ABSTRACT LIGHT, NUTRIENT, AND PLANKTIVORY EFFECTS ON ZOOPLANKTON COMMUNITIES AND FOOD CHAIN EFFICIENCY. by Jennifer M. Bobson

Transcription

1 STRCT LIGHT, NUTRIENT, ND PLNKTIVORY EFFECTS ON ZOOPLNKTON COMMUNITIES ND FOOD CHIN EFFICIENCY by Jennifer M. obson The biomass, production, and community composition of zooplankton are affected by many factors, such as food quantity, quality, and predation. In this study, we investigated the effects of light and nutrients on phytoplankton food quantity (chl-a) and quality (stoichiometric and taxonomic) and subsequent effects on zooplankton communities and food chain efficiency in the presence and absence of fish predation. The relative supply of light and nutrients had strong effects on phytoplankton food quantity and quality, and these parameters explained much variation in zooplankton community response. Zooplankton communities were also strongly affected by planktivory. oth the stoichiometric and taxonomic food quality of the phytoplankton, as well as the community composition of the zooplankton affected food chain efficiency across three trophic levels. This study highlights the importance of considering not only the effects of primary producers, but also of intermediate trophic levels on food web processes.

2 LIGHT, NUTRIENT, ND PLNKTIVORY EFFECTS ON ZOOPLNKTON COMMUNITIES ND FOOD CHIN EFFICIENCY Thesis Submitted to the Faculty of Miami University in partial fulfillment of the requirements for the degree of Master of Science Department of Zoology by Jennifer M. obson Miami University Oxford, Ohio 27 dvisor: Dr. María González Reader: Dr. Craig Williamson

3 TLE OF CONTENTS List of Tables List of Figures cknowledgements iii iv v Chapter 1. Zooplankton community responses to light, nutrients, and fish predation bstract 1 Introduction 1 Methods 5 Results 13 Discussion 18 References 49 Chapter 2. Light, nutrient, and planktivory effects on food chain efficiency bstract 53 Introduction 53 Methods 57 Results 66 Discussion 69 References 81 ii

4 LIST OF TLES Chapter 1 Table 1 23 Table 2 25 Table 3 27 Chapter 2 Table 1 73 Table 2 74 iii

5 LIST OF FIGURES Chapter 1 Figure 1 29 Figure 2 3 Figure 3 31 Figure 4 32 Figure 5 33 Figure 6 34 Figure 7 35 Figure 8 36 Figure 9 37 Figure 1 38 Figure Figure 12 4 Figure Figure Figure Figure Figure Figure Figure Figure 2 48 Figure Chapter 2 Figure 1 75 Figure 2 76 Figure 3 77 Figure 4 78 Figure 5 79 Figure 6 8 iv

6 CKNOWLEDGEMENTS First and foremost, I thank my advisor, María González for her continuing guidance on this project and her contagious enthusiasm for science and for life, in general. I have grown as a scientist and as a person while working with her. lso, I would like to thank eth Dickman for collaborating with me on this project. We have spent endless hours together in the field, in the lab, and at home discussing data; I have gained so much from working with her and from our friendship that has developed as a result. I am very grateful to Mike Vanni for introducing me to the wonderful field of limnology and for his continuing advice and guidance throughout this entire process. It has also been a pleasure working with my other committee member, Craig Williamson, as he has continued to challenge me and encourage me to think outside of the box. Jill Elder was an amazing field assistant, and I am especially grateful for her help with counting and measuring zooplankton samples; I could not have done it without her! The zooplankton deserve recognition as well; they are cute, and without them, I would not have a Master s Thesis. I would also like to thank Mike Hughes for providing much assistance with statistical analyses and the ERC staff, especially Rodney Kolb, for help in constructing and maintaining our mesocosms. I should also give a shout out to the mesocosm lids, as they made me a stronger field ecologist. ll members of the González and Vanni labs were such a pleasure to work with, and I would especially like to thank Lesley Knoll and Janelle Duncan for teaching me to identify zooplankton, Peter Levi and nnie owling for running numerous nutrient samples, Kate Seymour, Stephanie Doupnik, and Kelley ricker for assistance with sample processing, and Jim Stoeckel for answering numerous questions. Finally, I would like to thank my friends, specifically Lesley Knoll and eth Dickman, and my family, specifically my wonderful parents, Melanie and Mike obson, and now husband, George Newell for their continuing support and encouragement. This research was funded by USD # and the Miami University Summer Workshop Grant (Department of Zoology). v

7 Chapter 1 Zooplankton community responses to light, nutrients, and fish predation bstract Zooplankton biomass, production, and community structure are affected by many factors. In this study we investigated the effects of phytoplankton food quantity (chl-a), quality (stoichiometric and taxonomic), and fish predation on zooplankton community responses. We manipulated the supply of light (two levels) and nutrients (two levels), as well as the presence and absence of planktivorous fish (larval Dorosoma cepedianum) in mesocosms containing regional assemblages of phytoplankton and zooplankton. We measured the biomass, production, and compositional response of zooplankton to these treatments. Resource (light and nutrient) supply had significant positive effects on phytoplankton food quantity. Phytoplankton stoichiometric food quality was highest under low light conditions, and taxonomic food quality was highest under conditions of low light and high nutrients. We observed shifts in the relative abundance of major zooplankton taxonomic groups (cladocerans, copepods, and rotifers) with changes in the relative supplies of light, nutrients, and planktivory. Fish predation had a strong negative and positive effect on the biomass and production of crustaceans and rotifers, respectively. combination of phytoplankton food quantity and quality explained much variation in zooplankton biomass and production responses over the course of the experiment, but were often dependent on whether or not fish were present. Thus, this study emphasizes the importance of considering not only food quantity and quality when investigating zooplankton community dynamics, but also the number of trophic levels. Introduction Zooplankton represent an essential link in aquatic food webs due to their intermediate trophic position, as they regulate energy transfer from primary producers to upper trophic levels, such as fish. The composition and structure of zooplankton communities can be controlled by bottom-up and top-down factors (Carpenter et al. 1985, Carpenter et al. 1992), such as resource quantity and quality and predation by fish. Light and nutrients, specifically nitrogen (N) and phosphorus (P), are essential for the growth and persistence of phytoplankton (Reynolds 1984a). Furthermore, the relative supply of light and nutrients affects producer carbon (C): nutrient stoichiometry, thus affecting stoichiometric food quality for zooplankton (Sterner et al. 1997, Sterner and Elser 22). Light conditions can also affect predator-prey interactions. It has been demonstrated that larval fish select for smaller bodied zooplankton under high light conditions (Mills et al. 1

8 1986, Minor et al. 1993). Turbidity, however, can have positive and negative effects on larval fish foraging depending on light conditions and can also affect larval fish size selectivity of zooplankton prey (Minor et al. 1993, Utne-Palm 21, Granqvist and Mattila 24). Previous studies have examined the indirect effects of nutrients and light on zooplankton communities by testing the nutritional effects of producer C:nutrient on consumers under conditions of varying light. However, many of these addressed the effects of phytoplankton stoichiometry on a single species of zooplankton, usually Daphnia (Urabe and Sterner 1996, Sterner et al. 1998, Urabe et al. 22). In addition, rett et al. (2) addressed the effect of small, edible phytoplankton varying in phosphorus content on only Daphnia. lthough few studies have tested the effects of phytoplankton stoichiometry on zooplankton assemblages (Hall et al. 24b, Hall et al. 26, Hall et al. 27), to our knowledge, no study has yet examined the indirect effects of light and nutrients on zooplankton communities in the presence and absence of fish predation. The supply of nutrients and light and can vary widely within and among lake ecosystems (Grobbelaar et al. 1992, Litchman 1998). Nutrient concentrations are dependent on factors such as runoff from the surrounding watershed, consumer nutrient recycling, and nutrient release from bacteria and sediments (Vanni et al. 1997). Land use patterns affect the influx of nutrients from the watershed, and higher nutrient supplies can support higher primary production (Carpenter et al. 1998, Knoll et al. 23). The light regime in aquatic systems depends on the amount of light entering the system, the light attenuation within the water column, and the mixing depth (Wetzel and Likens 2). Furthermore, watershed inputs, such as non-volatile suspended solids, dissolved substances (e.g. chromophoric dissolved organic matter, CDOM), sediments, and even high densities of phytoplankton may decrease light penetration (Knowlton and Jones 1996). Consumers are indirectly affected by the supply of light and nutrients, as these directly affect carbon fixation through photosynthesis, growth, and reproduction of primary producers (Sterner et al. 1997). ecause phytoplankton biomass increases with increasing light and nutrient supply, ecosystems with abundant resources are generally able to support higher consumer biomass (erger et al. 26). In addition, the supply of 2

9 light and nutrients to primary producers affects their carbon (C): nutrient stoichiometry, and much variation is found in autotrophic stoichiometry both within and among species (Sterner et al. 1997, Sterner and Elser 22). High autotroph C:nutrient ratios are observed under conditions of high light, as autotrophs are able to fix more carbon with high irradiance. Likewise, increased nutrient supplies result in decreased C:nutrient ratios as autotrophs take up more nutrients relative to carbon fixed (Sterner et al. 1997, Sterner and Elser 22, Urabe et al. 22, Dickman et al. 26). Zooplankton communities are affected by the elemental composition of their food resources. Specifically, primary producers exhibiting low C:nutrient ratios yield higher stoichiometric food quality for zooplankton, and thus higher zooplankton production, than those with high C:nutrient ratios (Sterner et al. 1997, Sterner and Elser 22). s the elemental composition of zooplankton varies among taxa, the relative supply of light and nutrients may have implications for zooplankton community composition through variation in phytoplankton stoichiometry. For example, Daphnia exhibit low C:P and are thus more susceptible to phosphorus limitation, where as copepods tend to exhibit higher C:P ratios and are likely less susceptible to phosphorus limitation. Daphnia exhibit higher growth rates under conditions of low light and high nutrients than under conditions of high light and low nutrients, even though phytoplankton production is higher under the latter conditions (Sterner 1994, Urabe and Sterner 1996, Sterner et al. 1998). Hall et al. (24) showed that, due to their high P requirements, Daphnia dominate the zooplankton community under conditions of low phytoplankton C:P. In addition to stoichiometric food quality, variation in phytoplankton species composition ( taxonomic food quality ) can be an important factor regulating zooplankton communities (rett et al. 2). For example, in laboratory experiments, Daphnia growth rate was higher when fed diatoms or cryptomonads than when fed cyanobacteria. Phytoplankton size may also have implications for consumers. Zooplankton feeding may become gape limited in the presence of phytoplankton with greatest axial linear dimension (GLD) greater than 3µm (Cottingham 1999). Fish predation depresses zooplankton biomass and structures zooplankton communities (Reinertsen et al. 199, rooks and Dodson 1965, Carpenter et al. 1985). In fishless lakes, zooplankton communities are comprised primarily of large zooplankton 3

10 such as Daphnia and calanoids. Fish predation causes a higher proportional abundance of small zooplankton, such as small copepods and rotifers, as planktivores selectively feed on large zooplankton (rooks and Dodson 1965, Zaret 198, Vanni and Findlay 199, Dettmers and Wahl 1999). In Midwestern reservoirs, the most abundant fish species, including gizzard shad, are obligate zooplanktivores during their larval stage (Heinrichs 1982, Mundahl 1991, DeVries et al. 1998, Smoot and Findlay 2). Previous studies have documented changes in zooplankton composition when larval gizzard shad abundance is at its peak, specifically declines in Daphnia and large cladocerans (Dettmers and Stein 1996, Dettmers and Wahl 1999). However, under conditions of varying light and turbidity, these zooplankton-fish interactions become more complex (Mills et al. 1986, Minor et al. 1993, Utne-Palm 2, Granqvist and Mattila 24). Furthermore, the spectral quality of available light in the water column (i.e. ultraviolet vs. visual) has been shown to influence zooplankton-fish interactions (Losey et al. 1999, Tollrian and Heibl 24). However, since mid-western reservoirs often have high biological and physical turbidity, UV penetration, and thus effects, may be less significant. Our study seeks to experimentally test the main and interactive effects of light, nutrients, and fish predation on zooplankton composition, biomass and production. Specifically, we are interested in the relative importance of phytoplankton food quantity (chl-a) and measures of food quality (stoichiometric and taxonomic) on zooplankton communities. Previous emperical studies have focused on the effect of either food quantity (Lynch 1989, McCauley et al. 199) or stoichiometric food quality (Sterner et al. 1997, Sterner and Elser 22), but not both. We used a fully factorial design with two levels of light (low, high), nutrients (low, high), and fish (presence, absence). We hypothesized that zooplankton biomass, production, and community composition would be indirectly affected by light and nutrient conditions through phytoplankton quantity and quality. We predicted that zooplankton biomass and production would be highest under light and nutrient conditions that favor high chlorophyll concentrations, as well as phytoplankton with favorable elemental ratios (low C:nutrient) and high edibility (not too large in size, lack of physical defenses such as spines). Specifically, we predicted to observe highest food quantity for zooplankton (phytoplankton biomass) with high 4

11 resource supply (high light, high nutrient conditions), whereas we predicted the highest stoichiometric food quality for zooplankton, or the lowest phytoplankton C:nutrient ratios, with low light and high nutrients. Finally, we hypothesized that zooplankton community composition would be affected by phytoplankton stoichiometry, as some zooplankton exhibit lower C:nutrient ratios than others. For example, we predicted a higher proportional abundance of high body phosphorus taxonomic groups, such as Daphnia, under conditions of low light and high nutrients, and lower body phosphorus zooplankton such as copepods with high light and low nutrients. In addition, we predicted that fish predation would have a strong direct negative effect on zooplankton biomass and production, and that it would affect zooplankton community structure. Specifically, we predicted a higher abundance of large-bodied zooplankton such as Daphnia and calanoids in treatments without fish, whereas we predicted that smaller-bodied zooplankton, such as small cladocerans, cyclopoids, and rotifers, would dominate in the presence of fish. Methods Experimental design To investigate the effects of light, nutrients, and planktivorous fish on zooplankton communities, we conducted a mesocosm experiment at Miami University s Ecology Research Center (ERC), Oxford, Ohio ( Mesocosms are polyethylene circular tanks 1.4 m deep and 2 m in diameter, with a volume of ~5 L (Glaholt and Vanni 25). Mesocosms were filled 25% with water from cton Lake (a hypereutrophic reservoir) and 75% with water from an unproductive pond at the ERC, providing a regional assortment of plankton species representative of local water bodies and allowing us to assess their response to light, nutrients, and fish. The experiment lasted for 8 weeks from 6 June 25 y 25. We utilized a fullfactorial design, with two levels of light (high and low), two levels of nutrients (high and low), and the presence or absence of fish, for a total of 8 treatments with 3 replicates of each. Light was manipulated using lids with or without 9% light reduction Sudden Shade cloth (DeWitt Company). The lids were covered with clear plastic in order to minimize allochthonous inputs and were raised 3 inches from the top of the mesocosms 5

12 to allow for air exchange. Clear plastic was also placed over the high light mesocosms. Incident light levels were thus reduced to 8% or 68% of ambient levels to provide low and high light treatments, respectively. These levels are representative of natural conditions, as mean light in the mixed layer of the mesocosms ranged from 4-4% of ambient light, and mixed layer light in Ohio reservoirs has been shown to exhibit a comparable range of intensities (Knoll et al. 23, Vanni et al. 26). Nutrients (N and P) were added to each mesocosm three times per week at 5 µg N/L and 5 µg P/L as NH 4 NO 3 and NaH 2 PO 4 *H 2 O, respectively, to the high nutrient treatments in order to maintain high nutrient concentrations. We used larval gizzard shad (Dorosoma cepedianum) because they are very abundant in Midwestern reservoirs. We added larval fish rather than larger fish for two reasons. First, gizzard shad are planktivorous as larvae (< 3 mm; Yako et al. 1996). Second, larvae could grow (i.e. biomass would increase) during the experiment and allow us to quantify fish production and trophic efficiency. To obtain larval gizzard shad, we placed 1 adult gizzard shad collected from cton Lake during the spring of 25 in an experimental pond at the ERC, where they spawned. We collected larval gizzard shad from the pond and added them to half of the mesocosms at a density of 25 fish per mesocosm when they were /- 3.4 (SE) mm total length. Larval mortality can be substantial when handling these small fish (Drenner et al. 1982). Therefore, we added larvae to the mesocosms using a procedure designed to minimize mortality. We collected larvae at night by shining lights on the surface of the pond to attract the larvae. Larvae were then gently collected in plastic beakers and transfered to containers with pond water. Larvae were examined to ensure they were healthy, then added to mesocosms with a small amount of pond water. Nevertheless, we expected mortality because small larvae have relatively low survival rates even in lakes where gizzard shad are abundant (remigan and Stein 25). Thus, 1 dead larval gizzard shad were added to the no fish treatments to account for the expected initial rate of fish death and the associated potential nutrient subsidy (approximately 26.6 µg N/L and 3.32 µgp/l, i.e. less than one nutrient addition to the +Nutrient treatments). Initial samples were taken to assess zooplankton abundance in the mesocosms before treatments were applied (3 May, 25). Subsequently, zooplankton samples were collected weekly at and.5 m using a 1-L Schindler-Patalas trap to obtain samples 6

13 representative of the entire water column. Samples were preserved in 1% sugared formalin to quantify zooplankton biomass and community composition responses to light, nutrients, and fish. Sample analysis Chlorophyll-a samples were filtered onto Pall /E glass fiber filters (1. µm pore size) and frozen in the dark. Subsequently, chlorophyll-a was extracted from the filters in the dark at 4 C using acetone and measured on a Turner model TD-7 fluorometer. We screened seston C, N, and P samples through a 63 µm mesh to remove most zooplankton and subsequently filtered the seston onto pre-ashed Pall /E glass fiber filters (1. µm pore size). Phytoplankton enumeration, food quality index, and production Phytoplankton were enumerated in samples from alternate weeks throughout the study (4 sampling dates). Phytoplankton were counted and identified to the lowest possible taxonomic group with an inverted microscope using standard procedures (Wetzel and Likens 2). subset of phytoplankton cells of each taxonomic group (2 cells/ group) was measured, and biovolume was calculated by applying the formula of the nearest geometric shape (Wetzel and Likens 2). Phytoplankton food quality in terms of taxonomic composition was estimated because phytoplankton species differ in their edibility. Phytoplankton size may also have implications for consumers (Cottingham et al. 1999). Therefore, we created a food quality ranking based on information presented by rett et al. (2) on the relative growth of daphnids fed each of 4 common phytoplankton taxa (diatoms, cryptophytes, chlorophytes, and cyanobacteria). We ranked each phytoplankton taxon on a scale of -2, with higher values representing higher taxonomic food quality. ll phytoplankton with greatest axial linear dimension (GLD) greater than 3 µm were considered poor quality food (Cottingham 1999) and assigned a score of. For phytoplankton smaller than 3 µm GLD, food quality was assessed based on taxonomy. Cryptophytes and diatoms were considered high quality food and assigned a score of 2, cyanobacteria poor quality food (), and chlorophytes and all other groups as medium quality food (1.24), as estimated 7

14 from Figure 4 in rett et al. (2). n index of phytoplankton taxonomic food quality was calculated for each mesocosm by weighting the taxonomic quality of each taxon by its relative proportion of phytoplankton biovolume, averaged across the 8 weeks of the study. Phytoplankton primary production was measured weekly using 14 C uptake following the methods of Fee (199). NaH 14 CO 3 was added to water samples, which were incubated at mesocosm temperature in a climate-controlled chamber at a range of light levels to generate chlorophyll-specific photosynthesis-irradiance (PI) curves. We generated PI curves each week for each mesocosm. We then used the PI curve data together with light attenuation and chlorophyll data (obtained 3 times per week) and hourly data on incident PR, to estimate volumetric primary production in each mesocosm using the computer programs PSPRMS and YPHOTO (Fee 199) and light intensity (PR) in mesocosms. Incident PR was obtained from the meteorological station at the ERC; these data are part of the US EP s Clean ir Status and Trends (CSTNET) program ( PR data were adjusted for the effects of the mesocosm lids. For more detailed methodology on 14 C measurement of primary production, see Knoll et al. 23. Zooplankton enumeration Zooplankton were counted and identified using dissecting (for crustaceans) and compound (for rotifers) microscopes. Cladocerans and rotifers were identified to genus or species, and copepods were identified as cyclopoids, calanoids, and nauplii. For crustaceans, at least 2% of each sample was counted and measured. fter 2% of the sample was processed, those crustacean taxa that were estimated to be sufficiently abundant (>25 or more individuals) were counted, along with their eggs or neonates, in additional subsamples until a total of 5 individuals were counted and 22 individuals were measured. However, if the total estimated sample size for a taxon was sufficiently rare (<25 individuals), enumeration did not continue. For rotifers, 6% of the sample was processed or at least 2 individuals were counted. We used length to dry weight regression equations described by Downing and Rigler (1984) to estimate crustacean biomass, except for nauplii, for which we used the equation described by oucherle (1977). In order to yield more accurate biomass and 8

15 production estimates, we divided each major cladoceran taxonomic group (Daphnia, osmina, Chydoridae, and Scapholeberis) into.1mm size classes. We calculated the total biomass ( T ) of each crustacean group considering the size frequency distribution. For each mesocosm for each sampling date, we used the following formula: T =! (P i *D i )*W (dry,i) where P i = proportion of individuals in size class i D i = density of individuals in size class i (individuals/l) W (dry,i) = dry weight of size class i (µg) Rotifer biomass was calculated by using geometric formulas that approximate volume according to Ruttner-Kolisko (1977). Volume was converted to wet weight assuming a specific gravity of 1, and dry weight was estimated as.1 * wet weight (Doohan 1973). Zooplankton Production We estimated cladoceran production using the methodology described by Mason et al. (1991). Egg development times were determined from formulas provided in ottrell et al. (1976): Ln (D) = Ln (a) + b * Ln (T) + c * Ln (T) 2 where (D) is development time (days) and (T) is water temperature ( C). For Daphniidae: a= b=.2193 and c=-.3414; for other cladocerans: a=2.327, b= and c= The instantaneous birth rate (b ) was calculated as: b = Ln ((C/N) + 1)/D where C is the density of eggs or neonates (eggs+neonates/l) and (N) is the population density (individuals/l). The instantaneous rate of increase (r ) was calculated according to Edmondson (1977): r = (Ln Nt 2 Ln Nt 1 )/!t where N t1 and N t2 represent the population densities at times t 1 and t 2 respectively, and t is days between sampling dates. The instantaneous death rate (m ) was calculated according to Lynch (1982) as: 9

16 m = b r and the finite death rate (M) as: M = 1 e -m The turnover time (T) was defined as the reciprocal of M. Finally, net production of each cladoceran taxa (P taxa ) was calculated using turnover time (T) and biomass (W): P taxa = W/T Production is reported in µgc/l/day. We assumed that the organic carbon content of all crustacean and rotifer zooplankton was 48% of the dry weight (ndersen and Hessen, 1991). To obtain gross production (i.e. net production plus mortality), death rates were converted to C units and added to net production estimates. Copepod production was calculated as the sum of the production for three major stages: eggs, nauplii, and copepodites, using methods similar to ean (198), and based on the model proposed by Patalas (197): P = N e (W)/T + N n (W n )/T n + N c (W c )/T c Where N e = density of eggs (eggs/l) N n = density of nauplii (individuals/l) N c = density of copepodites (individuals/l) W = weight increment during each stage (g) T = time of duration of each stage (days) Since copepodites were not distinguished from adults during counting, the length of the smallest egg-bearing individual encountered across all samples was considered the minimum length of an adult. Smaller, non-naupliar individuals were considered copepodites. The relative densities of copepodites and adults were determined using the proportion of copepodites:adults in the subsample of individuals measured. Similarly, since cyclopoid and calanoid nauplii were not distinguished during counting, the relative proportions of the cyclopoid:calanoid densities (individuals/l) in the subsample of individuals measured was used to estimate nauplii densities. Nauplii and copepodites for each taxonomic group (cyclopoids, calanoids) were divided into.1mm size increments and densities and dry weights for each size class were calculated. The product of the 1

17 density for each size class (d sc ) and the dry weight gained when an organism went from one size class to the next (I w ) was divided by the developmental time and summed within a mesocosm. We estimated copepod production for each mesocosm according to the following equation: P mesocosm = " (d sc * I w ) / D where d sc = density of each size class (individuals/l), I w = dry weight increment from one size class to the next (µg) D = developmental time (days) Egg weight increment was considered to be 25% of the dry weight of the smallest measured nauplius. Developmental time equations were calculated using the equation described by ottrell (1975): Ln D = Ln (a+b) * Ln (T) 2 For calanoid eggs: a = and b = For calanoid nauplii: a = and b = For calanoid copepodites: a = and b = For cyclopoid eggs: a = 3.11 and b = For cyclopoid nauplii: a = and b = For cyclopoid copepodites: a = and b = For rotifers, the method used to calculate production depended on whether or not we had egg data from the mesocosms. Egg data were obtained for rotifers carrying external eggs. Production for these rotifers was calculated using finite birth rate and dry weight according to Edmondson and Winberg (1971). Egg development time was calculated using the formula from ottrell et al. (1976): Ln (D) = Ln (a) + b * Ln (T) + c * Ln (T) 2 where Ln a = ; b = ; c = and T = water temperature ( C). The finite birth rate was calculated as in Edmondson (196): = E/D 11

18 where E = average clutch size (no. eggs per individual) and D = egg development time in days. Recruitment of new individuals (P N ) was calculated as: P N = N f * where N f = density of females (individuals/l) and = finite birth rate. Finally, production was calculated as: P = P N * W where W = mean individual dry weight (#g). If egg data were not available for a particular taxon (i.e. lack of external eggs), production of that taxon was estimated using average production to biomass ratios (P/) calculated for rotifers for which egg data were available for each mesocosm for each sampling date (Winberg, 1971). Statistical analyses We employed a 3-way repeated measures NOV (MIXED Procedure, SS, Inc.) to assess the seasonal (time) and treatment effects of light, nutrients, and fish on zooplankton responses. Light, nutrients, and fish were between subjects factors, and time was the within subjects factor. Response variables included the biomass and production of total zooplankton, Daphnia, osmina, chydoridae, cyclopoids, calanoids, nauplii, and rotifers. We almost always observed significant interactions between time and one or more treatment effects (light, nutrients), and since we wished to know which treatments differed from each other, we conducted additional analyses. Specifically, we assessed zooplankton responses at the beginning, middle, and end of the study by averaging (within a mesocosm) the responses for all dates within each of these time periods (June 19-3, y 1-12, y 13-25). These averages were treated as observations (n=23) and were analyzed within each period using a 3-way NOV with Tukey tests to determine the rank order of treatment responses (GLM Procedure, SS, Inc.). Prior to all statistical analyses, zooplankton biomass and production values were lntransformed. Zooplankton composition was also analyzed using MNOV, with the average biomass or production of the major taxonomic groups (Daphnia, osmina, chydoridae, cyclopoids, calanoids, nauplii, and rotifers) as the dependent variables, and light, nutrients, and fish as independent variables to determine whether composition was 12

19 affected by the treatments, and therefore whether zooplankton groups should be analyzed independently with NOVs. Finally, to address the importance of phytoplankton food quantity and stoichiometric and taxanomic food quality on biomass and production responses of Daphnia, osmina, chydoridae, cyclopoids, calanoids, and nauplii, we performed step-wise multiple regressions. Predictor variables included chlorophyll-a (chl-a) concentration (food quantity), seston stoichiometry (C:P, C:N), and phytoplankton taxonomic food quality. We only report step-wise multiple regression results using biomass predictor variables because these were more often significant than for production responses. ll values, with the exception of phytoplankton taxonomic food quality, were ln-transformed for analyses. Results Phytoplankton biomass, seston stoichiometry, and taxonomic food quality Phytoplankton biomass (chlorophyll-a) exhibited significant time and treatment effects, with increasingly strong treatment effects as the study proceeded (Fig. 1a). Light and nutrients had positive main and interactive effects on phytoplankton biomass (Fig. 1a). Thus, the highest zooplankton food quantity was observed in the high light, high nutrient treatments. lthough treatment differences in chlorophyll-a were similar in all 3 time periods, this trend was the strongest at the end of the study, with highest phytoplankton biomass in the high light, high nutrient, plus fish treatment (Fig. 1a). Seston nutrient stoichiometry demonstrated similar treatment responses in each of the 3 time periods (June 19-3, y 1-12, y 13-25), with the most pronounced differences among treatments at the end of the study (Fig. 1b,c). s predicted by stoichiometric theory, seston C:N and C:P ratios increased with increased light supply. However, contrary to expectations, under high light conditions we observed a positive effect of nutrients on C:P. The highest C:nutrient ratios were observed in the high light, high nutrient treatments and the lowest C:nutrient ratios in the low light treatments (Fig. 1b,c). Therefore, the low light treatments yielded the highest stoichiometric food quality for zooplankton. C:P ratios showed an increasing trend during the second half of the experiment in both high light, high nutrient treatments, and by the end of the experiment, the C:P ratio exceeded 3, the P-limitation threshold for Daphnia (Fig. 1c). 13

20 Finally, we observed the highest phytoplankton taxonomic food quality under low light and high nutrient conditions, where cryptophytes were most abundant (Fig. 4c from Chapter 2). Zooplankton composition Over the course of the experiment, the zooplankton community in all treatments was dominated by crustaceans (Fig. s 2 and 4). We also observed drastic temporal changes in zooplankton composition in all treatments (Fig. s 2-5). The copepod community consisted of nauplii, calanoids, and cyclopoids. In the absence of fish, calanoids were the dominant copepod group (Fig. 2). Temporally, the proportion of total zooplankton biomass composed of nauplii drastically decreased in all no fish treatments, as did the relative biomass of cyclopoids following an initial increase over the first two weeks of the experiment (Fig. 2). However, the seasonal dynamics of calanoids, in the high light, low nutrient treatment differed from the other treatments. Specifically, calanoid relative biomass consistently increased with time and represented approximately 97 % of the total zooplankton biomass by the end of the experiment (Fig. 2b). In all the other no fish treatments, however, calanoid relative biomass increased with time only during the first three weeks of the experiment, followed by a decline over the remainder of the experiment (Fig. 2). The cladoceran assemblage included Scapheloberis, chydoridae, osmina, and Daphnia. In the absence of fish, cladocerans dominated the zooplankton community during the second half of the experiment, and we observed similar temporal trends in all treatments, except for in the high light, low nutrient treatment (Fig. 3b). In this treatment, the relative proportion of Daphnia decreased with time, whereas the relative proportion of osmina increased with time. In addition, although the relative proportion of chydoridae increased slightly over time in all treatments, this trend was much more pronounced in the high light, low nutrient treatment, specifically during the last two weeks of the experiment (Fig. 3b). With the exception of the high light, low nutrient treatment, the cladoceran community was comprised mainly of Daphnia and osmina with Daphnia representing between 23-98% and osmina representing between -77% of the total cladoceran biomass (Fig. 3). 14

21 Copepods constituted a greater proportion of total biomass than cladocerans in the presence of fish, especially during the first half of the experiment in the low nutrient treatments (Fig. 4). However, we also observed a drastic decline in the proportion of total biomass comprised of adult copepods (cyclopoids, calanoids) during the last half of the experiment in the plus fish treatments, particularly under low nutrient conditions (Fig. 4a,b). The relative proportion of nauplii was greater in the presence than in the absence of fish, particularly under conditions of low light (Fig. 2 and 4). In the presence of fish, the most drastic changes in zooplankton composition were also observed in the high light, low nutrient treatment. Unlike in the absence of fish, cladocerans did not dominate the zooplankton community during the second half of the experiment (Fig. 4). Overall, the relative proportion of Daphnia was lower in the low (Fig. 5a,b) than in the high nutrient treatments (Fig. 5c,d). During the final two weeks of the experiment, Daphnia biomass decreased considerably in the high light, low nutrient treatment (Fig. 5b). Chydoridae exhibited the opposite pattern representing a larger proportion of the cladoceran biomass in the low nutrient treatments, especially under high light conditions (Fig. 5a,c). The proportion of cladoceran biomass comprised of osmina decreased with time in all fish treatments (Fig. 5). We observed the highest proportion of Scapheloberis in the presence of fish during the first 3 weeks of the experiment. However, Scapheloberis composed less than 28% of the overall cladoceran biomass in fish treatments (Fig. 5). In summary, the most drastic temporal changes in cladoceran composition were observed in the high light, low nutrient treatments both in the presence and absence of fish. Finally, the proportion of total zooplankton biomass composed of rotifers was considerably lower than crustaceans. However, a higher proportion of rotifers was observed in the presence than in the absence of fish, specifically in the high light treatments during the last four weeks of the study (Fig. s 2 and 4). 15

22 Seasonal and treatment effects of light, nutrients, and fish on zooplankton biomass and production Zooplankton biomass Zooplankton communities exhibited significant responses to the treatments (MNOV, p =.3, Wilks Lambda =.942). Therefore, we used 3-way repeated measures NOVs on abundant taxonomic groups to determine biomass responses of these specific groups to light, nutrients, and fish. Total zooplankton biomass, as well as the biomass of all major crustacean groups (Daphnia, osmina, chydoridae, cyclopoid, calanoid, and nauplii) was significantly greater in the absence than in the presence of fish (Fig s 6a,c-12a,c; Table 1). Rotifer biomass showed the opposite trend (Fig.13a,c). In addition, the biomass responses of total zooplankton and all of the major crustacean groups, with the exception of osmina, varied significantly with time (Table 1). Furthermore, the responses of adult copepods (cyclopoids and calanoids) and nauplii to light and/or nutrient treatments varied with time (Table 1; significant time * treatment (light and/or nutrient interactions). Total zooplankton and Daphnia biomass significantly decreased under high light conditions and significantly increased under high nutrient conditions (Table 1). However, significant interaction effects (light*fish and nutrient*fish) indicated that these trends were stronger in the absence of fish (Fig. s 6a,c and 7a,c; Table 1). In the absence of fish, the analyses for each time period revealed a significantly greater biomass (total zooplankton, Daphnia, cyclopoid, and nauplii; Fig. s 6b, 7b, 1b and 12b) in the low light, high nutrient treatment. For total zooplankton, (Fig. 6b), this trend was consistent throughout the course of the experiment. However, we observed this significant trend only during two time periods for Daphnia (June 19-3 and y 1-12; Fig. 7b), for cyclopoids (June 19-3 and y 13-25; Fig. 1) and nauplii (y 1-12 and y 13-25; Fig. 12). Rotifer biomass was highest under high light, high nutrient conditions during 1 time period (June 19-3; Fig. 11b). In the presence of fish, we detected significant differences in biomass trends for calanoids and rotifers. Specifically, we observed the lowest calanoid biomass and highest rotifer biomass in the high light, high nutrient treatment during one time period (y 1-12; Figs. 11b and 13b). 16

23 Zooplankton production s for zooplankton biomass, the repeated measure MNOV also indicated significant treatment differences among production responses of zooplankton taxonomic groups (p<.1, Wilks Lambda =.658). Therefore, we conducted 3-way repeated measures NOVs to determine production responses of specific groups to light, nutrients, and fish. In general,production of total zooplankton and major crustacean groups exhibited similar trends to those of biomass, except that we observed a significant time effect on osmina production, but not on total zooplankton production (Fig. s 14a,c; 16a,c; Table 2). Tukey test comparisons for production among treatments during the three time periods showed similar results to those observed for biomass. In the absence of fish, the highest total zooplankton production and Daphnia production was observed in the low light, high nutrient treatment, but only during one time period (June 19-3; Fig. s 14b and 15b). Significant treatment differences were rarely observed in the presence of fish, with the exception of for rotifers (Fig. s 14d 21d). Importance of phytoplankton food quantity and stoichiometric and taxonomic food quality on zooplankton biomass responses In order to determine to what extent phytoplankton quantity (chl-a) and quality (stoichiometric and taxonomic) could explain the responses of zooplankton biomass and production, we performed step-wise multiple regressions for each time period in the absence and presence of fish. Here we only report biomass results because we detected a greater number of significant regressions. In the absence of fish, food quantity and quality characteristics explained more of the variability in the biomass response of cladocerans during the experiment (Table 3; Daphnia-whole experiment; osmina-2 time periods) than the variability in copepod response (Table 3; nauplii-two time periods; cyclopoids-1 time period). For cladocerans, food quantity (chl-a) and seston stoichiometry (C:P and C:N) together explained % of the Daphnia response for two time periods (June 19-3; y 13-25), while C:P ratios alone explained 73% of Daphnia response during y Stoichiometry explained a significant proportion of 17

24 variability in the osmina biomass response during y 1-12 (38%; C:N ratio) and during y (7%; C:N and C:P). For copepods, phytoplankton taxonomic quality and C:N ratio explained 79% of nauplli response during y 1-12, while phytoplankton taxonomic quality alone explained nauplii (7%) and cyclopoid (72%) responses during the last two weeks of the experiment (y 13-25). We observed the opposite pattern in the presence of fish, as food quantity and quality characteristics explained more of the variability in the response of copepod (Table 3; nauplii-whole experiment; cyclopoids-two time periods; calanoids-1 time period) than cladoceran biomass (Table 3; osmina-two time periods). For copepods, seston stoichiometry, specifically C:N, explained 47% of the variation in nauplii biomass response during June 13-25, whereas phytoplankton taxonomic quality explained 36% of the variation during y Stoichiometry explained a significant portion of variation in cyclopoid biomass response during y 1-12 (47%; C:P) and for calanoids during June (39%; C:N). combination of food quantity (chl-a), stoichiometry (C:N), and phytoplankton taxonomic quality explained 62% of the variation in calanoid biomass response during y For cladocerans, phytoplankton taxonomic quality explained 5% and 38% of the variation in osmina biomass response during y 1-12 and Discussion Overall fish effects s we predicted, fish predation had a strong effect on the biomass, production, and community composition of zooplankton. In general, crustacean biomass and production decreased, while rotifer biomass and production increased in the presence of fish. The increase in rotifer biomass and production in the presence of fish could be explained by a potential release from competition with large herbivore crustaceans such as Daphnia and calanoids that comprised only a small proportion of the zooplankton biomass in treatments with fish. Thus, the presence of visually foraging planktivorous fish (larval gizzard shad) likely affected the size structure of the zooplankton community, as has been previously demonstrated (Reinertsen et al. 199, Pont et al. 1991, rnott and Vanni 1993). nother potential explanation may be a decrease in predation pressure by invertebrate predators such as cyclopoid copepods in the presence of fish. 18

25 In addition to fish effects, we predicted that light and nutrient supply would affect zooplankton community composition, biomass, and production responses. Specifically, we predicted higher zooplankton biomass and production under conditions of high food quantity (chl-a) and/or high food quality (stoichiometric and taxonomic), as well as a higher proportional abundance of high body phosphorus zooplankton (i.e. Daphnia) with favorable phytoplankton elemental ratios (low C:nutrient). Similar to previous studies (Sterner et al. 1997), we observed the highest chl-a concentrations (food quantity) in the high light, high nutrient treatments. The lowest C:nutrient ratios, and thus the highest stoichiometric food quality, were observed in the low light treatments, and the highest taxonomic food quality was observed in the low light, high nutrient treatments (Fig. 1; Fig. 4c from Chapter 2). Light and nutrient effects Significant light (negative) and nutrient (positive) effects were observed for biomass and production of total zooplankton and Daphnia only (Tables 1 and 2). These effects were more pronounced in the absence of fish where biomass and production were not suppressed by predation (Fig. s 6-7 and 13-14). In the absence of fish, high body phosphorus zooplankton such as Daphnia often dominated the cladoceran community (Fig. 3). The highest total zooplankton biomass and production as well as the highest Daphnia production and biomass were observed under conditions of low light and high nutrients (Figs. 6 b,d; 8 b,d; 14 b,d and 16 b,d). This is also where seston C:P ratios were lowest (high stoichiometric food quality) and phytoplankton taxonomic food quality was highest (Fig. 1c; Fig. 4c from Chapter 2). The only treatment in which Daphnia were not the most abundant zooplankton group was the high light, low nutrient treatment, where the highest seston C:P ratios (low stoichiometric food quality) were observed (Fig. 3; Fig. 4c from Chapter 2). These results indicate the potential importance of phytoplankton food quality (both stoichiometric and taxonomic) on zooplankton response, as has been previously demonstrated (Sterner et al. 1997, Sterner and Elser 22, rett et al. 21). 19

26 Taxon-specific effects of phytoplankton food quantity, stoichiometric and taxonomic food quality, and fish on zooplankton The step-wise multiple regressions in the absence of fish suggest that, for Daphnia, phytoplankton quantity (chl-a) and stoichiometric (seston C:P, C:N), but not taxonomic food quality, explained a significant portion of variation (Table 3). When considering all three time periods in the absence of fish, phytoplankton stoichiometry was included in multiple regression models during all three time periods, phytoplankton quantity (chl-a) was included in two out of the three time periods, and phytoplankton taxonomy was never included as a significant predictor (Table 3). s these effects of phytoplankton food quantity and quality (stoichiometric) were stronger in the absence of fish, these results highlight the importance of considering the number of trophic levels when attempting to predict zooplankton community responses to controls of phytoplankton food quantity and quality. We observed the highest proportion of osmina in the high light, low nutrient treatment where phytoplankton stoichiometric and taxonomic food quality is lowest (Fig. 3; Fig. 1b,c; Fig. 4c from Chapter 2). osmina have higher body C:P ratios than Daphnia and have been shown to be less susceptible to phosphorus limitation (Schulz and Sterner 1999). Therefore, it is likely that osmina are capable of becoming abundant when Daphnia biomass decreases, specifically when phytoplankton stoichiometric food quality is poor. Stoichiometry did explain a significant proportion of variability in the osmina biomass response during y 1-12 (38%; C:N ratio) and during y (7%; C:N and C:P) in the absence of fish. In the presence of fish, however, phytoplankton taxonomic quality explained 5% and 4% of the variation in osmina biomass response during y 1-12 and y Finally, the negative fish effect on chydoridae was not strong, and the relative biomass of chydoridae was generally higher in the presence of fish (Fig. 5). In addition, the relative biomass of chydoridae increased temporally in the presence of fish, whereas the proportion of Daphnia and osmina decreased (Fig. 5). In the absence of fish, chydoridae were relatively abundant in the high light, low nutrient treatment only. Minimal information is available on the stoichiometric constraints of chydoridae. 2

27 However, this result suggests that chydoridae are not limited by low phytoplankton food quality (stoichiometric and taxonomic), thus potentially allowing them to replace zooplankton taxa that are (i.e. Daphnia) during low food quality conditions. In addition, multiple step-wise regressions for chydoridae demonstrated that phytoplankton quantity (chl-a) and quality (both stoichiometric and taxonomic) were not important in explaining variation in chydoridae biomass (Table 3). lthough less abundant on average than cladocerans, copepods (calanoids, cyclopoids, and nauplii) constituted a high proportion of biomass at certain times throughout the course of the experiment (Fig. s 2 and 4). In the plus fish treatments, unlike for many cladoceran taxa, seston C:P never explained a significant portion of variation in copepod biomass response, with the exception of for cyclopoids during y 1-12 (Table 3). Copepods are less likely to be limited by phosphorus as they exhibit high body N:P ratios (Gismervik 1997, Hassett et al. 1997, Sommer 23). However, seston C:N, taxonomic food quality, and quantity (chl-a) were potentially important controls on copepod biomass during various times throughout the season (Table 3). In treatments without fish, calanoids were extremely abundant in the high light, low nutrient treatment during the second and third time periods of the experiment (Fig. 2). lthough step-wise multiple regressions for these time periods did not reveal significant effects of any phytoplankton parameters considered on calanoid biomass, it is likely that calanoids were relatively more abundant than cladocerans in this treatment only because they are not limited by the high seston C:P observed here (Fig. 1c). Overall, our analyses indicate that measures of phytoplankton food quantity and quality explain a high proportion of the variation in the biomass response of the most abundant zooplankton groups. Specifically, in the absence of fish, phytoplankton quantity (chl-a) and stoichiometric (seston C:P, C:N) quality were important variables for cladocerans. In the presence of fish, seston C:N, taxonomic food quality, and quantity (chl-a) explained the most variation in copepod biomass (Table 3). Conclusion Zooplankton communities responded strongly to the presence of fish, usually with a decrease in biomass and production. In addition, the presence of fish contributed to zooplankton community structure, specifically by decreasing the relative 21

28 abundance of large cladocerans and copepods such as Daphnia and calanoids. Light and nutrients had strong negative and positive effects, respectively on total zooplankton and Daphnia biomass and production, and this response was largely explained by phytoplankton stoichiometric food quality. combination of stoichiometry, phytoplankton taxonomic food quality, and phytoplankton quantity (chl-a) explained much variation in biomass and production responses of other zooplankton taxonomic groups over the course of the experiment. lthough previous studies have tested the effects of producer C:nutrient and phytoplankton taxonomic food quality on consumers, most of these addressed these effects on a single species of zooplankton, usually Daphnia (Urabe and Sterner 1996, Sterner et al. 1998, rett et al. 2, Urabe et al. 22). To our knowledge, this is the first study to examine the effects of phytoplankton quantity and quality parameters on intact assemblages of zooplankton in the presence and absence of fish predation. This study highlights the importance of considering both top-down and bottom-up controls on zooplankton communities, as both are essential to our understanding and management of aquatic systems. 22

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33 Table 3: Results of multiple step-wise regressions for all three time periods of the experiment (June 19-3, y 1-12, y 13-25) for major zooplankton taxonomic groups that, on average, made up at least 1% of seasonal average total biomass. Phytoplankton variables that were predicted to affect biomass trends, namely phytoplankton quantity (chl-a), seston stoichiometry (C:N, C:P), and phytoplankton taxonomic quality were tested using step-wise multiple regressions. Plus fish and minus fish treatments were analyzed separately. No Fi s h Fi s h Time Peri o d June 19-3 y y Predict o r Daphnia Seston C: N Chlorophyll Seston C: P Taxonomic qualit y p-value June 19-3 y y osmina Seston C: N Chlorophyll Seston C: P. 5 9 Taxonomic qualit y p-value Chydoridae Seston C: N. 2 6 Chlorophyll. 1 9 Seston C: P Taxonomic qualit y. 2 5 p-value

34 Cyclopoids Seston C: N. 1 5 Chlorophyll Seston C: P Taxonomic qualit y p-value Calanoi d s Seston C: N Chlorophyll.5 Seston C: P Taxonomic qualit y p-value Nauplii Seston C: N Chlorophyll Seston C: P Taxonomic qualit y p-value

35 ) ) C) g/l) µ Chlorophyll-a ( Seston molar C:N Seston molar C:P Seston molar C:P Chlorophyll-a (ug/l) Seston molar C:N Fish - Fish + Fish - Fish + Fish - Fish + Fish - Fish 25 + Fish - Fish + Fish - Fish + Fish - Fish + Fish - Fish High nutrients Low nutrients High nutrients Low nutrients High nutrients Low nutrients High nutrients Low nutrients High Light Low Light High Light Low Light T:<.1 LxT:.1 LxNxT:.18 LxFxT:.464 NxFxT: Fish No Fish June 19-3 y 1-12 y C C C 25 Fish No Fish High nutrients Low nutrients High nutrients Low nutrients C D C Fish No Fish Fish No Fish June 19-3 y 1-12 y CD D D C DE C CD DEF F Fish No Fish Fish No Fish 5 Fish No Fish Fish No Fish Fish No Fish CDE CD C CD CD DE 1 CD C C D C CD CDE CD E E D May3 Jun27 25 Low High Low High Low HighLow High Low High Low High Light Figure 1: Left side (time series graphs): Cholorophyll-a and stoichiometric responses to the treatments across the duration of the study. Each point represents the average value for each date for all of the mesocosms within a treatment (2-4 mesocosms/treatment). The initiation of treatments is denoted with an arrow. Right side: Mean chlorophyll-a and stoichiometric responses in each treatment are denoted for each of 3 time periods. Each point represents the mean value for all mesocosms within a treatment for all dates in the indicated time period. Error bars represent standard error, and letters indicate treatments that are significantly different from each other as determined with a Tukey test that compared all 8 treatments. C CD CD CD CD CD C CD CD D EF

36 ) ROT Low Light Minus Fish ) High Light NU Low Nutrients CL CL CYC C) D) High Nutrients Figure 2: Percent relative biomass of major zooplankton taxonomic groups (ROT=rotifer, CL=cladoceran, NU=nauplii, CL=calanoid, CYC=cyclopoid) in minus fish treatments across the duration of the study

37 Minus Fish Low Light ) ) CHY High Light High Nutrients Low Nutrients OS DP C) D) Figure 3: Percent relative biomass of cladoceran taxonomic groups (SC=scapheloberis, CHY=chydoridae, OS=osmina, DP=Daphnia) in minus fish treatments across the duration of the study

38 ROT Low Light Plus Fish ) ) High Light Low Nutrients NU CL CL CYC C) D) High Nutrients Figure 4: Percent relative biomass of major zooplankton taxonomic groups (ROT=rotifer, CL=cladoceran, NU=nauplii, CL=calanoid, CYC=cyclopoid) in plus fish treatments across the duration of the study

39 Low Light ) ) SC Plus Fish High Light Low Nutrients OS CHY DP C) D) High Nutrients Figure 5: Percent relative biomass of cladoceran taxonomic groups (SC=scapheloberis, CHY=chydoridae, OS=osmina, DP=Daphnia) in plus fish treatments across the duration of the study

40 high light, high nutrients high light, low nutrients ) ) high nutrients Minus Fish low light, high nutrients low light, low nutrients low nutrients C) Total zooplankton biomass (!gc/l) May 3 Jun Jun 6 13 Jun 2 Jun D) C C C June 19-3 High Low Light C C C y 1-12 C C C C C C Low High Light y C C Plus Fish C C C C Low High Figure 6: Left side (time series graphs): Total zooplankton biomass trends across the duration of the study. Each point represents the average value for each date for all of the mesocosms within a treatment. The initiation of treatments occurred on 3-May. Right side: verage treatment biomass for each of 3 time periods. Each point represents the mean value for all mesocosms within a treatment for all dates within the indicated time period. Error bars represent standard error, and different letters indicate treatments that are significantly different from each other as determined with a Tukey test that compared all 8 treatments. Top graphs represent minus fish treatments and bottom graphs represent plus fish treatments.

41 high light, high nutrients high light, low nutrients ) ) low light, high nutrients low light, low nutrients high nutrients low nutrients Minus Fish Daphnia biomass (!gc/l) C) D) June 19-3 y 1-12 y Plus Fish 5 May 3 Jun Jun 6 13 Jun 2 Jun Low High Light Low High Light Low High Figure 7: Left side (time series graphs): Daphnia biomass trends across the duration of the study. Each point represents the average value for each date for all of the mesocosms within a treatment. The initiation of treatments occurred on 3-May. Right side: verage treatment biomass for each of 3 time periods. Each point represents the mean value for all mesocosms within a treatment for all dates within the indicated time period. Error bars represent standard error, and different letters indicate treatments that are significantly different from each other as determined with a Tukey test that compared all 8 treatments. Top graphs represent minus fish treatments and bottom graphs represent plus fish treatments.

42 high light, high nutrients high light, low nutrients ) ) low light, high nutrients low light, low nutrients high nutrients low nutrients Minus Fish osmina biomass (!gc/l) C) D) June 19-3 y y Plus Fish 1 May 3 Jun Jun 6 13 Jun 2 Jun Low High Light Low High Light Low High Figure 8: Left side (time series graphs): osmina biomass trends across the duration of the study. Each point represents the average value for each date for all of the mesocosms within a treatment. The initiation of treatments occurred on 3-May. Right side: verage treatment biomass for each of 3 time periods. Each point represents the mean value for all mesocosms within a treatment for all dates within the indicated time period. Error bars represent standard error, and different letters indicate treatments that are significantly different from each other as determined with a Tukey test that compared all 8 treatments. Top graphs represent minus fish treatments and bottom graphs represent plus fish treatments.

43 high light, high nutrients high light, low nutrients ) ) low light, high nutrients low light, low nutrients high nutrients low nutrients Minus Fish 4 2 June 19-3 y y Chydoridae biomass (!gc/l) C) D) May 3 Jun Jun 6 13 Jun 2 Jun Low Light High Low High Light Low Plus Fish High Figure 9: Left side (time series graphs): Chydoridae biomass trends across the duration of the study. Each point represents the average value for each date for all of the mesocosms within a treatment. The initiation of treatments occurred on 3-May. Right side: verage treatment biomass for each of 3 time periods. Each point represents the mean value for all mesocosms within a treatment for all dates within the indicated time period. Error bars represent standard error, and different letters indicate treatments that are significantly different from each other as determined with a Tukey test that compared all 8 treatments. Top graphs represent minus fish treatments and bottom graphs represent plus fish treatments.

44 high light, high nutrients high light, low nutrients ) ) high nutrients Minus Fish low light, high nutrients low light, low nutrients low nutrients 15 2 June y y C) Cyclopoid biomass (!gc/l) May 3 Jun 6 Jun 13 Jun 2 Jun D) Low High Light Low High Light Low Plus Fish High Figure 1: Left side (time series graphs): Cyclopoid biomass trends across the duration of the study. Each point represents the average value for each date for all of the mesocosms within a treatment. The initiation of treatments occurred on 3-May. Right side: verage treatment biomass for each of 3 time periods. Each point represents the mean value for all mesocosms within a treatment for all dates within the indicated time period. Error bars represent standard error, and different letters indicate treatments that are significantly different from each other as determined with a Tukey test that compared all 8 treatments. Top graphs represent minus fish treatments and bottom graphs represent plus fish treatments.

45 high light, high nutrients high light, low nutrients ) ) low light, high nutrients low light, low nutrients high nutrients low nutrients Minus Fish 2 25 June y 1-12 y Calanoid biomass (!gc/l) C) 2 D) May 3 Jun Jun 6 13 Jun 2 Jun Low Light High Low High Light Low Plus Fish High Figure 11: Left side (time series graphs): Calanoid biomass trends across the duration of the study. Each point represents the average value for each date for all of the mesocosms within a treatment. The initiation of treatments occurred on 3-May. Right side: verage treatment biomass for each of 3 time periods. Each point represents the mean value for all mesocosms within a treatment for all dates within the indicated time period. Error bars represent standard error, and different letters indicate treatments that are significantly different from each other as determined with a Tukey test that compared all 8 treatments. Top graphs represent minus fish treatments and bottom graphs represent plus fish treatments.

46 high light, high nutrients high light, low nutrients ) ) high nutrients Minus Fish low light, high nutrients low light, low nutrients low nutrients 6 3 June 19-3 y 1-12 y C) Nauplii biomass (!gc/l) May 3 Jun Jun 6 13 Jun 2 Jun D) Low High Light Low High Light Low Plus Fish High Figure 12: Left side (time series graphs): Nauplii biomass trends across the duration of the study. Each point represents the average value for each date for all of the mesocosms within a treatment. The initiation of treatments occurred on 3-May. Right side: verage treatment biomass for each of 3 time periods. Each point represents the mean value for all mesocosms within a treatment for all dates within the indicated time period. Error bars represent standard error, and different letters indicate treatments that are significantly different from each other as determined with a Tukey test that compared all 8 treatments. Top graphs represent minus fish treatments and bottom graphs represent plus fish treatments.

47 high light, high nutrients high light, low nutrients ) ) low light, high nutrients low light, low nutrients high nutrients low nutrients Minus Fish June 19-3 y 1-12 y Rotifer biomass (!gc/l) C) 15 D) 1 5 May 3 Jun 6 Jun 13 Jun 2 Jun C C C C High Low Light C C Low High Light Low Plus Fish High Figure 13: Left side (time series graphs): Rotifer biomass trends across the duration of the study. Each point represents the average value for each date for all of the mesocosms within a treatment. The initiation of treatments occurred on 3-May. Right side: verage treatment biomass for each of 3 time periods. Each point represents the mean value for all mesocosms within a treatment for all dates within the indicated time period. Error bars represent standard error, and different letters indicate treatments that are significantly different from each other as determined with a Tukey test that compared all 8 treatments. Top graphs represent minus fish treatments and bottom graphs represent plus fish treatments.

48 high light, high nutrients high light, low nutrients ) ) low light, high nutrients low light, low nutrients high nutrients low nutrients Minus Fish Total zooplankton production (!gc/l/day) C) D) May 3 Jun Jun 6 13 Jun 2 Jun June 19-3 Low High Light Low y 1-12 High Light y Plus Fish Low High Figure 14: Left side (time series graphs): Total zooplankton production trends across the duration of the study. Each point represents the average value for each date for all of the mesocosms within a treatment. The initiation of treatments occurred on 3-May. Right side: verage treatment production for each of 3 time periods. Each point represents the mean value for all mesocosms within a treatment for all dates within the indicated time period. Error bars represent standard error, and different letters indicate treatments that are significantly different from each other as determined with a Tukey test that compared all 8 treatments. Top graphs represent minus fish treatments and bottom graphs represent plus fish treatments.

49 high light, high nutrients high light, low nutrients ) ) low light, high nutrients low light, low nutrients high nutrients low nutrients Minus Fish June 19-3 y 1-12 y Daphnia production (!gc/l/day) C) D) 1 5 May 3 Jun 6 Jun 13 Jun 2 Jun Low High Light Low High Light Low Plus Fish High Figure 15: Left side (time series graphs): Daphnia production trends across the duration of the study. Each point represents the average value for each date for all of the mesocosms within a treatment. The initiation of treatments occurred on 3-May. Right side: verage treatment production for each of 3 time periods. Each point represents the mean value for all mesocosms within a treatment for all dates within the indicated time period. Error bars represent standard error, and different letters indicate treatments that are significantly different from each other as determined with a Tukey test that compared all 8 treatments. Top graphs represent minus fish treatments and bottom graphs represent plus fish treatments.

50 high light, high nutrients high light, low nutrients ) ) low light, high nutrients low light, low nutrients high nutrients low nutrients Minus Fish osmina production (!gc/l/day) C) D) May 3 Jun Jun 6 13 Jun 2 Jun June 19-3 Low High Light Low y 1-12 Light High Low y Plus Fish High Figure 16: Left side (time series graphs): osmina production trends across the duration of the study. Each point represents the average value for each date for all of the mesocosms within a treatment. The initiation of treatments occurred on 3-May. Right side: verage treatment production for each of 3 time periods. Each point represents the mean value for all mesocosms within a treatment for all dates within the indicated time period. Error bars represent standard error, and different letters indicate treatments that are significantly different from each other as determined with a Tukey test that compared all 8 treatments. Top graphs represent minus fish treatments and bottom graphs represent plus fish treatments.

51 high light, high nutrients high light, low nutrients ) ) low light, high nutrients low light, low nutrients high nutrients low nutrients Minus Fish Chydoridae production (!gc/l/day) C) D) 2 1 May 3 Jun 6 Jun 13 Jun 2 Jun June 19-3 Low Light High Low y 1-12 High Light Low y Plus Fish High Figure 17: Left side (time series graphs): Chydoridae production trends across the duration of the study. Each point represents the average value for each date for all of the mesocosms within a treatment. The initiation of treatments occurred on 3-May. Right side: verage treatment production for each of 3 time periods. Each point represents the mean value for all mesocosms within a treatment for all dates within the indicated time period. Error bars represent standard error, and different letters indicate treatments that are significantly different from each other as determined with a Tukey test that compared all 8 treatments. Top graphs represent minus fish treatments and bottom graphs represent plus fish treatments.

52 high light, high nutrients high light, low nutrients ) ) low light, high nutrients low light, low nutrients high nutrients low nutrients Minus Fish Cyclopoid production (!gc/l/day) C) D) 1 5 May 3 Jun Jun 6 13 Jun 2 Jun C C June 19-3 C C C Low High Light Low y 1-12 High Light Low y Plus Fish High Figure 18: Left side (time series graphs): Cyclopoid production trends across the duration of the study. Each point represents the average value for each date for all of the mesocosms within a treatment. The initiation of treatments occurred on 3-May. Right side: verage treatment production for each of 3 time periods. Each point represents the mean value for all mesocosms within a treatment for all dates within the indicated time period. Error bars represent standard error, and different letters indicate treatments that are significantly different from each other as determined with a Tukey test that compared all 8 treatments. Top graphs represent minus fish treatments and bottom graphs represent plus fish treatments.

53 high light, high nutrients high light, low nutrients ) ) low light, high nutrients low light, low nutrients high nutrients low nutrients Minus Fish Calanoid production (!gc/l/day) C) D) May 3 Jun Jun 6 13 Jun 2 Jun June 19-3 High Low Light Low y 1-12 High Light y Plus Fish Low High Figure 19: Left side (time series graphs): Calanoid production trends across the duration of the study. Each point represents the average value for each date for all of the mesocosms within a treatment. The initiation of treatments occurred on 3-May. Right side: verage treatment production for each of 3 time periods. Each point represents the mean value for all mesocosms within a treatment for all dates within the indicated time period. Error bars represent standard error, and different letters indicate treatments that are significantly different from each other as determined with a Tukey test that compared all 8 treatments. Top graphs represent minus fish treatments and bottom graphs represent plus fish treatments.

54 high light, high nutrients high light, low nutrients ) ) low light, high nutrients low light, low nutrients high nutrients low nutrients Minus Fish 15 4 June 19-3 y 1-12 y Nauplii production (!gc/l/day) C) D) May 3 Jun 6 Jun 13 Jun 2 Jun High Low Light Low High Light Plus Fish Low High Figure 2: Left side (time series graphs): Nauplii production trends across the duration of the study. Each point represents the average value for each date for all of the mesocosms within a treatment. The initiation of treatments occurred on 3-May. Right side: verage treatment production for each of 3 time periods. Each point represents the mean value for all mesocosms within a treatment for all dates within the indicated time period. Error bars represent standard error, and different letters indicate treatments that are significantly different from each other as determined with a Tukey test that compared all 8 treatments. Top graphs represent minus fish treatments and bottom graphs represent plus fish treatments.

55 high light, high nutrients high light, low nutrients ) low light, high nutrients ) low light, low nutrients high nutrients low nutrients Minus Fish Rotifer production (!gc/l/day) C) D) June 19-3 C C y 1-12 C y Plus Fish 2 May 3 Jun Jun 6 13 Jun 2 Jun High Low Light C C Low High Light C Low High Figure 21: Left side (time series graphs): Rotifer production trends across the duration of the study. Each point represents the average value for each date for all of the mesocosms within a treatment. The initiation of treatments occurred on 3-May. Right side: verage treatment production for each of 3 time periods. Each point represents the mean value for all mesocosms within a treatment for all dates within the indicated time period. Error bars represent standard error, and different letters indicate treatments that are significantly different from each other as determined with a Tukey test that compared all 8 treatments. Top graphs represent minus fish treatments and bottom graphs represent plus fish treatments.

56 References ndersen, T., and D.O. Hessen Carbon, nitrogen, and phosphorus content of freshwater zooplankton. Limnology and Oceanography 36: rnott, S.E., and M.J. Vanni Zooplankton assemblages in fishless bog lakes: influence of biotic and abiotic factors. Ecology 74: ean, D.J Crustacean zooplankton production in Lake Erie, 197. M.S. Thesis, The Ohio State University. erger, S.., S. Diehl, T. J. Kunz, D. lbrecht,. M. Oucible, and S. Ritzer. 26. Light supply, plankton biomass, and seston stoichiometry in a gradient of lake mixing depths. Limnology and Oceanography 51: ottrell, H.H The relationship between temperature and duration of egg development in some epiphytic Cladocera and Copepoda from River Thames, Reading, with a discussion of temperature functions. Oecologia 18: ottrell, H.H.,. Duncan, Z.M. Gliwicz, E. Grygeirek,. Herzig,. Hillibricht- Ilkowska, H. Kurasawa, P. Larsson, and J. Weglenska review of some problems in zooplankton production studies. Norwegian Journal of Ecology 24: oucherle, H.H Dry weight estimates of biomass of ten taxa of crustacean zooplankters from Lake Erie. M.S. Thesis, The Ohio State University. rett, M. T., D. C. Muller-Navarra, and S. K. Park. 2. Empirical analysis of the effect of phosphorus limitation on algal food quality for freshwater zooplankton. Limnology and Oceanography 45: rooks, J.L, and S.I. Dodson Predation, body size, and composition of the plankton. Science 15: Cajander, V.R Production of planktonic Rotatoria in Ormajarvi, an eutrophicated lake in southern Finland. Hydrobiologia 14: Caramujo, M.J., and M.J. oavida Characteristics of the reproductive cycles and development times of Copidodiaptomus numidicus (Copepoda: Calanoida) and canthocyclops robustus (Copepoda: Cyclopoida). Journal of Plankton Research 21: Carpenter, S.R., N.F. Caraco, D.L. Correll, R.W. Howarth,.N. Sharpley, and V.H. Smith Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecological pplications 8: Carpenter, S. R., J. F. Kitchell, and J. R. Hodgson Cascading Trophic Interactions and Lake Productivity. ioscience 35: Carpenter, S. R., C. E. Kraft, R. Wright, H. Xi, P.. Soranno, and J. R. Hodgson Resilience and Resistance of a Lake Phosphorus Cycle before and after Food Web Manipulation. merican Naturalist 14: Cottingham, K. L Nutrients and zooplankton as multiple stressors of phytoplankton communities: Evidence from size structure. Limnology and Oceanography 44: Dettmers, J.M., and D.H. Wahl Evidence for zooplankton compensation and reduced fish growth in response to increased juvenile fish density. Hydrobiolgia 4:

57 DeVries, D.R., M.T. remigan, and R.. Stein Prey selection by larval fishes as influenced by available zooplankton and gape limitation. Transactions of the merican Fisheries Society 127: Dickman, E. M., M. J. Vanni, and M. J. Horgan. 26. Interactive effects of light and nutrients on phytoplankton stoichiometry. Oecologia 149: Doohan, M n energy budget for adult rachionus plicatilis Muller (Rotatoria). Oecologia 13: Edmondson, W.T Reproductive rates of rotifers in natural populations. Mem. Ist. Ital. Idrobiol. 12: Edmondson, W.T Trophic Equilibrium of Lake Washington. Ecological Research Series, EP U.S. Environmental Protection gency, Washington, DC. Edmondson, W.T., and G.G. Wingerg manual on methods for the assessment of secondary productivity in freshwaters. IDP Handbook 17, lackwell Scientific, Oxford, US. Gismervik, I Stoichiometry of some marine planktonic cladocerans. Journal of Plankton Research 19: Granqvist, M., and J. Mattila. 24. The effects of turbidity and light intensity on the consumption of mysids by juvenile perch (Perca fluviatilis L.). Hydrobiologia 514: Grobbelaar, J.U.,.M.. Kroon, T. urger-wiersma, L.R. Mur Influence of medium frequency light/dark cycles of equal duration on the photosynthesis and respiration of Chlorella pyrenoidosa. Hydrobiologia 238: Hall, S. R., M.. Leibold, D.. Lytle, and V. H. Smith. 24b. Stoichiometry and planktonic grazer composition over gradients of light, nutrients, and predation risk. Ecology 85: Hall, S.R., M.. Leibold, D.. Lytle, and V.H. Smith. 26. Inedible producers in food webs: controls on stoichiometric food quality and composition of grazers. merican Naturalist 167: Hall, S.R., M.. Leibold, D.. Lytle, and V.H. Smith. 27. Grazers, producer stoichiometry, and the light:nutrient hypothesis revisited. Ecology 88: Hassett, R.P.,. Cardinale, L.. Stabler, J.J. Elser Ecological stoichiometry of N and P in pelagic ecosystems: comparison of lakes and oceans with an emphasis on the phytoplankton-zooplankton interaction. Limnology and Oceanography 42: Heinrichs, S.M Ontogenetic changes in the digestive tract of the larval gizzard shad, Dorosoma cepedianum. Transactions of the merican Microscopical Society 11: Knoll, L.., M. J. Vanni, and W. H. Renwick. 23. Phytoplankton primary production and photosynthetic parameters in reservoirs along a gradient of watershed land use. Limnol. Oceanogr. 48: Knowlton, M. F. and J. R. Jones Experimental evidence of light and nutrient limitation of algal growth in a turbid Midwest reservoir. rch.hydrobiol. 135: Litchman, E Population and community responses of phytoplankton to fluctuating light. Oecologia 117:

58 Losey, G.S., T.W. Cronin, T.H. Goldsmith, D. Hyde, N.J. Marshall, and W.N. McFarland The UV visual world of fishes: a review. Journal of Fisheries iology 54: Lynch, M How well does the Edmondson-Paloheimo model approximate instantaneous birth rates? Ecology 63: Lynch, M The life history consequences of resource depression in Daphnia pulex. Ecology 7: Mason, C.F., M.M. bdul-hussein Population dynamics and production of Daphnia hyalina and osmina longirostris in a shallow, eutrophic reservoir. Freshwater iology 25: McCauley, E Growth, reproduction, and mortality of Daphnia pulex Leydig: life at low food. Functional Ecology 4: Mills, E.L., J.L. Confer, D.W. Kretchmer Zooplankton selection by young yellow perch: the influence of light, prey density, and predator size. Trans. m. Fish. Soc. 115: Minor, J.G., R.. Stein Interactive influence of turbidity and light on larval bluegill (Leposmis macrochirus) foraging. Can. J. Fish. quat. Sci. 5: Mundahl, N.D Sediment processing by gizzard shad, Dorosoma cepedianum, in cton Lake, Ohio. Journal of Fish iology 38: Patalas, K Primary and secondary production in a lake heated by a thermal power plant. In Proc. 16 th nnual Tech. Meeting of Inst. Of Env. Sciences. Mt. Prospect, Illinois p. Pelaez-Rodriguez, M., and T. Matsumura-Tundisi. 22. Rotifer production in a shallow artificial lake (Lobo-roa Reservoir, SP, razil). razilian Journal of iology 62 Pont, D.,.J. Crivelli, and F. Guillot The impact of three-spines sticklebacks on the zooplankton of a previously fish-free pool. Freshwater iology 26: Reinertsen, H..,. Jensen, J.I. Koksvik,. Langeland, and Y. Olsen Effects of fish removal on the limnetic ecosystem of a eutrophic lake. Canadian Journal of Fisheries and quatic Sciences 47: Reynolds, C. S. 1984b. The ecology of freshwater phytoplankton. Cambridge University Press, New York. Ruttner-Kolisko, Suggestions for biomass calculations of plankton rotifers. rch. Hydrobiol. eih. Ergebn. Limnol. 8: Schaus, M.H., M.J. Vanni, and T.E. Wissing. 22. iomass-dependent diet shifts in omnivorous gizzard shad: implications for growth, food web, and ecosystem effects. Transactions of the merican Fisheries Society 131:4-45. Smoot, J.C., and R.H. Findlay. 2. Digestive enzyme and gut surfacant activity of detritivorous gizzard shad (Dorosoma cepedianum). Canadian Journal of Fisheries and quatic Sciences 57: Sommer, F. 23. comparison of the impact of major zooplankton taxa on marine, brackish and freshwater phytoplankton during the summer. er Inst Meeresk Univ Kiel 329. Stemberger, R.S., and J.J. Gilbert ody size, food concentration, and population growth in planktonic rotifers. Ecology 66: Sterner, R. W., J. Clasen, W. Lampert, and T. Weisse Carbon: phosphorus stoichiometry and food chain production. Ecology Letters 1:

59 Sterner, R. W. and J. J. Elser. 22. Ecological stoichiometry: the biology of elements from molecules to the biosphere. Princeton University Press. Sterner, R. W., J. J. Elser, E. J. Fee, S. J. Guildford, and T. H. Chrzanowski The light:nutrient ratio in lakes: the balance of energy and materials affect ecosystem structure and process. m.nat. 15: Sterner, R. W. and D. O. Hessen lgal nutrient limitation and the nutrition of aquatic herbivores. nnual Review of Ecology and Systematics 25:1-29. Tollrian, R. and C. Heibl. Phenotypic plasticity in pigmentation in Daphnia induced by UV radiation and fish kairomones. Functional Ecology 18: Urabe, J., M. Kyle, W. Makino, T. Yoshida, T. nderson, and J. J. Elser. 22. Reduced light increases herbivore production due to stoichiometric effects of light/nutrient balance. Ecology 83: Urabe, J., and R. W. Sterner Regulation of herbivore growth by the balance of light and nutrients. Proc. Natl. cad. Sci. 93: Utne-Palm,.C. 21. Visual feeding of fish in a turbid environment: physical and behavioural aspects. Mar. Fresh. ehav. Physiol. 35: Vanni, M.J., and D.L. Findlay Trophic cascades and phytoplankton community structure. Ecology. 71: Vanni, M. J., C. D. Layne, and S. E. rnott ''Top-down'' trophic interactions in lakes: Effects of fish on nutrient dynamics. Ecology 78:1-2. Wetzel, R.G. and G.E. Likens. 2. Limnological nalyses. 3 edition. Springer, New York. Winberg, G.G Methods for estimating the production of populations with continuous reproduction. In: G.G. Winberg, Methods for the estimation of secondary production of aquatic animals. Yako, L.., J.M. Dettmers, and R.. Stein. Feeding preferences of omnivorous gizzard shad as influenced by fish size and zooplankton density. Transactions of the merican Fisheries Society 125: Zaret, T.M Predation and freshwater communities. Yale University Press. New Haven. Connecticut. US. 53

60 Chapter 2 Light and nutrient effects on food chain efficiency bstract Food chain efficiency is hypothesized to be affected by producer C:nutrient stoichiometry, producer taxonomy, and number of trophic levels. Specifically, we sought to understand the relative contribution of bottom-up controls (C:nutrient stoichiometry, taxonomic food quality) on trophic efficiency across three trophic levels from phytoplankton, through zooplankton, to fish. The relative supply of light and nutrients was manipulated in mesocosms containing two or three trophic levels, thus allowing us to evaluate how phytoplankton response and food chain length mediates energy transfer. Phytoplankton C:nutrient stoichiometry, taxonomic food quality, and zooplankton community structure explained a significant portion of variation in trophic efficiency trends. Thus, this study suggests that the factors regulating food chain efficiency are complex and highlights the importance of considering the effects of intermediate trophic levels on energy transfer through the food chain. Introduction The biomass and production of higher trophic levels are affected by food chain efficiency, i.e. the efficiency at which primary production is converted to production of higher trophic levels (Sterner et al. 1998), or the efficiency of energy transfer up the food chain. Food chain efficiency may be influenced by the number of trophic levels (Hairston and Hairston 1993), the elemental imbalance between producers and consumers (Sterner et al. 1998), and the taxonomic composition of producers (rett et al. 2). Hairston et al. (196) suggested that each trophic level is controlled either by predation or competition for resources depending on its trophic position. Hairston and Hairston (1993) expanded on these ideas and suggested that energy flow, and hence trophic efficiency, depends on the number of trophic levels. Thus, in food chains with an even number of trophic levels, energy transfer efficiency from primary producers to primary consumers will be higher than in systems with an odd number of trophic levels. This is because herbivores are constrained by primary carnivores in food chains with three levels, reducing the efficiency at which the herbivore trophic level can convert primary production into herbivore production. Stoichiometric differences between primary producers and consumers may also affect trophic efficiency (Sterner et al. 1998). Consumers generally have lower C:nutrient 54

61 ratios in their tissues than primary producers, although producers exhibit flexibility in these ratios (Elser et al. 2, Sterner and Elser 22). Producers with lower C:nutrient ratios are generally high quality food for consumers, so their consumption should result in higher food chain efficiency. Producers exhibit variation in stoichiometry, both within and among species, and may vary greatly from the C:nutrient ratios of consumers (Elser et al. 2). In addition to stoichiometric food quality, variation in phytoplankton species composition ( taxonomic food quality ) can be an important determinant of the efficiency of energy transfer from phytoplankton to zooplankton (rett et al. 2). In laboratory experiments, Daphnia growth rate was higher when fed diatoms or cryptomonads than when fed cyanobacteria. In this study we assessed how trophic efficiency varies with the number of trophic levels, as well as the supplies of light and nutrients. The availability of resources such as light and nutrients can affect the stoichiometric quality and species composition of primary producers, and we attempt to evaluate how these two mechanisms (stoichiometric and taxonomic food quality) mediate food chain efficiency. The amount and relative supply of resources (light and nutrients) affects biomass, production, community composition, and C:nutrient stoichiometry of primary producers (McQueen et al. 1986, Tilman et al. 1986, Sterner et al. 1997). The supply of light and nutrients can vary widely within and among lake ecosystems. Nutrient concentrations are dependent on factors such as runoff from the surrounding watershed, consumer nutrient recycling, and nutrient release from bacteria and sediments (Vanni et al. 1997). The light regime in aquatic systems depends on the amount of light entering the system, the light attenuation within the water column, and the mixing depth. Since light has a complex pattern of temporal and spatial variability, fluctuations are common (Grobbelaar et al. 1992, Litchman 1998). Non-volatile suspended solids, dissolved substances (e.g. chromophoric dissolved organic matter, CDOM), sediments, and even high densities of phytoplankton may decrease light penetration (Knowlton and Jones 1996). ecause phytoplankton biomass increases with increasing resource supply, ecosystems with abundant resources are generally able to support higher consumer biomass (erger et al. 26). However, the relative supply of resources also has implications for the elemental and community composition of phytoplankton. Generally, 55

62 phytoplankton C:nutrient increases in response to increasing light supply and/ or decreasing nutrient supply (Sterner et al. 1997, Sterner and Elser 22, Elser et al. 23). Resource supply can also affect the community composition of the phytoplankton, as phytoplankton are differentially adapted to various resource conditions (Reynolds et al. 1993). For example, some phytoplankton are well-adapted to low or fluctuating light levels (diatoms), and others (some cyanobacteria) can fix atmospheric N, giving them an advantage under low N conditions (Smith 1983, Reynolds 1984, Tilman et al. 1986, Huisman et al. 24). Phytoplankton stoichiometry can affect zooplankton growth rates and community composition (Urabe and Sterner 1996, Urabe et al. 22, Hall et al. 24). Furthermore, phytoplankton community composition can influence biomass and taxonomic composition of zooplankton, because some phytoplankton taxa are considered higher quality food (rett et al. 2). Therefore, the relative supply of light and nutrients can have implications for tropic efficiency through food webs. In contrast to producers, consumers exhibit much less variability in their elemental composition (nderson and Hessen 1991, Gismervik 1997, Elser et al. 2). This contrast provides the basis for the theoretical and observed effects of phytoplankton stoichiometry on zooplankton production (Sterner and Hessen 1994). Specifically, primary producers exhibiting low C:nutrient ratios yield higher stoichiometric food quality for consumers, and thus higher consumer production, than those with high C:nutrient ratios (Sterner et al. 1997, Sterner and Elser 22). s the elemental composition of zooplankton varies among taxa, the relative supply of light and nutrients may have implications for zooplankton community composition through variation in phytoplankton stoichiometry. For example, rotifers exhibit low C:P ratios and are thus more susceptible to phosphorus limitation (Morales-aquero and Conde-Porcuna 2, Conde-Porcuna et al. 22), whereas copepods tend to exhibit higher C:P ratios and are less susceptible to phosphorus limitation (nderson and Hessen 1991). Several studies have demonstrated that Daphnia (also low body C:P) seem to respond to the phosphorus content of their algal food (Sterner 1994, Sterner et al. 1998, Urabe and Sterner 1996, Hall et al. 24). Daphnia exhibit greater growth rates under conditions of low light and high nutrients than under conditions of high light and low nutrients, even though phytoplankton production is higher under the latter conditions (Sterner 1994, Sterner et 56

63 al. 1998, Urabe and Sterner 1996). Hall et al. (24) showed that, due to their high P requirements, Daphnia dominate the zooplankton community under conditions of low phytoplankton C:P. Fish body C:nutrient ratios are also constrained and often low (Davis and oyd 1978, Penczak 1985, George 1994). Thus, fish may become nutrient limited by zooplankton with high C:nutrient ratios (Sterner and Elser 22). However, Schindler and Eby (1997) suggest that fish are rarely nutrient limited, but most often limited by energy availability. Therefore, assuming a sufficient availability of zooplankton body nutrients (nitrogen and phosphorus) for fish, higher zooplankton C:nutrient will still result in more efficient energy transfer to fish. Theory predicts that the greater the imbalance in C:nutrient ratios between consumers and their food, the less efficiently energy is transferred between these trophic levels (Sterner et al. 1998, Sterner and Elser 22). Previous studies have found a positive effect of lower C:nutrient phytoplankton on energy transfer efficiency from phytoplankton to zooplankton (Urabe and Sterner 1996, Sterner et al. 1998, Urabe et al. 22), but no studies have examined how variation in C:nutrient stoichiometry affects trophic efficiency from primary producers to carnivores. Therefore, we tested the proposed effect of phytoplankton C:nutrient stoichiometry on food chain efficiency, i.e. on energy transfer from phytoplankton through zooplankton to planktivorous fish. To accomplish this, we manipulated light and nutrient supply to aquatic communities and measured production at each trophic level. We also manipulated the number of trophic levels (two versus three) so that we could evaluate how food chain length mediates energy flow from phytoplankton to zooplankton as well. In accordance with Sterner et al. (1998), we predicted that the efficiency of energy transfer from phytoplankton to zooplankton to fish would be highest under low light, high nutrient conditions because phytoplankton C:nutrient ratios would be low, and likewise, that the lowest efficiency would be observed under high light, low nutrient conditions where phytoplankton C:nutrient ratios should be high (Figure 1). dditionally, we predicted that phytoplankton production and stoichiometry would have implications for community structure of higher trophic levels. For example, we predicted that we would observe a higher proportional abundance of zooplankton with high body 57

64 phosphorus (Daphnia, rotifers) with low light and high nutrients and a higher abundance of lower body phosphorus organisms (copepods) under the reverse conditions of high light and low nutrients. dditionally, we predicted that fish biomass would be affected indirectly by both phytoplankton production and stoichiometry through the effects these factors have on zooplankton biomass. Finally, in accordance with Hairston and Hairston (1993), we predicted that we would observe higher trophic efficiency from phytoplankton to zooplankton (TE PZ ) with two trophic levels than with three. Thus, our study not only provides insight into the responses of each trophic level to light and nutrients, but also to potentially complex food web dynamics essential to the understanding and management of aquatic systems. Methods Experimental design To investigate the effects of light, nutrients, and planktivorous fish on zooplankton communities, we conducted a mesocosm experiment at Miami University s Ecology Research Center (ERC), Oxford, Ohio ( Mesocosms are polyethylene circular tanks 1.4 m deep and 2 m in diameter, with a volume of ~5 L (Glaholt and Vanni 25). Mesocosms were filled 25% with water from cton Lake (a hypereutrophic reservoir) and 75% with water from an unproductive pond at the ERC, providing a regional assortment of plankton species representative of local water bodies and allowing us to assess their response to light, nutrients, and fish. The experiment lasted for 8 weeks from 6 June 25 y 25. We utilized a fullfactorial design, with two levels of light (high and low), two levels of nutrients (high and low), and the presence or absence of fish, for a total of 8 treatments with 3 replicates of each. Light was manipulated using lids with or without 9% light reduction Sudden Shade cloth (DeWitt Company). The lids were covered with clear plastic in order to minimize allochthonous inputs and were raised 3 inches from the top of the mesocosms to allow for air exchange. Clear plastic was also placed over the high light mesocosms. Incident light levels were thus reduced to 8% or 68% of ambient levels to provide low and high light treatments, respectively. These levels are representative of natural conditions. Light in the mixed layer of the mesocosms ranged from 4-4% of ambient 58

65 light, and mixed layer light in Ohio reservoirs has been shown to exhibit a comparable range of intensities (Knoll et al. 23, Vanni et al. 26). Nutrients (N and P) were added to each mesocosm three times per week at 5 µg N/L and 5 µg P/L as NH 4 NO 3 and NaH 2 PO 4 *H 2 O, respectively, to the high nutrient treatments in order to maintain high nutrient concentrations. We used larval gizzard shad (Dorosoma cepedianum) because they are very abundant in Midwestern reservoirs. We added larval fish rather than larger fish for two reasons. First, gizzard shad are planktivorous as larvae (< 3 mm; Yako et al. 1996). Second, larvae could grow (i.e. biomass would increase) during the experiment and allow us to quantify fish production and trophic efficiency. To obtain larval gizzard shad, we placed 1 adult gizzard shad collected from cton Lake during the spring of 25 in an experimental pond at the ERC, where they spawned. We collected larval gizzard shad from the pond and added to half of the mesocosms at a density of 25 fish per mesocosm when they were /- 3.4 (SE) mm total length. Larval mortality can be substantial when handling these small fish (Drenner et al. 1982). Therefore, we added larvae to the mesocosms using a procedure designed to minimize mortality. We collected larvae at night by shining lights on the surface of the pond to attract the larvae. Larvae were then gently collected in plastic beakers and transfered to containers with pond water. Larvae were examined to ensure they were healthy, then added to mesocosms with a small amount of pond water. Nevertheless, we expected mortality because small larvae have relatively low survival rates even in lakes where gizzard shad are abundant (remigan and Stein 25). Thus, 1 dead larval gizzard shad were added to the no fish treatments to account for the expected initial rate of fish death and the associated potential nutrient subsidy (approximately 26.6 µg N/L and 3.32 µgp/l, i.e. less than one nutrient addition to the +Nutrient treatments). Initial samples were taken to assess zooplankton abundance in the mesocosms before treatments were applied (3 May, 25). Subsequently, zooplankton samples were collected weekly at and.5 m using a 1-L Schindler-Patalas trap to obtain samples representative of the entire water column. Samples were preserved in 1% sugared formalin to quantify zooplankton biomass and community composition responses to light, nutrients, and fish. 59

66 The number of fish surviving in each mesocosm was determined at the end of the experiment, when mesocosms were drained; it was not possible to estimate fish abundance during the experiment. To estimate initial fish size, we measured a subsample of 68 fish during the introduction of the larval fish to the experiment. t the conclusion of the study, all remaining fish were measured to estimate final fish size in each mesocosm. We estimated fish weight (initial and final) using a length-weight regression developed from larval gizzard shad from cton Lake (. Pilati, personal communication): W =.5 * L where W is weight (g) and L is length (mm). We also removed 5 randomly captured fish from each mesocosm during the middle of the study (June 22, 25) and estimated fish length and weight using the procedure described above. We analyzed the C, N, and P content of 15 fish collected during the introduction of the larvae to the experiment and a minimum of 15 fish (or as many as remained in each mesocosm) at the end of the study. Sample analysis Chlorophyll-a samples were filtered onto Pall /E glass fiber filters (1. µm pore size) and frozen in the dark. Subsequently, chlorophyll-a was extracted from the filters in the dark at 4 C using acetone and measured on a Turner model TD-7 fluorometer. We screened seston C, N, and P samples through a 63 µm mesh to remove most zooplankton and subsequently filtered the seston onto pre-ashed Pall /E glass fiber filters (1. µm pore size). In order to assess fish body C, N, and P concentrations, fish were gutted and dried until they reached a constant weight at 6 C. Fish were then ground to a powder using a mortar and pestle. C and N of seston and fish were measured using a Perkin Elmer Series 24 CHN analyzer (Perkin Elmer, oston, US). We digested seston and fish P samples with HCl to convert particulate P to soluble reactive phosphorus (SRP) (Stainton et al. 1977) and measured SRP using a Lachat QC 8 FI autoanalyzer (Lachat Instruments, Loveland, US). Phytoplankton enumeration, food quality index, and production Phytoplankton were enumerated in samples from alternate weeks throughout the study (4 sampling dates). 6

67 Phytoplankton were counted and identified to the lowest possible taxonomic group with an inverted microscope using standard procedures (Wetzel and Likens 2). subset of phytoplankton cells of each taxonomic group (2 cells/ group) was measured, and biovolume was calculated by applying the formula of the nearest geometric shape (Wetzel and Likens 2). Phytoplankton food quality in terms of taxonomic composition was estimated because phytoplankton species differ in their edibility. Phytoplankton size may also have implications for consumers (Cottingham et al. 1999). Therefore, we created a food quality ranking based on information presented by rett et al. (2) on the relative growth of daphnids fed each of 4 common phytoplankton taxa (diatoms, cryptophytes, chlorophytes, and cyanobacteria). We ranked each phytoplankton taxon on a scale of -2, with higher values representing higher taxonomic food quality. ll phytoplankton with greatest axial linear dimension (GLD) greater than 3 µm were considered poor quality food (Cottingham 1999) and assigned a score of. For phytoplankton smaller than 3 µm GLD, food quality was assessed based on taxonomy. Cryptophytes and diatoms were considered high quality food and assigned a score of 2, cyanobacteria poor quality food (), and chlorophytes and all other groups as medium quality food (1.24), as estimated from Figure 4 in rett et al. 2. n index of phytoplankton taxonomic food quality was calculated for each mesocosm by weighting the taxonomic quality of each taxon by its relative proportion of phytoplankton biovolume, averaged across the 8 weeks of the study. Phytoplankton primary production was measured weekly using 14 C uptake following the methods of Fee (199). NaH 14 CO 3 was added to water samples, which were incubated at mesocosm temperature in a climate-controlled chamber at a range of light levels to generate chlorophyll-specific photosynthesis-irradiance (PI) curves. We generated PI curves each week for each mesocosm. We then used the PI curve data together with light attenuation and chlorophyll data (obtained 3 times per week) and hourly data on incident PR, to estimate volumetric primary production in each mesocosm using the computer programs PSPRMS and YPHOTO (Fee 199) and light intensity (PR) in mesocosms. Incident PR was obtained from the meteorological station at the ERC; these data are part of the US EP s Clean ir Status and Trends 61

68 (CSTNET) program ( PR data were adjusted for the effects of the mesocosm lids. For more detailed methodology on 14 C measurement of primary production, see Knoll et al. 23. Zooplankton enumeration Zooplankton were counted and identified using dissecting (for crustaceans) and compound (for rotifers) microscopes. Cladocerans and rotifers were identified to genus or species, and copepods were identified as cyclopoids, calanoids, and nauplii. For crustaceans, at least 2% of each sample was counted and measured. fter 2% of the sample was processed, those crustacean taxa that were estimated to be sufficiently abundant (>25 or more individuals) were counted, along with their eggs or neonates, in additional subsamples until a total of 5 individuals were counted and 22 individuals were measured. However, if the total estimated sample size for a taxon was sufficiently rare (<25 individuals), enumeration did not continue. For rotifers, 6% of the sample was processed or at least 2 individuals were counted. We used length to dry weight regression equations described by Downing and Rigler (1984) to estimate crustacean biomass, except for nauplii, for which we used the equation described by oucherle (1977). In order to yield more accurate biomass and production estimates, we divided each major cladoceran taxonomic group (Daphnia, osmina, Chydoridae, and Scapholeberis) into.1mm size classes. We calculated the total biomass ( T ) of each crustacean group considering the size frequency distribution. For each mesocosm for each sampling date, we used the following formula: T =! (P i *D i )*W (dry,i) where P i = proportion of individuals in size class i D i = density of individuals in size class i (individuals/l) W (dry,i) = dry weight of size class i (µg) Rotifer biomass was calculated by using geometric formulas that approximate volume according to Ruttner-Kolisko (1977). Volume was converted to wet weight assuming a specific gravity of 1, and dry weight was estimated as.1 * wet weight (Doohan 1973). 62

69 Zooplankton Production We estimated cladoceran production using the methodology described by Mason et al. (1991). Egg development times were determined from formulas provided in ottrell et al. (1976): Ln (D) = Ln (a) + b * Ln (T) + c * Ln (T) 2 where (D) is development time (days) and (T) is water temperature ( C). For Daphniidae: a= b=.2193 and c=-.3414; for other cladocerans: a=2.327, b= and c= The instantaneous birth rate (b ) was calculated as: b = Ln ((C/N) + 1)/D where C is the density of eggs or neonates (eggs+neonates/l) and (N) is the population density (individuals/l). The instantaneous rate of increase (r ) was calculated according to Edmondson (1977): r = (Ln Nt 2 Ln Nt 1 )/!t where N t1 and N t2 represent the population densities at times t 1 and t 2 respectively, and t is days between sampling dates. The instantaneous death rate (m ) was calculated according to Lynch (1982) as: m = b r and the finite death rate (M) as: M = 1 e -m The turnover time (T) was defined as the reciprocal of M. Finally, net production of each cladoceran taxa (P taxa ) was calculated using turnover time (T) and biomass (W): P taxa = W/T Production is reported in µgc/l/day. We assumed that the organic carbon content of all crustacean and rotifer zooplankton was 48% of the dry weight (ndersen and Hessen, 1991). To obtain gross production (i.e. net production plus mortality), death rates were converted to C units and added to net production estimates. Copepod production was calculated as the sum of the production for three major stages: eggs, nauplii, and copepodites, using methods similar to ean (198), and based on the model proposed by Patalas (197): P = N e (W)/T + N n (W n )/T n + N c (W c )/T c 63

70 Where N e = density of eggs (eggs/l) N n = density of nauplii (individuals/l) N c = density of copepodites (individuals/l) W = weight increment during each stage (g) T = time of duration of each stage (days) Since copepodites were not distinguished from adults during counting, the length of the smallest egg-bearing individual encountered across all samples was considered the minimum length of an adult. Smaller, non-naupliar individuals were considered copepodites. The relative densities of copepodites and adults were determined using the proportion of copepodites:adults in the subsample of individuals measured. Similarly, since cyclopoid and calanoid nauplii were not distinguished during counting, the relative proportions of the cyclopoid:calanoid densities (individuals/l) in the subsample of individuals measured was used to estimate nauplii densities. Nauplii and copepodites for each taxonomic group (cyclopoids, calanoids) were divided into.1mm size increments and densities and dry weights for each size class were calculated. The product of the density for each size class (d sc ) and the dry weight gained when an organism went from one size class to the next (I w ) was divided by the developmental time and summed within a mesocosm. We estimated copepod production for each mesocosm according to the following equation: P mesocosm = " (d sc * I w ) / D where d sc = density of each size class (individuals/l), I w = dry weight increment from one size class to the next (µg) D = developmental time (days) Egg weight increment was considered to be 25% of the dry weight of the smallest measured nauplius. Developmental time equations were calculated using the equation described by ottrell (1975): Ln D = Ln (a+b) * Ln (T) 2 64

71 For calanoid eggs: a = and b = For calanoid nauplii: a = and b = For calanoid copepodites: a = and b = For cyclopoid eggs: a = 3.11 and b = For cyclopoid nauplii: a = and b = For cyclopoid copepodites: a = and b = For rotifers, the method used to calculate production depended on whether or not we had egg data from the mesocosms. Egg data were obtained for rotifers carrying external eggs. Production for these rotifers was calculated using finite birth rate and dry weight according to Edmondson and Winberg (1971). Egg development time was calculated using the formula from ottrell et al. (1976): Ln (D) = Ln (a) + b * Ln (T) + c * Ln (T) 2 where Ln a = ; b = ; c = and T = water temperature ( C). The finite birth rate was calculated as in Edmondson (196): = E/D where E = average clutch size (no. eggs per individual) and D = egg development time in days. Recruitment of new individuals (P N ) was calculated as: P N = N f * where N f = density of females (individuals/l) and = finite birth rate. Finally, production was calculated as: P = P N * W where W = mean individual dry weight (#g). If egg data were not available for a particular taxon (i.e. lack of external eggs), production of that taxon was estimated using average production to biomass ratios (P/) calculated for rotifers for which egg data were available for each mesocosm for each sampling date (Winberg, 1971). Sample analysis Chlorophyll-a samples were filtered onto Pall /E glass fiber filters (1. µm pore size) and frozen in the dark. Subsequently, chlorophyll-a was extracted 65

72 from the filters in the dark at 4 C using acetone and measured on a Turner model TD-7 fluorometer. We screened seston C, N, and P samples through a 63 µm mesh to remove most zooplankton and subsequently filtered the seston onto pre-ashed Pall /E glass fiber filters (1. µm pore size). In order to assess fish body C, N, and P concentrations, fish were gutted and dried until they reached a constant weight at 6 C. Fish were then ground to a powder using a mortar and pestle. C and N of seston and fish were measured using a Perkin Elmer Series 24 CHN analyzer (Perkin Elmer, oston, US). We digested P samples with HCl to convert particulate P to soluble reactive phosphorus (SRP) (Stainton et al. 1977) and measured SRP using a Lachat QC 8 FI autoanalyzer (Lachat Instruments, Loveland, US). Fish production We estimated fish production from measurements of fish biomass and body C content. In each mesocosm, we calculated the fish biomass (initial or final) by multiplying mean individual fish mass (g/individual) by the number of fish. These values were multiplied by the C content of fish to express biomass as g C/ mesocosm. verage fish production (gc/l/day) was calculated as: P fish = (C final C initial ) / t where P fish = production rate of fish C, C final = total fish C per mesocosm at the end of the study, C initial = total fish C per mesocosm at the beginning of the study, and t = length of the study. Trophic efficiency calculations Trophic efficiency was calculated as the ratio of production at one trophic level to production at a lower trophic level, using production values averaged across the experiment for each trophic level in each mesocosm. We calculated efficiency between each successive trophic level (TE PZ = zooplankton production / primary production; TE ZF = fish production / zooplankton production) and food chain trophic efficiency (TE PF = fish production / primary production) (Sterner et al. 1998, Sterner and Elser 22). 66

73 Statistical analyses To assess the effects of light, nutrients, and fish (and their interactions) on phytoplankton, zooplankton, and fish production and trophic efficiency, we employed a 3 or 2-way NOV with a Tukey test. Due to the scarcity of zooplankton in the treatments with fish, we analyzed the trends in treatments with and without fish separately. Step-wise multiple regressions were also used to better understand factors that can predict trophic efficiencies. Predictor variables included phytoplankton (seston C:P, C:N, and taxonomic food quality) and zooplankton (percent of zooplankton biomass comprised of cladocerans, adult copepods, nauplii and rotifers) variables. Only phytoplankton parameters (phytoplankton stoichiometry and food quality) were used in the TE PZ regression, and only zooplankton parameters (percent biomass composition) were used in the TE ZF regression. We also employed regression models that included the relative contributions of taxonomic groups to total zooplankton production (i.e. percent of total production instead of biomass), but these models were dropped because those with percent biomass consistently explained a higher proportion of variance. In order to assess zooplankton and fish responses to phytoplankton primary production, we regressed zooplankton and fish parameters against primary production, using either linear or polynomial regression, as appropriate. Fish parameters at the end of the experiment (whole-body C, N, P and their ratios) were analyzed for main and interactive effects using NCOV with treatment as a categorical variable and fish wet mass as a covariate. Prior to analyses, all values were ln-transformed, with the exception of percent composition of zooplankton groups, which was arcsin square-root transformed, and phytoplankton food quality, which was not transformed. ll statistical analyses were performed using JMP (SS Institute Inc., 25). Results Trophic efficiency Food chain efficiency (fish production/phytoplankton production) was highest under low light, high nutrient conditions, and lowest with high light, low nutrients. Overall, nutrients had a positive effect and light a negative effect on food chain trophic efficiency (Fig. 2a; Table 1). ased on stepwise multiple regressions, phytoplankton taxonomic food quality, phytoplankton stoichiometry, and zooplankton 67

74 community composition explained approximately 9% of the variation in phytoplankton to fish trophic efficiency (Table 1). We also assessed TE PZ and TE ZF to determine to what extent they can predict food chain trophic efficiency. 3-way NOV revealed significant negative effects of fish and light on TE PZ when all treatments were considered. ecause fish had very strong negative effects on TE PZ (Fig. 2c) and because we wished to understand the factors responsible for regulating trophic efficiency in the 3-level food chain, we also conducted 2-way NOVs separately on mesocosms with and without fish. We found a significant negative nutrient effect on TE PZ in fish treatments and a significant negative effect of light on fishless treatments (Fig. 2c,d; Table 1). Seston stoichiometry (C:P and C:N) explained 42-51% of the variation in TE PZ, depending on whether analyses were done with all treatments or subsets with and without fish (Table 1). TE ZF showed trends among treatments similar to those observed for food chain efficiency. Overall, nutrients had a significant positive effect on TE ZF (Fig. 2b; Table 1). Percent adult copepod biomass and percent rotifer biomass together explained approximately 57% of the variation in TE ZF ; TE ZF increased with increasing relative abundance of adult copepods and rotifers (Table 1). Phytoplankton responses The effects of light and nutrients differed between fish and fishless treatments. However, we observed the highest primary production under high light and high nutrient conditions (Fig. 3a). Phytoplankton production responded positively to light and nutrients in the presence fish, but a significant interaction revealed greater primary production under high light conditions only when nutrients were enhanced. Phytoplankton production also responded positively to nutrients in fishless treatments (Table 2; Chapter 1). Similarly, phytoplankton C:nutrient ratios responded positively to light and nutrients, with highest C:P and C:N observed under high light, high nutrient conditions, and lowest C:nutrient under low light conditions (Table 2; Fig. 4a,b; Chapter 1). Therefore, low light treatments yielded the highest stoichiometric food quality for zooplankton. In several treatments by the end of the study phytoplankton C:P ratios had exceeded 3 (Chapter 1), the P-limitation threshold for Daphnia, although when averaged over the course of the experiment, no treatment exhibited C:P >3 (Fig. 68

75 4a). We observed the highest taxonomic food quality of the phytoplankton under low light and high nutrient conditions, where cryptophytes were most abundant (Fig. 4c; Chapter 1). Zooplankton responses Fish had a strong negative effect on both zooplankton biomass and production (Fig. 3b,c; Table 2). The highest biomass and production were observed in the low light, high nutrient treatments, but treatment effects were statistically significant only within the fishless treatments. In the absence of fish, biomass and production responded negatively to light and positively to nutrients. We observed the lowest zooplankton biomass and production under high light and low nutrient conditions (Fig 3b,c; Table 2). Fish responses Fish survival was linearly and positively correlated with phytoplankton production, and was highest in the high light, high nutrient treatment (Fig. 5a). In contrast, mean individual fish biomass, total fish biomass, and fish production responded unimodally to primary production (Fig. 5b,c,d). ll three of these response variables were maximal under low light / high nutrient conditions, even though both primary production and fish survival were only intermediate in this treatment. Phytoplankton taxonomic food quality showed a response similar to that of fish production, responding unimodally to primary production, and being highest under these low light / high nutrient conditions (Fig. 4e; Fig. 5e). Phytoplankton stoichiometric food quality (seston C:P) also responded unimodally to phytoplankton production, with lowest C:P under low light, high nutrient conditions. Fish biomass increased during the study, with final fish biomass ranging from mg fish/l (wet weight). Fish C, P, C:N, C:P, and N:P showed significant responses to treatments. We observed the most variation in fish body carbon (C) and phosphorus (P) content under low light conditions. The highest fish C occurred under low light, low nutrients, and the lowest fish C under low light, high nutrients. Fish P exhibited the opposite trend, with highest P under low light, high nutrient conditions, and lowest P with low light and low nutrients (Fig. 6a,c). dditionally, fish C:N, C:P, and N:P stoichiometry varied among treatments, with highest C:N, C:P, and N:P under low light, 69

76 low nutrient conditions (Fig. 6d,e,f). Fish C, nitrogen (N) and C:N response to the treatments was dependent on fish size, with treatment and fish weight having interactive effects on these response variables (Fig. 6a,b,d). Discussion Trophic efficiency responses Food chain efficiency is driven by phytoplankton taxonomic food quality, C:nutrient stoichiometry, and zooplankton community composition. Considering bottom-up factors, we had predicted that phytoplankton C:nutrient stoichiometry would affect the efficiency of energy transfer between trophic levels, in accordance with the predictions of Sterner et al. (1998) and the findings of previous studies addressing trophic efficiency from phytoplankton to zooplankton (Urabe and Sterner 1996, Sterner et al. 1998, Urabe et al. 22). However, we found that although producer C:nutrient did help explain some of the food chain efficiency (TE PF ) trends we observed, phytoplankton taxonomic food quality explained most of the observed variation (Table 1). rett et al. (2) found similar results when considering phytoplankton effects on Daphnia growth. However, rett et al. (2) s study addressed the effect of small, edible phytoplankton on only Daphnia, but our results offer support for a taxonomic food quality effect in intact food chains with assemblages of phytoplankton and zooplankton. lthough previous studies suggest that phytoplankton stoichiometric food quality will drive food chain efficiency through the nutritional effects of producer C:nutrient on consumers, most of these studies addressed the effects of phytoplankton stoichiometry on a single species of zooplankton, usually Daphnia (Urabe and Sterner 1996, Sterner et al. 1998, Urabe et al. 22). In addition, recent studies have demonstrated that consumer homeostasis is not as constrained as previously thought and that the degree of homeostasis varies with species (Ferrão-Filho et al. 27, DeMott et al. 24, DeMott and Pape 25). Therefore, since we used complete assemblages of phytoplankton and zooplankton, our results may be confounded by other factors, but also more closely resemble natural systems. It is necessary to consider potential covariation between phytoplankton taxonomic composition and seston C:P. Phytoplankton community composition may not be 7

77 completely independent of stoichiometric response of the phytoplankton assemblage, as it has been suggested that the relative abundance of several phytoplankton taxa is correlated with the C:P response of the seston (Hall et al. 27, Chapter 1 from eth Dickman s Thesis, 27). Specifically, seston C:P is negatively correlated with the proportional abundance of cryptomonads and diatoms. Since we classified both cryptomonads and diatoms as high taxonomic quality food and they also exhibit low C:P, this suggests that effects of phytoplankton taxonomic composition on food chain efficiency may be confounded by differential stoichiometric responses of phytoplankton groups. dditionally, seston C:P was negatively correlated (r 2 =.13, p=.4) with phytoplankton taxonomic food quality, making it difficult to disentangle the effects of phytoplankton stoichiometric and taxonomic food quality on consumers. Our stoichiometric predictions for food chain efficiency (TE PF ) were based on the elemental constraints of consumers, specifically zooplankton. Thus, we predicted that we would observe similar trends for food chain efficiency (TE PF ), TE PZ, and TE ZF. In accordance with this prediction, TE PZ in the fishless treatments was similar to TE PF, i.e. trophic efficiency was highest under low light conditions (low C:P). However, trends in TE PZ in the plus fish treatments were opposite to those for TE ZF (Fig. 2b,c,d). When all eight treatments for TE PZ were analyzed together, phytoplankton stoichiometry (C:P, C:N) explained approximately 42% of the variation, indicating the potential importance of producer elemental composition for the efficiency of energy transfer across two trophic levels. However, when plus fish and minus fish treatments were analyzed separately, C:P alone explained only 15% of the variation in plus fish treatments, whereas C:P and phytoplankton taxonomic food quality together explained 43%. In minus fish treatments, C:P explained over half (51%) of the variation (Fig.2c,d; Table 1). These results highlight the importance of considering phytoplankton parameters other than stoichiometry, as well as the potential importance of considering the number of trophic levels (two or three) (Hairston and Hairston, 1993). lthough when analyzed separately, TE PZ in treatments with and without fish appeared to be regulated by different factors, it is important to note the intense fish predation effect on zooplankton biomass and production. Zooplankton biomass was very low in treatments with fish, which made it difficult to examine factors regulating zooplankton production in these treatments. 71

78 In the presence of fish, food chain efficiency was more closely related to TE ZF than to TE PZ (Fig. 2a,b,c,d). Zooplankton biomass parameters, namely the percentage of zooplankton biomass composed of adult copepods and rotifers, explained 57% of the variation in TE ZF (Table 1). However, the mechanism by which these particular zooplankton groups increase TE ZF is not clear. It is possible that the quantity and quality of lipids in zooplankton may directly affect fish, as lipids are essential for growth (Kim and Lee, 25). Zooplankton and fish responses We predicted that phytoplankton biomass and stoichiometric responses to light and nutrients would have implications for higher trophic levels. Zooplankton biomass and production were not significantly correlated with primary production, indicating that food quantity alone cannot explain observed responses (Fig. 5). In treatments without fish, zooplankton biomass and production was highest under low light, high nutrient conditions. In addition, cladoceran biomass was highest in this treatment, and production was highest under low light conditions (Fig. 3b,c). These results can be at least partially explained by the low C:P ratios (high stoichiometric food quality) and high taxonomic food quality of the phytoplankton in this treatment (Fig. 4a,b,c). However, zooplankton biomass and production in the presence of fish did not show significant treatment differences, likely due to the scarcity of zooplankton that made it difficult to distinguish treatment differences (Fig. 2b,c). Fish survival was positively affected by primary production (Fig.5a). Highest phytoplankton production and fish survival was observed under high light, high nutrient conditions (Fig. 3d; Fig. 5a). However, individual fish size, total fish biomass and production varied unimodally with primary production; fish size, biomass, and production were highest under low light, high nutrient conditions. Primary production was intermediate in this treatment, but phytoplankton taxonomic and stoichiometric food quality was highest. This suggests that while the total amount of phytoplankton production (food quantity) affected fish survival rates, phytoplankton food quality (taxonomic and stoichiometric) affects individual fish size, total biomass and production of fish. Fish body C, N, P, and stoichiometry responded to the light and nutrient treatments, resulting in variation in C:nutrient responses of the fish among treatments 72

79 (Fig. 6a,b,c,d,e,f). This variation in response to light and nutrient manipulations provides evidence for potential homeostatic flexibility in consumers. In conclusion, we found that food chain efficiency is driven by both bottom-up and top-down factors, including phytoplankton taxonomic food quality, C:nutrient stoichiometry, and zooplankton community composition. Our empirical findings suggest that C:nutrient producer stoichiometry is not the sole predictor of trophic efficiency from phytoplankton to zooplankton to fish, as predicted by Sterner et al. (1998). It is difficult to disentangle the effects of stoichiometric and taxonomic food quality on consumers, as we observed the highest taxonomic and stoichiometric food quality in the same treatment. Our study emphasizes the importance of examining intermediate trophic efficiency trends (TE pz and TE zf ), as results suggest that zooplankton may be directly regulating the efficiency of energy transfer to fish (TE zf ), as well as of considering the number of trophic levels (Hairston and Hairston, 1993). Future research should seek to validate the applicability of our findings to a wide range of systems. 73

80 Table 1: Results of stepwise multiple regressions and NOVs. Variables that were predicted to affect the trophic efficiency trends were tested using stepwise multiple regressions and 2 and 3-way NOVs, as appropriate. 2-way NOVs testing the effects of light and nutrients on trophic efficiency were used for all of the above analyses except for phytoplankton to zooplankton trophic efficiency (all treatments), which was analyzed with a 3-way NOV. Stepwise multiple regression NOV Response variable Predictor variables r 2 Model p-value Predictor variables p-value phytoplankton to fish trophic efficiency (TE FP) phytoplankton food quality light.3 seston C:N.77 nutrients.53 seston C:P.82 % rotifer biomass.86 % nauplii biomass.9 % adult copepods biomass -- % cladoceran biomass -- phytoplankton to zooplankton trophic efficiency (TE ZP ) seston C:P light.83 (all treatments) seston C:N.42 fish <.1 phytoplankton food quality -- light x fish.17 phytoplankton to zooplankton trophic efficiency (TE ZP ) seston C:P nutrients.167 (fish treatments) phytoplankton food quality.43 seston C:N -- phytoplankton to zooplankton trophic efficiency (TE ZP ) seston C:P light.246 (no fish treatments) seston C:N -- phytoplankton food quality -- zooplankton to fish trophic efficiency (TE FZ ) % adult copepod biomass nutrients.21 % rotifer biomass.57 % nauplii biomass -- % cladoceran biomass --

81 Response variable Predictor variables p-value phytoplankton primary production light.279 (fish treatments) nutrients <.1 light x nutrients.14 phytoplankton primary production nutrients.28 (no fish treatments) seston C:P light <.1 (fish treatments) nutrients.553 light x nutrients <.1 seston C:P light <.1 (no fish treatments) light x nutrients.33 seston C:N light.15 (fish treatments) nutrients.194 light x nutrients <.1 seston C:N light <.1 (no fish treatments) light x nutrients.88 phytoplankton food quality light <.1 (fish treatments) nutrients <.1 light x nutrients.6 phytoplankton food quality light.75 (no fish treatments) nutrients.39 zooplankton production (fish treatments) zooplankton production light.31 (no fish treatments) nutrients.8 zooplankton biomass nutrients.75 (fish treatments) zooplankton biomass light.22 (no fish treatments) nutrients.7 fish production light.1 nutrients <.1 Table 2: Results of 2-way NOVs analyzing the main and interactive effects of light and nutrients on phytoplankton, zooplankton, and fish parameters. Only significant predictors (p<.5) are included in this table.

82 Food chain efficiency: HighLow Fish Carbon Dissipation Zooplankton Phytoplankton C:nutrient Figure 1: Predicted effects of phytoplankton C:nutrient stoichiometry on food chain efficiency. Since consumers (zooplankton and fish) are more stoichiometrically constrained than primary producers, we predict that food chain efficiency will be lower with high C:nutrient phytoplankton food. (dapted from Sterner et al. 1998, Sterner and Elser 22).

83 ) Plus Fish ) Plus Fish TE pf (FP/PPr) C C TE zf (FP/ZP) TE pz (ZP/PPr) C) Plus Fish D) C C C C High Low High Low Nutrients Nutrients HL HN HL LN LL HN LL LN High light Low light TE pz (ZP/PPr) C Minus Fish C High Low High Low Nutrients Nutrients HL HN HL LN LL HN LL LN High light Low light Figure 2: Each bar represents seasonal average trophic efficiency for all mesocosms within a treatment. Error bars represent standard error, and letters indicate treatments that are significantly different from each other as determined by a Tukey test. For TEpz (ZP/PPr), all 8 treatments (plus fish and minus fish) were analyzed together, although graphically depicted separately. Note the difference in scale between the plus and minus fish treatments.

84 ) PPr (ugc/l/day) Plus fish C C Minus fish ) 25 Cladocerans dult copepods Nauplii Rotifers 25 iomass (ug/l) C C) Production (ugc/l/day) FP (ugc/l/day) D) C High HL HN HL Low LN Nutrients LL High HN LL Low LN Nutrients High light Low light HL High HN HL Low LN LL High HN LL Low LN Nutrients Nutrients High light Low light Fig 3: Each bar represents a seasonal Fig average 3: Seasonal for all average mesocosms primary within production a (PPr), zooplankton treatment. biomass Error bars and represent production, standard fish production error, and (FP). letters Each indicate bar represents treatments a seasonal that are average for all mesocosms within a treatment. Error significantly different from each other as bars represent standard error, and letters indicate determined by a Tukey test. Plus fish and treatments that are significantly different from each other minus as determined fish treatments by a Tukey were analyzed test. Plus and and minus are fish graphically treatments represented were analyzed separately. and are graphically depicted separately. Note the difference in scale between the plus and minus fish treatments.

85