Automated workflow for incorporation and evaluation of data uncertainty in ecological networks with autoLIMR

Abstract

Ecosystems are often modelled as food webs or ecological networks, which map energy flows between species or functional groups. While there are multiple sources of uncertainty in ecological network models, a fundamental difficulty is quantifying the uncertainty of flow magnitudes, even if derived from field data, limiting the degree to which the model captures the inherent uncertainty of the empirical system. Popular techniques of incorporating data uncertainties of flow magnitudes in ecological network models are calculating multiple plausible representations of the food web within data constraints using linear inverse modelling and Markov Chain Monte Carlo (LIM-MCMC) methods. Hurdles in this process include coding input files for data-intense networks and evaluating the quality of the multiple plausible representations. To address these, we present the R package autoLIMR: an open-source automated workflow using LIM-MCMC to incorporate and evaluate data uncertainty in ecological network models. The main objectives are to improve the accessibility of LIM-MCMC, reduce errors, and provide a consistent framework to enhance model output reporting and reproducibility. We display autoLIMR on a small food web time series example and provide guidelines to facilitate the uptake of this robust methodology in the broader ecological systems sciences.