{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Silvestro D"],"funding":["Swiss National Science Foundation"],"pagination":["415-424"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7612764"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["5(5)"],"pubmed_abstract":["Over a million species face extinction, urging the need for conservation policies that maximize the protection of biodiversity to sustain its manifold contributions to people. Here we present a novel framework for spatial conservation prioritization based on reinforcement learning that consistently outperforms available state-of-the-art software using simulated and empirical data. Our methodology, CAPTAIN (Conservation Area Prioritization Through Artificial INtelligence), quantifies the trade-off between the costs and benefits of area and biodiversity protection, allowing the exploration of multiple biodiversity metrics. Under a limited budget, our model protects substantially more species from extinction than areas selected randomly or naively (such as based on species richness). CAPTAIN "],"journal":["Nature sustainability"],"pubmed_title":["Improving biodiversity protection through artificial intelligence."],"pmcid":["PMC7612764"],"funding_grant_id":["187012"],"pubmed_authors":["Sterner T","Antonelli A","Silvestro D","Goria S"],"additional_accession":[]},"is_claimable":false,"name":"Improving biodiversity protection through artificial intelligence.","description":"Over a million species face extinction, urging the need for conservation policies that maximize the protection of biodiversity to sustain its manifold contributions to people. Here we present a novel framework for spatial conservation prioritization based on reinforcement learning that consistently outperforms available state-of-the-art software using simulated and empirical data. Our methodology, CAPTAIN (Conservation Area Prioritization Through Artificial INtelligence), quantifies the trade-off between the costs and benefits of area and biodiversity protection, allowing the exploration of multiple biodiversity metrics. Under a limited budget, our model protects substantially more species from extinction than areas selected randomly or naively (such as based on species richness). CAPTAIN ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 May","modification":"2025-04-21T21:24:13.102Z","creation":"2025-02-19T04:04:54.576Z"},"accession":"S-EPMC7612764","cross_references":{"pubmed":["35614933"],"doi":["10.1038/s41893-022-00851-6"]}}