cosimmr: an R package for fast fitting of Stable Isotope Mixing Models with covariates

TL;DR

cosimmr is an R package that significantly accelerates the estimation of animal diet proportions in Stable Isotope Mixing Models by using Variational Bayes instead of traditional MCMC, enabling more efficient ecological analysis.

Contribution

The paper introduces cosimmr, a novel R package that incorporates covariates into SIMMs and employs Fixed Form Variational Bayes for faster computation without losing accuracy.

Findings

Up to tenfold speed improvement over MCMC methods.

Produces comparable results to state-of-the-art models in less time.

User-friendly design based on existing frameworks.

Abstract

The study of animal diets and the proportional contribution that different foods make to their diets is an important task in ecology. Stable Isotope Mixing Models (SIMMs) are an important tool for studying an animal's diet and understanding how the animal interacts with its environment. We present cosimmr, a new R package designed to include covariates when estimating diet proportions in SIMMs, with simple functions to produce plots and summary statistics. The inclusion of covariates allows for users to perform a more in-depth analysis of their system and to gain new insights into the diets of the organisms being studied. A common problem with the previous generation of SIMMs is that they are very slow to produce a posterior distribution of dietary estimates, especially for more complex model structures, such as when covariates are included. The widely-used Markov chain Monte Carlo (MCMC) algorithm used by many traditional SIMMs often requires a very large number of iterations to reach convergence. In contrast, cosimmr uses Fixed Form Variational Bayes (FFVB), which we demonstrate gives up to an order of magnitude speed improvement with no discernible loss of accuracy. We provide a full mathematical description of the model, which includes corrections for trophic discrimination and concentration dependence, and evaluate its performance against the state of the art MixSIAR model. Whilst MCMC is guaranteed to converge to the posterior distribution in the long term, FFVB converges to an approximation of the posterior distribution, which may lead to sub-optimal performance. However we show that the package produces equivalent results in a fraction of the time for all the examples on which we test. The package is designed to be user-friendly and is based on the existing simmr framework.