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

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.

Sample acidification has a predictable effect on isotopic ratios of particulate organic matter along the Chilean coast

“…As such, where researchers are interested in δ 15 N values at the base of the food web, e.g. to estimate consumer trophic position, it may be necessary to take a precautionary principle and determine δ 15 N values from non‐acidified samples of POM.…”

“…58,59 As such, where researchers are interested in δ 15 N values at the base of the food web, e.g. to estimate consumer trophic position, 60 Figure 2B). There was considerably more scatter in the δ 15 N values than in the δ 13 C values ( Figure 3B).…”

Effects of preservation methodology on stable isotope compositions of sea stars

“…The increasing use of stable isotope analysis has also led to the development of new ecological metrics and models for assessing between-and/or within-group trophic diversity and variability, [32][33][34] source partitioning, 35 or trophic levels of organisms. 36 All these approaches take into account the isotopic variability of consumers (intragroup or intergroup comparisons) and allow us to calculate diverse ecologically meaningful isotopic metrics. However, to date only one study has investigated the impact of preservation on mixing model performance.…”

Fruit of the forest ‐ larval sea lamprey Petromyzon marinus are fuelled by allochthonous resources

“…We used the R package tRophicPosition to calculate the trophic position of larval lamprey, benthic invertebrates and all other fishes in each river (Quezada‐Romegialli et al, ). This approach implements a Bayesian analogue of the two‐source estimation of trophic position described by Post ().…”