ABSTRACT

COMPLEX RELATIONSHIPS AMONG WATERSHED LAND COVER AND RESERVOIR MORPHOMETRY, PRODUCTIVITY, AND ZOOPLANKTON COMMUNITIES

by Elisabeth J. Hagenbuch

Proper management of aquatic systems requires knowledge of the links among landscape-level anthropogenic disturbances, terrestrial subsidies, and aquatic ecosystem properties. Due to their large watershed area to surface area ratios, reservoirs often receive substantial subsidies of nutrients and sediments. To better understand relationships between environmental variables and reservoir ecosystem properties, we examined relationships among variables representing landscape-level features, reservoir morphometry, water quality parameters and zooplankton community biomass for 109 reservoirs spanning a wide productivity gradient. A principal components analysis (PCA) identified two significant environmental gradients, the first representing reservoir productivity and land use cover and the second representing reservoir morphometry. A regression tree analysis used landscape-level and morphometric parameters to classify reservoirs according to their productivity level into four groups. Significant correlations were detected between small-bodied zooplankton (rotifers and/or copepod nauplii) and several environmental parameters. Non-metric multidimensional scaling plots identified a separation between reservoirs in the highest and lowest productivity groups, but only for rotifer communities.

COMPLEX RELATIONSHIPS AMONG WATERSHED LAND COVER AND RESERVOIR

MORPHOMETRY, PRODUCTIVITY, AND ZOOPLANKTON ASSEMBLAGES

Submitted to the

Faculty of Miami University

in partial fulfillment of

the requirements for the degree of

Master of Science

Department of Zoology

by

Elisabeth J. Hagenbuch

Miami University

Oxford, Ohio

2010

Advisor______María J. González

Reader______Martin H. H. Stevens

Reader______Michael J. Vanni

Reader______William H. Renwick

TABLE OF CONTENTS

List of Tables iii List of Figures iv Acknowledgements v

Introduction 1 Methods 4 Results 9 Discussion 12 References 30 Appendices 34

ii

LIST OF TABLES

Table 1 19 Table 2 20 Table 3 21 Table 4 21 Table 5 22 Table 6 23

iii

LIST OF FIGURES

Figure 1 24 Figure 2 25 Figure 3 26 Figure 4 27 Figure 5 28 Figure 6 29

iv

Acknowledgements

Foremost, I would like to express my appreciation to my advisor, María González. María provided consistent direction for this project and without her help and encouragement this project would not have been possible. Each member of my graduate committee—Hank Stevens, Mike Vanni, and Bill Renwick—were integral in that they each provided assistance on a specific area of expertise within this project. In addition, I would like to thank Jon Denlinger and Scott Hale of the Ohio Department of Natural Resources Division of Wildlife for working toward the grant which made this project possible. The initial sample kit organization, preparation, collection, and processing was performed by Lesley Knoll. I would also like to thank Robbyn Abbitt and Stacy Xanakis for their help with GIS and Joe Conroy for his valuable draft comments.

Funding for this research was provided by the Ohio Department of Natural Resources Division of Wildlife (Project: FADR48) and Miami University’s Summer Workshop Grant (Department of Zoology).

v

Introduction In an effort to understand the increasingly global effects of eutrophication on aquatic ecosystems, relationships between nutrients and primary productivity have been heavily studied (Smith 2003, Schindler 2006, Smith and Schindler 2009). However, in order to effectively inform management strategies, it is also crucial to identify links between anthropogenic landscape-level disturbances, such as agriculture (e.g., Knoll et al. 2003) and urbanization (e.g., Dodson et al. 2005, Gélinas and Pinel-Alloul 2008), and indicators of eutrophication. Effects of anthropogenic disturbance may be more pronounced in human-made reservoirs, which tend to have smaller surface water areas (Whittier et al. 2002) and larger watershed area:surface water area ratios (WA:SA) when compared to natural lakes (Kimmel et al. 1990). Consequently, relative to natural lakes, reservoirs receive proportionately greater subsidies of nutrients and sediments (Kimmel et al. 1990, Vanni et al. 2005). Reservoirs are essential for supplying water for crop irrigation, livestock, drinking water, and recreation in many areas, and their numbers are increasing worldwide (Downing et al. 2006). Furthermore, predicted patterns for worldwide human population growth and the associated need for increased food production will result in the conversion of available land for agricultural crop and pasture use and be accompanied by increases in nitrogen and phosphorus fertilizer usage (Tilman et al. 2001). In addition, recent demands for alternative fuel sources, such as grain-based ethanol, may increase rates of land cover conversion and nutrient subsidies to aquatic systems (Simpson et al. 2008). Given the substantial increases in both the number of reservoir impoundments and conversion of land for agricultural uses, it is surprising that few studies have looked specifically at landscape-level influences on reservoir ecosystems (but see Knowlton and Jones 2000, Knoll et al. 2003, Jones et al. 2004, Bremigan et al. 2008, Hale et al. 2008). Recent syntheses show that many reservoirs in the United States suffer from symptoms of eutrophication, including elevated concentrations of nutrients and phytoplankton (Whittier et al. 2002, Dodds et al. 2009). However, the internal and external factors driving trophic status are not well known. As in natural lakes, positive correlations exist between indicators of reservoir eutrophication such as high concentrations of total phosphorus (TP), total nitrogen (TN) and chlorophyll concentrations (Knoll et al. 2003, Jones et al. 2004). In addition, total phosphorus concentration is related to the extent of agricultural land cover and watershed area:water body volume ratios (Knoll et al. 2003). Row-crop agriculture has been linked to higher TN:TP ratios,

1 whereas this ratio was lower in agricultural areas containing primarily pasturelands and animal operations (Arbuckle and Downing 2001). In addition, reservoir trophic status and watershed size have been used to explain variation in fish biomass (Hale et al. 2008). In 11 Ohio reservoirs, models including agricultural land use and reservoir depth explained the most variation in nutrient concentrations and chlorophyll, while zooplankton biomass was only related to chlorophyll (Bremigan et al. 2008). Zooplankton represent a key group in reservoir food chains, linking phytoplankton and fish, especially during fish larval and juvenile stages (Welker et al.1994, Bremigan and Stein 1997, Bunnell et al. 2003, Bremigan et al. 2008). Thus, in reservoirs spanning a large gradient of productivity, information on factors explaining variation in zooplankton communities can provide fisheries managers with information on potential available prey for fish (Stein et al. 1995). In addition, zooplankton communities have potential as biological indicators of lake trophic status (Gannon and Stemberger 1978, Bays and Crisman 1983). Positive relationships have been identified between total crustacean biomass and productivity in natural lakes and reservoirs (Pinto-Coelho et al. 2005, Pace 1986, Gyllström et al. 2005). In oligotrophic lakes, crustacean biomass increases in conjunction with residential cover, total phosphorus and total nitrogen (Gélinas and Pinel-Alloul 2008). Several studies have identified a unimodal relationship between primary productivity and crustacean species richness (Dodson et al. 2000, Mittelbach et al. 2001, Hoffmann and Dodson 2005) and changes in crustacean richness have been associated with indirect effects of riparian landscape-level changes (Dodson et al. 2005). Similarly, rotifer (Whitman et al. 2004, Yoshida et al. 2003) and microzooplankton biomass (including rotifers and nauplii; Bays and Crisman 1983) have been positively related to lake productivity. Changes in rotifer species composition have been documented along a trophic gradient (Duggan et al. 2001, Yoshida et al. 2003). However, studies using both rotifer and crustacean zooplankton illustrate the possible contrasting responses of different zooplankton groups to altered trophic conditions. Stemberger and Lazorchek (1994) observed that nutrient-poor lakes were dominated by large cladocerans and calanoid copepods whereas nutrient-rich lakes contained small cladocerans, cyclopoid copepods, nauplii, and rotifers. Similarly, rotifer and small cladoceran abundance (Whitman et al. 2004) and microzooplankton biomass (including rotifers and nauplii; Bays and Crisman 1983) have been positively related to increasing lake productivity. In temperate Ohio reservoirs, phytoplankton biomass (chlorophyll) was negatively related to

2 crustacean biomass, but positively related to rotifer biomass (Bremigan et al. 2008). Such variable responses demonstrate the benefit of including both rotifers and crustaceans in predictive analyses. In this study, we wished to examine how landscape-level variables and within-reservoir physical features can predict reservoir water quality and zooplankton communities. We first analyzed relationships among three categories of environmental parameters: 1) water quality (chlorophyll, suspended solids and nutrients), 2) morphometric (maximum depth, surface area), and 3) landscape-level (percent of land cover type, watershed area:lake area ratio (WA:SA)) using data from 109 Ohio reservoirs whose watersheds contain a variety of land cover types. We predicted that reservoirs with higher levels of agricultural cropland cover within the watershed and with greater WA:SA would have higher concentrations of sediments and nutrients, because inputs of these materials presumably increase with these landscape features. In contrast, reservoirs with higher levels of forested land cover and smaller WA:SA should have fewer sediment and nutrient inputs and therefore are predicted to exhibit lower levels of suspended sediments, nutrients and chlorophyll. Second, to further examine the relationships between reservoir productivity levels and morphometric and landscape-level parameters, we used a combination of these parameters to develop a classification scheme that predicts reservoir productivity level (trophic state). Finally, we analyzed how much of the variation in zooplankton biomass and community composition among these reservoirs could be explained by landscape-level, morphometric, and water quality parameters. Previous studies have shown changes in zooplankton composition along a productivity gradient, but how these changes relate to watershed features is largely unknown. We predicted that small-bodied zooplankton, including rotifers, small-bodied cladocerans, and copepod nauplii, as well as predatory cyclopoid copepods, would be positively associated with concentrations of non-volatile suspended solids, nutrients, and chlorophyll, and hence with agricultural land use. We predicted that larger-bodied cladocerans and herbivorous calanoid copepods would show the opposite response, i.e., that they will be negatively correlated with watershed agriculture and the concentrations of nutrients, sediments and phytoplankton.

3

Methods Study Area and Study Parameters We sampled 109 reservoirs located throughout Ohio whose watersheds contain a variety of land cover types (Fig. 1) and whose productivity levels range from mesotrophic to hypereutrophic (based on total phosphorus concentrations as defined in Nürnberg 1996; Table 1). All reservoirs were sampled at least once during July or August in 2006 or 2007. In order to assess whether sampling in just one year is likely to provide representative data, a subset of 34 of these reservoirs were sampled once in both 2006 and 2007. In addition, 10 reservoirs were sampled at least once per month during July and August of 2006 and 2007. We quantified zooplankton biomass and community composition, and several environmental parameters that we group into three categories: 1) water quality, 2) morphometric, and 3) landscape.

Sample Collection and Analyses Water and zooplankton samples were collected at the outflow end of each reservoir. Using an integrated tube sampler or water pump, water samples were collected throughout the epilimnion. The epilimnion was defined as the surface through the deepest depth at which the dissolved oxygen remained about 2 mg/L. Water samples for chlorophyll (Chl) and non-volatile suspended solids (NVSS) were filtered through Pall A/E glass fiber filters (1.0 μm nominal pore size) and frozen for later laboratory analysis. NVSS concentration was used as an index of inorganic turbidity (Knowlton and Jones 2000). Although NVSS concentration can increase due to wind-induced sediment resuspension, it also increases greatly after large storms due to soil erosion (Vanni et al. 2006). Therefore, NVSS provides a potentially useful indicator of watershed influence (Jones and Knowlton 2005). Water samples for total phosphorus (TP) and total nitrogen (TN) were acidified and stored at 4˚C prior to analysis. A single zooplankton sample was collected from 1-2 meters above the maximum depth to the surface using a 63 µm mesh plankton net. To avoid loss of eggs from cladoceran brood chambers, zooplankton were anesthetized using Alka-Seltzer and preserved in 70% ethanol or 10% sugar formalin. Although preservation can change body shape of zooplankton, Black and Dodson (2003) found that there is no difference in zooplankton body length between samples preserved in alcohol and formalin. Water sample processing for Chl, NVSS and TP followed the analytical procedures outlined in Knoll et al. (2003). Briefly, Chl was extracted from the filters in the dark at 4˚C using

4 acetone and measured on a Turner model TD-700 fluorometer. Filters for NVSS were pre- weighed to determine the total weight of suspended solids and the filter, placed in a muffle furnace for four hours at 550°C to remove organic material, and reweighed to determine NVSS. TP was converted to soluble reactive phosphorus via potassium persulfate digestion and analyzed using the molybdenum blue technique method on a Lachat FIA+ QuikChem 8000 series autoanalyzer. TN was converted to nitrate via low-N potassium persulfate digestion and analyzed using second-derivative spectroscopy on a Perkin Elmer Lambda 35 UV/VIS spectrophotometer (Crumpton et al. 1992). The (molar) ratio of total nitrogen to total phosphorus (TN:TP) was also included as an additional water quality variable. Crustacean zooplankton was identified under a dissecting microscope according to Aliberti et al. (2007). For each sample, we counted at least 100 each of copepods, cladocerans, and nauplii, or 60% of the sample. Copepods were identified as calanoids, cyclopoids, or nauplii. Cladocerans were identified to at least the genus level, with the exception of Chydoridae and Macrothricidae, which were identified to the family level. For each taxon, the lengths of the first 20 (or all available) identified individuals were measured. The biomass of each crustacean group (μg dry mass L-1) was calculated using length vs. dry mass regressions (Peters and Rigler 1973, Dumont et al. 1975, Bottrell et al. 1976, McCauley 1984, Culver et al. 1985, Eisenbacher 1998). Rotifers were identified under a compound microscope using Stemberger (1979) and Aliberti et al. (2007). For each sample, at least 200 rotifers or 8% of the sample were counted and identified to at least the genus level. Rotifer biomass, except for Asplanchna spp., was calculated using the mean taxon-specific individual mass for rotifers collected in Ohio reservoirs (M.J. González et al., unpublished data). Due to their relatively large and variable size, 20 (or all available) Asplanchna spp. individuals were measured under a compound microscope and their biomass was calculated using a biovolume equation (McCauley 1984). We recognize that the resolution with which we identified taxa varied with taxonomic group. This was necessary for us to effectively process the large number of samples. Although resolution was uneven among taxonomic groups, it was consistent across lakes and thus should not affect our ability to draw inferences regarding community composition.

Reservoir Morphometric Parameters

5

Because detailed bathymetric data were not available for all reservoirs, the maximum sampling depth used for dissolved oxygen/temperature profiles (MaxDepth) was used as a surrogate for the maximum physical depth. A shapefile of reservoir boundaries was obtained through the Ohio Department of Resources-Division of Wildlife and reservoir surface areas (Area) were calculated in ESRI ArcGIS 9.3 (ESRI, Inc., Redlands, CA).

Landscape Parameters All GIS data were quantified in ESRI ArcGIS 9.3. A 30-meter digital elevation model (DEM) of Ohio was downloaded from the United States Geological Survey national map seamless server (http://seamless.usgs.gov/). Reservoir watersheds were delineated from reservoir polygon shapefiles using the Arc Hydro Tools 9 extension. DEM manipulation steps included DEM reconditioning using the National Hydrography Dataset 1:100,000 scale flowlines (http://nhd.usgs.gov/) and fill sinks. Land cover percentages for agricultural crop (PercentAgCrop); agricultural pasture (PercentAgPasture); deciduous, evergreen, and mixed forest (PercentForest); open, low, medium, and high intensity developed (PercentDeveloped) were calculated using the designations provided by 2001 30 m National Land Cover Database (NLCD; Homer et al. 2007), the most recent data available in GIS format. The watershed area to reservoir surface area ratio (WA:SA) was calculated using the delineated watersheds and digitized reservoir surface areas.

Relationships among Environmental Parameters in Reservoirs To determine the efficacy of sampling in just one year (which was the case for the majority of the reservoirs), we compared water quality parameters (Chl, TP, TN, NVSS, TN:TP) in the subset of reservoirs sampled both years by regressing 2006 vs. 2007 values (α = 0.05). In addition, to verify similarity between year-to-year patterns, we conducted simple linear regressions between pairs of water quality parameters (Chl, NVSS, TP, TN, TN:TP) as well as relationships between single water quality parameters and morphometric (MaxDepth, Area) and landscape-level parameters (PercentAgCrop, PercentAgPasture, PercentForest, PercentDeveloped, WA:RA) for each year separately. Based on similarities between 2006 and 2007 regression results, we combined these datasets and conducted regressions as above using the full data set. When multiple samples were available for a reservoir within one year, averages

6 were time-weighted using the proportion of time represented by each sample. For the full data set and for those lakes sampled in both years, these values were averaged over two years to obtain a single value for each reservoir. All statistical analyses were performed in R (R Development

Core Team 2009). To improve normality, all data were log10(x+1) transformed with the exception of land cover percentages, which were arcsine square-root transformed. Ranges and mean values for study parameters used are shown in Table 1. We used a principal components analysis (PCA) to identify those parameters that explained the most among-lake environmental variation and to reduce the environmental data set by using the resulting principal components axes as uncorrelated composite variables (McCune and Grace 2002). The PCA was performed on centered and scaled environmental data using the ―prcomp‖ function in the vegan package in R (Oksanen et al. 2008). PCA results can be influenced by a lack of multivariate normality. Therefore, we searched for multivariate outliers by standardizing the data set by using the z-scores, calculating the Mahalanobis distance between each sample unit, and identifying those reservoirs for which this value was greater than the relevant Chi-square value (P < 0.001; McCune and Grace 2002). Based on this test, no multivariate outliers were identified. The number of significant PCA axes was determined using the Kaiser-Guttman criterion which assumes that significant axes have an eigenvalue greater than 1 (McCune and Grace 2002). Pearson correlations between the environmental parameters and the PCA axes were considered significant where r > 0.30 (Tabachnick and Fidell 1996) and P < 0.05. In order to group the reservoirs by their level of productivity, a regression tree analysis was performed using the rpart package (Therneau and Atkinson 2008) in R. Because our data set consisted of continuous variables, the ―anova‖ method was selected. This method divides the reservoirs by minimizing the residual sums of squares within each group at each level of the tree, with each split decision being made independently of prior splits (Maindonald and Braun 2007). Prior to regression analysis, water quality parameters associated with productivity (Chl, TP, TN, NVSS) were entered into a PCA analysis to create a composite ―productivity‖ variable, which was used as the response variable in the regression analysis. Possible predictor variables entered into the analysis were MaxDepth, Area, PercentAgCrop, PercentForest, and WA:SA. A regression tree was over-fitted and then trimmed using the one-standard-deviation rule to select the minimum size tree, where the cross-validated error is less than the minimum cross-validated

7 error plus one standard deviation. When considering the accuracy of the regression tree, both relative error and cross-validated error were considered. The relative error provides an error estimate based on the current data set from which the current regression tree was built, while the cross-validated error uses subsets of the data to estimate the accuracy of the model for new data sets (Maindonald and Braun 2007).

Environmental Parameters as Predictors of Zooplankton Community Composition and Biomass Simple linear regressions were used to examine relationships between the biomass of major zooplankton taxa and environmental parameters. The biomass of zooplankton groups (rotifers, cladocerans, calanoid copepods, cyclopoid copepods, and copepod nauplii) were regressed against water quality (Chl, NVSS, TP, TN, TN:TP), morphometric (MaxDepth, Area), and landscape parameters (PercentAgCrop, PercentAgPasture, PercentForest, WA:SA). We conducted a permutational multivariate analysis of variance (PERMANOVA) to examine the relationships between the community structure and biomass of crustaceans and rotifers and the composite environmental variables obtained from the initial environmental PCA using the ―adonis‖ function of the vegan package in R (Oksanen et al. 2008). Because distance matrices can be disproportionately influenced by rare taxa, crustacean and rotifer taxa occurring in fewer than 5% of the total reservoirs were removed from the data sets. This reduced the rotifer data set from 26 to 21 genera and the crustacean data set from 12 to 11 taxa. For all but one reservoir, rare taxa accounted for less than 1% of total crustacean or rotifer biomass. In the single case where a ―rare‖ rotifer genus represented 7% of the sample biomass, 11 additional rotifer genera were identified and included for this reservoir. Due to the non-normality of the zooplankton community data set, a semimetric distance measure, Bray-Curtis, was used (McCune and Grace 2002). P-values were determined using 10,000 permutations. In addition, Spearman correlations were used to investigate taxa-specific relationships between the biomass of individual taxa and the first two PCA axes, and these correlations were considered significant where r > 0.30 (Tabachnick and Fidell 1996) and P < 0.05. To further visualize differences between rotifer and crustacean assemblages in relation to the reservoir groups obtained from the regression analysis, non-metric multidimensional scaling (NMDS) was performed using the ―metaMDS‖ function in the VEGAN package in R (Oksanen et al. 2008). The appropriate

8 number of dimensions was determined subjectively from a screeplot by looking for the point at which the least number of dimensions had a substantial decrease on the overall stress.

Results Relationships among Environmental Parameters in Reservoirs All water quality parameters were significantly correlated between 2006 and 2007, with the strongest relationships (highest r2) observed for TP and Chl (Fig. 2). With the exception of NVSS, 95% confidence intervals for slopes and intercepts of all regression lines were inclusive of 1 and 0, respectively (Appendix 1a). In addition, when analyzed separately, regressions using all available 2006 data or 2007 data displayed trends similar to those identified when the two yearly datasets were combined (Appendix 1b; Table 2). As anticipated, water quality parameters where highly correlated with each other (Table 2). Strong positive relationships were observed among Chl, TP, and TN. Additionally, NVSS was positively correlated with Chl, TN, and TP. We also detected negative correlations between Chl and TN:TP and between Chl and NVSS. We also detected significant relationships between water quality and morphometric parameters (Table 2). MaxDepth showed a negative relationship with Chl, TP, TN, and NVSS and a positive relationship with TN:TP. Reservoir area showed only a weak positive relationship with Chl. Of the land cover types, we detected significant positive relationships between PercentAgCrop and Chl, NVSS, TP, and TN, while PercentForest was negatively related to these parameters (note that PercentAgCrop and PercentForest are strongly negatively related). WA:SA exhibited weak positive relationships with Chl, NVSS, TP, TN, and a weak negative relationship with TN:TP. PercentDeveloped showed only a weak negative relationship with TN:TP and a weak positive relationship with TP. The first two axes of the PCA performed on water quality, morphometric, and landscape parameters accounted for 46 percent and 16 percent of the environmental variation between reservoirs (Fig. 3, Table 3). Due to weak correlations with water quality parameters, PercentAgPasture and PercentDeveloped were not included in the PCA. Both axes were significant according to the Kaiser-Guttman criterion (axis eigenvalues: PC1 = 2.037, PC2 = 1.214). Pearson correlations between the environmental parameters and first principal component axis (PC1) showed the strongest significant (p <0.05) associations with water quality and landscape-level parameters including negative correlations with Chl, NVSS, TP, TN, and

9

PercentAgCrop and a positive correlation with PercentForest (Fig. 3, Table 3). Parameters representing reservoir morphometry, Area and MaxDepth, showed the strongest association with the second principal component axis (PC2). Overall, PC1 represented a gradient of decreasing row-crop cover and productivity, and increasing forest cover and depth, and PC2 represented a gradient of increasing reservoir surface area and depth (Fig. 3). A regression tree analysis was used to examine relationships between reservoir productivity levels and morphometric and landscape-level parameters (Fig. 4). Due to high levels of collinearity among water quality parameters (Chl, NVSS, TP, and TN), a composite variable, representing reservoir productivity, was calculated using a second PCA analysis considering only these four parameters. The first principal component explained 71 percent of the variance among reservoirs and was significantly correlated with all water quality parameters (Pearson correlations, Chl: -0.88, NVSS: -0.68, TP: -0.94, TN: -0.84). The scores from the first principal component axis for individual reservoirs, representing this composite reservoir productivity variable, ranged from -4.65 (high productivity) to 3.21 (low productivity) and were used as the response variable in the regression tree analysis. Of the five morphometric and landscape parameters entered as possible predictors in the regression tree analysis (MaxDepth, Area, PercentAgCrop, PercentForest, and WA:SA), Area was not included in the final model. The remaining four parameters explained 67% of the variation between reservoirs (relative error = 0.33, where relative error = 1-r2). The cross-validated error rate (0.48) showed that after resubstitution using subsets of the data, the model explained 52% of the variance between reservoirs. The best regression tree model divided the reservoirs into groups with mean productivity (PCA) scores of -2.68, -0.833, -0.707, 0.634, and 2.09, yielding five groups of reservoirs (Fig. 4). However, each split decision was made independently of prior splits, and the second and third groups (-0.833 and -0.707) showed similar values in terms of productivity. Therefore, reservoirs were ultimately placed into four groups according to productivity (Fig. 4). These productivity groups, ―very high,‖ ―high,‖ ―moderate‖ and ―low,‖ correspond closely to hypereutrophic, eutrophic/hypereutrophic, mesotrophic/eutrophic, and mesotrophic freshwaters as defined by Nürnberg et al. (1996) based on Chl, TP, and TN. The first ―branch‖ of the tree separated reservoirs based on the percentage of watershed land comprised of row-crop agriculture, whereas the next two divisions were based on reservoir depth or the percentage of land cover comprised

10 of forest (Fig. 4). Watershed area:reservoir surface area (WA:SA) was also an important variable. Thus, very high productivity levels were associated with shallow reservoirs in watersheds containing higher levels of agricultural row-crop cover. The ―high‖ productivity group contained reservoirs that were either a) associated with high row-crop agriculture but were relatively deep, or b) associated with lower amounts of row-crop cover, but also lower amounts of forested cover and large watershed:surface area ratios. The ―moderate‖ productivity group differed from the previous subgroup of ―high‖ productivity reservoirs only in having smaller watershed area:surface area ratios. As expected, the low productivity group had high levels of forested cover.

Environmental Parameters as Predictors of Zooplankton Community Composition and Biomass In general, the environmental variables considered in the study explained relatively little variation in zooplankton biomass (r2 <0.08-0.24, depending on taxon). We detected significant correlations only between small-bodied zooplankton biomass (rotifers and copepod nauplii) and water quality, morphometric, and landscape parameters (Fig. 5). We observed the strongest relationships between rotifer biomass and TP (positive) and between rotifer biomass and MaxDepth (negative). Rotifer biomass also showed positive correlations with Chl, TN, and NVSS as well as a negative relationship with PercentForest and a weak positive relationship with PercentAgCrop. Like rotifer biomass, copepod nauplii biomass showed the strongest positive relationship with TP and negative relationship with MaxDepth. In addition, copepod nauplii biomass showed a positive relationship with Chl and a weak negative relationship with PercentForest. Furthermore, the ratio of crustacean to rotifer biomass showed a weak negative relationship with Chl (r2 = 0.10, P <0.001). Additional significant regressions (p < 0.05) were observed, but were very weak (all r2 <0.08). These included cladocerans and MaxDepth, cyclopoids and MaxDepth, calanoids and TP, calanoids and MaxDepth, nauplii and TN, and nauplii and PercentDeveloped. Total zooplankton biomass showed only weak positive relationships with TP and NVSS and a weak negative relationship with PercentForest (all r2 <0.09). The composite environmental axes from the initial PCA of environmental parameters (Fig. 3) explained a greater percentage of the variance in the rotifer community composition and

11 biomass (16%) than that of the crustaceans (9%; Table 4). Spearman correlations between the biomass of individual taxa and PCA axes indicate changes in zooplankton community composition along the productivity (PC1) and reservoir morphometry (PC2) gradients (Table 5). Asplanchna spp., Brachionus spp., Filinia spp., Synchaeta spp., and Trichocerca spp. biomasses were positively related to productivity and row-crop coverage, and negatively related to depth. Filinia spp. biomass was related to smaller surface areas. Conversely, Ascomorpha spp., Gastropus spp., Kellicottia spp., and Keratella spp. biomasses were negatively related to productivity and positively related to depth and forest cover. Conochilus spp. biomass was positively related to productivity and negatively related to reservoir surface area. Within the crustaceans, calanoid copepods, copepod nauplii, Diaphanosoma spp., and Moina spp. biomasses were positively related to productivity and row-crop cover and negatively related to depth. Copepod nauplii and Moina spp. biomasses were also associated with smaller surface areas. Bosmina spp., Daphnia spp., and Macrothricidae biomasses were negatively related to productivity and positively related to depth, and forested cover. Cerodaphnia spp. biomass was associated with smaller surface areas and shallower depths, while cyclopoids were associated with greater surface areas and depths. We used NMDS plots to visualize the differences in rotifer and crustacean community composition and biomass along the four productivity levels obtained from the regression tree analysis (Fig. 6). For both rotifers and crustaceans, differences in community composition and biomass were best represented by 3-dimensional plots with low to moderate stress (rotifer NMDS stress = 14.11, crustacean NMDS stress = 8.90), yielding two usable plots. Adding an additional dimension did not substantially decrease the overall stress of the figures. The NMDS plots show that with our level of taxonomic resolution, greater overall variation was observed among rotifer communities than among crustacean communities. Zooplankton community composition and biomass appeared to overlap greatly between our reservoir groups with only potential differences between ―very high‖ and ―low‖ productivity groups for rotifers.

Discussion Relating Landscape-level Parameters to Reservoir Productivity Our study identified significant linear relationships among landscape-level parameters and indicators of reservoir productivity (Table 2). As we predicted, increases in nutrient,

12 sediment, and chlorophyll concentrations were observed along a gradient of increasing agricultural crop cover and decreasing forest cover. In addition, one of the two morphometric parameters, maximum depth, was significantly related to lower reservoir productivity, while higher WA:SA ratios were significantly related to increases in productivity. However, relationships with WA:SA were relatively weak and multivariate analysis illustrated that the environmental variability among reservoirs were primarily explained by differences in reservoir productivity and land cover, as illustrated by both the principle components and regression tree analyses (Fig. 3, Table 3). The strongest links between reservoir productivity and land cover type were identified for the two primary land cover types within our reservoirs: forest and agricultural row-crop. The lack of significant relationships between reservoir productivity and other types of land cover, such as developed or agricultural pasture land cover, suggests that our ability to identify relationships may be limited to the dominant land cover types within the area being studied. For example, changes in residential development within the watersheds of oligotrophic lakes were found to substantially influence zooplankton communities (Dodson et al. 2005, Gélinas and Pinel-Alloul 2008). However, only 15 of our watersheds contained > 25% developed land cover. Conversely, 78 of the watersheds contained at least 25% of either agricultural row-crop or forest cover and 17 contained >25% of both of these dominant types of land cover. In addition, oligotrophic lakes such as those included in the study by Gélinas and Pinel-Alloul (2008) have a relatively small range of watershed area:surface area ratios (WA:SA range: 2.9–19.6). The reservoirs in our study have variable but often large ratios (WA:SA range: 1.2–1730) and thus may be potentially affected by both alterations to the landscape some distance from the reservoir as well as by direct lakeshore disturbances. As the use of landscape-level data becomes more commonplace, it is important to point out the potential differences between the use and definitions of specific land cover types. Similar to previous studies (Knoll et al. 2003, Jones et al. 2004), our study suggests significant positive relationships between agricultural land cover and nutrient inputs. This is despite a difference in how land use was classified to generate the data sets used in these studies. Whereas our study used data from the 2001 NLCD (Homer et al. 2007), Knoll et al. (2003) and Jones et al. (2004) used imagery from 1994 and 1993, respectively. In addition, classification criteria differ among data sets. For example, ―agriculture‖ includes row-crop cover plus pasture in Knoll et al. (2003),

13 whereas Jones et al. (2004) combined pasture with grassland cover and treated agricultural row- crops separately. Our study considered agricultural row-crop and agricultural pasture cover separately from each other and from grassland cover (which only accounted for <2% of total land cover, on average, in our watersheds). Similar to the pasture/grassland category used in Jones et al. (2004), we found that the percentage of agricultural pasture cover was not significantly related to TP or TN. This suggests that analyzing the most detailed level of each land cover category may not always provide insight into relational patterns. For example, deciduous and evergreen forest coverage showed similar responses in our study and were therefore combined into a single ―forest‖ category. However, our results show that when gauging water quality responses of reservoirs to agricultural inputs, row-crop and pasture cover should be considered separately. Previous comparisons of row-crop and pasture cover showed that higher TN:TP is associated with row-crop coverage, while lower TN:TP is associated with pasture coverage, presumably due to the high TN:TP of crop fertilizers vs. the low TN:TP of wastes produced by farm animals (Arbuckle and Downing 2001). Although our range of TN:TP was similar to that of Arbuckle and Downing (2001), we did not detect significant relationships between TN:TP and agricultural land cover. Within Ohio, areas classified as agricultural pasture are predominantly located in the southeast portion of the state, where forest cover is also more pronounced (Fig. 1). The topography within this region is marked by hills and valleys and the uneven landscape results in smaller watersheds than those found in the remainder of the state. In addition, many small-scale meat and poultry farms have consolidated into large confined animal feeding operations (Renwick et al. 2008) and are not located in this area, but rather are more concentrated in the northwestern part of the state (ODA 2009, OEPA 2009). In addition, within our region, these relationships may be temporally dependent. Analyses on a restricted set of reservoirs sampled throughout the spring revealed significant relationships between TN:TP and forested, pasture, and agricultural crop cover (Hagenbuch et al., unpublished data). This suggests that the influence of land cover type may be limited to the spring, when the amount of runoff is typically higher than during the summer months. Between 14 and 65% of the variation in nutrient and sediment concentrations were explained by land-use parameters using simple linear regressions (Table 2). We also used a regression tree analysis because it could potentially increase the variance explained, and would

14 allow us to group reservoirs according to productivity. Using a combination of morphometric and landscape-level variables, we were able to accurately classify reservoirs into four groups that coincide with well established trophic state classifications (oligotrophic, mesotrophic, eutrophic and hypereutrophic; Nürnburg et al. 1996). Using the regression tree output, we can infer that reservoirs in our study area with watersheds containing greater than 71% forest will most likely be mesotrophic. For reservoirs with <71% watershed forest, trophic status is determined by the relative extent of watershed row crops, and either WA:SA or maximum depth. The extent to which we could accurately classify reservoirs was likely limited by the availability of additional factors potentially influencing reservoir productivity, such as nutrient cycling within the reservoir, the distance between a particular type of land cover and the reservoir (King et al. 2007), and variation in agricultural practices within a type of land cover. For example, conservation tillage, which has been related to lower levels of soluble reactive phosphorus and suspended sediments in streams (Richards and Baker 2002, Renwick et al. 2008), may decrease lake productivity. However, data on tillage practices are generally not available on a watershed-specific basis and so could not be factored into this study. Where available, the inclusion of detailed information on agricultural practices will most likely aid in explaining relationships between land cover type and productivity levels in water bodies.

Environmental Parameters as Predictors of Zooplankton Community Composition and Biomass For zooplankton, we identified the strongest relationships when predicting the biomass of small-bodied taxa (Fig. 5). Our results support other studies that have shown positive relationships between nutrients/chlorophyll and rotifer and/or copepod nauplii abundances in natural lakes (Table 6, Bays and Crisman 1983, Pace 1986, Stemberger and Lazorchek 1994, Canfield and Jones 1996, Yoshida et al. 2003, Whitman et al. 2004, Bremigan et al. 2008). Although Bays and Crisman (1983), whose study covered a similar productivity gradient, found significant relationships between large and small-bodied zooplankton and chlorophyll levels, relationships were much stronger for microzooplankton (rotifers and ciliates, r2>0.5) than for crustaceans (r2<0.35). Compared to productivity variables, land cover type was not as effective in predicting the biomass of small-bodied zooplankton (Fig. 5). However, the relationships identified between

15 water quality parameters and land cover type (Table 2) suggest that zooplankton community composition and biomass are indirectly affected by changes at the landscape scale. Composite environmental parameters describing gradients of reservoir productivity and reservoir morphometry allowed us to explain a greater amount of the variation in rotifer communities than in crustacean communities (Table 4). In addition to changes in overall biomass, multivariate analyses also revealed changes in rotifer community composition along a productivity gradient (Table 5). For some rotifer taxa, our results support previous findings, for example the positive relationships between productivity and Brachionus and Filinia biomass (Duggan et al. 2001) and Asplanchna (Stemberger and Lazorchak 1994), and the negative relationships between productivity levels and Conochius biomass (Duggan et al. 2001, Yoshida et al. 2003). However, the responses of some taxa in our study reservoirs did not agree with previous findings. For example, Keratella biomass was negatively related to productivity in our study, whereas previous studies either did not see a relationship between Keratella and productivity (Stemberger and Lazorchak 1994) or found that this genus was associated with higher productivity (Duggan et al. 2001, Yoshida et al. 2003). It is possible that some of this variation can be explained by species-specific responses, i.e., different species within a genus may respond differently to changes in productivity levels. In relation to other studies, the responses of crustacean biomass were mixed. Canfield and Jones (1996) identified positive relationships between chlorophyll and all major groups of zooplankton. Similarly, Whitman et al. (2004) found that all species of crustaceans, with the exception of one copepod species, were positively related to chlorophyll. Wang et al. (2007a) identified significant positive, though weak, relationships between crustacean biomass and total phosphorus. However, all of these sampling schemes differed from ours. Whitman et al. (2004) sampled on a bi-weekly basis throughout the spring, summer, and fall; Canfield and Jones (1996) sampled each water body twice between the months of May through September; and Wang et al. (2007a) sampled from April through September. Our sampling strategy consisted of mostly single samples collected between July and August when copepods and rotifers accounted for 77 percent of the total zooplankton biomass. The results of Stemberger and Lazorchak (1994), who sampled each lake once during the summer, coincided more closely with our results in that the responses to inputs differed by crustacean taxa. Although copepod nauplii biomass was correlated with higher productivity in our study, cyclopoid biomass was significantly associated

16 only with the second environmental principal component, which represented increasing reservoir area and reservoir depth. We expected calanoid biomass to increase in less impacted systems (due to a possible increase in algal quality, e.g., lower relative biomass of cyanobacteria in less- impacted reservoirs). However, calanoid biomass was positively influenced by factors related to increased reservoir productivity. Because we did not collect data reflecting the quality of the phytoplankton available to zooplankton within our reservoirs, it is difficult to interpret this finding. However, in a study with a similar sampling scheme, Stemberger and Lazorchek (1994) divided copepods into two groups, based on size, which showed opposite responses. Our study, which combined all calanoid juvenile and adult size classes, supported the positive relationship seen in Stemberger and Lazorchek (1994) between chlorophyll and small-bodied calanoid copepods. Although we originally predicted that small-bodied cladocerans would increase with higher nutrient levels, neither Chydoridae nor Eubosmina spp. biomass was significantly influenced by productivity levels. Bosmina spp. biomass was negatively influenced by higher productivity, which contrasts with the findings of both Stemberger and Lazorcheck (1994) and Whitman et al. (2004). The apparent negative impact of nutrients on Bosmina spp. may be related to predation. Zooplanktivores such as juvenile gizzard shad, which are very common in highly productive Ohio reservoirs (Hale et al. 2008), select for Bosmina spp. during their intermediate larval stage (Bremigan and Stein 1994, Welker et al. 1994). In additional, larval bluegill can exert substantial predation pressure by selectively preying on Bosmina sp. and Ceriodaphnia spp. during their intermediate and late larval stage (Welker et al. 1994). Because zooplanktivores will feed upon the largest prey items available within the confines of gape limitation, we also expected medium to large-bodied cladocerans to have a negative relationship with higher productivity levels. However these relationships, although significant, were very weak. Although previous studies of zooplankton communities have identified positive relationships between crustacean biomass and productivity indicators, the productivity levels represented in previous studies were generally lower than in our study (Table 6, Stemberger and Lazorchak 1994, Whitman et al. 2004, Gélinas and Pinel-Alloul 2008). Although several studies suggest that crustacean responses are stronger in lower-productivity systems, a study of systems over a variety of latitudes suggests that temperature may also be an important factor influencing relationships between productivity and zooplankton (Gyllström et al. 2005). However, because

17 reservoir temperatures were relatively similar across our study area, temperature was not considered in our analysis. In highly productive reservoir communities in temperate regions where rotifers are abundant (Canfield and Jones 1996), the community composition of small- bodied organisms most likely offers the best possible insight into the effects of anthropogenic disturbance.

Study Implications In summary, many of the reservoirs within our region are heavily impacted by human land use and receive high levels of watershed subsidies. We have developed a classification scheme which predicts reservoir productivity levels according to landscape-level and morphometric parameters. In addition, this study contributes to the understanding of zooplankton dynamics in reservoirs by identifying not only a direct link between small-bodied zooplankton and productivity levels, but also by extending our analysis to indirect relationships with landscape-level and morphometric parameters. Overall, our study suggests that due to potential temporal variation and impacts of predation on zooplankton communities, when a single- sampling strategy is utilized, water quality indicators (i.e. chlorophyll, total phosphorus, and total nitrogen) are better predictors of landscape disturbance.

18

Table 1. Range and mean values for abiotic and biotic characteristics of all reservoirs and a subset of reservoirs used in 2006/2007 comparisons. Reservoirs sampled in All Reservoirs 2006 and 2007 (n=109) (n=34) Range Mean Range Mean Chlorophyll (µg/L) 1.4-365.1 38.7 3.4-111.86 32.9 Non-Volatile Suspended Solids (mg/L) 0-58.8 3.4 0-6.7 1.0 Total Phosphorus (µg/L) 11.9-715.3 76.3 14.3-255.4 51.3 Total Nitrogen (µg/L) 129.6-301.8 1074.4 353.5-4359.6 924.1 Total Nitrogen: Total Phosphorus (molar) 5.0-254.9 45.9 11.4-276.2 48.1 Maximum Depth (m) 1.0-15.0 6.3 3.0-16.0 7.2 Lake Area (km2) <0.1-65.1 3.9 0.1-65.1 6.6 Land Cover: % Developed 1.1-86.9 14.6 1.2-65.6 16.6 Land Cover: % Forest 0-94.0 36.3 5.0-83.2 36.0 Land Cover: % Agricultural (Pasture) 0-45.5 12.7 0-30.0 12.7 Land Cover: % Agricultural (Crop) 0.4-78.4 25.0 0-78.4 21.9 Watershed Area: Reservoir Surface Area 1.2-730.0 105.7 1.8-371.2 59.0 Total Zooplankton (µg dry wgt/L) 2.1-2834.5 131.9 1.0-104.1 11.3 Rotifers (µg dry wgt/L) 0.2-464.3 19.0 0.1-100.9 9.08 Total Cladocerans (µg dry wgt/L) 0-614.7 57.5 0-437.3 36.0 Total Copepods(µg dry wgt/L) 0.3-491.1 55.5 0.6-208.0 47.4 Cyclopoids (µg dry wgt/L) <0.1-119.7 14.1 <0.1-163.6 16.3 Calanoids (µg dry wgt/L) 0-348.7 30.8 <0.1-169.4 21.9 Copepod nauplii (µg dry wgt/L) 0.2-49.2 10.6 0.3-29.8 9.2 Crustacean Biomass: Rotifer Biomass 0.2-209.1 28.3 0.2-499.0 39.3

19

2 Table 2: r values for simple linear regressions among log10 (x+1) transformed environmental parameters (Chl: chlorophyll (µg/L), NVSS: non-volatile suspended solids (mg/L), TP: total phosphorus (µg/L), TN: total nitrogen (µg/L), TN:TP: total nitrogen:total phosphorus ratio (molar), Area: reservoir surface area (m), MaxDepth: maximum reservoir sampling depth (m), WA:SA: watershed area:reservoir surface area ratio) and arcsine square root transformed land cover percentages (PercentDeveloped: open, low, medium, and high intensity developed; PercentForest: deciduous, evergreen, and mixed forest; PercentAgCrop: agricultural row-crop; PercentAgPasture: agricultural pasture). Chl NVSS TP TN TN:TP Water Quality NVSS 0.14 (+)** – – – – TP 0.65 (+)** 0.41 (+)** – – – TN 0.50 (+)** 0.14 (+)** 0.50 (+)** – – TN:TP 0.11 (-)** 0.21 (-)** – – – Morphometric Area 0.10 (+)** ns ns ns ns MaxDepth 0.19 (-)** 0.23 (-)** 0.34 (-)** 0.10 (-)** 0.19 (+)** Landscape PercentDeveloped ns ns 0.04 (+)* ns 0.10 (-)** PercentForest 0.11 (-)** 0.08 (-)* 0.24 (-)** 0.43 (-)** ns PercentAgCrop 0.29 (+)** 0.18 (+)** 0.31 (+)** 0.35 (+)** ns PercentAgPasture ns ns ns ns ns WA:SA 0.08 (+)* 0.10 (+)** 0.11 (+)** 0.06 (+)* 0.04 (-)* ns = not significant, * P < 0.05, ** P < 0.001, (-) and (+) refer to the direction of the relationship

20

Table 3. Pearson correlations between the first two principal components (PC1 and PC2) and log10 (x+1) transformed (Chl, NVSS, TP, TN, Area, MaxDepth, WA:SA) and arcsine square root transformed (PercentForest, PercentAgCrop) environmental parameters. Abbreviations are as defined in Table 2. Values with an r > 0.30 and P < 0.05 (shown in bold) were considered significant. PC1 PC2

Proportion of 46% 16% Variance Explained Chl -0.83** 0.14 NVSS -0.67** -0.21* TP -0.92** -0.13 TN -0.86** 0.10 Area -0.15 0.80** MaxDepth 0.53** 0.72** PercentForest 0.63** 0.13 PercentAgCrop -0.76** 0.40** WA:SA -0.39** 0.22* * P < 0.05, ** P < 0.001

Table 4. PERMANOVA results showing the amount of variance in log10 (x+1) transformed rotifer (LogRotifers) and crustaceans (LogCrus) biomass that can be explained by each of the composite environmental axes obtained in a PCA of environmental parameters (PC1 and PC2). PC1 represents a decrease in productivity, increase in depth, and a land cover gradient from primarily row-crop agriculture to forested watershed cover and PC2 represents a morphometric gradient of increasing depth and surface area. Sums of 2 F r P Squares LogRotifers PC1 3.651 15.73 0.13 <0.001 PC2 0.775 3.34 0.03 0.002 Residuals 24.594 0.84 LogCrus PC1 0.475 4.21 0.04 0.002 PC2 0.658 5.85 0.05 <0.001 Residuals 11.94 0.91

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Table 5: Spearman correlations between log10 (x+1) transformed common rotifer and crustacean taxa biomass and the first two principal components from a PCA of environmental parameters (PC1 and PC2). Positive correlations with PC1 illustrate relationships with less productive, deeper reservoirs with more forested watersheds. Negative correlations show relationships with more productive, shallower reservoirs with more row-crop agriculture in the watershed. Positive correlations with PC2 indicate relationships with increasing reservoir depth and reservoir area. Taxa are listed in the order of their relationship to PC1. Values with an r > 0.30 and P < 0.05 (shown in bold) were considered significant. PC1 PC2 PC1 PC2 Rotifers Crustaceans Brachionus spp. -0.77** -0.08 Copepod nauplii -0.38** -0.27* Filinia spp. -0.42** -0.27* Moina spp. -0.25* -0.27* Asplanchna spp. -0.38** -0.02 Diaphanosoma spp. -0.21* -0.18 Synchaeta spp. -0.35** 0.14 Calanoids -0.20* -0.15 Trichocerca spp. -0.33** -0.14 Chydoridae -0.15 -0.04 Polyarthra spp. -0.14 0.09 Eubosmina spp. -0.08 0.01 Pompholyx spp. -0.09 0.07 Ceriodaphnia spp. 0.09 -0.32** Anuraeopsis spp. -0.04 -0.02 Cyclopoids 0.11 0.20* Notholca spp. 0.00 -0.19 Bosmina spp. 0.19* -0.18 Testudinella spp. 0.02 -0.15 Macrothricidae 0.21* -0.02 Conochiloides spp. 0.03 -0.16 Daphnia spp. 0.25* 0.15 Collotheca spp. 0.06 0.14 Platyias spp. 0.09 -0.23* Hexarthra spp. 0.10 -0.10 Lecane spp. 0.14 -0.18 Monostyla spp. 0.14 0.01 Ascomorpha spp. 0.19* 0.08 Conochilus spp. 0.33** -0.30* Keratella spp. 0.33** -0.03 Gastropus spp. 0.42** 0.05 Kellicottia spp. 0.60** 0.13 * P < 0.05, ** P < 0.001

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Table 6. Summary of results from selected multi-lake/reservoir studies examining the relationships between productivity levels and zooplankton communities. TP: Total phosphorus, Chl: chlorophyll, Total Zoops = crustacean and rotifer zooplankton, na: not applicable. Where available, the mean or median (†) values for TP and Chl have been provided. Sampling Zooplankton Response (biomass or abundance) to Greater Productivity: Study Location TP (µg/L) Chl (µg/L) Period Crustaceans Rotifers Total Zoops

This Study 109 Ohio reservoirs Summer 11.9-715.3 1.4-365.1 nauplii weak (76.3) (38.7) 1 Bays and Crisman 39 Florida lakes Year NA 1-120 moderate for nauplii, cyclopoids, strong strong 1983 Around cladocerans 2 Pace 1986 12 Quebec lakes Spring – 3.7-56 1.3-29 ,specifically nauplii, cyclopoids, in abundance, not Fall and cladocerans biomass 3 Stemberger and 19 New England Summer 0.9-30 1.3-21.4 cladocerans, small calanoids Lazorchak 1994 lakes (8.61) (5.48) 1 1 4 Canfield and 45 Midwest US lakes Spring – 5-389.3 3-99 Jones 1996 and reservoirs Summer (71.8) (32.2) 5 Yoshida et al. 34 Canadian lakes Early – 2.6-36.4 1.8-47.1 na na 2003 Mid (9.4)3 (7.4)3 Summer 6 Whitman et al. 11 NE Lake Spring - 20-60 3.7-51 ,except L. diap.minutus † † 2004 Michigan Coastal Fall (39) (16.74) lakes 7 Gyllström et al. 81 shallow lakes Summer - 4-532 0.5-378 ,specifically proportions of None (using proportion 2005 across Europe Fall (100) (39.6) cyclopoids and daphnia in ice lakes rotifer biomass) 8 Pinto-Coelho et 55 Canadian lakes, Spring - 4-269 1-59 specifically cladocerans and na na al. 2005 5 Florida lakes, 4 Summer cyclopoids Brazilian reservoirs 2 2 9 Wang et al. 2007a 29 China Lakes Spring – 8-1448 0.7-146.1 weak with TP only, not Chl-a na na Fall (190.59) (32.31) 10 Bremigan et al. 11 Ohio reservoirs Spring 27.8-166 2.4-45.7 none 2008 11 Gélinas and Pinal- 13 Canadian lakes Early – 4.6-14.2 0.7-4.6 ,specifically Bosmina, na na Alloul 2008 Late (8.22) (2.38) Ceriodaphnia, Diaphanasoma, and Summer Cyclopoids 1 Knowlton and Jones 1989, Knowlton and Jones 1993, 2 Wang et al. 2007b, 3personal communication

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Figure 1. Map of study locations and land cover using the 2001 National Land Cover Database.

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Non-volatile Chlorophyll (µg/L) Suspended Solids (µg/L) 2.5 1.0 y = 0.64x + 0.54 y = 0.59x + 0.07 r² = 0.55 r² = 0.33 P < 0.001 P < 0.001 2.0

1.5 0.5

1.0

0.5 0.0 0.5 1.0 1.5 2.0 2.5 0.0 0.5 1.0

Total Phosphorus (µg/L) Total Nitrogen (µg/L) 2.5 4.0 y = 0.82x + 0.27 y = 0.28x + 2.09 r² = 0.86 r² = 0.23 P < 0.001 P = 0.004

2.0 3.5

Transformed Parameters 1.5 3.0

(x+1)

10

Log 1.0 2.5 1.0 1.5 2.0 2.5 2.5 3.0 3.5

2007 Total Nitrogen:Total Phosphorus (molar) 2.5 y = 0.41x + 1.00 r² = 0.31 P < 0.001

2.0

1.5

2006 1.0 1.5 2.0 2.5

2006 Log (x+1) Transformed Parameters 10 Figure 2. Simple linear regressions between 2006 and 2007 on log10 (x+1) transformed water quality parameters.

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Figure 3. Principal components analysis of environmental parameters using log10 (x+1) transformed (Chl, NVSS, TN, TP, Area, MaxDepth, WA:SA) and arcsine square root transformed (PercentForest, PercentAgCrop) parameters. Each number represents an individual reservoir and are listed in Appendix 1c.

26

Percent AgCrop >= 49%

Y N

MaxDepth < 4.91 m Percent Forest < 71%

Y N Y N WA:SA >= 88.54 -2.68 -0.833 n=16 n=11 Y N 2.09 n=12 -0.707 0.634 n=13 n=57

Productivity Level: Very High High Moderate Low 400

300 100

Chl (µg/L)Chl 0 103.71 (H) 40.97(E) 25.26 (M) 10.92 (M) 800

600

200

TP (µg/L) 0 229.49 (H) 84.26 (E) 41.82 (E) 19.60 (M) 6000

4000

2000

TN (µg/L) 0 2264.11 (H) 1414.71 (H) 744.30 (E) 375.09 (M) 80 60 40 20

NVSS (mg/L) 0 13.50 3.92 0.88 0.35

Figure 4. Regression tree analysis showing significant predictors of composite water quality response variable representing productivity. Log10 (x+1) transformed parameters include MaxDepth and WA:SA and arcsine square root transformed parameters include PercentAgCrop and Percent Forest. The height of the branch coincides with relative reduction on the total sums of squares. Abbreviations are as defined in Table 2. In addition, boxplots diagrams, showing the range of concentrations within each group, are shown. The numbers above each boxplot represent the mean values for productivity parameters within each level. For Chl, TP, and TN, the defined trophic state per Nürnberg (1996) is provided (M = mesotrophic, E = eutrophic, and H = hypereutrophic). Individual reservoir names for each productivity level are provided in Appendix 1c.

27

3.0 3.0 Rotifers 2.5 2.5 Nauplii

2.0 2.0

r²: 0.16** 1.5 1.5 r²: 0.12** 1.0 1.0

r²: 0.19** 0.5 0.5 r²: 0.24** 0.0 0.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 0.5 1.0 1.5

Log10 (Chlorophyll (µg/L) +1) Log10 (Maximum Depth (m) +1) 3.0 3.0

2.5 2.5

2.0 2.0

1.5 r²: 0.10* 1.5

) +1) ) 1.0 -1 1.0 r²: 0.09* 0.5 0.5 r²: 0.11**

0.0 0.0 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5

Log10 (Non-volatile Suspended Solids (mg/L) +1) ArcSine Sqrt Percent Forest 3.0 3.0

2.5 2.5

(Biomass (µg dry weight L weight (Biomass dry (µg 10 2.0 2.0

Log r²: 0.24** 1.5 r²: 0.18** 1.5 r²: 0.08* 1.0 1.0

0.5 0.5

0.0 0.0 1.0 1.5 2.0 2.5 3.0 3.5 0.0 0.5 1.0 1.5

Log10 (Total Phophorus (µg/L) +1) ArcSine Sqrt Percent Row-crop Agriculture 3.0

2.5 Figure 5. Simple linear regressions between the biomass of rotifers and 2.0 nauplii and log10 (x+1) transformed

1.5 (Chl, NVSS, TP, TN, MaxDepth), and r²: 0.14** arcsine square root transformed 1.0 (PercentForest, PercentAgCrop)

0.5 environmental parameters. Regression lines with an r2 > 0.08 and P < 0.05 are 0.0 shown. Abbreviations are as defined in 2.0 2.5 3.0 3.5 4.0 Table 2. Log10 (Total Nitrogen (µg/L) +1)

28

a. Very High High

1.0 Moderate

Low

0.5

0.0

NMDS2

-0.5

-1.0 -1.5

-1.5 -1.0 -0.5 0.0 0.5 1.0

NMDS1

2.0 b.

1.5

1.0

0.5

NMDS2

0.0

-0.5 -1.0

-1 0 1 2

NMDS1

Figure 6. NMDS plots of log10 (x+1) transformed (a) rotifer and (b) crustacean community composition and biomass showing the first two dimensions. ―Very high‖, ―high‖, ―moderate‖, and ―low‖ refer to the productivity level of the reservoir as determined in the regression tree analysis. Final 3-dimensional stress: plot a = 14.11, plot b = 8.90.

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Appendix 1a. Confidence intervals for the 95th percentile for the slopes and intercepts of the regression equations in Fig. 2. Intercept Slope Chl -0.233 - 0.595 0.572 - 1.130 NVSS 0.043 - 0.220 0.276 - 0.848 TP -0.296 - 0.211 0.896 - 1.205 TN -1.049 - 2.102 0.279 - 1.364 TN:TP -0.293 - 1.056 0.342 - 1.156

2 Appendix 1b. r values for simple linear regressions among log10 (x+1) transformed environmental parameters for 2006 and 2007, separately. Abbreviations are as defined in Table 2. 2006 Chl NVSS TP TN TN:TP Water Quality NVSS 0.14(+)** – – – – TP 0.65(+)** 0.36(+)** – – – TN 0.45(+)** 0.12(+)* 0.43 (+)** – – TN:TP 0.07 (-)* 0.13(-)* – – – Morphometric Area 0.06(+)* ns ns ns ns MaxDepth 0.17(-)** 0.13(-)* 0.31(-)** ns 0.22(+)** Landscape PercentDeveloped ns ns 0.05(+)* ns 0.15(-)** PercentForest 0.08(-)* 0.07(-)* 0.21(-)** 0.39(-)** ns PercentAgCrop 0.27(+)** 0.16(+)** 0.24 (+)** 0.53(+)** ns PercentAgPasture ns ns ns ns ns WA:SA 0.11(+)* 0.20(+)** 0.17(+)** 0.09(+)* ns 2007 Chl NVSS TP TN TN:TP Water Quality NVSS 0.10 (+)* – – – – TP 0.64 (+)** 0.33 (+)** – – – TN 0.31 (+)** 0.07 (+)* 0.23 (+)** – – TN:TP ns 0.08 (-)* – – – Morphometric Area 0.06(+)* ns ns ns ns MaxDepth 0.14(-)* 0.18(-)** 0.29(-)** 0.12(-)* ns Landscape PercentDeveloped ns ns ns ns ns PercentForest 0.15(-)* 0.08(-)* 0.29(-)** 0.15 (-)* ns PercentAgCrop 0.24(+)** 0.19(+)** 0.33(+)** 0.11 (+)* ns PercentAgPasture ns ns ns 0.09 (-)* ns WA:SA ns ns 0.07 (+)* ns ns ns = not significant, * P < 0.05, ** P < 0.001

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Appendix 1c. Reservoir names and selected environmental parameters within each productivity level defined in the regression tree analysis (VH = very high, H = high, M = moderate, L = low). Prod Percent Percent Percent Percent Max Reservoir Name WA:SA Level AgCrop AgPasture Forest Developed Depth (m) 1 Lake Loramie VH 76.5 3.3 7.5 8.3 57.2 2.0 2 Oakthorpe Lake VH 73.5 5.2 14.7 5.5 109.5 4.0 3 Griggs Reservoir VH 72.2 6.3 7.6 12.1 1730.0 4.0 4 Swift Run Lake VH 71.7 6.0 13.6 6.4 139.9 3.0 5 Clark Lake VH 71.4 9.4 5.4 5.3 47.3 1.0 6 Bucyrus Reservoir #2 VH 70.8 7.4 7.9 11.2 54.0 2.0 7 Madison Lake VH 69.2 12.0 6.0 10.4 359.0 3.0 8 Grant Lake VH 61.9 5.6 20.0 11.5 93.5 3.0 9 Harrison Lake VH 61.7 21.7 4.7 6.0 239.0 4.0 10 Grand Lake St Marys VH 59.9 5.8 2.6 11.5 5.6 2.0 11 Indian Lake VH 59.0 13.6 9.1 8.6 13.2 3.0 12 Stonelick Lake VH 56.3 11.1 25.1 5.6 94.9 4.0 13 Kiser Lake VH 56.3 7.7 20.7 6.9 14.5 3.0 14 Oxbow Lake VH 56.0 0.0 6.0 11.7 4.9 2.0 15 Buckeye VH 50.8 10.3 12.8 13.8 8.9 4.0 16 Charles Mill Lake VH 50.3 11.7 25.8 9.8 103.1 2.0 17 Deer Creek Lake H 78.4 7.8 5.3 6.4 138.0 11.5 18 Upper Sandusky Reservoir H 78.4 0.0 1.5 8.3 31.6 6.0 19 Paint Creek Lake H 77.8 6.3 8.8 6.2 309.8 10.0 20 Acton Lake H 77.1 3.7 12.8 5.4 108.2 8.0 21 O'Shoughnessy Reservoir H 75.4 6.6 7.8 8.5 700.0 11.0 22 Caesar Creek Lake H 71.3 6.6 13.4 6.3 53.8 15.0 23 Delaware Lake H 68.7 4.8 15.0 10.2 205.8 7.3 24 Cowan Lake H 67.8 6.4 14.9 8.7 45.5 7.0 25 Rush Creek Lake H 54.3 18.2 18.7 6.9 63.9 6.0 26 East Fork Lake H 54.1 9.5 27.7 6.9 104.0 15.0 27 Alum Creek Lake H 52.8 9.8 24.8 7.8 24.2 13.0 28 Nettle Lake H 39.1 28.0 11.4 7.7 112.8 4.0 29 Dillon Lake H 35.3 17.0 35.2 10.5 371.2 7.0 30 Lake Milton H 27.2 20.2 30.7 14.0 103.5 5.0 31 Pleasant Hill Reservoir H 27.2 14.1 47.1 9.6 163.0 7.3 32 Twin Churches Lake H 24.5 17.4 51.9 5.5 91.8 2.0 33 Clouse Lake H 23.6 32.6 34.6 7.2 284.1 2.0 34 Mill Creek Lake H 13.6 10.2 21.5 49.4 1408.9 3.0 35 Wills Creek Lake H 9.2 14.5 63.5 8.2 1435.4 4.0 36 Aquilla Lake H 7.0 12.0 60.2 8.6 266.2 4.0 37 Hinckley Lake H 6.7 10.2 54.1 21.1 174.4 3.0 38 Veto Lake H 5.1 25.5 61.2 7.0 89.3 5.0 39 Sharon Woods Lake H 3.2 2.7 20.7 72.7 92.3 4.5 40 Winton Woods Lake H 0.1 1.1 22.6 74.2 115.5 3.0 41 Hoover Reservoir M 48.3 13.7 25.7 8.9 42.1 11.3 42 Hargus Creek Lake M 47.9 16.7 25.3 6.9 29.1 12.0 43 Rush Run Lake M 38.7 21.5 31.6 3.9 21.8 8.0 44 Findley Lake M 35.1 11.0 40.1 4.1 45.1 4.5 45 Dale Walborn Reservoir M 33.3 25.2 27.7 7.0 30.6 4.5 46 Deer Creek Reservoir M 31.2 23.3 30.5 7.2 71.6 4.0 47 Rocky Fork Lake M 30.8 19.8 36.3 7.9 37.2 10.0 48 Guilford Lake M 30.5 25.9 25.8 11.4 19.4 5.5 49 Clear Fork Reservoir M 30.4 12.2 42.2 10.1 21.4 7.0 50 Berlin Lake M 28.5 20.9 29.7 14.0 50.9 12.5 51 Shreve Lake M 28.3 30.0 21.6 6.2 7.4 3.5 52 Mosquito Creek Lake M 26.3 12.1 30.4 9.2 8.5 6.5 53 St. Joseph Lake M 22.1 36.3 32.2 7.5 71.3 3.0 54 LaDue Reservoir M 20.7 9.2 44.0 12.1 15.5 6.0 55 East Branch Reservoir M 19.4 14.0 52.5 5.2 29.4 4.0

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Prod Percent Percent Percent Percent Max Reservoir Name WA:SA Level AgCrop AgPasture Forest Developed Depth (m) 56 Lake La Su An M 17.4 19.8 17.9 11.3 4.4 4.0 57 Pymatuning Lake M 16.6 14.5 33.2 10.7 5.7 6.5 58 West Branch Reservoir M 15.0 17.1 46.2 9.9 19.2 12.5 59 McKarns Lake M 14.0 1.9 0.0 4.8 1.2 10.0 60 Mogadore Reservoir M 13.1 14.2 34.4 18.4 7.7 5.5 61 Flagdale Lake M 13.0 38.8 41.4 5.8 87.9 2.0 62 Salt Fork Lake M 11.8 12.0 65.7 6.3 33.1 11.0 63 Leesville Lake M 10.8 16.3 63.2 5.4 30.6 11.0 64 Belmont Lake M 10.0 26.3 42.1 14.9 28.8 13.0 65 Adams Lake M 9.5 24.1 53.9 10.4 84.6 4.0 66 Monroe Lake M 9.2 38.8 44.4 6.0 75.4 11.0 67 Slope Creek Reservoir M 9.2 27.6 54.8 4.4 39.0 15.0 68 Atwood Lake M 8.6 20.2 56.9 8.9 28.6 8.0 69 Barton Lake M 8.6 0.0 0.0 1.1 1.2 6.5 70 Piedmont Lake M 7.8 20.9 56.7 6.3 23.1 8.0 71 Lake Snowdon M 7.7 45.4 35.6 6.3 17.9 11.0 72 Seneca Lake M 7.0 10.9 70.8 6.5 21.1 6.0 73 Jackson Lake M 6.7 36.4 36.0 6.9 47.8 4.0 74 Clendending Lake M 6.4 15.4 68.0 5.3 26.1 8.5 75 Tycoon Lake M 5.2 25.0 45.8 5.6 5.7 4.0 76 Lake White M 4.8 17.8 60.6 6.2 67.8 5.0 77 Jackson City Reservoir M 4.5 15.7 66.5 3.9 12.4 13.0 78 Tappan Lake M 4.5 12.2 71.1 6.2 20.1 7.8 79 Nimisila Reservoir M 3.8 11.0 28.9 37.3 7.0 7.5 80 Lake Logan M 3.0 19.6 68.0 6.7 28.4 6.0 81 Turkeyfoot Lake (Portage) M 2.9 9.4 27.4 47.6 11.9 5.0 82 West Reservoir (Portage) M 2.7 8.5 27.0 48.3 63.8 5.0 83 East Reservoir (Portage) M 2.5 7.4 25.1 52.3 39.4 7.5 84 Sippo Lake M 1.5 0.0 7.7 86.0 25.0 2.0 85 Lake Medina M 1.0 4.3 40.4 13.8 3.2 5.0 86 Springfield Lake M 0.3 2.1 14.6 65.6 7.5 5.5 87 Fox Lake M 0.2 27.8 65.3 3.8 48.8 7.0 88 New Lexington Reservoir M 0.1 21.0 63.6 9.2 15.6 6.0 89 Antrim Lake M 0.0 0.0 8.8 52.4 2.5 8.0 90 Eastwood Lake M 0.0 0.0 0.0 54.9 2.3 6.0 91 Granger Pond M 0.0 0.0 0.0 62.3 2.1 3.0 92 Miami Whitewater Lake M 0.0 9.1 70.6 7.2 23.8 1.0 93 New Lyme Lake M 0.0 0.0 26.8 1.2 1.8 4.0 94 North Reservoir (Portage) M 0.0 0.0 18.8 56.3 4.5 3.0 95 Punderson Lake M 0.0 1.3 44.1 27.0 11.8 10.0 96 Resthaven Pond #10 M 0.0 0.0 11.9 4.8 1.5 2.0 97 Silver Creek Lake M 0.0 0.0 0.0 75.2 3.9 2.0 98 Lake Alma L 7.9 1.8 71.1 4.3 6.6 5.0 99 Lake Rupert L 3.4 10.6 78.1 5.6 43.4 6.0 100 Ross Lake L 2.5 7.2 80.2 5.7 19.2 7.0 101 Forked Run Lake L 2.2 4.3 86.8 4.8 50.8 7.0 102 Wolf Run Lake L 1.9 6.9 73.0 12.2 18.1 13.0 103 Highlandtown Lake L 1.5 13.8 72.5 6.4 19.8 6.5 104 Lake Vesuvius L 1.2 5.4 89.9 3.4 64.7 6.5 105 Burr Oak Lake L 0.4 5.9 84.5 6.4 30.8 8.1 106 Timber Ridge Lake L 0.3 13.9 73.9 4.1 14.5 11.0 107 Dow Lake L 0.2 1.0 91.2 5.0 26.7 9.0 108 Turkey Creek Lake L 0.1 0.8 93.3 4.9 113.8 10.0 109 Lake Hope L 0.0 0.2 94.0 5.5 47.4 6.0

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