Submerged aquatic vegetation correlations with depth and light attenuating materials in a shallow subtropical lake

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1 Hydrobiologia 493: , Kluwer Academic Publishers. Printed in the Netherlands. 173 Submerged aquatic vegetation correlations with depth and light attenuating materials in a shallow subtropical lake Karl E. Havens South Florida Water Management District, West Palm Beach, FL , U.S.A. khavens@sfwmd.gov Received 20 August 2002; in revised form 30 December 2002; accepted 19 January 2003 Key words: submerged aquatic vegetation, shallow lakes, alternative stable states Abstract A 3-year study was done to quantify the biomass of submerged aquatic vegetation (SAV) and its relationship with environmental attributes in Lake Okeechobee, the largest lake in the southeastern United States. Plants were sampled on 21 occasions at sites located along 15 fixed transects around the shoreline, giving rise to 721 observations of SAV species (Chara spp., Vallisneria americana, Hydrilla verticillata, Potamogeton illinoinensis) dry weight biomass. Environmental sampling focused on factors that attenuate light, including phytoplankton chlorophyll a (chl a), total suspended solids (TSS), non-volatile suspended solids (NVSS) and color. Depth and Secchi transparency also were measured. Based on regression analysis, NVSS was considerably more important in attenuating light than chl a or color. Total biomass of SAV varied from 0 to 271 g dw m 2, with a mean of 4.7 gdwm 2, and strong dominance by Chara. The SAV biomass was lower than average for Florida lakes, and may reflect the influence of suspended solids on underwater irradiance, as well as high water level in the late 1990s. Dense SAV was found only where depth was < 2mandTSS< mg l 1. At locations where high biomass of SAV occurred, the plants may have influenced water quality, because concentrations of TSS, NVSS, and chl a were 2 3 fold lower than at sites with no plants. The potential effects of SAV also were apparent at a regional scale. The shoreline region of the lake displayed a pattern of rising and falling chl a and NVSS with water depth. This occurred both at sites with and without plants, suggesting that it may be driven by physical processes, such as water circulation patterns, which are influenced by depth. However, the pattern was dampened at sites with SAV, indicating a potential to influence these attributes of water quality. Introduction An array of factors can influence the distribution and abundance of submerged aquatic vegetation (SAV) in freshwater lakes. These factors include sediment type (Gafny & Gasith, 1999), water depth (Wallsten & Forsgren, 1989), transparency (Canfield et al., 1985; Schwarz et al., 2002), bottom slope (Duarte & Kalff, 1986), wave disturbance (Hudon et al., 2000), and benthic algae (Weisner et al., 1997). In lakes where conditions favor development of high biomass of SAV over large areas, there is potential for large-scale reductions in water column concentrations of nutrients and phytoplankton, and increased transparency (Scheffer et al., 1994; Jeppesen et al., 1998; Blindow et al., 2002). This occurs because plant beds stabilize sediments (Vermaat et al., 2000), support epiphyton that sequesters nutrients (Burkholder et al., 1990; Hansson, 1990), and give rise to physical and chemical conditions that lead to removal of both particulate and soluble nutrients from the water column (Kufel & Kufel, 2002). In contrast, when plants are scarce, the same lakes may have high concentrations of nutrients, blooms of phytoplankton, and high turbidity (Scheffer et al., 1994; Moss et al., 1997). An understanding of SAV dynamics is therefore critical to management of shallow lakes where submerged plants can flourish and potentially control ecosystem dynamics.

2 174 Much of the research on SAV in shallow lakes has occurred in temperate regions of Europe and North America, with less attention given to sub-tropical and tropical systems. Recently, Bachmann et al. (2002) carried out a comprehensive survey (319 lakes) of SAV in relation to trophic state indicators in Florida, U.S.A. They found significant inverse relationships between the biomass of SAV and concentrations of nutrients and chl a, and a positive relationship between biomass of SAV and Secchi transparency. However, the amount of variability explained by the correlations was quite low, and there was considerable overlap in trophic state attributes in lakes with vs. without plants. In the present study, I assembled a comparable database from a 3-year study carried out in Florida s largest lake, Lake Okeechobee. The data were used to examine the factors that might control SAV, and to evaluate potential effects of SAV on lake water quality. The following specific questions were addressed: (1) How does the biomass of SAV in Lake Okeechobee compare to what has been documented for other shallow eutrophic lakes, in particular, lakes in Florida? (2) What environmental factors primarily control the biomass of SAV in this lake? (3) Is there evidence that the SAV affects water quality, at local or regional scales? Methods Field sampling Sampling was conducted on 15 fixed transects (Fig. 1) along the south, west, and north shoreline of the lake, in a region between the emergent marsh community and the pelagic zone. This is a shallow region with typical depths between 0.5 and 1.5 m, which historically has supported SAV. The 15 transects are a subset of those surveyed in an earlier study (Zimba et al., 1995). Samples were collected on 21 occasions (May, July, and October 1999; February, April August, and October 2000; January, April June, August November 2001; January, March, and April 2002). Sampling was done at fixed stations along each transect, which were located in the field based on previously established GIS coordinates. In 1999 and from January to May 2000, three stations were sampled on each transect. After May 2000, when low lake levels exposed some stations close to the shore, additional stations were added to the lakeward end of some transects. On each transect, sampling occurred outward from the first shoreline station with standing water until a station was encountered in deeper water with no plants. No sampling was done lakeward of that site. In some cases, as many as six sites were sampled along a given transect, while in other cases, only one site was sampled. The distance between points along each transect ranged from 200 to 2100 m, depending on total transect length. At each site, water depth was measured with a calibrated plastic rod to the nearest 5 cm, and Secchi depth was measured with a 20-cm black and white disk. Water temperature was measured at middepth with a YSI meter, and samples were collected for analysis of phytoplankton chl a, total suspended solids (TSS), volatile suspended solids (VSS), and color. Concentrations of non-volatile suspended solids (NVSS) were calculated as the difference between measured TSS and VSS. The NVSS is considered to be a good signal of transport of inorganic solids from the central pelagic region, where there is frequent resuspension of mud bottom sediment (Phlips et al., 1995). Irradiance measurements were taken at the water surface and near bottom (+20 cm) with a LiCor spherical quantum sensor, but the frequency of sampling was low, precluding use of these data in evaluation of SAV/water quality relationships. From 1999 to summer 2000, vascular plants were sampled by harvesting all material contained within 0.5 m 2 plastic frames, which were randomly thrown to three locations from the anchored boat. Sampling was done by snorkeling, with divers breaking the plant material at the sediment surface, and then collecting it inside cloth mesh bags. The bags were immersed into the water several times to dislodge loosely attached periphyton, and the plant material was placed into plastic bags in an ice chest until processing in the laboratory. During winter, or at sites with water depths in excess of 2 m, divers first surveyed the site by randomly swimming over approximately a 5 5 m area of the bottom. If no plants were observed, sampling frames were not deployed. Sampling of Chara, a benthic macro-alga that grows to a maximal height of 30 cm in this lake, was done with triplicate samples of a petite Ponar dredge (total area sampled 0.08 m 2 ). Starting in August 2000, when a spatially intensive SAV mapping program was conducted on the lake (Havens et al., 2002), the sampling method for plants was modified, to increase efficiency and ensure greater consistency in sampling at different times of the year and at locations of varying depths. The SAV (vascular

3 Figure 1. Map of Lake Okeechobee, showing the locations of 15 shoreline transects (solid black lines) where submerged aquatic vegetation was sampled in 1999 to The grey shaded area is emerged vegetation and the inset map shows the location of this lake in Florida, U.S.A. 175

4 176 plants and Chara) was collected by triplicate samples of a tool constructed from two standard garden rakes, bolted together at midpoint to create a tongs like device. The degree of opening was constrained by a chain connecting the two handles, so that three replicate samples with the device removed approximately 1 m 2 of bottom cover. When the rakes were in a closed position (collecting plants), the distance between adjacent tines was approximately 10 mm. The plants were placed into plastic bags and stored on ice as above. Periphyton removal was done in the laboratory, by agitation of the plants under water in plastic trays. A recently completed comparison of quadrat vs. tong sampling (Andrew Rodusky, South Florida Water Management District, personal communication) indicates a very good agreement between methods, over the range of plant taxa and sediment types sampled. Laboratory methods Concentrations of TSS and VSS were determined following Standard Methods (APHA, 1985). Concentrations of chl a were determined with a spectrophotometer, after filtration of water onto Whatman GF/F filters, grinding on a tissue grinder, and acetone extraction for at least 2 h in the dark at 0 C. Plant samples were sorted by species and dried in ovens at 60 C until two successive measurements confirmed that weight loss no longer was occurring. Dry weight was expressed on a per area basis (g dw m 2 ). Data manipulation and statistical analysis The SAV data set contained 721 observations. However, water quality sampling was done at a lower frequency. As a result, there are between 467 and 718 observations with concurrent data for SAV and particular physical or chemical attributes (higher sample sizes for readily measured field parameters, compared to laboratory analyses). Statistical analyses were done using all of these data, or in certain instances, subsets of data falling into different categories (e.g., categories of plant biomass, sediment type, or wind / wave exposure). To address the problem of heterogeneous variability and highly skewed distributions of data, the SAV biomass and water quality data were log- 10 transformed prior to statistical analysis. Percent Secchi depths (Secchi divided by total depth) were arcsine transformed. Before taking the logarithms of SAV biomass, a value of 0.01 was added to each measurement. Statistical analyses were done using Table 1. Summary statistics for environmental attributes measured in concert with sampling of submerged aquatic vegetation in Lake Okeechobee, Florida, from April 1999 to April Statistics for absolute Secchi depths (m) include only observations where Secchi was less than total depth. Percent Secchi depths (Secchi divided by total depth) include all observations Attribute Mean Median Minimum Maximum N Depth (m) Secchi (m) Secchi (%) Temperature ( C) TSS (mg l 1 ) NVSS (mg l 1 ) Chl a (µgl 1 ) Color (Pt Units) SYSTAT Version 10.2 (SYSTAT Software Inc., California, U.S.A.). Least-squares regression analysis was used to determine the extent to which different water quality attributes (solids, chl a, color) accounted for observed variations in Secchi transparency. Spearman rank correlation analysis was done to evaluate possible relationships between SAV species biomass and water quality attributes, and Factor Analysis was done to provide a summary of these interactions. Factor optimization was done using the varimax orthogonal rotation method, which minimizes the number of variables that have high loading on each factor, thereby simplifying factor interpretation. To examine potential effects of SAV on water quality, one-way analysis of variance (ANOVA) was used to test for significant differences in water quality attributes between sites with different plant biomass (no plants, 10, , and >100 g dw m 2 plants). The General Linear Models procedure was used, due to different sample sizes between categories. Tukey s post hoc test was used to identify significant (p < 0.05) differences between means for these categories. The potential influence of SAV was further evaluated by comparing temporal (3-year) patterns in TSS and chl a at sites with SAV vs. sites with no plants. Results Physical and chemical conditions Water depth ranged from 0.05 to 3.70 m over all of the sites sampled, with a mean depth of 1.30 m (Table 1). Any given site displayed a range of water depths over

5 177 Table 2. Spearman correlation coefficients between the environmental attributes. For any given correlation, sample size (N) is the smaller one associated with the pair of variables, from Table 1. Correlations that are significantly significant at p < 0.05 are in bold. The correlation coefficients are based on Log-10 transformed data, except for%secchi, where data were arcsine transformed. The correlations between%secchi and depth, and%secchi and Secchi are spurious (sp), and therefore are not provided Depth Secchi %Secchi Temp TSS NVSS Chl a Color Depth sp Secchi sp %Secchi Temp TSS NVSS Chl a Table 3. Summary statistics for submerged aquatic vegetation (biomass, g dw m 2 ) at the transect sampling stations in Lake Okeechobee, from April 1999 to April 2002 Taxon Mean Median Minimum Maximum N Chara Hydrilla Vallisneria Potamogeton Total Among the three light attenuating substances (chl a, solids, and color), the strongest correlation (Table 2) with Log-10 Secchi transparency was for Log-10 TSS, and a significant linear regression model was fit to these data (r 2 = 0.56, p<0.001, Fig. 2). Suspended solids appear to be the most important factor attenuating light in the near-shore region of the lake, as they are in the deeper pelagic zone (Phlips et al., 1995). Submerged aquatic vegetation Figure 2. Relationship between log-10 transformed Secchi transparency and log-10 total suspended solids (TSS) in the shoreline region of Lake Okeechobee, based on data collected in along the transects shown in Figure 1. The solid line is based on the linear regression model. its sampling history. Secchi transparency at the sites ranged from 0.05 to 1.64 m, with a mean of 0.60 m, and the percent Secchi depth ranged from 4 to 100% (mean 56%). Total suspended solids ranged from 0 to 142 mg l 1 (mean 16 mg l 1 ) and NVSS ranged from 0 to 96 mg l 1 (mean 8 mg l 1 ), accounting for 50% of TSS. Concentration of phytoplankton chl a ranged from 0 to 96 µg l 1 (mean 19 µg l 1 ), and color ranged from 8 to 536 Pt units (mean 59 Pt units). Total dry weight biomass of all species of SAV present at a site varied from 0 to 271 g dw m 2 (Table 3); the most common situation (65% of observations) encountered during the study was that of no plants. The mean biomass was 7.1 g dw m 2. Considering only the sites with SAV present, the mean biomass was 20 gdwm 2. Chara spp. displayed the highest biomass, with a range from 0 to 271 g dw m 2 (mean 4.7 g dw m 2 ). Biomass ranges for the vascular plant species were: Hydrilla verticillata, 0 to g dw m 2 (mean 1.1gdwm 2 ), Vallisneria americana, 0 to 75.1 g dw m 2 (mean 0.7 g dw m 2 ), and Potamogeton illinoinensis, 0 to 58.6 g dw m 2 (mean 0.5 g dw m 2 ). Chara occurred over a large portion of the sampling region, whereas vascular plants occurred in distinct beds in the north, west, or south. There was generally little overlap between Chara and the vascular plants in time and space. Vegetation water quality relationships There were significant negative correlations between total SAV biomass and water depth, TSS, NVSS, and chl a, and significant positive correlations between SAV biomass and Secchi depth, percent Secchi depth, and water temperature (Table 4). The strongest correlations were with concentrations of TSS and NVSS, and with percent Secchi. Similar results were obtained

6 178 Table 4. Spearman rank correlation coefficients for environmental attributes vs. submerged aquatic vegetation taxa and total biomass. For any given correlation, sample size (N) is the lowest number shown for the two attributes in Tables 1 and 2. Correlations that are statistically significant at p<0.05 are in bold. Correlation coefficients are based on Log-10 transformed data, except for%secchi, where data are arcsine transformed. Prior to transformation, a value of 0.01 was added to all SAV taxa and total biomass values Chara Hydrilla Vallisneria Potamogeton Total SAV Depth Secchi %Secchi Temperature TSS NVSS Chl a Color Chara Hydrilla Vallisneria Potamogeton for Chara biomass, while fewer significant correlations were observed for individual vascular plant taxa. Considering water depth and the attributes related to attenuation of light, a stepwise multiple regression model retained TSS and depth as significant predictors of total SAV and Chara biomass: Figure 3. Results of Factor Analysis of the SAV and environmental data collected in the shoreline region of Lake Okeechobee from 1999 to The first two factors are shown; they explain 32% and 16% of the variation in the underlying data. Factor 1 identifies two different states in the shoreline region, based on positive correlations with Log-10 transformed depth (LDEPTH), chl a (LCHL), and TSS (LTSS), and negative correlations with arcsine transformed%secchi transparency (ARSD), and Log-10 transformed water temperature (LTEMP), and biomass of Chara (LCHA). Other variables, including Log-10 transformed Vallisneria (LVAL), Potamogeton (LPOT), Hydrilla (LHYD), and color (LCOLOR), do not significantly contribute to the pattern. Log-10 Total Biomass = 1.04(Log-10 depth) 1.69(Log-10 TSS) , R 2 = 0.18,p <0.01,n= 482 Log-10 Chara Biomass = 0.71(Log-10depth) 1.48(Log-10 TSS) 0.52, R 2 = 0.18,p <0.01,n= 482 Models with NVSS, instead of TSS, had nearly identical R 2 values. Significant regression models were not obtained for any of the other plant species. Slightly better models for total biomass and Chara biomass were developed based solely on percent Secchi depth: Log-10 Total Biomass =1.55 (arcsine%secchi) 2.99, r 2 = 0.24,p<0.01,n= 702 Log-10 Chara Biomass = 1.24 (arcsine%secchi) 3.14, r 2 = 0.21,p <0.01,n= 702 The results of factor analysis (Fig. 3) provide a summary of SAV vs. environmental relationships in the near-shore region of Lake Okeechobee. The first factor explains 37% of the variation in the data, while the second factor explains just 16%. Subsequent factors contribute less to explanation of the data structure. Factor 1 describes a significant difference between alternative states: a state with high TSS (r =+0.75), high chl a (r =+0.58), and high depth (r =+0.65), and a state with high biomass of Chara (r = 0.57), high%secchi transparency (r = 0.86), and high water temperature (r = 0.66). In general, this differentiates between typical winter and summer conditions in this shallow subtropical lake.

7 179 Figure 4. (A) Total biomass of submerged aquatic vegetation (SAV) plotted as a function of water depth and total suspended solids (TSS). (B) Biomass of Chara plotted as a function of depth and TSS. SAV-free sites. This substantially reduced the power of statistical tests, yet was essential for logistical reasons. To test for possible local effects of SAV on water quality, one-way ANOVA was performed, with particular water quality attributes (chl a, Secchi, TSS) as dependent variables, and four categories of SAV biomass (as defined previously) as the independent variable. Tukey s test indicated significantly lower chl a, lower TSS, and significantly higher Secchi transparency at sites with the highest plant biomass (>100 gdwm 2 ) vs. sites with no plants. Mean chl a was 10 µg l 1 at sites with dense plants, compared to 22 µg l 1 at sites with no plants. Likewise, mean Secchi depths were 0.7 m (dense plants) vs. 0.4 m (no plants), and mean TSS concentrations were 5 mg l 1 (dense plants) vs. 21 mg l 1 (no plants). Contrasts between other plant density categories were not statistically significant. To test for possible regional effects of SAV, I examined whether the data support a hypothesis that at low lake stage, high biomass of SAV is the reason for reduced concentrations of chl a and solids that previously were documented (Maceina, 1993; Phlips et al., 1993) in the sampling region. When the data from sites with vs. without plants are plotted in a time series (Fig. 5A C), there clearly are synchronized patterns of rising and falling chla and NVSS with water depth, regardless of whether plants are present or not. Therefore, the underlying pattern does not appear to be driven by the SAV. However, the pattern was noticeably dampened at sites with plants, indicating that SAV does have the potential to influence these water quality attributes. Potential effects of SAV on water quality The relationships between SAV or Chara biomass vs. depth and TSS can be visualized in a threedimensional graph (Fig. 4A,B). In both cases, high biomass observations are restricted to depths below 2 m and TSS values below mg l 1. These graphs also help to explain the low regression coefficients obtained in the preceding models very few data points occur in the region of the X Y plane with depths >2 mortss>50 mg l 1, even though these conditions characterize much of the lake. The sampling program used here, where lakeward sampling along each transect stopped once a station was reached with no plants, limited the number of deep water, high TSS, Discussion The results of this study indicate that while average biomass of SAV is relatively low in Lake Okeechobee, the plants still may have effects on water quality (transparency, phytoplankton chl a, solids) in the lake s shoreline region. If hydrologic conditions become more favorable for plant growth (e.g., lower water levels) and SAV were to expand further, the potential effects observed here might become even more pronounced. This trend might occur, because a key objective of an ongoing regional restoration program in south Florida, the Comprehensive Everglades Restoration Plan (USACE, 1999), is to maintain lower average water levels in Lake Okeechobee. The following discussion draws contrasts between the present con-

8 180 Potamogeton,andVallisneria) ranged from 59 to 158 g dw m 2. The values are low when compared to results from shallow eutrophic lakes at temperate latitudes, where most SAV research has occurred. For example, Blindow et al. (2002) recorded summer biomass maxima for Chara in a shallow, eutrophic Swedish lake of 800, 550, and 380 g dw m 2 (three successive years). Fernandez-Alaez et al. (2002) reported maxima of g dw m 2 for Chara, and 430 g dw m 2 for Potamogeton in a shallow lake in Spain. As a result of a longer growing season, higher water temperatures, and higher solar irradiance, Florida lakes might be expected to support a higher biomass of SAV. Indeed, Bachmann et al. (2002) reported SAV biomass as high as 1760 g dw m 2, in a survey of 319 shallow lakes in north-central Florida, where the average biomass was 96gdwm 2. Considering only the sites with SAV present, they found an average biomass of 160 g dw m 2. The corresponding value for Lake Okeechobee is 20 g dw m 2. Hence, even when conditions are suitable for occurrence of SAV in this lake, the plants display a mean biomass that is considerably lower than the average observed in other Florida lakes. In contrast, the maximum SAV biomass observed in Lake Okeechobee, 271 g dw m 2, indicates that under certain conditions, this lake can support SAV at a level that is more typical for the region. In the following discussion, some potential constraints on SAV biomass in Lake Okeechobee are considered. Vegetation correlations with environmental conditions Figure 5. Water depth (A), phytoplankton chlorophyll a (B), and non-volatile solids (C), during the 3-year period of SAV sampling. Data were collected along the 15 transects shown in Figure 1. The dashed line shows results from observations with SAV present; the solid line shows results from observations with no plants. ditions in Lake Okeechobee and other shallow lakes, considers major factors that constrain SAV in the lake, examines the concept of alternative stable states, and considers how changes in water level management might influence the dynamics of the community. Comparison with other lakes The average biomass of SAV in Lake Okeechobee was 7.1 g dw m 2, with a maximum of 271 g dw m 2,and dominance by the macro-alga Chara. The maximal biomass of the three vascular plant species (Hydrilla, Of the various attributes that were directly measured in this study, water depth and the concentration of suspended solids were most strongly correlated with the biomass of SAV. This finding is consistent with the concept that SAV generally is limited by light availability, rather than water column nutrients in eutrophic lakes (Wallsten & Forsgren, 1989; Gafny & Gasith, 1999; Hudon et al., 2000). Previous studies of SAV in Lake Okeechobee also identified water depth and transparency as key environmental variables. Steinman et al. (1997) found inverse correlations between biomass of Chara and depth, and positive correlations with Secchi transparency at sites in the southern end of the lake. They carried out laboratory photosynthesisirradiance experiments, which documented that at the time of seasonal Chara declines in the lake, ambient irradiance was below the level necessary for net production. Havens et al. (2002) developed maps of Chara and other SAV taxa in Lake Okeechobee at a

9 181 relatively fine scale ( m) of spatial resolution, sampling over 2000 locations in the shoreline region during August September A simple decision model with a critical depth (1.5 m) explained 55% of the spatial distribution of Chara. This seems to be a better result than obtained in the present study, where depth and TSS, taken together, explained just 20% of the variability in SAV biomass. There are several reasons for the different results. First, the mapping study evaluated only presence/absence of vegetation, a much easier attribute to predict than biomass. Second, the mapping study was a snapshot in time, with SAV sampled at the end of the summer growing season, in a year when water depths and other environmental conditions were relatively constant for >1 month before samples were taken. Third, the spatial modeling in Havens et al. (2002) only was successful for Chara, and could not be done for vascular plants, whose spatial distribution appeared to be strongly controlled by the locations of historic beds. The depth to which SAV can occur in any given lake is largely governed by the transparency of the water. A number of authors have quantified the linear or non-linear relationships between critical depth for SAV and Secchi transparency (e.g., Chambers & Kalff, 1985; Chambers & Prepas, 1988; Middelboe & Markager, 1997; Hudon et al., 2000), including Florida lakes (Canfield et al., 1985). With a mean Secchi depth of 0.5 m in Lake Okeechobee s near-shore zone, the predicted maximal depth of SAV using these published models ranges from 0.7 to 1.8 m. The Florida lake model (Canfield et al., 1985) predicts a depth of 1.3 m. The observed maximum depth of SAV in Lake Okeechobee was 2.0 m, somewhat higher than predicted by the models. This may reflect the predominance of plant taxa that are relatively low light adapted in this turbid lake. We have documented in outdoor tank experiments that Vallisneria and Chara from Lake Okeechobee maintain net growth at very low irradiances. For Vallisneria, net growth occurs at irradiances down to 30 µmol photons m 2 s 1 (Grimshaw et al., 2002), while for Chara, netgrowth occurs at roughly half of this irradiance (Grimshaw, unpublished data). Most studies dealing with SAV focus on phytoplankton or benthic algae as the major light attenuating material (e.g., Li, 1998; Meijer, 2000; Blindow et al., 2002). However, in certain systems, light attenuation by inorganic solids can be important. A focus on solids in the SAV literature occurs mainly in studies dealing with river systems, for example tributaries to the Chesapeake Bay (Orth, 1993). In Lake Okeechobee, inorganic solids (indicated by NVSS concentrations) were clearly more important than chl a in attenuating light in the shoreline region. The important role of solids in this lake has been documented for the pelagic zone,whichisveryshallow(4 5m),underlain by soft mud, and frequently exposed to winds that mix sediments into the water column (Phlips et al., 1995). Nutrient and solids transport from the pelagic zone to the shoreline region has been indicated by empirical observations (Maceina, 1993) and hydrodynamic model results (Sheng & Lee, 1991). The fundamental role of sediment resuspension in driving the dynamics of this shallow lake can best be appreciated by considering the morphometry of Lake Okeechobee in the context of other Florida lakes. The mean depth of Lake Okeechobee (2.7 m) is typical for the region, but the surface area is 3 4 orders of magnitude greater than most Florida lakes. The dynamic ratio, the square root of surface area divided by mean depth (Hakanson, 1982), is approximately 15 km m 1, and under this circumstance, it has been estimated that 100% of the lake bottom is subject to sediment resuspension 40% of the time (Bachmann et al., 2000). The primary mechanism for solids transport to the shoreline region is wind-driven current (Jin et al., 2000, 2002). One possibility for reducing the magnitude of solids transport to the shoreline region is to manage the lake at lower water levels, which as indicated above, is one goal of a regional restoration project slated to occur over the next 30 years (Steinman et al., 2002a). Hydrodynamic modeling suggests that at lower lake stages, most of the resuspended sediment material is kept within the central basin of the lake, with considerably less transport of solids to the shoreline region than at higher stage. A rock reef between the central pelagic and south/southwest shoreline prevents solids transport at lower stage. Results of the present study indicate that if water depths in the shoreline region could be maintained at <2 m and TSS held below mg l 1, there might be favorable conditions for more widespread SAV growth. This, in turn, might lead to water quality improvements (see below). It is important to note, however, that while the results indicate conditions where SAV cannot occur (constraints), they do not indicate clearly whether or not SAV will attain high biomass under the favorable conditions. This conclusion seems to be a common one in studies of SAV in shallow lakes (e.g., Scheffer et al., 1992; Bachmann et al., 2002). It points to factors, in addition to depth and light attenuating sub-

10 182 stances, as being potentially important in controlling the community. Additional factors controlling SAV A number of factors not directly measured in this study might influence the biomass and distribution of SAV. For example, in lakes with a large fetch, wave energy can be an important variable limiting the plants (Chambers, 1987; Hudon et al., 2000). To evaluate the potential for such effects in Lake Okeechobee, I subdivided the sampling region into two general exposure classes high and low based on the location of sampling stations relative to the direction of prevailing winds and fetch. The most frequent wind direction over the lake is from due east to west; therefore, all stations except those located in small bays at the south end of the lake (Fig. 1) were classified as high exposure. Two of the inner transect stations on the southwest shore, protected behind small rock reefs and dense beds of Scirpus, also were classified as low exposure. I found significant differences in total SAV biomass, and the biomass of all plant taxa, between these exposure classes (one-way ANOVA, p<0.001); higher biomass always occurred in the low exposure class. Mean total biomass of SAV was 3.9 g dw m 2 under high exposure conditions, vs g dw m 2 under low exposure. Mean biomass of Chara was3.6gdw m 2 (high exposure) vs. 7.7 g dw m 2 (low exposure). However, when statistical analyses were run separately for the two exposure classes, there were not any substantive improvements in correlation or regression coefficients. In both the high and low exposure categories, a depth of 2 m is clearly the cut-off point beyond which plants are not found, and the cut-off for TSS is 50 mg l 1, with high biomass values occurring at TSS below mg l 1 in both exposure categories. These results indicate that exposure may be an important attribute to consider in development of SAV models for Lake Okeechobee, in terms of placing another constraint on plant presence/absence and biomass. However, exposure does not appear to affect the manner in which plants respond to other (directly measured) environmental attributes. This finding is somewhat different from the results of Hudon et al. (2000), who examined the importance of exposure and other factors in controlling SAV in the St. Lawrence and Ottawa Rivers, Canada. They also found that in a hierarchical analysis, exposure (sheltered vs. open locations) was the first decision variable in a model of SAV biomass. However, they also found that within each exposure class, there were differences in SAV response to depth and irradiance. At this time, a reason for the different results is uncertain, but they may simply reflect the different dynamics of temperate rivers vs. shallow subtropical lake ecosystems. Another factor that could influence the biomass of SAV is sediment type, as has been documented in temperate lakes (Carpenter & Titus, 1984; Barko & Smart, 1986; Gafny & Gasith, 1999). Sediments were found to be important in the SAV mapping project in Lake Okeechobee (Havens et al., 2002). Whereas depth explained 55% of the spatial distribution of Chara, a hierarchical model with sediment type and depth correctly predicted 75% of the spatial distribution of plants in the >2000 cell sampling grid. To determine whether the transect data supports the hypothesis that sediment type is an important factor controlling SAV, I categorized the data based on the three dominant sediment types found in the shoreline region mud, sand, and peat. This study did not include quantitative analysis of sediments, but divers made observations of surface sediment types during 1999, when underwater sampling was done. Sediment type was not evaluated in this manner at deeper sites added during To categorize sediment types at those locations, a lake sediment map developed by Fisher et al. (2001) was used. Sediment type appears to affect SAV biomass, although not to the degree observed by Havens et al. (2002). Total biomass of SAV averaged 4.9 g dw m 2 at sites with peat sediments, 8.5 g dw m 2 at sites withsand,and1.6gdwm 2 at sites with rock. Oneway ANOVA, with sediment type as the class variable, indicated a significant difference in total SAV biomass (sand = peat > rock), but no significant differences for individual SAV taxa. At sites with peat sediments, the maximal depth where plants were found was approximately 1.5 m, while at sand sediment sites, plants occurred to a depth of 2 m. In this case, I suspect the result is spurious, and simply reflects the fact that peat sediments occur in the shallowest part of the nearshore zone. The maximal TSS concentration at which SAV was observed was approximately 50 mg l 1 for all sediment types. Sorting the data by sediment type prior to correlation analysis did not substantially change the results. As with exposure, sediment type may explain some of the variation in SAV biomass, but it does not appear to substantially effect how SAV respond to other environmental attributes.

11 183 Potential vegetation effects on water quality The model of alternative stable states (Scheffer, 1989) has become the paradigm for shallow lake research and management. It predicts that when shallow lakes become dominated by dense beds of SAV, those lakes can maintain a stable condition with low concentrations of chl a and nutrients, and high transparency. In contrast, lakes without SAV have a stable state with high concentrations of chl a and nutrients, and low transparency. There are a number of processes whereby SAV can maintain a stable clear-water state. These include: direct competition with phytoplankton for nutrients (Howard-Williams, 1981; Sand-Jensen & Borum, 1991); reduction of water flow velocity, such that sedimentation of algal cells is increased (Schriver et al., 1995); co-precipitation of phosphorus with calcium at high ph, driven by intense plant photosynthesis (Murphy et al., 1983); and oxidation of sediments, such that Fe +3 binds with sediment phosphorus (Wigand et al., 1997). Dense beds of SAV not only sequester nutrients (Kufel & Kufel, 2002), but they also reduce shearing stress on the sediment bed (Vermaat et al., 2000), reducing sediment nutrient resuspension. There are observations of lakes switching between alternative states (Blindow, 1992) and of clear and turbid states occurring at different locations within in a single lake (Scheffer et al., 1994). The data from Lake Okeechobee, as well as field observations and photographs (Havens et al., 2002) indicate that there are sometimes clear water zones in the proximity of dense beds of SAV. I have experienced diving in the southern bays of the lake in the late 1990s, when water was deep and extremely turbid (without SAV), and then again in crystal clear water over dense Chara beds at the same locations in summer At that time, the beds extended over nearly hectares of lake bottom. Steinman et al. (2002b) provided data to support the hypothesis that these Chara beds at the south end of the lake set up a positive feedback loop that stabilizes water clarity, consistent with the model of alternative stable states (Scheffer, 1989). The present study confirmed that dense SAV beds are correlated with reduced concentrations of chl a, TSS, NVSS, and increased transparency of the water. The data from this study cannot confirm a causal relationship, but they are consistent with the hypothesis that SAV can affect water quality in shallow lakes. For Lake Okeechobee, an important question is whether or not SAV can bring about switches from clear to turbid states at a regional scale (i.e., over the entire shoreline region that was sampled in this study). Phlips et al. (1993) noted that in the shoreline region of the lake, there is an inverse relationship between the biomass of SAV and water column chl a. They postulated that during the summer, SAV removes nutrients from the water column, suppressing phytoplankton density. They considered this to be the reason for the inverse relationship between SAV (maximum in summer) and chl a (maximum in winter) that they observed in that region of the lake. Maceina (1993) proposed a different explanation for the variation of chl a that under lower lake stage (which typically occurs in spring summer), there is reduced nutrient transport from the pelagic zone, for reasons previously described. Maceina (1993) noted that the difference between pelagic and shoreline total phosphorus decreases with increasing lake stage, a pattern that also is found in more contemporary data (Havens & Walker, 2002). The present data allowed for a test of these hypotheses, which relate directly to how important SAV is in affecting water quality at a regional scale in this lake. I examined variation in chl a and NVSS over the study period, and documented that both attributes were directly related to water depth (and lake stage). I then re-examined the patterns, looking separately at data from sites with vs. without SAV. In both cases, there was a strong correspondence between chl a, NVSS, and depth, indicating that some factor other than SAV is responsible for the general pattern. This finding is consistent with Maceina s (1993) hypothesis. However, the temporal patterns were dampened at sites with SAV, possibly supporting the hypothesis of Phlips et al. (1993), that plants play a role in controlling water quality in the shoreline region. In summary, physical factors may drive major seasonal changes in water chemistry in the shoreline region, while SAV may influence the magnitude of this change. The degree of influence of the SAV at any given time will depend on its biomass and spatial extent. Implications for lake management The shoreline region that supports SAV in Lake Okeechobee is a critical one, both for native flora and fauna, and for human uses of the lake (Havens & Walker, 2002). The SAV in this region provides habitat for a recreational fishery (Furse & Fox, 1994), foraging habitat for wading birds (Smith et al., 1995), habitat for migratory waterfowl, and is the location of

12 184 water intake structures for several local municipalities. This study indicates that water depth in the shoreline region may be a key determinant of SAV biomass, and therefore an important factor affecting these valued attributes of the system. In recent years, Lake Okeechobee has displayed wide fluctuations in its water level. In the late 1990s, deep water occurred for 6 consecutive years, as a result of heavy rainfall and a flood control schedule that held high levels of water in the lake. During that period of time (including the first year of this study) there was very little SAV in the lake. In 2000, the South Florida Water Management District released approximately 0.3 m of water from the lake, in an attempt to provide more desirable conditions for SAV growth. A major drought occurred in , dropping the lake level to historic lows, and exposing much of the shoreline area. The SAV (mostly Chara) covered nearly hectares by September 2000 (Havens et al., 2002), but then declined dramatically when very low water levels occurred in the subsequent year. By fall 2001, water levels had recovered and SAV biomass increased. Dense beds of vascular SAV, in particular Vallisneria, developed in certain regions of the lake, but as of summer 2002, had not spread across the near-shore landscape. Although a new regulation schedule for the lake may result in some attenuation of these high and low stages, it is uncertain whether widespread dense beds of SAV will persist for long periods of time (multiple years) in this lake. The Comprehensive Everglades Restoration Program (USACE, 1999), approved by the U.S. Congress in 2000 and funded at nearly $8.5 billion U.S. for the next 30 years, is expected to provide alternative water storage locations that are expected to reduce the range of water level variation in Lake Okeechobee. This should benefit the SAV. We are in the process of developing a mechanistic model of SAV that will be linked with an existing lake hydrodynamic sediment transport model (Jin et al., 2000, 2002), and intend to use this tool to assist in optimizing the regulation schedule for the lake under the regional restoration program. The results of this study, coupled with information from ongoing experimental work with SAV, will help to develop a model that accurately represents the responses of SAV to depth and other environmental attributes. Acknowledgements Bruce Sharfstein provided oversight for all of the field sampling and laboratory analyses in support of this project. Field and laboratory work was carried out by Mark Brady, Therese East, Ryan Maki, and Andrew Rodusky. Matt Harwell developed the rake sampling method that was used after summer Roger Bachmann, Richard Bartleson, Peter Doering, Binhe Gu, Bruce Sharfstein, and an anonymous reviewer provided constructive comments on an earlier version of this manuscript. References APHA, Standard Methods for the Examination of Water and Waste Water (16th edn). American Public Health Association, Washington, D.C., U.S.A.: 1268 pp. Bachmann, R. W., M. V. Hoyer & D. E. Canfield, Jr., The potential for wave disturbance in shallow Florida lakes. Lake Reserv. Manage. 16: Bachmann, R. W., C. A. Horsburgh, M. V. Hoyer, L. K. Mataraza & D. E. Canfield, Jr., Relations between trophic state indicators and plant biomass in Florida lakes. Hydrobiologia 470: Barko, J. W. & M. Smart, Effect of sediment composition on growth of submersed aquatic vegetation. Technical Report, U.S. Army Corps of Engineers, Waterways Experiment Station, Vicksburg, MS, U.S.A. Blindow, I., Long and short term dynamics of submerged macrophytes in two shallow lakes. Freshwat. Biol. 28: Blindow, I., A. Hargeby & G. Andersson, Seasonal changes of mechanisms maintaining clear water in a shallow lake with abundant Chara vegetation. Aquat. Bot. 72: Burkholder, J. M., R. G. Wetzel & K. L. Klomparens, Direct comparison of phosphate uptake by adnate and loosely attached microalgae within an intact biofilm matrix. Appl. Environ. Microbiol. 56: Canfield, D. E., K. A. Langeland, S. B. Linda & T. T. Haller, Relations between water transparency and maximum depth of macrophyte colonization in lakes. J. Aquat. Plant Manage. 23: Carpenter, S. R. & J. E. Titus, Composition and spatial heterogeneity of submersed vegetation in a soft water lake in Wisconsin. Vegetatio 57: Chambers, P. A., Nearshore occurrence of submersed aquatic macrophytes in relation to wave action. Can. J. Fish. aquat. Sci. 44: Chambers, P. A. & J. Kalff, Depth distribution and biomass of submerged aquatic macrophyte communities in relation to Secchi depth. Can. J. Fish. aquat. Sci. 42: Chambers, P. A. & E. Prepas, Underwater spectral attenuation and its effect on the maximum depth of angiosperm colonization. Can. J. Fish. Aquat. Sci. 45: Duarte, C. M. & J. Kalff, Littoral slope as a predictor of maximum biomass of submerged macrophyte communities. Limnol. Oceanogr. 31:

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