Ecological stoichiometry quantitatively predicts responses of tadpoles to a food quality gradient. Issue 8 (1st August 2015)

• Record Type: Journal Article
• Authors: Stephens, Jeffrey .P; Berven, Keith A.; Tiegs, Scott D.; Raffel, Thomas R.
• Is Part Of: Ecology. Volume 96:Issue 8(2015)
• Page Start: 2070
• Page End: 2076
• Publication Date: 2015-08-01
• Subjects: amphibian decline -- Anaxyrus americanus -- canopy -- community -- detritivore -- frog -- gradient -- leaf litter -- Lithobates sylvaticus -- spatial subsidy -- stoichiometry -- toad; Ecology -- Periodicals; Ecology -- Periodicals; Écologie -- Périodiques; Ecologie; Écologie; Écologie animale; Écologie végétale; Ecology; Periodicals; 577.05
• DOI: 10.1890/14-2439.1
• Languages: English
• ISSNs: 0012-9658

Abstract

Abstract : Ecological stoichiometry (ES) uses elemental ratios and mass balance to explain organismal growth, an important parameter in ecological systems. In this study, we tested quantitative predictions of the ES "minimal model" for the growth rates of two tadpole species (wood frogs, Lithobates sylvaticus and American toads, Anaxyrus americanus ), by manipulating light and the quality of a leaf litter mixture in a seminatural mesocosm experiment. We predicted that wood frogs, which consume leaf litter as a resource, would respond more strongly to leaf litter quality than toads, which forage on periphyton and algae. The ES minimal model, parameterized from literature values, provided strikingly accurate quantitative predictions of nonlinear wood frog growth patterns across gradients of leaf litter quality, both in this experiment and when applied to previously published data on wood frog growth responses to various leaf litter species. In contrast, toad growth was best explained by the biomass of periphyton, which was driven primarily by light availability and only indirectly influenced by litter‐derived soluble polyphenols. This study demonstrates the power of ES to predict organism growth rates, and highlights potential applications of this theory to predicting population‐ and community‐level responses to changing forest environments.