气候变化领域集成服务门户(CCPortal): Improving biodiversity protection through artificial intelligence

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DOI	10.1038/s41893-022-00851-6
	Improving biodiversity protection through artificial intelligence
	Silvestro, Daniele; Goria, Stefano; Sterner, Thomas; Antonelli, Alexandre
发表日期	2022
ISSN	2398-9629
起始页码	415
结束页码	424
卷号	5 期号:5 页码:10
英文摘要	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.
学科领域	Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies
语种	英语
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
WOS记录号	WOS:000780305100003
来源期刊	NATURE SUSTAINABILITY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/272890
作者单位	University of Fribourg; Swiss Institute of Bioinformatics; University of Gothenburg; University of Gothenburg; University of Oxford; Royal Botanic Gardens, Kew
推荐引用方式 GB/T 7714	Silvestro, Daniele,Goria, Stefano,Sterner, Thomas,et al. Improving biodiversity protection through artificial intelligence[J],2022,5(5):10.
APA	Silvestro, Daniele,Goria, Stefano,Sterner, Thomas,&Antonelli, Alexandre.(2022).Improving biodiversity protection through artificial intelligence.NATURE SUSTAINABILITY,5(5),10.
MLA	Silvestro, Daniele,et al."Improving biodiversity protection through artificial intelligence".NATURE SUSTAINABILITY 5.5(2022):10.