Environmental and Organismal Predictors of Intraspecific Variation in the Stoichiometry of a Neotropical Freshwater Fish

The elemental composition of animals, or their organismal stoichiometry, is thought to constrain their contribution to nutrient recycling, their interactions with other animals, and their demographic rates. Factors that affect organismal stoichiometry are generally poorly understood, but likely reflect elemental investments in morphological features and life history traits, acting in concert with the environmental availability of elements. We assessed the relative contribution of organismal traits and environmental variability to the stoichiometry of an insectivorous Neotropical stream fish, Rivulus hartii. We characterized the influence of body size, life history phenotype, stage of maturity, and environmental variability on organismal stoichiometry in 6 streams that differ in a broad suite of environmental variables. The elemental composition of R. hartii was variable, and overlapped with the wide range of elemental composition documented across freshwater fish taxa. Average %P composition was ∼3.2%(±0.6), average %N∼10.7%(±0.9), and average %C∼41.7%(±3.1). Streams were the strongest predictor of organismal stoichiometry, and explained up to 18% of the overall variance. This effect appeared to be largely explained by variability in quality of basal resources such as epilithon N∶P and benthic organic matter C∶N, along with variability in invertebrate standing stocks, an important food source for R. hartii. Organismal traits were weak predictors of organismal stoichiometry in this species, explaining when combined up to 7% of the overall variance in stoichiometry. Body size was significantly and positively correlated with %P, and negatively with N∶P, and C∶P, and life history phenotype was significantly correlated with %C, %P, C∶P and C∶N. Our study suggests that spatial variability in elemental availability is more strongly correlated with organismal stoichiometry than organismal traits, and suggests that the stoichiometry of carnivores may not be completely buffered from environmental variability. We discuss the relevance of these findings to ecological stoichiometry theory

Open access perpetuates differences between higher- and lower-income countries

When a group of concerned scientists initiated the Open Access (OA) movement in 2001 with the Budapest Open Access Initiative (https://www.budap estopenaccessinitiative.org), their primary objective was to facilitate free and universal access to scientific articles through the elimination of readers’ subscription fees. For authors, the increased visibility and impact associated with OA papers came at the expense of high publication costs. Twenty years later, the number of OA science journals has skyrocketed (Piwowar et al. 2018), including those focused on ecological research. Now, many traditional ecology journals (those that have not become fully OA) offer OA options as hybrid journals, and funding agencies help cover the publication fees associated with OA (referred to as article publication or processing charges), which may add up to several thousand dollars per paper

Analysis of elemental composition and stoichiometry of adult <i>Rivulus hartii</i> using a general linear model (GLM) and variance decomposition (η²).

<p>All values are F ratios, and symbols indicate degree of significance. DF is degrees of freedom used for each variable. C∶P, N∶P and C∶N were log transformed before analysis.</p>**<p>indicates P<0.01.</p>*<p>indicates P<0.05.</p>#<p>P∼0.043.</p

Spatial variability in the organismal stoichiometry of adult <i>Rivulus hartii</i>.

<p>Stream and the interaction of stream×community are the strongest predictors of elemental composition and organismal stoichiometry of <i>R. hartii</i>. Values are least squares means generated from a general linear model (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032713#pone-0032713-t003" target="_blank">Table 3</a>). They are standardized to body size = 35 mm. Community designations are RO = <i>Rivulus</i> Only, RG = <i>R. hartii</i> and Guppies, HP = High Predation sites. ARM is the Arima, ARP is Aripo, GUA is Guanapo, MAR is Marianne, QUA is Quare and TUR is the Turure.</p

A statistical assessment of how much of the stream effect in organismal stoichiometry is described by the quality of benthic resources or by the overall availability of resources.

*<p>values were log transformed prior to analysis.</p

The distribution of elemental composition in all <i>Rivulus hartii</i> individuals collected in this study compared to the distribution of elements and elemental ratios of 31 fish species compiled in a recent review [14].

<p>N/R indicates that values were not reported in the original study.</p

Map displaying locations sampled in this study.

<p>(1) Marianne, (2) Arima, (3) Guanapo, (4) Aripo, (5) Quare, and (6) Turure. All streams were located in the Northern Range Mountains of Trinidad and Tobago (location shown in inset).</p

Correlations between body size, elemental composition or organismal stoichiometry of adult <i>Rivulus hartii</i>.

<p>Correlations between body size, elemental composition or organismal stoichiometry of adult <i>Rivulus hartii</i>.</p

Correlations between resources and organismal stoichiometry of adult <i>Rivulus hartii</i>.

<p>Different aspects of the organismal stoichiometry of adult <i>R. hartii</i> are significantly correlated with the invertebrate standing stocks (a), with the stoichiometry of epilithon (b), and with the stoichiometry of benthic organic matter (c).</p

Environmental characteristics among <i>Rivulus hartii</i> sites.

<p>HP indicates high predation sites, RG indicates <i>R. hartii</i>/guppy sites, while RO indicates <i>R. hartii</i> only sites. Values are means and brackets represent standard deviations of three replicates when available (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032713#s2" target="_blank">methods</a>). N/A indicates sites where logistical difficulties hindered collection of environmental variables. Sampling of environmental variables is described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032713#pone.0032713.s001" target="_blank">Appendix S1</a>.</p