Machine learning applied to global scale species distribution models
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 a persistent species. We also test the sensitivity of BART to pseudo-absence data and compare its performance with MaxEnt and Generalized Additive Models (GAMs). Results indicate that BART performs slightly better overall, particularly under pseudo-absence settings, showing higher accuracy and more stable sensitivity and specificity. These findings highlight BART as a reliable alternative for long-term, global-scale species distribution modeling in marine systems.
Authors
- Xavier Barber (ORCID: https://orcid.org/0000-0003-3079-5855)
- J.M. Bellido-Millán (ORCID: https://orcid.org/0000-0002-6887-4391)
- Antonio López‐Quílez (ORCID: https://orcid.org/0000-0002-7022-9260)
- José Carlos Báez (ORCID: https://orcid.org/0000-0003-2049-0409)
- Marta Coll (ORCID: https://orcid.org/0000-0001-6235-5868)
- David Conesa (ORCID: https://orcid.org/0000-0002-5442-5691)
- Villy Christensen (ORCID: https://orcid.org/0000-0003-0688-2633)
- Jeroen Steenbeek
- María Grazia Pennino (ORCID: https://orcid.org/0000-0002-7577-2617)
- Jorge Mestre‐Tomás (ORCID: https://orcid.org/0000-0002-8983-3417)
- Alba Fuster‐Alonso (ORCID: https://orcid.org/0000-0002-7283-291X)
Institutions
- Universitat de Miguel Hernández d'Elx (ES)
- Fisheries and Oceans Canada (CA)
- University of British Columbia (CA)
- Universitat de València (ES)
- Universidad Autónoma de Chile (CL)
- Instituto Español de Oceanografía (ES)
- Ecopath International Initiative (ES)
- Institut de Ciències del Mar (ES)
- Universitat Politècnica de València (ES)
Publication Details
- Journal
- Scientific Reports
- Published
- 2025-10-27
- DOI
- https://doi.org/10.1038/s41598-025-20797-x
- Citations
- 1
- Primary Topic
- Species Distribution and Climate Change
- Type
- article
- Field-Weighted Citation Impact
- 1.84
Funders
- Ministerio de Ciencia, Innovación y Universidades
- European Commission
- Generalitat Valenciana
- Ministerio de Ciencia e Innovación
- European Regional Development Fund
- Agencia Estatal de Investigación
- Spanish National Plan for Scientific and Technical Research and Innovation