Taxonomic identity best explains variation in body nutrient stoichiometry in a diverse marine animal community.

Animal-mediated nutrient dynamics are critical processes in ecosystems. Previous research has found animal-mediated nutrient supply (excretion) to be highly predictable based on allometric scaling, but similar efforts to find universal predictive relationships for an organism's body nutrient content have been inconclusive. We use a large dataset from a diverse tropical marine community to test three frameworks for predicting body nutrient content. We show that body nutrient content does not follow allometric scaling laws and that it is not well explained by trophic status. Instead, we find strong support for taxonomic identity (particularly at the family level) as a predictor of body nutrient content, indicating that evolutionary history plays a crucial role in determining an organism's composition. We further find that nutrients are "stoichiometrically linked" (e.g., %C predicts %N), but that the direction of these relationships does not always conform to expectations, especially for invertebrates. Our findings demonstrate that taxonomic identity, not trophic status or body size, is the best baseline from which to predict organismal body nutrient content.

Introduction

Animals are important drivers of nutrient dynamics in many ecosystems[1-4]. n class="Chemical">Nutrienpan>ts frequenpan>tly limit key ecosystem processes, such as primary production[5], and animals can represenpan>t a major source of labile nutrienpan>ts through their excretion, at times alleviating nutrienpan>t limitation[6-8]. Because they are oftenpan> the dominant pool of biomass (particularly in many aquatic systems), animals can also represenpan>t an important reservoir of nutrienpan>ts stored in their tissue[9]. While previous work has shownpan> the supply of nutrienpan>ts from animals via excretion is highly predictable based on rules of allometry[10-12], no unpan>iversal factor has beenpan> founpan>d to broadly predict the amounpan>t of nutrienpan>ts stored in the body tissue of organisms[13-18].

Animal body nutrient composition, an indicator of an organism’s relative demand for nutrients, is a product of myriad evolutionary, ecological, and environmental factors[19,20]. Structural characteristics, such as n class="Chemical">phosphorus (P)-rich bonpan>es or pan> class="Chemical">nitrogen (N)-rich carapaces or spines, are often phylogenetically conserved and can contribute substantially to the nutritional makeup and demands of an animal. Ecological (e.g., diet) and environmental (e.g., temperature) factors, on the other hand, influence physiological processes and may also affect body nutrient composition[21,22]. The high degree of interdependence of ecological and evolutionary influences makes identifying specific factors that drive patterns of nutrient content in animals difficult. For example, the Growth Rate Hypothesis (GRH) proposes that body P content is highly predicted by growth rate because a higher growth rate requires higher levels of P-rich RNA[23,24]. However, this hypothesis was developed from studies of organisms with low structural demand for P, e.g., invertebrates[23], and is less supported in vertebrates. This interaction between evolutionary and ecological processes limits the predictability of body nutrient content[15,17,18,25-27], but suggests that such processes are ultimately contingent on the taxonomic identity of organisms[28,29].

Researchers have sought to identify predictable relationships of body nutrient content across taxa using three primary frameworks. First, allometric relationships are generalizable across highly diverse taxa[30], and for a wide array of ecological processes, based on predictable fractal scaling associated with metabolic rates—the Metabolic Theory of Ecology (or MTE)[31]. However, it is less clear that such relationships apply to the storage of nutrients in somatic tissues. Empirical support for body size-body nutrient content relationships has been mixed[18,27,32-34]. Davis and Boyd[32] and Gonzalez et al.[18] found positive, negative, and non-significant relationships between single elements and body size in fish and invertebrates. Sterner and George[35] and El-Sabaawi et al.[26] found mixed (negative and non-significant) support for body nutrient content and body size relationships in fish. Others, including Fagan et al.[14] and Lemoine et al.[17], found mixed and weak support across groups of invertebrates.

Second, the trophic status of organisms has been identified as a useful predictor of body nutrient content in arthropods and other invertebrates[18,25], temperate and tropical fishes[15], and across other taxa[17]. This suggests that organisms, over evolutionary time, reduced the stoichiometric imbalances between their body tissues and their prey to maximize utilization of their food resources[36]. This concept is a basic tenet of Ecological Stoichiometry Theory (EST) and suggests that animals within trophic groups are inherently limited by the quality of their food resource (plant matter is rarely as nutrient-rich as animal tissue)[19], and body nutrient composition has indeed been shown to largely reflect diets[16,26,37]. For example, herbivores and detritivores feed on resources with high C:nutrient ratios, and also tend to have higher body C:nutrient ratios than organisms at higher trophic levels[15,18,38]. This suggests an inverse relationship between trophic position and body C:nutrient ratios.

A third avenue for predicting organismal body nutrient content is using one body nutrient to predict another, i.e., examining how nutrients covary. Hendrixson et al.[15] found relationships between body content of C and P were negatively correlated across multiple species of temperate fishes. This approach has not received widespread attention (but see[18]), but does offer important insight into the degree to which elements are fundamentally linked, whereby the demand for one nutrient coincides with the demand for another[15,38-40], herein termed “stoichiometrically linked.” One expectation would be a negative relationship between elements, as found by Hendrixson et al.[15], as this suggests net conservation of elemental mass-balance, whereby increases in one nutrient results in reductions in another. Further, if trophic group is a good predictor of body stoichiometry then the relationship (i.e., the slope) by which elements are stoichiometrically linked may differ across trophic groups or other levels of classification that have large differences in body nutrient content. One example of this is the difference between vertebrates and invertebrates, whereby the P-rich internal skeleton of vertebrates should lower body C:P and n class="Chemical">N:P ratios, anpan>d thus genpan>erate relationpan>ships betweenpan> these pairs of elemenpan>ts with steeper slopes.

We take advantage of a large dataset of somatic n class="Chemical">carbon (C), n class="Chemical">nitrogen (N), phosphorus (P), and their ratios for 738 individuals of 105 invertebrate and vertebrate species (52 families, 68 genera) in a single tropical marine community to test for predictive relationships in body nutrient content. We ask three questions:

Question 1: Does the trophic level of an organism explain variation in body nutrient content, such that body C:nutrient ratios decrease with increasing trophic position? We also test an alternative hypothesis that taxonomic identity is a superior predictor of body nutrient content than trophic level.

Question 2: Does body mass improve prediction of body nutrient content beyond trophic level or taxonomic identity alone? We test the hypothesis that body nutrient content scales allometrically.

Question 3: Are body nutrient concentrations (C, n class="Chemical">N, P) stoichiometrically linpan>ked? We test the hypothesis that body C is negatively related to pan> class="Chemical">N and P. We further hypothesize that the slopes of these relationships will vary predictably according to trophic level classification.

A key strength of our analysis is that all organisms come from the same ecological community, eliminating any confounding effects of variation in environmental factors such as temperature and resource availability. This provides an alternative and complementary approach to meta-analyses or other data compilations across ecosystems and study organisms.

Methods

Individual organisms were collected using hook and line, traps, cast nets, and dip nets between 2008 and 2011, within the same large ~ 12 km2 embayment (the Bight of Old Robinson) on Abaco Island, The Bahamas (Allgeier et al.[10]). Habitat types within the embayment consisted of seagrass, mangrove, and coral complexes. This system is a continuum of habitats without clear boundaries, partly because there are no rivers and thus no brackish estuaries; as a result, patches of coral can be found within mangrove and seagrass ecosystems. n class="Chemical">Nearly all, if not all, the organpan>isms collected inpan> the embaymenpan>t could be found withinpan> anpan>y onpan>e of these sub-ecosystem types. Of the 738 inpan>dividuals (51 families, 68 genpan>era, 105 species), 195 were inpan>vertebrate anpan>d 543 were vertebrates. Inpan>vertebrate species collected were diverse inpan> taxonpan>omy (22 families, 26 genpan>era, 31 species) anpan>d morphology/physiology—inpan>cludinpan>g shrimp, jellyfish, anpan>d gastropods, amonpan>g manpan>y other groups. Vertebrates were also very diverse (30 families, 42 genpan>era, 74 species), anpan>d inpan>cluded organpan>isms with rather differenpan>t morphologies, such as moray eels (e.g., pan> class="Species">Gymnothorax funebris), and pipefish (e.g., Cosmocampus brachycephalus). For comparison with published body nutrient content data, we extracted the range of values for %C, %N, and %P from the five studies cited in Fig. 1.

Figure 1

Histograms of percent nutrient content for carbon (C), nitrogen (N), and phosphorus (P), and their ratios for invertebrates (light green) and vertebrates (purple). The bars below indicate the range of values reported in the studies of the corresponding color. For each study we indicate the number of species used in the analysis. The current study analyzed 105 species.

Histograms of percent nutrient content for n class="Chemical">carbon (C), n class="Chemical">nitrogen (N), and phosphorus (P), and their ratios for invertebrates (light green) and vertebrates (purple). The bars below indicate the range of values reported in the studies of the corresponding color. For each study we indicate the number of species used in the analysis. The current study analyzed 105 species.

Individuals were identified, weighed for wet mass, and measured to standard length. Animals were either dissected to remove stomach contents (fish) or allowed to incubate for 6–12 h to clear gut passage (invertebrates) and then frozen for transport to n class="Species">Odum School of Ecology anpan>d processed for elemenpan>tal conpan>tenpan>t (see below). Samples were lyophilized to a conpan>sistenpan>t dry weight thenpan> ground to a powder with a ball mill grinpan>der. Larger inpan>dividuals required blenpan>dinpan>g to homogenpan>eity before mill grinpan>dinpan>g. Ground samples were anpan>alyzed for %C anpan>d %pan> class="Chemical">N content and δ15N (as a proxy for trophic position; see below) with a CHN Carlo-Erba elemental analyzer (NA1500) CN Analyzer and for %P using dry oxidation-acid hydrolysis extraction followed by colorimetric analysis (Alpkem RF300). Elemental content was calculated on a dry weight basis, with nutrient ratios expressed on a molar basis.

To create a categorical variable for trophic level (herein Trophic Group, TG, used in Questions 1, 2, and 3), each organism was placed into one of eight trophic groups: detritivore, herbivore, microinvertivore, macroinvertivore, piscivores-invertivore, piscivores, zooplanktivore, and omnivore. Classifications were designated based on n class="Chemical">Newmanpan> et al.[41] anpan>d Munro[42] for fish anpan>d inpan>vertebrates, anpan>d the authors’ data anpan>d observationpan>s from this system (Allgeier anpan>d Laymanpan> unpublished). δ15pan> class="Chemical">N values were used as a continuous measure for the trophic level of an individual (Questions 2), following the rationale that the higher the δ15N value the higher on the food chain an individual is feeding[43-46]. We did not correct for potential spatio-temporal variation in isotopic baselines for three reasons: (1) the myriad basal resources within these systems makes correcting for specific isotopic baselines exceedingly difficult[43,45], (2) we are not attempting to use these data to calculate trophic position sensu stricto and instead were interested in relative trophic position among focal taxa, and (3) previous research using the same species from the same study area found clear gradients of δ15N values that accurately depict relative trophic levels in this system with relatively little inter-annual variation in isotopic values among consumers[44]. We acknowledge potential error associated with this approach, but suggest the continuous nature of δ15N renders it preferable to simply assigning species to exact trophic levels based on presumed natural history or literature sources from studies conducted in other systems. The University of Georgia’s Institutional Animal Care and Use Committee approved protocols for the capture and handling of fish (AUP # A2009-10003-0). All methods were carried out following relevant guidelines and regulations.

Statistical methods

Question 1: We used simple linear regression to test the degree to which every nutrient and nutrient ratio was explained by an individual’s trophic group. Because previous work has indicated the importance of taxonomy for predicting animal-mediated nutrient dynamics, we also tested the relative importance of each of the following predictors: vertebrate/invertebrate, class, order, family, genus, and species (e.g., % C ~ family). We, therefore, tested six competing models for each of the six response variables (C, n class="Chemical">N, P, pan> class="Chemical">N:P, C:N, and C:P). Comparisons among competing models were made using the Akaike information criterion adjusted for small sample size (AICc) and the r2 statistic[47,48]. Sample sizes were always the same; i.e., any sample that had a %N value had corresponding data for all levels of taxonomic identity. All assumptions for general linear models were met for these analyses. These and subsequent analyses were conducted using R software[49].

Question 2: To test for the importance of body size on body nutrient content and stoichiometry, we ran mixed-effects models using mass as a fixed effect with a random slope of taxonomy following[10]. For taxonomy, we used the best supported trophic group or taxonomic level from our statistical test for Question 1. Statistical tests for Question 1 and Question 2 were conducted separately rather than simultaneously with a global model (e.g.,[10]) because our tests of such global models showed frequent failure to converge. We also tested the importance of trophic group (TG) and δ15n class="Chemical">N (as a proxy for trophic positionpan>) by addinpan>g all combinpan>ationpan>s of these terms anpan>d mass as fixed effects to models with a ranpan>dom effect of taxonpan>omy (againpan> based onpan> the level determinpan>ed from Questionpan> 1). Comparisonpan>s amonpan>g competinpan>g models were made usinpan>g AICc anpan>d the r2 statistic (r2total; which inpan>cludes fixed anpan>d ranpan>dom effects, as well as r2fixed for fixed effects alonpan>e[50]). Mixed-effects models were run usinpan>g the lme4 package inpan> R[51].

Question 3: We used linear mixed-effects models to conduct three tests regarding the extent to which single elements were stoichiometrically linked. First, we tested for a significant relationship between elements (i.e., n class="Chemical">N ~ P, C ~ pan> class="Chemical">N, C ~ P). Second, to test if these relationships differed among vertebrates and invertebrates, an interaction term was included in the model (N or P × vertebrate/invertebrate). Finally, to test if these relationships were best explained at the family or trophic group level, we ran two separate models with the same fixed effects (body N or P, vertebrate/invertebrate, and their interaction), but with different random effects, either for family-level taxonomy (determined from Question 1) or for trophic group. For each class of model (with random effects for family or trophic group) we identified the best model using AICc. The best family-level or trophic group-level models were then compared using AICc. We also calculated Pearson’s correlation coefficients for the relationships between elements (C:N, C:P, N:P) for invertebrates, vertebrates, and both groups combined. All assumptions were met for mixed-effects models.

Results

We used a total of 738 individuals from 105 species, 68 genera, 51 families, and 8 functional feeding groups in our analyses. The vertebrate assemblage collected for this analysis accounted for ~ 46% of all species observed across 122 surveys of coral reef, seagrass, and mangrove locations in the n class="Chemical">Northern Antilles, but represenpan>ted > 90% of the biomass amonpan>g these communities[52]. Onpan> average, there were 2 species per family (ranpan>ge 7–1 species; 26 families had onpan>e species). Onpan> average, there were 14 inpan>dividuals per family, fifteenpan> families (29%) had fewer thanpan> three inpan>dividuals, anpan>d six (11%) had onpan>e inpan>dividual.

Variation in body nutrient content ranged substantially (%n class="Chemical">N: 2.32–14.22, %P: 0.19–8.7, %C: 11–60.1, n class="Chemical">NP: 2.9–128.2, C:N: 3.5–7.7, C:P: 11.3–562.2) relative to previous community-level studies: Small et al.[53]: %N ~ 10.1–10.6, %P ~ 3.6–5, 12 species, 4 families; Cross et al.[21]: %N ~ 6–12, %P 0–1.8, 40 species; Vanni et al.[54]: %N ~ 6.5–12, %P ~ 0.75–5, 28 species, 13 families; Hendrixson et al.[15]: %N ~ 9.3–12.2, %P ~ 2–4.1, %C ~ 40–52, 20 species and meta-analyses; Fagan et al.[25]: %N ~ 8–12, 162 species, 65 families (Fig. 1).

Question 1: Differences in body nutrient content (%n class="Chemical">N, %P, %C) were best explainpan>ed at the level of family, as was body nutrienpan>t stoichiometry for C:P, based on lowest AICc (Fig. 2 and Table 1). Differenpan>ces inpan> body nutrienpan>t stoichiometry for n class="Chemical">N:P and C:N were best explained at the genus level (Fig. 2). In each case, the best model explained a large proportion of the variation in the data: > 80% for all response variables, except for C:P for which 78% of the variation in the data was explained.

Figure 2

Raw values for C, N, and P across families delineated by invertebrates and vertebrates. Bars indicate standard deviation around the mean (center point with black dot). Family names are associated with numbers on the x-axis and the list on the right side of the figure (having a consistent color in all three plots). Families are ordered from smallest to largest %N body content for all three plots for ease of comparison across plots.

Table 1

Model comparisons of the relative ability of taxonomic levels (species, genus, family, and class), invertebrate/vertebrate category, and trophic groups to explain diffence in body nutrient content of organisms.

Tax.level	%N		%P		%C		N:P		C:P		C:N
	r2	AICc	r2	AICc	r2	AICc	r2	AICc	r2	AICc	r2	AICc
Vert/invert	0.57	− 407	0.73	− 28.73	0.49	− 519.25	0.06	− 490.36	0.00	− 741.30	0.16	90.33
Class	0.67	− 608.82	0.92	− 775.34	0.64	− 788.79	0.06	− 483.25	0.13	− 838.98	0.16	99.77
Order	0.85	− 1,185.76	0.94	− 917.17	0.81	− 1,247.84	0.10	− 493.76	0.72	− 1667.27	0.23	59.79
Family	0.89	− 1,377.44	0.96	− 1,240.00	0.85	− 1,407.79	0.79	− 1,316.31	0.78	− 1,820.06	0.79	− 648.96
Genus	0.87	− 1,229.57	0.94	− 919.33	0.82	− 1,240.62	0.92	− 1886.89	0.71	− 1572.02	0.83	− 768.50
Species	0.80	− 866.61	0.89	− 490.67	0.78	− 1,050.85	0.80	− 1,272.10	0.63	− 1,351.83	0.78	− 575.51
Trophic group	0.25	20.42	0.25	617.83	0.20	− 177.35	0.09	− 498.91	0.09	− 803.18	0.12	122.74

Bold text indicates models with the best r and AICc.

Raw values for C, n class="Chemical">N, and P across families delinpan>eated by inpan>vertebrates and vertebrates. Bars inpan>dicate standard deviation arounpan>d the mean (cenpan>ter poinpan>t with black dot). Family names are associated with numbers on the x-axis and the list on the right side of the figure (havinpan>g a consistenpan>t color inpan> all three plots). Families are ordered from smallest to largest %n class="Chemical">N body content for all three plots for ease of comparison across plots.

Question 2: Body mass was a weak predictor of body %n class="Chemical">N, %P, and n class="Chemical">N:P, but was retained in the top four models for %C, C:N, and C:P (Table 2; Fig. 3). In all cases, this parameter explained very little of the variance in the data. Only in the cases of %C and C:N did models with body mass have AICc values more than two points better than models without it (Table 2). For all response variables, the inclusion of other covariates had only a marginal effect on model performance relative to the random effects of taxonomy, which explained between 66% (C:N) and 95% (%P) of the variance in the data. Trophic group generally explained the most variance of all fixed effects, and appeared to be the most effective in the models for %P, %N, and N:P, but only in the case of %N was it retained in all models with ΔAIC < 2. The parameter δ15N had essentially no effect on model performance, e.g., it was not retained in all models with ΔAIC < 2 for N:P and C:P, but was the only model with ΔAIC < 2 for %C (Table 2).

Table 2

Statistics for mixed-effects model output and rank for body nutrient composition that include random effects of taxonomic level and ecological covariates: Mass = wet body mass (continuous; log10 transformed), δ15N (continuous), and TG = trophic group classifications (categorical).

Response	Tax. level	Model rank	Mass	δ15N	TG	r2fixed	r2total	LogLik	AICc	AAIC	Weights
N	Family	1	–	–	1	0.14	0.92	200.01	− 379.68	0	0.38
		5	–	–	–	–	0.92	190.21	− 374.38	5.31	0.03
P	Family	1	–	–	1	0.21	0.95	205.64	− 390.87	0	0.26
		5	–	–	–	–	0.95	197.54	− 389.03	1.84	0.10
C	Family	1	0.12 (0.04)	0.11 (0.04)	–	0.04	0.87	79.14	− 148.19	0	0.85
		7	–	–	–	–	0.88	64.43	− 122.83	25.37	0.00
N:P	Genus	1	0.08 (0.05)	− 0.11 (0.05)	1	0.24	0.82	− 67.92	160.44	0	0.37
		7	–	–	–	–	0.85	− 83.64	173.32	12.88	0.00
C:N	Genus	1	0.20 (0.05)	–	–	0.03	0.71	− 229.43	466.91	0	0.56
		5	–	–	–	–	0.66	− 236.04	478.11	11.20	0.00
C:P	Family	1	0.13 (0.05)	− 0.07 (0.05)	–	0.01	0.87	− 45.90	101.91	0	0.35
		5	–	–	–	–	0.87	− 50.64	107.33	5.42	0.02

r and r indicates the variation in the data explained by the fixed effects and the full model, respectively[50]. For Mass and δ15N, values are parameter estimates (with standard error). For TG, a “1” indicates inclusion in the model (estimates not shown).

Figure 3

Left panels: Body nutrient content and stoichiometry across trophic groups. Bars indicate standard deviation around the mean (center point with black dot): “detri” = detritivore; “herb” = herbivore; “macroi” = macroinvertivore; “microi” = microinvertivore; “omni” = omnivore; “p-i” = piscivore and invertivore; “pisc” = piscivore; “zoop” = zooplanktivore. Right panels: data for log(body mass) and body nutrient content and stoichiometry. Note mass data was log10 transformed in analysis. Colors indicate datasets: gray = invertebrates only, black = vertebrates only.

Statistics for mixed-effects model output and rank for body nutrient composition that include random effects of taxonomic level and ecological covariates: Mass = wet body mass (continuous; log10 transformed), δ15n class="Chemical">N (continpan>uous), and TG = trophic group classifications (categorical).

r and r indicates the variation in the data explained by the fixed effects and the full model, respectively[50]. For Mass and δ15n class="Chemical">N, values are parameter estimates (with standard error). For TG, a “1” inpan>dicates inpan>clusion inpan> the model (estimates not shownpan>).

Left panels: Body nutrient content and stoichiometry across trophic groups. Bars indicate standard deviation around the mean (center point with black dot): “detri” = detritivore; “herb” = herbivore; “macroi” = macroinvertivore; “microi” = microinvertivore; “omni” = omnivore; “p-i” = piscivore and invertivore; “pisc” = piscivore; “zoop” = zooplanktivore. Right panels: data for log(body mass) and body nutrient content and stoichiometry. n class="Chemical">Note mass data was log10 tranpan>sformed inpan> anpan>alysis. Colors inpan>dicate datasets: gray = inpan>vertebrates onpan>ly, black = vertebrates onpan>ly.

Question 3: There was a significant relationship between all elements, and in all cases the slope of this relationship was significantly different for vertebrates and invertebrates (i.e., a significant interaction term; Table 3). In all cases, models including the random effect for family outperformed those containing random effects for trophic group; however, in most cases, both of these levels of classification explained a substantial proportion of the variation in the data much more than the fixed effect (Table 3). Correlations between elements were significant when all animals were grouped, as well as when invertebrates and vertebrates were analyzed independently (Fig. 4). The direction of the n class="Chemical">N:P anpan>d C:P relationpan>ships differed betweenpan> inpan>vertebrates anpan>d vertebrates.

Table 3

Statistics for mixed-effects models of stoichiometric relationships between elements.

Relationship	Level of org	# Obs	%Nut	V/I	%Nut * V/I	r2fixed	r2total	AICc	Weight
N ~ P	Family	444 V/65 I	0.44	3.27	− 0.84	0.69	0.93	− 148.58	1.00
	Trophic	444 V/65 I	1.08	4.55	− 1.61	0.55	0.91	33.62	1.00
C ~ P	Family	434 V/65 I	0.24	0.65	− 0.42	0.63	0.88	− 1,066.31	1.00
	Trophic	434 V/65 I	0.38	0.69	− 0.56	0.71	0.87	− 934.52	1.00

All models include fixed effects for % body nutrients (% Nut; continuous), vertebrate or invertebrate (V/I; categorical), their interaction (%Nut * V/I), and random effects at either family or trophic group level of organization. Lower AICc scores indicate the better model; r and r indicate the variation in the data explained by the fixed effects and the full model, respectively. The taxonomic level of classfication that provided by the best model is in bold. Mean parameter estimates are shown for %Nut, V/I, and % Nut * V/I.

Figure 4

Correlations of stoichiometric relationships between elements. Colors indicate datasets: green = invertebrates only, purple = vertebrates only, black = full dataset. Text in each plot indicates the correlation, * = significant at alpha < 0.05. Dashed black line indicates a slope of 1 for perspective.

All models include fixed effects for % body nutrients (% n class="Chemical">Nut; continpan>uous), vertebrate or inpan>vertebrate (V/I; categorical), their inpan>teraction (%n class="Chemical">Nut * V/I), and random effects at either family or trophic group level of organization. Lower AICc scores indicate the better model; r and r indicate the variation in the data explained by the fixed effects and the full model, respectively. The taxonomic level of classfication that provided by the best model is in bold. Mean parameter estimates are shown for %Nut, V/I, and % Nut * V/I.

Discussion

Developing a predictable understanding of the factors that control how nutrients flow through ecosystems is an important challenge in ecology. We took advantage of an extensive dataset from a diverse marine community of vertebrates and invertebrates in a single ecological system that allows novel insight into the ability to explain the variation in the storage of nutrients by animals in their tissues. We found that animal body nutrient concentration was poorly explained by the basic traits of an individual organism (e.g., body size), but instead was best explained by the taxonomic rank of family. This finding suggests that our ability to predict the storage of nutrients in animals is minimal without a priori knowledge of family-level body nutrient concentrations. However, building on a somewhat neglected approach (and basic tenets of ecological stoichiometry theory), we show that correlations between single elements may provide an alternative avenue of prediction.

Previous studies have found trophic position to be a well-supported predictor of body nutrient content. This idea is rooted in EST, based on the principle that organisms should evolve to minimize the imbalances between their nutritional demands (their body nutrient content) and their diet. In theory, this principle encompasses both ecological (i.e., diet) and evolutionary drivers[36]. Our study confirms previous findings that trophic level can explain some of the variation seen in the real world[17,18,25]. n class="Chemical">Notably, the most distinpan>ct trophic group was detritivores, represenpan>ted by two inpan>dividuals of onpan>e species, pan> class="Species">Holothuria floridana, a sea cucumber. Interestingly, this species has extremely low %P (~ 0.2%; the lowest in our dataset), %C (15%, 22%), and %N (4%, 7%), numbers that set this species apart as being among of the lowest in all three body nutrients (values below 3%, 34%, 9% represent the 1st quartiles for P, C, and N). Theory would predict that organisms should evolve to minimize the stoichiometric mismatch between body nutrient content and dietary nutrient content. Because detritivores and herbivores consume resources with high C:nutrient ratios, they should also have high C:nutrient ratios, thus spurring the notion you are what you eat. Our data show that detritivores do appear to have lower body nutrient content and ratios than predators. However, an important caveat is that there is only one species with two individuals represented in the detritivore group (an invertebrate), whereas there are 22 species (with 158 individuals) of herbivores, which have slightly lower body nutrient content, but still substantially overlap with higher level consumers (Fig. 4).

In contrast, zooplanktivores (including two species, n class="Species">Atherinomorus stipes, pan> class="Species">silversides fish, and Cassiopea andromeda, a jellyfish) that feed on invertebrates of low nutrient content (relative to piscivores), had, on average, slightly higher body nutrient content than piscivores. Further, we found that the level of explanatory power of trophic group as a predictor of body nutrient content is minimal compared to differences among families (and/or genus). These findings, in conjunction with others (e.g.,[35]), support the idea that evolutionary processes, such as the development of particular structures to deter predation or those that relate to sexual selection, outweigh ecological factors in determining body nutrient content[55,56].

We found that family was overall best supported as the taxonomic level at which body nutrient content is conserved, although genus was a better predictor of some nutrient ratios. This finding is consistent with past studies testing EST[10-12] and inconsistent with others. For example, Gonzalez et al.[18] found that the level of taxonomy that best predict body nutrient content was dependent on the element in question, and Wiesenborn[57] found P in invertebrates was best predicted by order. Interestingly, two independent analyses of vertebrates and invertebrates[10,11] that found the family of the organism (in addition to body size) to be the strongest predictor of excretion rates of n class="Chemical">N anpan>d P, also found that body nutrienpan>t conpan>tenpan>t was a poor predictor of nutrienpan>t excretionpan>. Takenpan> at face value, this finpan>dinpan>g is somewhat inpan>conpan>sistenpan>t with EST. However, conpan>siderinpan>g our finpan>dinpan>g that body nutrienpan>t conpan>tenpan>t is stronpan>gly correlated with family-level taxonpan>omy suggests that the importanpan>ce of body nutrienpan>t conpan>tenpan>t for excretionpan> could have beenpan> masked whenpan> conpan>trollinpan>g for the importanpan>ce of family-level taxonpan>omy inpan> the anpan>alysis. Improvinpan>g the resolutionpan> of the excretionpan> anpan>alysis by inpan>cludinpan>g organpan>ismal phylogenpan>y may help clarify past results. Either way, the conpan>sistenpan>cy across these data-rich anpan>alyses stronpan>gly supports the idea that taxonpan>omy is a key determinpan>anpan>t of stoichiometry.

Extending our analysis to include any combination of continuous (body size and δ15n class="Chemical">N) anpan>d categorical (trophic group) predictors, while still accountinpan>g for variationpan> inpan> taxonpan>omy through the use of ranpan>dom effects, yielded models with onpan>ly marginpan>al improvemenpan>ts over the basic taxonpan>omic models. These finpan>dinpan>gs are conpan>sistenpan>t with some studies[32,35], anpan>d inpan> conpan>trast to others[17], especially whenpan> conpan>siderinpan>g the potenpan>tially divergenpan>t stoichiometry found amonpan>g conpan>specific inpan>dividuals[37]. Although we had some withinpan>-species replicationpan> inpan> our data, it was not conpan>sistenpan>t enpan>ough to provide a robust test of inpan>dividual-level variationpan> (e.g., some species had onpan>ly a sinpan>gle inpan>dividual, some had > 30). However, the high taxonpan>omic diversity enpan>compassed by our dataset, anpan>d the substanpan>tial ranpan>ge inpan> body sizes it inpan>cludes (0.04–2,597 g), lenpan>ds conpan>fidenpan>ce to our finpan>dinpan>gs: beyonpan>d taxonpan>omy, neither body size nor anpan>y obvious conpan>tinpan>uous covariate is a good predictor of body nutrienpan>t conpan>tenpan>t. This finpan>dinpan>g is importanpan>t for theory anpan>d conpan>servationpan>, as it suggests that onpan>ce data are available for a particular family, body chemistry canpan> be predicted with reasonpan>able conpan>fidenpan>ce for members of that family. However, genpan>eratinpan>g stoichiometry data is time- anpan>d cost-inpan>tenpan>sive anpan>d represenpan>ts a substanpan>tial challenpan>ge whenpan> studyinpan>g diverse communities, such as those found inpan> the tropics, whereas relationpan>ships with conpan>tinpan>uous variables (e.g., body size) require substanpan>tially fewer baselinpan>e requiremenpan>ts. pan> class="Chemical">Nonetheless, our study represents an important advance by clearly distinguishing the family level of classification as key for predicting body nutrient content.

Despite the lack of predictive power found with continuous covariates, our study showed that elemental composition was generally well predicted by other elements across the diverse community. Previously, Hendrixson et al.[15] reported strong relationships between %C and %P (negative), and a lack of significance between %C and %n class="Chemical">N, but the authors were cautionpan>ary about their finpan>dinpan>gs inpan> that they were limited to onpan>ly relatively few temperate fish species. Usinpan>g a more comprehenpan>sive dataset we found signpan>ificanpan>t relationpan>ships betweenpan> all corresponpan>dinpan>g elemenpan>ts. Because C, pan> class="Chemical">N, and P make up a relatively large proportion of the total mass of an organism, a negative relationship between elements could be expected because increasing amounts of one element should come at the expense of another (i.e., if organisms were made up exclusively of C, N, and P, then we would expect perfect relationships among these elements). We found the expected negative relationship for vertebrate C and P and vertebrate N and P, but not vertebrate C and N (Fig. 4). For invertebrates, all relationships were positive rather than negative. Most notable was the tight positive relationship between C and N for invertebrates (r2 = 0.98).

One hypothesis to explain this result is that many invertebrates have a high proportion of n class="Chemical">chitin, anpan> pan> class="Chemical">N- and C-rich polysaccharide largely associated with invertebrate exoskeletons. Thus, chitinous organisms with greater surface area to volume ratio should have higher amounts of both elements. For example, the families with the highest %N were represented by shrimps (e.g., Stenopodidae and Penaeidae, mean %N = 11.1 and 10.4, %C = 46.6 and 37.4, respectively) and lobsters (Palinuridae, mean %N = 12.9, %C = 43.2) with high surface area to volume ratio and a chitinous shell. In contrast, the jellyfish family Cassiopeidae, with no chitinous material, had among the lowest %C = 14.9 and %N = 3.07. We also found only weak support for our hypothesis that trophic group would explain variation in the slopes of these relationships, providing further support that dietary restrictions do not appear to be key drivers of the composition of body nutrients across a diverse group of organisms. Instead, the finding that taxonomic identity (at the family level) was a superior predictor highlights the key take-home message from our study: taxonomic identity is the baseline from which to predict stoichiometric relationships among elements.

Supplementary file1 (XLS 197 kb)