[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125413-en":3,"doc-seo-125413-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125413,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning applied to global scale species distribution models - Applied research summary","Species Distribution Models (SDMs) support ecological research by linking species presence or absence to environmental drivers and forecasting distribution changes. With climate change increasingly reshaping marine conditions, Bayesian Additive Regression Trees (BART) are applied to estimate and forecast global habitat suitability for marine turtle species under contrasting scenarios. The study models individual species and a combined functional group, evaluates key predictors, tests sensitivity to pseudo-absence data, and compares BART with MaxEnt and GAMs. Results show slightly higher overall accuracy, improved pseudo-absence robustness, and more stable sensitivity and specificity for long-term global-scale modeling.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nMachine learning applied to global scale species distribution models  \nAlba Fuster-Alonso1,2􀀍, Jorge Mestre-Tomás1,3, Jose Carlos Baez4,5, Maria Grazia Pennino1,6, Xavier Barber7, Jose María Bellido8, David Conesa2, Antonio López-Quílez2,  \nJeroen Steenbeek9, Villy Christensen9,10 & Marta Coll1,9  \nSpecies 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:  \na 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.  \nKeywords Marine turtles, Global scale, Long-term prediction, Spatial distributions, Environmental change, Machine learning, BART, Simulation  \nThe impact of climate change on marine ecosystems has been increasingly recognized as a global phenomenon, with numerous studies highlighting its effects worldwide1–4. As environmental conditions continue to change, marine species must adapt and potentially shift their distributions to areas with more suitable conditions for their survival and reproduction1,5–11. Therefore, understanding the present spatiotemporal distribution of marine species and accurately predicting their future changes is a critical challenge in the current context of global warming12, 13.  \nFor this reason, macroecological approaches have gained importance in recent decades14–21, providing broad insights into large-scale patterns of species distributions22. These global approaches are essential for evaluating climate change12, 13,23, contributing to the development of effective management strategies with global policy objectives14,24.  \nSpecies Distribution Models (SDMs), also referred to as Ecological Niche Models (ENMs) or Habitat Suitability Models (HSMs), are widely used tools for understanding species and community distributions in space and their potential shifts over time25,26. The terminology often depends on the focus of the study: SDMs emphasize spatial distributions, while ENMs highlight the ecological and niche drivers underlying those distributions27,28. In this study, we use SDMs as a general term, encompassing all approaches that estimate species’ ecological requirements to predict their distributions across space and time. However, we acknowledged the difference between ENMs and SDMs27,28. While ENMs aim to estimate a species’ fundamental ecological niche based on  \n1Present address: Renewable Marine Resources Department, Institute of Marine Sciences (ICM)-CSIC, Barcelona 08003, Spain. 2Department of Statistics and Operations Research (VaBar), Universitat deValència, Valencia, Spain.  \n3Department of Applied Statistics and Operational Research, Universitat Politècnica de València (UPV), 46022 Valencia, Spain. 4Spanish Institute of Oceanography (IEO)-CSIC, Oceanographic Center of Málaga, Fuengirola 29640, Spain. 5Ibero-American Institute for Sustainable Development (IIDS), Auto","cbCaiq70EH4s4n4V","https://ap.wps.com/l/cbCaiq70EH4s4n4V","pdf",6213064,1,17,"English","en",105,"# Abstract\n## SDM background and terminology\n## Modeling approach: BART and comparative methods\n## Simulation design and evaluation results","[{\"question\":\"What is the main goal of using BART in this study?\",\"answer\":\"To estimate and forecast the global distribution and habitat suitability of marine turtle species under different climate change scenarios using SDM frameworks.\"},{\"question\":\"How did the study evaluate BART’s performance?\",\"answer\":\"Through a simulation study under two contrasting distributional scenarios, testing sensitivity to pseudo-absence data, and comparing results with MaxEnt and Generalized Additive Models (GAMs).\"},{\"question\":\"What were the key findings when comparing BART with other SDM methods?\",\"answer\":\"BART performed slightly better overall, especially under pseudo-absence settings, delivering higher accuracy and more stable sensitivity and specificity.\"}]","Machine learning applied to global scale species distribution models - 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