Light and nutrient supply mediate intraspecific variation in the nutrient stoichiometry of juvenile fish

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1 Light and nutrient supply mediate intraspecific variation in the nutrient stoichiometry of juvenile fish Kelsea N. Downs, 1 Nicole M. Hayes, 1,2,3 Amber M. Rock, 1,2 Michael J. Vanni, 1,2, and María J. González 1,2 1 Department of Biology, Miami University, Oxford, Ohio USA 2 Graduate Program in Ecology, Evolution, and Environmental Biology, Miami University, Oxford, Ohio USA Citation: Downs, K. N., N. M. Hayes, A. M. Rock, M. J. Vanni, and M. J. González Light and nutrient supply mediate intraspecific variation in the nutrient stoichiometry of juvenile fish. Ecosphere 7(10):e /ecs Abstract. Intraspecific variation in the nutrient stoichiometry of animals and their waste products can be substantial, but the factors mediating this variation are unclear. We experimentally manipulated light, nutrient supply, and nutrient ratio (N:P) in a field experiment to generate variation in algal quantity and elemental composition, and assessed the stoichiometric responses of juvenile carnivorous fish (bluegill, Lepomis macrochirus). Algal quantity (primary production) and stoichiometry (ratios of carbon [C], nitrogen [N], and phosphorus [P]) varied greatly in response to manipulations, as did bluegill body and excretion stoichiometry. Algal primary production (PPr) was greatest at high light and high P supply (HL HP). Algal stoichiometry responded in accordance with the light:nutrient hypothesis; that is, C:P ratios were highest at HL LP and lowest at LL HP. Variation in fish body stoichiometry was strongly related to basal resource supply (PPr), while excretion stoichiometry was driven by both PPr and algal stoichiometry. Bluegill growth and body C and N concentrations were highest at HL HN and lowest at LL LN, whereas body P showed the opposite pattern. Thus, bluegill body C:P, C:N, and N:P were highest under HL HN and lowest under LL LN. Per capita excretion rates of bluegill were strongly related to body mass and hence were highest at HL HN, where fish grew largest. However, P excretion rates normalized for body mass increased with phytoplankton P and zooplankton production. In accordance with ecological stoichiometry theory, N:P excretion ratio increased with basal resource (algal) N:P and decreased with estimated bluegill P ingestion. Variation in both body and excretion stoichiometry in this single cohort of fish was comparable to or exceeded variation observed in other studies of bluegill stoichiometry across multiple ecosystems. Our results illustrate the pronounced intraspecific variation in carnivore nutrient stoichiometry as a function of basal resource quantity and quality. Key words: algae; bluegill; ecological stoichiometry; excretion; light:nutrient hypothesis; nitrogen; nutrient ratios; phosphorus; phytoplankton. Received 26 April 2016; accepted 5 May Corresponding Editor: D. P. C. Peters. Copyright: 2016 Downs et al. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. 3 Present address: Department of Biology, University of Regina, Regina, Saskatchewan S4S 0A2 Canada. vannimj@miamioh.edu Introduction Ecological stoichiometry presents a framework for examining the relative balance of multiple elements found in, and recycled by, organisms (Sterner and Elser 2002). Variation in the stoichiometry of animal bodies, and their waste products, has implications at several levels of ecological organization. For individuals, the variation in body composition can reflect the relative abundance of biomolecules tied to fitness; for example, body P content is correlated with RNA and hence with individual growth rate (Elser et al. 1996), and high body C content can result from lipid storage, which is sometimes correlated with survival or reproductive output 1

2 (e.g., Karimi et al. 2010, El- Sabaawi et al. 2012b, Ebel et al. 2015). At the ecosystem level, animals can be important in storing and recycling nutrients, but there is great variation among species and ecosystems in the importance of fluxes mediated by animals, in part because of the variation in their body or diet stoichiometry (Vanni 2002, McIntyre et al. 2008, Small et al. 2011). Thus, it is important to understand the drivers underlying the variation in the stoichiometry of animal bodies and their wastes. The purpose of this study was to quantify intraspecific variation in the body and excretion stoichiometry of a carnivorous fish species in response to the variation in two important environmental drivers, solar radiation and nutrients. Early ecological stoichiometry theory recognized great variation in body elemental composition among animal species, but assumed little or no variation within a species (Sterner 1990, Andersen and Hessen 1991). Indeed, intraspecific variation in body elemental content is usually much less than interspecific variation (e.g., Sterner and George 2000, Evans- White et al. 2005, McIntyre and Flecker 2010, González et al. 2011). However, recent studies suggest that intraspecific variation in animal body composition is much greater than previously recognized and, in some cases, is nearly as great as interspecific variation (e.g., Small and Pringle 2010, El- Sabaawi et al. 2012a, b). Intraspecific variation in animal stoichiometry can be mediated by genetics, ontogeny/life history stage, environmental factors, and interactions among these drivers. Different genotypes of the same species can exhibit evolved differences in body and/or excretion stoichiometry (e.g., Jeyasingh et al. 2014, Tobler et al. 2016). Ontogeny and life history stage can drive intraspecific variation in several ways. For example, larval/juvenile and adult stages within the same species often have divergent body elemental composition (e.g., Villar- Argaiz et al. 2002, Pilati and Vanni 2007, Back and King 2013, Boros et al. 2015, Ebel et al. 2015, Showalter et al. 2016, Tiegs et al. 2016), and body stoichiometry can vary with morphological phenotype (e.g., Vrede et al. 2011, El- Sabaawi et al. 2016) and reproductive status (Ebel et al. 2015). The presence of predators can also mediate the stoichiometry of prey bodies and excretion rates, phenotypically by eliciting fear- induced decreases in prey feeding rates or increases in respiration rates (Hawlena and Schmitz 2010, Leroux et al. 2012, Dalton and Flecker 2014), or by inducing evolutionary changes in life histories that are correlated with organismal stoichiometry (e.g., Sullam et al. 2015, El- Sabaawi et al. 2016). Several recent studies demonstrate the substantial intraspecific variation in animal stoichiometry among habitats or ecosystems. Although much of this intraspecific variation is unexplained, in some cases it is related to the quantity and/or quality of basal resources (e.g., Hambäck et al. 2009, Small and Pringle 2010, Vrede et al. 2011, El- Sabaawi et al. 2012a, b, Halvorson et al. 2015). In many ecosystems, nutrients and solar radiation (light) limit primary production and thus the quantity of food resources (hence nutrients) available for herbivores and ultimately other consumers. In aquatic ecosystems, the relative balance of light and nutrients also can regulate the quality of algae. The light:nutrient hypothesis states that a high supply of light relative to P yields low- quality, P- poor primary producers (high C:P cell content), while a low light:p supply ratio yields P- rich, high- quality producers (low C:P content) (Sterner et al. 1997). Although less studied, the light:nutrient ratio can similarly influence algal C:N (Dickman et al. 2006, Liess and Kahlert 2007, Rowland et al. 2015). Thus, light and nutrients can regulate both the quantity and quality of basal resources. While resource quantity and quality can modulate the stoichiometry of animal bodies and their waste products, these relationships are highly variable. Some studies show that animal body stoichiometry is relatively homeostatic against the variation in food resources; that is, body elemental composition varies little in the face of great variation in the elemental content and ratios of their food (Persson et al. 2010, Hessen et al. 2013). However, some studies show that the concentration of a particular element in an animal s body, and/or the rate at which that element is excreted, increases with the concentration of that element in food, or with higher ecosystem resource (element) supply (e.g., Hood and Sterner 2010, Small and Pringle 2010, Benstead et al. 2014). Similarly, animal excretion rates and excretion N:P ratio vary with diet in some studies, but not others (Hessen et al. 2013, Allgeier et al. 2015). Some studies also show that algal quality may travel 2

3 up food chains to carnivores. Herbivorous zooplankton grown on P- replete phytoplankton have higher body P content and thus are better quality food for carnivorous fish compared with zooplankton fed on P- poor phytoplankton, resulting in higher carnivore growth and condition when algal quality is high (Malzahn et al. 2007, Boersma et al. 2008). Some studies show that carnivore production per unit herbivore production is higher when algal quality is high, clearly indicating that algal food quality travels up the food chain (Malzahn et al. 2007, Boersma et al. 2008, Dickman et al. 2008). However, some carnivores are relatively insensitive to the variation in herbivore quality induced by the variation in algal quality, and in these cases, algal quality effects do not travel up the food chain (Malzahn et al. 2010; Rock et al., in press). In this study, we asked the question: How much does the stoichiometry of a single carnivore species respond to the variation in light and nutrients? To explore this question, we conducted a field experiment in which we manipulated light and nutrients and quantified the intraspecific variation in the body and excretion stoichiometry of a carnivorous fish, bluegill (Lepomis macrochirus). We set up food webs with bluegill as the top consumer under 12 different light and nutrient regimes. This three- way factorial experiment had two levels of light, two levels of P supply, and three N:P supply ratios. Although many studies have examined the effects of nutrients and light on algae on herbivore performance (Hessen et al. 2013) and energy flow to higher consumers (Boersma et al. 2008, Dickman et al. 2008, Lesutiene et al. 2014, Verspagen et al. 2014, Faithfull et al. 2015, Rowland et al. 2015), to our knowledge this is the first study to experimentally assess how carnivore body and excretion stoichiometry vary in response to the variation in primary producer quality and quantity induced by experimental manipulation of light, nutrient supply, and nutrient ratios. We have two main objectives in this study. The first is to quantify the intraspecific variation in the stoichiometry (in bodies and in excretion) of a carnivorous fish in response to light and nutrients. We expected our experimental manipulation of light and nutrients to produce wide variation in both algal resource quantity and quality, which in turn should induce variation in bluegill food resources, namely herbivore biomass, production, and perhaps quality. The stoichiometry of bluegill bodies has been studied in a number of different North American ecosystems, revealing a considerable variation in elemental concentrations and ratios (Davis and Boyd 1978, Hendrixson et al. 2007, Torres and Vanni 2007, Showalter et al. 2016). We hypothesize that intraspecific variation in bluegill stoichiometry in our experiment will be similar to that observed in bluegill across multiple ecosystems. Our second objective is to quantify which factors (e.g., bluegill body mass, algal biomass, algal C: nutrient ratios) best predict bluegill body and excretion stoichiometry. Animal stoichiometry depends on both resource quantity and quality and their interactions, and excretion stoichiometry is a function of the stoichiometry of both an animal s body and its food. Because of the potentially highly interactive nature of our treatments and response variables, we generate broad hypotheses for our study. First, we hypothesize that bluegill growth will be positively correlated with algal primary production and will therefore be highest in treatments with high light and high P supply. Growth rate is important because many aspects of animal stoichiometry are functions of body size; for example, per capita excretion rates generally increase with body size, and several studies show that body P (percentage of dry mass) increases with body size in bluegill (Davis and Boyd 1978, Hendrixson et al. 2007, Torres and Vanni 2007, Showalter et al. 2016). Therefore, we expect many aspects of bluegill stoichiometry to differ among treatments simply because body size will differ. However, we also hypothesize that once body size is accounted for, the concentration of a particular element in bluegill bodies, and/or the rate at which that element is excreted, will be positively related to the availability of that element. Specifically, body N and P contents and N and P excretion rates should broadly correlate with N and P availability in algae and/or in herbivores. Similarly, body C, C:N, and C:P should all be positively correlated with primary production because a high organic C supply rate should promote the storage of lipids in fish tissue, thereby diluting other elements such as N and P (Karimi et al. 2010, Dalton 2015, Ebel et al. 2015). Finally, following ecological stoichiometry theory, we hypothesize that body N:P will be 3

4 positively related to resource N:P and that excretion N:P will be positively related to resource N:P, but negatively related to body N:P. Methods Experimental design We conducted a 7- week field experiment at Miami University s Ecology Research Center in summer Light and nutrients were manipulated in cylindrical polyethylene mesocosms (5000 L, 2.25 m diameter, 1.4 m depth), also used in previous experiments (e.g., Dickman et al. 2008, Rowland et al. 2015). We used a food chain with three trophic levels, with juvenile bluegill as the carnivore. Light intensity was manipulated to allow the passage of 30% and 90% of ambient light, P supply rates were 2 and 20 μg P L 1 week 1, and N:P supply ratios were 40:1, 20:1, and 3:1 (molar). Thus, we implemented 12 treatments, each with three replicates, for a total of 36 mesocosms. N was added as NH 4 NO 3 and P as NaH 2 PO 4. H 2 O. Light intensity was mani pulated with Sudden Shade Cloth (DeWitt Company, Sikeston, Missouri, USA) placed over mesocosms as in Dickman et al. (2008). The high- P treatments reflect the external P loading rate to nearby Acton Lake, a eutrophic reservoir with a watershed dominated by agriculture (Vanni et al. 2001). N:P supply ratios were designed to yield algal assemblages that were either limited by N or P, or co- limited (low, high, and intermediate N:P, respectively). Treatments are labeled here using abbreviations; for example, the treatment with high light, low P, and intermediate N:P is designated HL LN IN:P. Mesocosms were filled with water from a nearby fishless mesotrophic pond, which introduced plankton. Zooplankton tows were also taken from this pond and Acton Lake and added to mesocosms to create diverse assemblages. After 1 week, 40 juvenile bluegill (mean wet mass g ± SD, length 21.3 mm ± 4.19) were placed in each mesocosm. Bluegill was used as a model species because they are widely distributed and abundant, and because bluegill stoichiometry has been examined in several other studies (Davis and Boyd 1978, Hendrixson et al. 2007, Torres and Vanni 2007, Showalter et al. 2016), providing benchmarks against which we can compare our observed stoichiometric variation. We used juveniles because they are developing and building P- rich skeletons and hence should be more sensitive than adults to treatment differences. Fish were obtained from Ohio Division of Wildlife Hatchery ponds in Hebron, OH, using seines. As fish were added to mesocosms, a subsample (n = 124) was taken to estimate the initial length, mass, and body nutrient contents. To minimize the handling stress, individuals added to the mesocosms were not measured or weighed. During weekly sampling, integrated water samples were taken from the water surface to near bottom using a bilge pump, to quantify phytoplankton biomass, production, and stoichiometry. Zooplankton samples were collected with a 10- L Schindler- Patalas trap at 0 and 0.5 m, then pooled to make one integrated sample, and preserved in 10% sugared formalin. To assess environmental conditions and to calculate primary production, abiotic parameters such as light (measured as photosynthetically available radiation, PAR), temperature, and dissolved oxygen were also measured. Algal quality and quantity The biomass, production, and elemental content of two algal groups, phytoplankton and periphyton, were used as measures of basal food quantity and quality. Juvenile bluegill undergo an ontogenetic feeding shift from zooplankton to benthic invertebrates (Werner and Hall 1988), so it is possible that they also consumed both invertebrate groups. In nearby Acton Lake, larval bluegill feed entirely on zooplankton, but switch to a diet of benthic insects when they are ~20 35 mm total length, after which they remain entirely benthivorous (Showalter et al. 2016). Thus, we measured both periphyton and phytoplankton quantity and quality and used stable isotopes to trace food sources. Although we quantified periphyton and phytoplankton elemental composition, we were unable to measure that of zooplankton because once fish were added the zooplankton community shifted to small species, which were nearly impossible to separate from seston for nutrient analyses. Even though algal elemental composition is an indirect measure of food quality for carnivores, some studies have shown that the effects of phytoplankton stoichiometry can travel up the food chain ; that is, zooplankton feeding on P- replete 4

5 phytoplankton are higher- quality food for carnivores, because these zooplankton have higher P content than those fed P- limited phytoplankton (Malzahn et al. 2007, 2010, Boersma et al. 2008). On the other hand, some herbivores are homeostatic with respect to body elemental composition (Persson et al. 2010, Hessen et al. 2013), so there is great variation among species in the extent of elemental homeostasis. Even species in the same genus (Daphnia) vary greatly in body elemental homeostasis in response to the changes in food stoichiometry (Hood and Sterner 2010). Phytoplankton net primary production rates (PPr) were estimated weekly using the 14 C method (Peterson 1980, Fee 1990). However, because quantifying PPr is time- consuming, we estimated it in only 24 mesocosms each week (two replicates of each treatment, alternating so that PPr was measured four times in each mesocosm). Periphyton PPr was quantified in weeks 4 and 7 in all mesocosms; biomass was too low to quantify it earlier in the experiment. PPr was estimated by incubating algal samples with NaH 14 CO 3 at a range of light intensities in the laboratory to generate chlorophyll- specific photosynthetic irradiance curves, which were used with ambient light and chlorophyll data to estimate PPr (Fee 1990, Dickman et al. 2008). Phytoplankton samples were taken from the integrated water samples. Periphyton samples were obtained using polyethylene strips, affixed to mesocosm walls from top to bottom in each cardinal direction. For each sampling event, we removed one strip in each cardinal direction (four in total) and carefully scrubbed periphyton into 2 L of prefiltered mesocosm water. To quantify algal C, N, and P contents, the samples were filtered onto preashed Pall A/E glass fiber filters; phytoplankton samples were first screened through 63- μm mesh to remove most zooplankton, while periphyton samples were taken from the same slurry used to estimate PPr. Zooplankton enumeration and production Zooplankton were enumerated in weeks 0, 1, 3, and 6 using a dissecting (crustaceans) or compound (rotifers) microscope. Cladocerans were identified to species (Daphnia), family (chydorids), or genus (others), and copepods as calanoid, cyclopoid, or nauplii. For crustaceans, at least 20% of the individuals in the sample were counted and measured, after which enumeration continued only for sufficiently abundant taxa ( 25 individuals). Enumeration of sufficiently abundant taxa, and their eggs/embryos, continued until a total of 50 individuals were counted, and 25 individuals were measured, to estimate biomass (Rowland et al. 2015). For rotifers, a total of 200 individuals, or 60% of the sample, were enumerated, and biomass was calculated using geometric formulas (Ruttner- Kolisko 1977). Zooplankton production was calculated for each week using taxon- specific biomass production regressions taken from Dickman et al. (2008), as described in Rowland et al. (2015). Bluegill growth, stoichiometry, and food sources Mesocosms were drained at the end of the experiment using submersible utility pumps, and all fish were collected when the water level dropped to ~10 cm, which took min. Six of these fish, randomly selected, were collected with nets from each mesocosm and used to quantify the excretion rates. Within 15 min of collection, excretion fish were incubated at mesocosm temperatures in individual plastic bags with 1 L of mesocosm water, prefiltered through 1- μm Pall A/E glass fiber filters to remove phytoplankton and bacteria that could take up excreted nutrients. Control bags without fish were filled with 500 ml of prefiltered water. After h, fish were removed and sacrificed, and water was filtered through Pall A/E filters. The samples were preserved with sulfuric acid, stored at 4 C, and analyzed for NH 4 - N and soluble reactive P (SRP) with the phenol hypochlorite and molybdenum blue methods, using a QC 8000 FIA autoanalyzer (Lachat Instruments, Loveland, Colorado, USA), as in Torres and Vanni (2007). Excretion rates (μg N or P fish 1 h 1 ) were determined as final minus initial nutrient mass, corrected for controls. All surviving fish, including those used for excretion measurements, were counted, measured, and weighed to estimate fish biomass and growth rates. Fish condition was calculated as 1000 M/L 3, where M is g wet mass and L is mm total length. To analyze the body nutrients, fish from excretion incubations were weighed and stored at 20 C. Fish were then gutted, dried at 60 C until reaching a constant mass, then ground into a fine powder. For C and N, two samples 5

6 per fish (six fish per mesocosm) were analyzed using a FLASH 2000 CN analyzer (CE Elantech, Lakewood, New Jersey, USA). Body P content (one sample per fish) was determined by digesting samples with HCl to convert P to SRP, which was then quantified as for excretion samples. Stable isotope ratios (δ 13 C and δ 15 N) were analyzed to infer energy source(s) and trophic position for bluegill. Samples, collected and prepared as for nutrient analyses, of phytoplankton (weeks 4 and 6), periphyton (weeks 4 and 6), and bluegill (week 6) were sent to the UC Davis Stable Isotope Laboratory. Contributions of pelagic (phytoplankton) and benthic (periphyton) energy sources to bluegill were estimated using two- source mixing models (Vander Zanden and Vadeboncoeur 2002); for example, the contribution of periphyton to bluegill production was estimated as [(δ 13 Cfish δ 13 Cphytoplankton)/(δ 13 C periphyton δ 13 Cphytoplankton)] 100%. For both phytoplankton and periphyton, we used means of weeks 4 and 6. If the δ 13 C value of fish fell beyond either end member in the mixing model (indicating >100% benthic reliance or <0% benthic reliance), the values were set to either 100% or 0%. δ 15 N data were used to infer bluegill trophic position (Vander Zanden and Vadeboncoeur 2002). In order to try to relate the stoichiometry of bluegill to their food sources, we estimated bluegill P ingestion rates. First, we estimated C ingestion by assuming a gross growth efficiency of 15% for C; that is, C growth/c ingestion = 0.15 (Schindler and Eby 1997). Then, using the δ 13 C mixing model results, we apportioned C ingestion to benthic invertebrates and zooplankton. We then assumed that bluegill consumed the four arthropod zooplankton groups (cladocerans; copepod adults and copepodites; copepod nauplii; and Chaoborus) in proportion to their relative biomass; we assumed that bluegill did not ingest rotifers. We assigned each zooplankton group a body P content based on the literature values (Appendix S1: Table S4), and then estimated P ingestion from each of these zooplankton groups as C ingested from that group divided by its body C:P; these P ingestion values were summed to estimate the total P ingestion from zooplankton. Similarly, we estimated P ingestion from benthic invertebrates by using the mixing model to infer the proportion of diet from benthic invertebrates and the C and P values of chironomids (the only common benthic invertebrate in our mesocosms). Then, the total P ingestion was calculated as the sum of P ingested from chironomids plus zooplankton. To evaluate the feasibility of algal food quality traveling up the food chain, we explored two scenarios. In the homeostasis scenario, invertebrate body P content was assumed to be constant (within a taxon) in all treatments, and we used a mean value for each invertebrate group from the literature (Appendix S1: Table S4). In the flexibility scenario, we assumed that invertebrate body P was higher when algal P content was higher (i.e., algal C:P was lower). Hood and Sterner (2010) found that Daphnia body P differed on average by ~30% when grown on high- vs. low- P phytoplankton. Using this value, we varied the body P contents by 30% across our treatments, based on algal C:P. Thus, we assigned invertebrates in the lowlight, low- nutrient (LL LN) treatments a body P content equal to that used in the homeostasis scenario, because these treatments had intermediate phytoplankton and periphyton C:P (see Results). Algae in the low- light, high- nutrient (LL HN) treatments had the lowest C:P; therefore, we assigned invertebrate body P contents that were 30% higher than those in the LL LN treatments. Finally, algae in the high- light treatments (regardless of P supply) had the highest C:P, so invertebrates were assigned a body P content equal to 70% of that in the LL LN treatments. For both the homeostasis and flexibility scenarios, we estimated bluegill P ingestion rates over the entire experiment (using zooplankton data from weeks 0, 1, 3, and 6) and related that to bluegill body elemental composition. We also estimated bluegill P ingestion rates using data only from the final zooplankton sampling (week 6) and related that to bluegill excretion rates. Our rationale for using different timescales is that body elemental composition should respond to the variation in food resources over the entire experiment, whereas excretion rates likely reflect more recent consumption. We realize that we have made several important assumptions in doing these calculations, namely that bluegill consume zooplankton groups in proportion to their relative abundance and, in the flexibility scenario, that body P contents of all invertebrate groups respond similarly to the variation in algal quality. Nevertheless, we feel that comparing the two scenarios allows us 6

7 to gauge whether the variation in bluegill stoichiometry can be influenced by algal quality traveling up the food chain. Bluegill elemental gain Because we were interested in the relative storage of elements by fish, we estimated the mass of C, N, and P sequestered in bluegill bodies during the experiment. If storage of lipids (C) is high, body N and P contents may be relatively low because of a dilution effect (Karimi et al. 2010). If so, element gains may be more informative than body concentrations for understanding the changes in stoichiometry. The gain of an element E by an individual fish was calculated as mass of E at the end of the experiment minus mean mass of E in initial fish (g E/fish). Final E mass for each mesocosm was calculated by multiplying final fish dry mass (g) times mean body E content (g E per g dry mass, from the fish used to measure body E). Initial element mass was obtained similarly, using the fish used to estimate body nutrients (n = 14). The ratio of C gain to P gain was also calculated for each mesocosm and regressed against log(growth rate) to determine the relative sequestration of C vs. P as a function of growth. Statistical analyses To analyze the treatment differences, we used three- way ANOVA with light, P supply, and N:P supply ratio as categorical variables and included all interactions. Bluegill excretion rates, body nutrients, growth, and condition were dependent variables. All statistical analyses were carried out with JMP (SAS Institute, Cary, North Carolina, USA), using log- transformed data because the mean and variance of untransformed data were positively correlated. Per capita nutrient excretion rates and ratios were regressed against body mass (log- transformed to account for negative allometry). Because excretion rates are likely to be allometric functions of body mass, and growth rates are likely to differ among treatments, treatment differences in body size may result in differences in excretion rates. Such growth (size)- associated effects on stoichiometry can have important consequences for ecosystem function. Nevertheless, size differences among treatments could obscure proximate effects of food resources and body stoichiometry on excretion, because excretion rates are likely to be influenced more by short- term events (e.g., on the order of gut passage time or daily feeding cycles) rather than interactions occurring over the duration of the experiment (which will determine the growth). Therefore, we used mass- normalized excretion rates to compare treatments, calculated as X n = X/W b, where X n is the mass- normalized excretion rate (μg N or P μg g 1 h 1 ) and X is the non- normalized excretion rates (μg N or P μg fish 1 h 1 ), W is body mass (g), and b is the scaling exponent (Torres and Vanni 2007). The scaling exponent b was obtained empirically as the slope of the log(mass) vs. log(excretion rate), with all individuals from all treatments pooled together. Body elemental composition may also be influenced by body mass. To account for this, we regressed mass (independent variable) against body nutrient content and ratios and found mass to be significantly correlated with all of these. Therefore, to compare the treatments, we obtained the residuals from these regressions, which removes the mass effect. Three- way ANOVAs on the residuals were then used to determine the effects of light, P supply, and N:P supply ratios on body nutrients and nutrient gain. Finally, we used multiple regression analysis with stepwise model selection to predict excretion rates and ratios, using bluegill body mass, bluegill body nutrients, phytoplankton stoichiometry, periphyton stoichiometry, zooplankton production, and bluegill P ingestion as predictor variables. Model selection was based on ΔAIC. Results Bluegill energy sources and trophic position Phytoplankton and periphyton δ 13 C differed significantly in 24 of 36 mesocosms, distributed across all treatments, so we used these mesocosms to estimate the relative contributions of pelagic (phytoplankton) and benthic (periphyton) energy sources to bluegill diets. Benthic contribution to diets increased with decreasing light and P supply, from 0% in the HL HP HN:P treatment to 99% in the LL LP LN:P treatment (Appendix S1: Table S1). δ 15 N data showed that bluegill were carnivorous and that trophic position did not vary among treatments. Among these 24 mesocosms, bluegill δ 15 N was on average 7.53 (SD = 0.38) and 6.99 (SD = 0.73) greater than that of phytoplankton and periphyton (Appendix S1: 7

8 Table S1), respectively, indicating that bluegill were two trophic levels above both algal groups. Algal quality and quantity Primary production (PPr) of both algal groups was highest in the HL HP treatments (Fig. 1A, B). Effects of light and P supply were significant for both groups, but the N:P supply ratio affected only phytoplankton (Table 1), mainly driven by high PPr in the HL HP IN:P treatment. At the ecosystem (mesocosm) scale, phytoplankton PPr was much greater than periphyton PPr (Fig. 1A, B). Fig. 1. Algal production (A, B) and stoichiometry (C H) across treatments. Shapes represent replicate mesocosms, while black dots represent treatment means. Treatments are arranged to generally represent highest to lowest resource supply rates, from left to right. 8

9 Table 1. Results of three- way ANOVA on algal PPr and stoichiometry; bluegill growth, body condition, and survivorship; bluegill body nutrients and elemental gains, and bluegill mass- normalized nutrient excretion, in responses to light and nutrient manipulations. Response variables Predictor variables F ratio P Periphyton and phytoplankton PPr and stoichiometry Phytoplankton PPr Light < P supply Light P supply N:P Periphyton PPr Light < P supply Light P supply Periphyton C:N Light < P supply < P supply N:P Phytoplankton C:N Light < P supply < Light P supply Light N:P Periphyton C:P Light < P supply N:P Light N:P Phytoplankton C:P Light < P supply < Light P supply N:P Periphyton N:P Light < P supply N:P Light P supply Light P supply N:P Phytoplankton N:P Light < P supply N:P Light P supply Light P supply N:P Bluegill growth, condition, and survivorship Bluegill survivorship Light P supply Light P supply N:P Body condition Light < P supply Growth rate Light < P supply < Light P supply N:P Bluegill body nutrients Body P P supply N:P C:N Light P supply N:P C:P Light P supply N:P C Gain Light < P supply N:P Light N:P Light P supply N:P

10 Table 1. Continued. Response variables Predictor variables F ratio P P Gain Light < P supply < N:P Light P supply Light P supply N:P N Gain Light < P supply < N:P Light N:P Light P supply N:P Bluegill nutrient excretion Mass- normalized P excretion P supply Light P supply Light N:P Mass- normalized N excretion N:P Mass- normalized N:P excretion P supply Light P supply Note: Only significant and marginally significant predictor variables (P- value <0.1) are included. Algal stoichiometry responded generally as predicted by the light:nutrient hypothesis; that is, C:nutrient ratios were overall higher at high light and lower at low nutrients (Fig. 1C F). Relative responses to treatments were similar in the two algal groups, although periphyton C:nutrient ratios were higher and more variable than phytoplankton ratios. For both groups, C:N and C:P increased with increasing light and decreasing P supply, and C:nutrient ratios were lowest (algal quality was best) in the LL HP treatments (Fig. 1C F). Main effects of light and N:P, as well as the light P supply and light N:P interactions, were significant for both phytoplankton and periphyton N:P (Fig. 1G, H). At high light, N:P of both algal groups was highest in the high- P supply treatments and increased as N:P supply ratio increased; however, these trends were not evident at low light (Fig. 1G, H). Fish growth and body elemental composition Bluegill growth rates increased with increasing light and P supply (Fig. 2A, Table 1). The threeway interaction was also significant, because the growth rate trends along the N:P gradient differed with P and light supply (Fig. 2A, Table 1). Elemental gains paralleled those of growth rates; that is, gains of C, N, and P increased with light and P supply, were highest in the HL HP treatments, and were lowest in the LL LP treatments; several interactions were also significant (Fig. 2B D, Table 1). Differences between LL LP and HL HP treatments in C gain were 80, much greater than those for growth rate (17 ), whereas the differences in N gain (25%) and P gain (22%) were similar to those for growth rate. Treatment differences in fish condition were similar to those of growth rate; that is, fish in the HL HP treatments had better condition than those in the LL LP treatments (Appendix S1: Fig. S1). Bluegill survivorship was high in all treatments (overall mean, 85%; Appendix S1: Fig. S1). At the end of the experiment, bluegill body C (percentage of dry mass) was highest in the HL HP HN:P treatment (43 ± 0.02 SD) and lowest in the LL HP HN:P and LL LP IN:P treatments (39 ± 0.02, Fig. 3A). Final body N was highest in the HL HP LN:P treatment (12.04 ± 0.02) and lowest in the LL LP IN:P treatment (11.26 ± 0.01, Fig. 3B). Final body C and N were positively correlated with fish mass (Appendix S1: Table S2). After accounting for mass, there were no treatment effects on body C or N (ANOVA on residuals of the mass vs. body C or N regression). Body P responded oppositely to C and N; that is, it was highest in the LL LP IN:P treatment (3.60% ± 0.001) and lowest in the HL HP LN:P treatment (2.71% ± 0.002) and was negatively correlated with mass (Fig. 3C; Appendix S1: Table S2). Three- way ANOVA on residuals of the body P vs. mass regression revealed a marginally significant P supply N:P supply interaction 10

11 Fig. 2. Bluegill growth rate (A) and elemental gain (B D) across treatments. Symbols as in Fig. 1. (Appendix S1: Table S1). In LP treatments, body P increased with decreasing N:P supply, while in HP treatments the relationship between N:P supply and body N:P was inconsistent (Fig. 3C). Body P responded more strongly to treatments than C or N. Thus, the highest treatment mean for body P (LL LP IN:P) was 33% higher than the lowest treatment mean (HL HP LN:P); relative differences among treatments in body C (10%) and N (7%) were much smaller. Bluegill body C:P, N:P, and C:N were positively correlated with mass (Fig. 3D F; Appendix S1: Table S2). Based on ANOVAs on the residuals of the mass vs. body ratio regressions, none of the main effects (light, P supply, N:P supply ratio) were significant for any body ratio, although we did observe a significant three- way interaction for body C:N and a marginally significant interaction for body C:P (Table 1). In general, raw body C:P and N:P (i.e., uncorrected for body mass) were higher at high light than at low light and showed no clear trends with P or N:P supply (Fig. 3E, F). Note that body P content (percentage of dry mass) responded oppositely to P gain, especially to light; that is, %P decreased, while P gain increased, with increasing light (contrast Figs. 2A and 3C). Bluegill C gain/p gain increased with growth rate (Fig. 4). Thus, fish with the highest growth rates stored proportionally more C relative to P. Nutrient excretion Per capita N excretion was strongly and positively correlated with mass, pooling all treatments (Appendix S1: Fig. S2). Because mass increased with increasing light and P supply, fish in HL HP treatments were larger and therefore excreted N at higher per capita rates (Appendix S1: Fig. S2). Per capita P excretion also increased with mass, but both the variance explained and the slope were much lower than for N excretion (Appendix S1: Fig. S2). As for N, P excretion increased with increasing light and P supply. Because N excretion increased with mass more strongly than P excretion, N:P excretion was also positively correlated with mass (Appendix S1: Fig. S2). Mass- normalized N excretion rate was not affected by light or P supply, but it increased with decreasing N:P supply (marginally significant; Table 1), although the N:P effect was not evident in the LL LP treatments (Fig. 5A). Mass- normalized P excretion rate was significantly affected by P supply; in addition, the light P supply and light N:P supply interactions were marginally significant (Fig. 5B; Appendix S1: Table S1). On average, mass- normalized P excretion rates were 11

12 Fig. 3. Bluegill body elemental contents (percentage of dry mass, A C) and ratios (D F) across treatments. Symbols as in Fig. 1. Dashed line shows elemental contents and ratios of fish at the beginning of the experiment. Fig. 4. Bluegill C gain/p gain regressed against growth rate. Each point is a mesocosm mean, of all fish recovered at the end of the experiment. higher in the HP treatments than in the LP treatments (3.4 ± 1.4 vs. 2.6 ± 1.0 μg P μg 1 h 1 ), but this trend was much stronger at low light than at high light (Fig. 5B). In the HL treatments, P excretion increased with decreasing N:P supply, while in the LL treatments P excretion was much more variable, but tended to show the opposite pattern (Fig. 5B). Mass- normalized N:P excretion ratios were significantly affected by P supply (Fig. 5C; Appendix S1: Table S1). There was also a light P supply interaction, such that N:P excretion decreased with P supply at high light, but increased with P supply at low light (Fig. 5C, Table 1). The highest mean mass- normalized N:P excretion ratio was found in the LL LP treatments (17.1 ± 6.9 SD), while the lowest was in the LL HP treatment (8.9 ± 2.2; Fig. 5B). 12

13 Fig. 5. Mass- normalized excretion rates (A, B) and ratio (C) across treatments. Symbols as in Fig. 1. To determine whether energy source influenced excretion, we regressed mass- normalized excretion rates and ratio against percentage benthic contributions to diet, and found no significant relationships. Predicting excretion rates and ratios Multiple regression analysis with stepwise model selection showed that the model with bluegill mass and phytoplankton C:N best predicted per capita N excretion, although this model Fig. 6. Graphical illustration of the best models for predicting excretion rates (A, B) and N:P excretion ratio (C) based on stepwise model selection. Each point is a mesocosm mean. was not significantly better than one with only body mass (based on ΔAIC; Fig. 6A, Table 2; Appendix S1: Table S3). For P excretion, the best model included phytoplankton P and 13

14 Table 2. Results of multiple regression analyses with stepwise model selection, to predict bluegill excretion. Model rank Body mass Body C:N Phyto N Phyto C:N Peri N Peri C:N Zoop prod Bluegill P ingestion R 2 AIC ΔAIC N excretion 1 X X X X X X Model rank Body mass Body C:P Phyto P Phyto C:P Peri P Peri C:P Zoop prod Bluegill P ingestion R 2 AIC ΔAIC P excretion (Homeostasis scenario) 1 X X X X X X X X X X X X X X X P excretion (Flexibility scenario) 1 X X X X X X X X X X Model rank Body mass Body N:P Phyto N:P Peri N:P Zoop prod Bluegill P ingestion R 2 AIC ΔAIC N:P excretion (Homeostasis scenario) 1 X X X N:P excretion (Flexibility scenario) 1 X X X X X X Notes: Listed here are the best models (ΔAIC < 2); results from all models are given in Appendix S1: Table S3. Phyto = phytoplankton; peri = periphyton; Zoop prod = zooplankton production. zooplankton production, which were both positively related to P excretion (Fig. 6B, Table 1). Bluegill P ingestion was not included in the best model of P excretion, regardless of whether P ingestion was calculated with the homeostasis or flexibility scenario (Appendix S1: Table S3). Finally, for N:P excretion ratio, the best model differed depending on whether bluegill P ingestion was estimated under the homeostasis or flexibility scenarios. Under the homeostasis scenario, the best model included only periphyton N:P, which was positively related to excretion N:P. In contrast, under the flexibility scenario, the best model included both periphyton N:P and P ingestion; the latter was negatively related to N:P excretion (Fig. 6C, Table 1; Appendix S1: Table S3). Stoichiometric variation compared with other bluegill studies The intraspecific variation in bluegill body and excretion stoichiometry we observed in our experiment is comparable to the variation among other studies of this species across multiple ecosystems, even though the body size range in our study was narrower than that of others (Figs. 7 and 8). Variation in body N, P, and N:P in our study was greater than that observed across multiple lakes 14

15 and ponds (Davis and Boyd 1978, Hendrixson et al. 2007). Variation in body stoichiometry in our study was also greater than ontogenetic variation observed in bluegill in a single lake (Showalter et al. 2016), even though that study used a much greater range in bluegill body size that included larvae as small as 10 mm (Fig. 7). The variation we observed in excretion rates was also substantial compared with other studies, although the patterns differed for N and P. At a given size, we observed much more variation in N excretion rate than did either Showalter et al. (2016) or Torres and Vanni (2007) for bluegill N excretion in Acton Lake, Ohio (Fig. 8). In contrast, the variability we observed in P excretion rate, at a given fish size, was comparable to that of Showalter et al. (2016). Over a comparable range of fish sizes, the relative variation we observed in excretion N:P was similar to that of Showalter et al. (2016) although bluegill from our experiment excreted at a lower N:P than those in Acton Lake (Fig. 8). In particular, fish in our low- light, high- P treatments excreted at very low N:P compared with other fish (Fig. 8; Appendix S1: Fig. S2). Discussion Intraspecific variation in body and excretion stoichiometry By experimentally manipulating light and nutrients, we produced a wide variation in the quantity and quality of basal resources (algae), which in turn yielded tremendous intraspecific variation in the growth, body stoichiometry, and excretion stoichiometry of carnivorous fish. Indeed, the variation we observed in bluegill body and excretion stoichiometry was comparable to or exceeded the variation observed in previous studies of bluegill stoichiometry conducted across multiple lakes and ponds and using a broader range in body size (Figs. 7 and 8). Notably, we observed great intraspecific variation within a cohort of individuals that were likely of very similar age, and presumably had very little genetic variation (because fish were obtained from a hatchery population). Thus, intraspecific variation was substantial even with little or no variation in some of the key factors that can drive stoichiometric variation, such as ontogenetic stage and genetics. These findings contribute to the growing body of literature revealing the considerable intraspecific variation in the stoichiometry of animals, including terrestrial invertebrates (Hambäck et al. 2006, Hawlena and Schmitz 2010, González et al. 2011), aquatic invertebrates (Villar- Argaiz et al. 2002, Back and King 2013, Jeyasingh et al. 2014, Halvorson et al. 2015), fish (Pilati and Vanni 2007, Small and Pringle 2010, Vrede et al. 2011, El- Sabaawi et al. 2012a, b, 2016, Boros et al. 2015, Ebel et al. 2015, Showalter et al. 2016, Tobler et al. 2016), and amphibians (Milano vich and Hopton 2014, Rowland et al. 2015, Tiegs et al. 2016). Furthermore, the intraspecific variation we observed is on par with observed interspecific variation in fish, a pattern also noted in other recent studies (El- Sabaawi et al. 2012b, 2016). Growth and body nutrients We hypothesized that bluegill growth would be highest at high light and light nutrients, following trends in primary production, and that body size variation would drive several aspects of bluegill stoichiometry. Overall, this hypothesis was supported. To a large extent, amongtreatment differences in bluegill body nutrient concentrations and ratios were driven largely by the variation in bluegill growth rate, which was positively related to algal primary production. Thus, bluegill growth increased with light and P supply, and elemental concentrations and ratios varied with growth rate. These results suggest that algal quantity (primary production) was more important than algal stoichiometric quality in regulating bluegill growth and body stoichiometry. It is not surprising that growth rate responded greatly to basal resource quantity given that fish production is generally positively correlated with primary production (Downing et al. 1990). Even when algal quality travels up the food chain and mediates the efficiency at which energy is converted from primary production to carnivore production (Malzahn et al. 2007, Boersma et al. 2008), carnivore production is still positively correlated with primary production (Dickman et al. 2008). Furthermore, bioassays (similar to those of DeMott and Tessier 2002) using Daphnia grown on phytoplankton from this mesocosm experiment showed that Daphnia were limited more by phytoplankton quantity than by phytoplankton P content (A. M. Rock, unpublished data). Finally, a previous experiment, conducted in these same mesocosms, showed that juvenile bluegill production is highly correlated with 15