Machine learning applied to global scale species distribution models.

ABSTRACT: Species Distribution Models (SDMs) are widely used in ecology to analyze historical and future patterns of marine species distributions. Given the growing impact of climate change, predicting potential shifts in species ranges has become a key challenge. In this study, we apply Bayesian Additive Regression Trees (BART), a non-parametric machine learning algorithm, to estimate and forecast the global distribution of marine turtle species under different climate change scenarios. We model both individual species and their combined functional group, assess their historical and future habitat suitability, and examine the contribution of key environmental predictors. To evaluate BART's performance, we conduct a simulation study under two contrasting distributional scenarios: a cosmopolitan and

SUBMITTER: Fuster-Alonso A 

PROVIDER: S-EPMC12559289 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature