"New models to infer the origin, evolution, and future dynamics of biodiversity"
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Throughout the long evolutionary history of life, species of all kingdoms have undergone staggering diversification and faced countless environmental changes and extinction events. Since the great majority of species that lived on Earth have since gone extinct, it is difficult to infer how the evolution of biodiversity unfolded over billions of years. Today, with over a million species threatened with extinction, biodiversity is facing unprecedented challenges, urging the need for conservation policies that maximize its protection and sustain its manifold contributions to people. Here we present a suite of new supervised and unsupervised models with applications in evolutionary and conservation biology. Specifically, we develop Bayesian and deep learning methods to infer the age of major clades in the tree of life, model species richness through time, and test hypotheses on past extinctions. We then develop probabilistic methods to predict the effect of forecasted extinctions on biodiversity and compare future trends with historical extinction trajectories. We show that machine learning can be used 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 introduce a novel framework for spatial conservation prioritization based on reinforcement learning that can quantify the trade-offs between the costs and benefits of area and biodiversity protection. Coupling artificial intelligence with evolutionary models holds great promise for advancing our understanding of biodiversity and its evolution across time scales, and for improving conservation action in a rapidly changing and resource-limited world.
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