Improving biodiversity protection through artificial intelligence

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. Artificial intelligence methods can help biodiversity conservation planning in a rapidly evolving world. A framework based on reinforcement learning quantifies the trade-off between the costs and benefits of area and biodiversity protection and achieves better solutions with empirical data than alternative methods.

Automated summary

The study proposes a framework for prioritizing conservation areas based on artificial intelligence, which can protect species from extinction more effectively and generate more interpretable prioritization maps. Artificial intelligence methods hold great promise for biodiversity conservation planning.