Species Distribution Models: Ecological Explanation and Prediction Across Space and Time

TLDR

Species distribution models combine occurrence data with environmental estimates to infer ecological patterns and predict species distributions across terrestrial, freshwater, and marine landscapes, yet their realism and robustness depend on predictor selection, modeling method, scale, and extrapolation, and current practice often lacks strong ties to ecological theory. The study aims to overcome remaining challenges in SDMs by improving methods for presence‑only data, refining model selection and evaluation, incorporating biotic interactions, and quantifying model uncertainty. The authors propose methodological enhancements that target presence‑only data handling, model selection and evaluation, biotic interaction integration, and uncertainty assessment to strengthen SDM predictions.

Abstract

Species distribution models (SDMs) are numerical tools that combine observations of species occurrence or abundance with environmental estimates. They are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time. SDMs are now widely used across terrestrial, freshwater, and marine realms. Differences in methods between disciplines reflect both differences in species mobility and in “established use.” Model realism and robustness is influenced by selection of relevant predictors and modeling method, consideration of scale, how the interplay between environmental and geographic factors is handled, and the extent of extrapolation. Current linkages between SDM practice and ecological theory are often weak, hindering progress. Remaining challenges include: improvement of methods for modeling presence-only data and for model selection and evaluation; accounting for biotic interactions; and assessing model uncertainty.