tRophicPosition, an r package for the Bayesian estimation of trophic position from consumer stable isotope ratios

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Authors Claudio Quezada-Romegialli, Andrew L. Jackson, Brian Hayden, Kimmo K. Kahilainen, Christelle Lopes, Chris Harrod
Journal/Conference Name Methods in Ecology and Evolution
Paper Category , ,
Paper Abstract Stable isotope analysis provides a powerful tool to identify the energy sources which fuel consumers, to understand trophic interactions and to infer consumer trophic position (TP), an important concept that describes the ecological role of consumers in food webs. However, current methods for estimating TP using stable isotopes are limited and do not fulfil the complete potential of the isotopic approach. For instance, researchers typically use point estimates for key parameters including trophic discrimination factors and isotopic baselines, and do not explicitly include variance associated with these parameters when calculating TP. We present “tRophicPosition,” an r package incorporating a Bayesian model for the calculation of consumer TP at the population level using stable isotopes, with one or two baselines. It combines Markov Chain Monte Carlo simulations through JAGS and statistical and graphical analyses using R. We model consumer and baseline observations using relevant statistical distributions, allowing them to be treated as random variables. The calculation of TP—a random parameter—for one baseline follows standard equations linking 15N enrichment per trophic level and the trophic position of the baseline (e.g. a primary producer or primary consumer). In the case of two baselines, a simple mixing model incorporating δ13C allows for the differentiation between two distinct sources of nitrogen, thus including heterogeneity derived from alternatives sources of δ15N. Methods currently implemented in “tRophicPosition” include loading, plotting and summarizing stable isotope data either from multiple sites and/or communities or a local assemblage; loading trophic discrimination factors from an internal database or generating them; defining and initializing a Bayesian model of TP; sampling posterior parameters; analysing, comparing and plotting posterior estimates of TP and other parameters; and calculating a parametric (non-Bayesian) TP estimate. Additionally, full documentation including examples, multiple vignettes and code are available for download.
Date of publication 2018
Code Programming Language R

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