Improving biodiversity protection through artificial intelligence.
Name
s41893-022-00851-6.pdf
Description
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Size
2.02 MB
Format
Adobe PDF
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Resource type
Journal article
Date published
March 24, 2022
Abstract
Over a million species face extinction, highlighting the urgent need for conservation policies that maximize the protection of biodiversity to sustain its manifold contributions to people’s lives. 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, conservation area prioritization through artificial intelligence (CAPTAIN), 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 significantly more species from extinction than areas selected randomly or naively (such as based on species richness). CAPTAIN achieves substantially better solutions with empirical data than alternative software, meeting conservation targets more reliably and generating more interpretable prioritization maps. Regular biodiversity monitoring, even with a degree of inaccuracy characteristic of citizen science surveys, further improves biodiversity outcomes. Artificial intelligence holds great promise for improving the conservation and sustainable use of biological and ecosystem values in a rapidly changing and resource-limited world.
Funder
| Funder name | Awards |
Stiftelsen för Strategisk Forskning, Sweden | FFL15-0196 |
Vetenskapsrådet, Sweden | VR 2019-05191 - VR: 2019-04739 |
Royal Botanic Gardens, Kew, United Kingdom | |
Sveriges Regering, Sweden | Strategic Research Area Biodiversity and Ecosystem Services in a Changing Climate, BECC |
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Switzerland | PCEFP3_187012 |
Journal title
Nature Sustainability
Publisher
Springer Science and Business Media LLC
Place of publication
Berlin/Heidelberg, Germany
eISSN
2398-9629
Date accepted
January 17, 2022
Official URL
Rights statement
In Copyright