Methods and uncertainties in bioclimatic envelope modelling under climate change

TLDR

Bioclimatic envelope models link species distributions to climatic variables to project future ranges under climate change. This review examines methodological uncertainties in bioclimatic modelling and highlights the need to integrate land cover, CO₂ effects, biotic interactions, and dispersal into future models. The authors conduct a comprehensive review of recent methodological advances and remaining challenges in bioclimatic envelope modelling, focusing on model choice, validation, and the treatment of non‑climatic factors. They conclude that bioclimatic envelope models are useful only when users fully understand their limitations and uncertainties.

Abstract

Potential impacts of projected climate change on biodiversity are often assessed using single-species bioclimatic ‘envelope’models. Such models are a special case of species distribution models in which the current geographical distribution of species is related to climatic variables so to enable projections of distributions under future climate change scenarios. This work reviews a number of critical methodological issues that may lead to uncertainty in predictions from bioclimatic modelling. Particular attention is paid to recent developments of bioclimatic modelling that address some of these issues as well as to the topics where more progress needs to be made. Developing and applying bioclimatic models in a informative way requires good understanding of a wide range of methodologies, including the choice of modelling technique, model validation, collinearity, autocorrelation, biased sampling of explanatory variables, scaling and impacts of non-climatic factors. A key challenge for future research is integrating factors such as land cover, direct CO 2 effects, biotic interactions and dispersal mechanisms into species-climate models. We conclude that, although bioclimatic envelope models have a number of important advantages, they need to be applied only when users of models have a thorough understanding of their limitations and uncertainties.