Using artificial intelligence to predict future biodiversity trends and guide conservation efforts


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 suite of new methods aimed to help guiding conservation efforts using artificial intelligence. Specifically, we develop machine learning methods to predict the effect of current and forecasted extinction risks on biodiversity and compare future trends with historical extinction trajectories. We present a deep learning approach to evaluate the extinction risk across thousands of species, complementing the Red List compiled by the International Union for Conservation of Nature (IUCN). Finally, we will introduce 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. This model, 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, the model protects significantly more species from extinction than areas selected randomly or naively and meets conservation targets more reliably than alternative software. 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.

Speaker: Professor Daniele Silvestro, Professor Alexandre Antonelli

Affiliation: University of Fribourg, Royal Botanic Gardens, Kew

Time: 4:30 PM, Tuesday, Jun. 7, 2022

Venue: 瞩目会议平台 会议 ID:1000451372 会议密码 PWD:666666 

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