Richard Inger

TL;DR: This work outlines a framework that builds on recently published Bayesian isotopic mixing models and presents a new open source R package, SIAR, to allow for continued and rapid development of this core model into an all-encompassing single analysis suite for stable isotope research.

TL;DR: The ellipses are unbiased with respect to sample size, and their estimation via Bayesian inference allows robust comparison to be made among data sets comprising different sample sizes, which opens up more avenues for direct comparison of isotopic niches across communities.

TL;DR: This overview should serve as a widely accessible code of best practice for applying LMMs to complex biological problems and model structures, and in doing so improve the robustness of conclusions drawn from studies investigating ecological and evolutionary questions.

TL;DR: Stable isotope mixing models are increasingly used to quantify consumer diets, but may be misused and misinter- preted, and major challenges to their effective application are addressed.

TL;DR: Carry-over effects are likely to be far more widespread than currently indicated, and they could feasibly be responsible for a large amount of the observed variation in performance among individuals, and warrant a wealth of new research designed specifically to decompose components of variation in fitness attributes related to processes across and within seasons.