 2010, res-idential land use is expected to account for 67%of the watershed, with forested/uplands decliningto 12%, and wetlands down to 12% as well (Fig.2). Forested/uplands were estimated to have com-prised ca. 75% of the watershed in pre-develop-ment times, with wetlands comprising the remain-ing 25%.

The increased urbanization of the watershed ismodeled to have caused an increase in freshwaterinflow into Lemon Bay associated with the in-creased impervious nature of the landscape. Av-erage annual runoff into Lemon Bay in pre-devel-opment times (1850) is modeled to have been ap-proximately 7.10 3 1010 liters. The 1995 estimatesare 19% higher (8.47 3 1010 liters), and estimatesfor 2010 suggest a further 17% increase (9.93 31010 liters).

Dry season nitrogen loads into Lemon Bay areestimated at 50,319 kg TN for 1995 conditions with66% of the load (33,095 kg TN) coming fromstormwater runoff (Table 3). Nitrogen loads fromseptic tank systems are 28% of the dry season load.Rainfall comprises 7% of 1995 loads with baseflowat , 1%. Estimated dry season nitrogen loads are77% higher than loads in pre-development times.

Wet season nitrogen loads into Lemon Bay are


Fig. 3. A) light attenuation coefficients (k m21) versus chlo-rophyll a (mg l21) for all site and date combinations (n 5 96).Line is best-fit relationship with 95% prediction limits (see text).B) light attenuation coefficients (k m21) versus turbidity (NTU)for all site and date combinations (n 5 96). Line is best-fit re-lationship with 95% prediction limits (see text). C) predictedversus observed light attenuation coefficients (k m21) for all siteand date combinations (n 5 96). Line is best-fit relationshipwith 95% prediction limits (see text). Dashed line is 1:1 rela-tionship.

estimated at 79,394 kg TN for 1995 conditions with83% of the load (65,679 kg TN) coming fromstormwater runoff (Table 3). Nitrogen loads fromseptic tank systems are ca. 6% of the wet seasonload. Rainfall is 11% of 1995 loads with baseflowat , 1%. Estimated wet season nitrogen loads are50% higher than loads in pre-development times.

Total annual nitrogen loads into Lemon Bay areestimated at 129,713 kg TN yr21 for 1995 condi-tions with 76% of the load (98,774 kg TN) comingfrom stormwater runoff (Table 3). Nitrogen loadsfrom septic tank systems are estimated at 14% ofthe annual load, followed by rainfall at 10% of theannual load. Baseflow is , 1% of the annual load.Annual nitrogen loads are estimated to be ca. 59%higher than in pre-development times. With theremoval of 90% of the septic tank system nitrogenload, annual nitrogen loads would be expected toincrease ca. 45% from 1995 to 2010, to 188,696 kgTN yr21. If existing septic tank system nitrogenloads are not removed, annual nitrogen loadswould be expected to increase ca. 58% from 1995to 2010 to 205,426 kg TN yr21 (Table 3).

After normalizing for the size of the contribut-ing watershed, modeled nitrogen loads in pre-de-velopment times are estimated at 5.3 kg TN ha21

yr21, versus 8.4 kg TN ha21 yr21 in 1995. Estimatesfor 2010 are 12.2 and 13.3 kg TN ha21 yr21, re-spectively, depending on whether existing nitrogenloads from septic tank systems are removed or al-lowed to continue.

OPTICAL MODEL

Linear regression revealed a significant relation-ship between light attenuation coefficients and chla concentrations in Lemon Bay (r2 5 0.37, p ,0.01; Fig. 3a). There was also a significant corre-lation between light attenuation coefficients andturbidity values (r2 5 0.33, p , 0.01; Fig. 3b). Nocorrelation was found between light attenuationcoefficients and color (p . 0.10).

Using values for chl a and turbidity alone, atten-uation coefficients can be predicted (r2 5 0.56, p, 0.01; Fig. 3c). However, the best-fit line tends tooverestimate light attenuation at the low range ofobserved values and underestimate light attenua-tion at the high range of observed values. Basedon the results of the multiple regression analysis,the equation of the fitted model relating light at-tenuation coefficients to chl a concentrations andturbidity values (Fig. 3c) is:

k 5 0.65 1 0.04 3 chl a [mg l21] 1 0.06 3 turbidity

[NTU]

Using site-specific mean attenuation coefficientsand chl a concentrations, the percent of total light


Fig. 4. Annual average chlorophyll a (mg l21) versus annualtotal nitrogen load (kg TN ha21 yr21) for the years 1964 to 1990in Hillsborough Bay, Florida. Lemon Bay values are representedby open symbol. Line is best-fit relationship with 95% predictionlimits (see text).

Fig. 5. Observed mean light attenuation coefficients (k m21)and predicted light attenuation coefficients with a 29% increasein chlorophyll a concentrations for all sites (see text). GPBI 5Gasparilla Pass Barrier Island, GPML 5 Gasparilla Pass Main-land, DPBI 5 Don Pedro Barrier Island, DPML 5 Don PedroMainland, SPBI 5 Stump Pass Barrier Island, SPML 5 StumpPass Mainland, IMBI 5 Indian Mound Barrier Island, andIMML 5 Indian Mound Mainland.attenuation due to phytoplankton biomass was es-

timated as in McPherson and Miller (1987). Valuesranged from 12.4% at GPBI to 38.5% at IMBI. Thepercent of light attenuation associated with phy-toplankton levels for sites located next to the main-land (GPML, DPML, SPML, and IMML) was27.0%, 29.0%, 26.7%, and 37.4%, respectively. Theremaining sites (DPBI and SPBI) had values of31.3% and 26.2%, respectively.

NITROGEN LOADING:CHL A RELATIONSHIP

Nitrogen loading rate estimates from Hillsbor-ough Bay (in the northeastern portion of TampaBay) were normalized for watershed size to pro-duce estimates in units of kg TN ha21 yr21. Linearregression found a significant correlation betweenannual nitrogen loads and annual average chl aconcentrations (p , 0.01, r2 5 0.50, 21 df), as hasbeen previously reported by Johansson (1991). Ni-trogen loading estimates and mean chl a concen-trations from Lemon Bay fit within the 95% pre-diction interval for the empirical relationship de-veloped for Hillsborough Bay (Fig. 4). The rela-tionship between annual average chl aconcentrations in Hillsborough Bay and annual ni-trogen loads can be expressed as:

21chl a [mg l ] 5 8.24 1 (1.78 3 nitrogen load21 21[kg TN ha yr ])

Using the best-fit equation for linear regressionof the Hillsborough Bay data set, a 45% increasein nitrogen loading rates (2010 A scenario nitro-gen loads versus 1995 nitrogen loads) would beexpected to increase annual average chl a concen-trations by 29%, as the predicted annual average

chl a concentration for the 2010 A scenario is 29%higher than the predicted chl a concentration forthe 1995 nitrogen loading estimate.

A prediction was then made that a future 45%increase in Lemon Bay nitrogen loads would beexpected to increase the annual average chl a con-centration in Lemon Bay by 29%, and future lightattenuation coefficients were predicted for individ-ual locations with a 29% increase in the site-spe-cific mean chl a concentration (Fig. 5). The meanincrease in light attenuation coefficients associatedwith a 29% increase in chl a concentrations is8.7%. Perhaps due to errors associated with pre-dicting light attenuation coefficients, values atGPBI, where observed phytoplankton levels ac-count for only 12.4% of total light attenuation, areslightly lower for the future scenario than the pres-ently observed value (Fig. 5).

SEAGRASS DEPTH DISTRIBUTIONS

The calculated percent of subsurface irradianceat the deep edges of the sampled seagrass meadowsranged between 15.2% and 21.1% of subsurfaceirradiance for sites GPML, DPBI, DPML, SPBI,IMBI, and IMML with a mean value for these sixlocations of 18.2%. Mean values for SPML andGPBI were significantly higher: 31.8% and 45.4%of subsurface irradiance, respectively. The valuesfor SPML and GPBI are also much higher thanprevious estimates of 20% to 25% of subsurfaceirradiance as the minimum light requirement forT. testudinum in Tampa Bay (Dixon 1999), SarasotaBay (Dixon and Kirkpatrick 1995), and Charlotte


Fig. 6. Predicted versus observed depth limits (m belowMTL) for sites GPML 5 Gasparilla Pass Mainland, DPBI 5 DonPedro Barrier Island, DPML 5 Don Pedro Mainland, SPBI 5Stump Pass Barrier Island, IMBI 5 Indian Mound Barrier Is-land, and IMML 5 Indian Mound Mainland. Line is best-fitrelationship with 95% confidence intervals (see text).

Fig. 7. Observed maximum depth limits (m below MTL)and predicted maximum depth limits with a 29% increase inchlorophyll a concentrations for sites GPML 5 Gasparilla PassMainland, DPBI 5 Don Pedro Barrier Island, DPML 5 DonPedro Mainland, SPBI 5 Stump Pass Barrier Island, IMBI 5Indian Mound Barrier Island, and IMML 5 Indian MoundMainland.

Harbor (Dixon and Kirkpatrick 1999b). Values forSPML and GPBI were not used for estimating po-tential changes in depth distribution as related topotential changes in water clarity. By using themean percent of subsurface irradiance at the deepedge for the six remaining locations (18.2%) as anestimate of minimum light requirements and usingmean attenuation coefficients for individual sites,predicted depth limits were found to be signifi-cantly correlated with observed depth limits (p ,0.05, r2 5 0.46; Fig. 6).

Figure 7 compares observed depth limits withestimated depth limits if light attenuation coeffi-cients were to increase due to a 29% increase inchl a concentrations at individual sites. Predictedreductions in depth limits ranged from 10% to35% (DPML and GPML, respectively), with the av-erage reduction for all light-limited sites (i.e., ex-cluding GPBI and SPML) being 24%.

Areal blade productivity (gdw m22 d21) was notcorrelated with the percent of subsurface irradi-ance reaching the depths at which seagrass sam-pling efforts took place (p . 0.05). Areal bladeproductivity was weakly yet significantly associatedwith water temperature (p , 0.01, r2 5 0.11).

DiscussionWATER QUALITY

Water clarity in Lemon Bay was most probablyinfluenced by flushing rates, as the lowest meanlight attenuation coefficients were at the two sitesclosest to passes (GPBI and SPBI), and the highestmean attenuation coefficients were at sites (IMBIand IMML) located in a null zone for circulation

(i.e., Sheng and Peene 1992). The importance ofphytoplankton biomass, as related to water clarityin Lemon Bay, is illustrated both by the correlationbetween light attenuation and chl a concentrations(Fig. 3a) and the finding that locations with thegreatest water clarity (GPBI and SPBI) also had thelowest chl a concentrations.

Residence time appears to be an important fac-tor relating to phytoplankton biomass in LemonBay, as the four sites located in null zones for cir-culation (DPBI, DPML, IMBI, and IMML) had thehighest mean chl a concentrations. These findingsare consistent with a previous review by Nixon etal. (1996) who examined relationships between nu-trient loads, flushing rates, and phytoplankton bio-mass for various estuaries in the North AtlanticOcean.

Using an empirical modeling approach devel-oped by McPherson and Miller (1987), the calcu-lated mean percent of light attenuation due to phy-toplankton biomass in Lemon Bay ranged from12% (GPBI) to 39% (IMBI). The mean percent oflight attenuation due to phytoplankton for all siteand date combinations was 29%. This value is sim-ilar to estimates from Tampa Bay (27%; McPher-son and Miller 1993) but higher than in SarasotaBay, where the mean percent of light attenuationdue to phytoplankton biomass ranged between 4%and 12% (Dixon and Kirkpatrick 1995). In Char-lotte Harbor, the mean percent of light attenuationdue to phytoplankton biomass was 4% (Dixon andKirkpatrick 1999b). In contrast to Lemon Bay, wa-ter clarity in adjacent Charlotte Harbor appears tovary mostly in response to rainfall-derived changes


in the amount of dissolved organic matter broughtinto Charlotte Harbor from its substantially largerwatershed (see Tomasko and Hall 1999). The par-tial attenuation coefficient for phytoplankton de-rived in this study (k 5 0.041 3 chl a [in mg l21])is nearly three times higher than the commonly-cited coefficient developed by Lorenzen (1972),perhaps reflecting the estuarine locations thatwere the source of data for this study as opposedto the marine locations used by Lorenzen (1972).

SEAGRASS PARAMETERS

Short shoot densities, aboveground standingcrop and areal blade productivity of T. testudinumfrom this study were similar to values reportedfrom Tampa Bay (Dawes and Tomasko 1988; Dixon1999) and Sarasota Bay (Tomasko et al. 1996).Aboveground standing crop and areal blade pro-ductivity values in Lemon Bay were mostly higherthan values reported for Charlotte Harbor (To-masko and Hall 1999), perhaps reflecting the low-er light attenuation coefficients of Lemon Bay ascompared to Charlotte Harbor. Areal blade pro-ductivity was correlated with water temperature,but not percent of subsurface irradiance at sampledepths, similar to results from Charlotte Harbor(Tomasko and Hall 1999).

NITROGEN LOADING MODEL

Annual nitrogen loads to Lemon Bay were mod-eled to have increased by 59% versus pre-devel-opment (1850) conditions, a rate of increase lowerthan estimates from Sarasota Bay (200% increase;Heyl 1992). Nitrogen loads appear to be dominat-ed by nonpoint sources (i.e., stormwater runoff),as has been previously reported for Sarasota Bay(Heyl 1992) and Charlotte Harbor (Squires et al.1998).

While nitrogen loads from septic tank systemswere only 14% of the total annual load, they wereestimated to contribute 28% of the dry season ni-trogen load (Table 3). The increased importanceof septic tank-derived nitrogen loads in the dry sea-son (as compared to annual loads and wet seasonloads) is mostly due to the decrease in nonpointsource loads that occurs due to decreased rainfalland runoff volumes during the dry season. Al-though the removal of septic tank nitrogen loadsinto Lemon Bay may not have a dramatic effect onnitrogen loads on an annual basis, this activitymight have a more substantial effect during the 9mo (October 1 to June 30) of Lemon Bay’s typicaldry season.

The estimated nitrogen yield for Lemon Bay’swatershed (8.4 kg TN ha21 yr21) is higher thanyields independently calculated for the watershedsof Charlotte Harbor and Tampa Bay (2.6 and 5.9

kg TN ha21 yr21, respectively; Stacey et al. 2001),but lower than the yield calculated for SarasotaBay’s watershed (10.9 kg TN ha21 yr21; Stacey et al.2001). Lemon Bay’s 1995 watershed nitrogen yieldis substantially lower than yields estimated for var-ious mid-Atlantic estuaries (range of 15.1 to 27.2kg TN ha21 yr21; Stacey et al. 2001). The best-casescenario for Lemon Bay’s 2010 estimated water-shed nitrogen yield (12.2 kg TN ha21 yr21) is high-er than Sarasota Bay’s present-day yield, and justslightly lower than Delaware Bay’s estimated pre-sent-day yield (15.1 kg TN ha21 yr21; Stacey et al.2001).

NITROGEN LOADING:CHL A:SEAGRASS DEPTHDISTRIBUTION RELATIONSHIPS

Based on an empirically-derived nitrogen load:chl a relationship developed for a portion of near-by Tampa Bay, the expected best-case scenario forfuture nitrogen load increases (45%) would be ex-pected to result in a 29% increase in mean chl aconcentrations. If the estimate of a 29% increasein expected chl a concentrations is used, the meanlight attenuation coefficient for all sites would in-crease by 9% (Fig. 5).

As was previously found for one of two T. testu-dinum meadows in the Tampa Bay area studied byDawes and Tomasko (1988), it appears that the sea-grass meadows at GPBI and SPML are limited intheir depth distribution by factors other than lightavailability alone. The presence of nearby boatingchannels is perhaps responsible for preventing sea-grass meadows in these two areas from growingdown to the depths they could reach if water claritywas the only factor limiting their offshore devel-opment. The mean percent of subsurface irradi-ance at the deep edges at the other six locations(18.2%) matches up fairly well with the estimatedminimum light requirement for T. testudinum inTampa Bay (20.1% to 23.4% of subsurface irradi-ance; Dixon 1999) and Sarasota Bay (21% of sub-surface irradiance; Dixon and Kirkpatrick 1995). Itshould be noted that the role of epiphyte coveragein further reducing light availability was not ex-amined in this study (see Dixon 1999).

Depth limits for the seagrass meadows in LemonBay (excluding GPBI and SPML) could be pre-dicted based on water clarity alone (Fig. 5). Thereduction in depth limits that would be broughtabout by a 29% increase in chl a concentrationswas examined to estimate the reduction in seagrasscoverage that might be expected to occur with fu-ture water quality degradation. The range of ex-pected reductions was 10% to 35% of presentdepth limits, with a mean reduction of 24%.

There is a reasonable comfort level with pre-dicting the average percent reduction in depth


limits that would be expected to occur for LemonBay’s light-limited seagrass meadows, based on ex-pected reductions in water clarity in the year 2010.There is more uncertainty when attempting to pre-dict the change in biomass and productivity thatmight also occur in remaining seagrass meadowswith a reduction in water clarity. The inverse rela-tionship found between watershed nitrogen loadsand seagrass biomass and productivity in adjacentSarasota Bay (Tomasko et al. 1996) suggests thatLemon Bay would not only be expected to havefewer acres of seagrass meadows by the year 2010,but remaining seagrass beds would probably be-come sparser and less productive as well.

ConclusionsFurther increases in the degree of urbanization

of Lemon Bay’s watershed are expected by the year2010. This increased urbanization is expected togenerate increased nitrogen loads into Lemon Bay,mostly as a function of increased nonpoint sourcepollution. While septic tank systems are not as im-portant a source of nitrogen loads as nonpointsource pollution, they appear to be considerablymore important during the 9-mo dry season thanthe 3-mo wet season. With or without a septic tanksystem replacement program, Lemon Bay is ex-pected to experience degraded water clarity by theyear 2010, due to nitrogen-fueled increases in phy-toplankton biomass. Using an estimate of the de-gree of water clarity degradation expected, thedeep edges of light-limited seagrass meadows inLemon Bay would retreat by 24%. Concurrent withfuture reductions in seagrass acreage, remainingmeadows would be expected to become sparserand less productive. The combination of fewer,sparser, and less productive seagrass meadowswould almost certainly result in a decline in theabundance of seagrass-dependent species of finfishand shellfish in Lemon Bay.

ACKNOWLEDGMENTS

The authors thank the Governing Board of the SouthwestFlorida Water Management District and the Florida Departmentof Environmental Protection for financial and logistical supportfor this effort. Thanks are given to the members of the Char-lotte Harbor Surface Water Improvement and Management(SWIM) Program’s Technical Advisory Committee for adviceand comment throughout the course of this project. Specialthanks are given to Dr. Tom Frazer (University of Florida) forhis critical review of light attenuation data, Dr. Penny Hall forlogistical support and the use of laboratory space, and to theanonymous reviewers whose comments have resulted in a muchimproved manuscript.

LITERATURE CITED

ALEXANDER, R. B., R. A. SMITH, G. E. SCHWARZ, S. D. PRESTON,J. W. BRAKEBILL, R. SRINIVASAN, AND P. A. PACHECO. 2001. At-mospheric nitrogen flux from the watersheds of major estu-

aries of the United States: An application of the SPARROWwatershed model, p. 119–170. In R. A. Valigura, R. B. Alex-ander, M. S. Castro, T. P. Meyers, H. W. Paerl, P. E. Stacey,and R. E. Turner (eds.), Nitrogen Loading in Coastal WaterBodies: An Atmospheric Perspective. American GeophysicalUnion, Washington, D.C.

ANITA, N., P. HARRISON, AND L. OLIVEIRA. 1991. The role of dis-solved organic nitrogen in phytoplankton nutrition, cell bi-ology and ecology. Phycologia 30:81–89.

AVERY, W. 1997. Distribution and abundance of macroalgae andseagrass in Hillsborough Bay, Florida, from 1986 to 1995, p.151–166. In S. Treat (ed.), Proceedings, Tampa Bay Area Sci-entific Information Symposium 3. Tampa Bay Regional Plan-ning Council, St. Petersburg, Florida.

BURWELL, D., M. VINCENT, M. LUTHER, AND B. GALPERIN. 2000.Modeling residence times: Eulerian versus Lagrangian, p.995–1009. In M. L. Spaulding and H. L. Butler (eds.), Estu-arine and Coastal Modeling. American Society of Civil Engi-neers, Reston, Virginia.

CHARLOTTE HARBOR NATIONAL ESTUARY PROGRAM (CHNEP).1997. Synthesis of Existing Information for the Greater Char-lotte Harbor Watershed. Charlotte Harbor National EstuaryProgram, North Fort Myers, Florida.

DAWES, C. J. AND D. A. TOMASKO. 1988. Depth distribution ofThalassia testudinum in two meadows on the west coast of Flor-ida: A difference in effect of light availability. Publicazioni dellaStazione Zooligica de Napoli I: Marine Ecology 9:123–130.

DELWICHE, L. D. AND D. A. HAITH. 1983. Loading functions forpredicting nutrient losses from complex watersheds. Water Re-sources Bulletin 19:951–959.

DIXON, L. K. 1999. Establishing light requirements for the sea-grass Thalassia testudinum: An example for Tampa Bay, Flori-da, p. 9–31. In S. A. Bortone (ed.), Seagrasses: Monitoring,Ecology, Physiology, and Management. CRC Press, Boca Ra-ton, Florida.

DIXON, L. K. AND G. J. KIRKPATRICK. 1995. Light Attenuationwith Respect to Seagrasses in Sarasota Bay, Florida. SarasotaBay National Estuary Program, Sarasota, Florida.

DIXON, L. K. AND G. J. KIRKPATRICK. 1999a. Biological Effects ofAtmospheric Deposition on Algal Assemblages. Sarasota BayNational Estuary Program, Sarasota, Florida.

DIXON, L. K. AND G. J. KIRKPATRICK. 1999b. Causes of Light At-tenuation with Respect to Seagrasses in Upper and LowerCharlotte Harbor. Surface Water Improvement and Manage-ment Program. Southwest Florida Water Management Dis-trict, Tampa, Florida.

ESTEVEZ, E. D. 1992. Tidal wetlands, p. 6.1–6.20. In P. Roat, C.Ciccolella, H. Smith, and D. Tomasko (eds.), Sarasota BayFramework for Action. Sarasota Bay National Estuary Pro-gram, Sarasota, Florida.

FLORIDA DEPARTMENT OF ENVIRONMENTAL PROTECTION. 1994.Southwest Florida District Water Quality 1994 305 (b) Tech-nical Appendix. Florida Department of Environmental Pro-tection, Tallahassee, Florida.

FLORIDA DEPARTMENT OF HEALTH AND REHABILITATIVE SERVICES.1994. The Fate of Nutrients and Pesticides in Southwest Flor-ida Soils. Florida Department of Health and RehabilitativeServices, Tallahassee, Florida.

FLORIDA DEPARTMENT OF NATURAL RESOURCES (FDNR). 1991.Lemon Bay Aquatic Preserve Management Plan. Bureau ofSubmerged Lands and Preserves, Florida Department of Nat-ural Resources, Tallahassee, FL.

HARPER, H. H. 1991. Estimation of Loading Rate Parameters forthe Tampa Bay Watershed. Southwest Florida Water Manage-ment District, Brooksville, Florida.

HEYL, M. G. 1992. Point and non-point source pollutant loadingassessment, p. 12.1–12.9. In P. Roat, C. Ciccolella, H. Smith,and D. Tomasko (eds.), Sarasota Bay Framework for Action.Sarasota Bay National Estuary Program, Sarasota, Florida.


JOHANSSON, J. O. R. 1991. Long-term trends in nitrogen loading,water quality and biological indicators in Hillsborough Bay,Florida, p. 157–176. In S. F. Treat and P. A. Clark (eds.), Pro-ceedings, Tampa Bay Areas Scientific Information Symposium2. Tampa Bay Regional Planning Council, St. Petersburg, Flor-ida.

JOHANSSON, J. O. R. AND H. S. GREENING. 1999. Seagrass resto-ration in Tampa Bay: A resource-based approach to estuarinemanagement, p. 279–293. In S. A. Bortone (ed.), Seagrasses:Monitoring, Ecology, Physiology, and Management. CRCPress, Boca Raton, Florida.

JOHANSSON, J. AND T. RIES. 1997. Seagrass in Tampa Bay: Historictrends and future expectations, p. 139–150. In S. Treat (ed.),Proceedings, Tampa Bay Area Scientific Information Sympo-sium 3. Tampa Bay Regional Planning Council, St. Peters-burg, Florida.

KURZ, R. C., D. A. TOMASKO, D. BURDICK, T. F. RIES, K. PATTER-SON, AND R. FINCK. 1999. Recent trends in seagrass distribu-tions in Southwest Florida coastal waters, p. 157–166. In S. A.Bortone (ed.), Seagrasses: Monitoring, Ecology, Physiology,and Management. CRC Press, Boca Raton, Florida.

LAPOINTE, B. E., J. D. O’CONNELL, AND G. S. GARRETT. 1990.Effects of on-site sewage disposal systems on nutrient relationsof groundwaters and nearshore surface waters of the FloridaKeys. Biogeochemistry 10:289–307.

LORENZEN, C. J. 1972. Extinction of light in the ocean by phy-toplankton. Journal de Conseil International pour l’Exploration dela Mer 34:262–267.

MCPHERSON, B. F. AND R. L. MILLER. 1987. The vertical attenu-ation of light in Charlotte Harbor, a shallow subtropical es-tuary, south-western Florida. Estuarine, Coastal and Shelf Science25:721–737.

MCPHERSON, B. F. AND R. L. MILLER. 1993. Causes of light atten-uation in estuarine waters of Southwestern Florida, p. 227–234. In L. J. Morris and D. A. Tomasko (eds.), Proceedingsand Conclusions of Workshops on Submerged Aquatic Vege-tation Initiative and Photosynthetically Active Radiation. Spe-cial Publication SJ93-SP13. St. Johns River Water ManagementDistrict, Palatka, Florida.

MONTGOMERY, R. T., B. F. MCPHERSON, AND E. E. EMMONS. 1991.Effects of Nitrogen and Phosphorus Additions on Phytoplank-ton Productivity and Chlorophyll a in a Subtropical Estuary,Charlotte Harbor, Florida. U.S. Geological Survey, Water Re-sources Investigations Report 91-4077. Tampa, Florida.

NIXON, S. W., J. W. AMMERMAN, L. P. ATKINSON, V. M. BEROUNSKY,G. BILLEN, W. G. BOICOURT, W. R. BOYNTON, T. M. CHURCH,D. M. DITORO, R. ELMGREN, J. H. GARBER, A. E. GIBLIN, R. A.JAHNKE, N. J. P. OWENS, M. E. PILSON, AND S. P. SEITZINGER.1996. The fate of nitrogen and phosphorus at the land-seamargin of the North Atlantic Ocean. Biogeochemistry 15:141–180.

PEIERLS, B. L. AND H. W. PAERL. 1997. The bioavailability of at-mospheric organic nitrogen deposition to coastal phytoplank-ton. Limnology and Oceanography 42:1819–1823.

RYTHER, J. AND W. DUNSTAN. 1971. Nitrogen, phosphorus, andeutrophication in the coastal marine environment. Science171:1008–1012.

SEITZINGER, S. AND J. SANDERS. 1997. Contribution of dissolved

organic nitrogen from rivers to estuarine eutrophication. Ma-rine Ecology Progress Series 159:1–12.

SHENG, Y. P. AND S. PEENE. 1992. Circulation and its effect onwater quality, p. 5.1–5.18. In P. Roat, C. Ciccolella, H. Smith,and D. Tomasko (eds.), Sarasota Bay Framework for Action.Sarasota Bay National Estuary Program, Sarasota, Florida.

SOUTHWEST FLORIDA WATER MANAGEMENT DISTRICT. 1999.Southern Coastal Watershed Comprehensive ManagementPlan. Southwest Florida Water Management District, Brooks-ville, Florida.

SQUIRES, A. P., H. ZARBOCK, AND S. JANICKI. 1998. Loadings oftotal nitrogen, total phosphorus and total suspended solidsto Charlotte Harbor, p. 187–200. In S. F. Treat (ed.), Pro-ceedings of the Charlotte Harbor Public Conference andTechnical Symposium; 1997 March 15–16; Punta Gorda, Flor-ida. Charlotte Harbor National Estuary Program TechnicalReport No. 98-02. West Palm Beach (Fla.); South Florida Wa-ter Management District, Florida.

STACEY, P. S., H. S. GREENING, J. N. KREMER, D. PETERSON, AND

D. A. TOMASKO. 2001. Contributions of atmospheric nitrogendeposition to U.S. estuaries, p. 187–226. In R. A. Valigura, R.B. Alexander, M. S. Castro, T. P. Meyers, H. W. Paerl, P. E.Stacey, and R. E. Turner (eds.), Nitrogen Loading in CoastalWater Bodies: An Atmospheric Perspective. American Geo-physical Union, Washington, D.C.

STANLEY, D. 2001. An annotated summary of nitrogen loadingto US estuaries, p. 227–252. In R. A. Valigura, R. B. Alexander,M. S. Castro, T. P. Meyers, H. W. Paerl, P. E. Stacey, and R. E.Turner (eds.), Nitrogen Loading in Coastal Water Bodies: AnAtmospheric Perspective. American Geophysical Union,Washington, D.C.

TOMASKO, D. A. AND B. E. LAPOINTE. 1991. Productivity and bio-mass of Thalassia testudinum as related to water column nutri-ent availability and epiphyte levels: Field observations and ex-perimental studies. Marine Ecology Progress Series 75:9–17.

TOMASKO, D. A. AND M. O. HALL. 1999. Productivity and biomassof the seagrass Thalassia testudinum along a gradient of fresh-water influence in Charlotte Harbor, Florida. Estuaries 22:592–602.

TOMASKO, D. A., C. J. DAWES, AND M. O. HALL. 1996. The effectsof anthropogenic nutrient enrichment on turtle grass (Thal-assia testudinum) in Sarasota Bay, Florida. Estuaries 19:448–456.

TURNER, R. E., D. STANLEY, D. BROCK, J. PENNOCK, AND N. N.RABALAIS. 2001. A comparison of independent N-loading es-timates for U.S. estuaries, p. 107–118. In R. A. Valigura, R. B.Alexander, M. S. Castro, T. P. Meyers, H. W. Paerl, P. E. Stacey,and R. E. Turner (eds.), Nitrogen Loading in Coastal WaterBodies: An Atmospheric Perspective. American GeophysicalUnion, Washington, D.C.

WALKER, W. G., J. BOUMA, D. R. KENNEY, AND P. G. OLCOTT. 1973.Nitrogen transformations during subsurface disposal of septictank effluent in sands: II. Ground water quality. Journal of En-vironmental Quality 2:521–525.

SOURCE OF UNPUBLISHED MATERIALS

ENGLEWOOD WATER DISTRICT. Personal Communication. 201Selma Avenue, Englewood, Florida 34223.

Received for consideration, April 14, 2000Accepted for publication, May 16, 2001

assessment of present and future nitrogen loads, water ......q 2001 estuarine research federation...

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