Conservation Area Prioritization
Through Artificial INtelligence

Improving biodiversity protection through artificial intelligence

Silvestro, D., Goria, S., Sterner, T., and Antonelli, A. (2022) Nature Sustainability, DOI:10.1038/s41893-022-00851-6

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A simulated natural system

CAPTAIN uses simulations based on an individual-based spatially explicit model of biodiversity to train policies through Reinforcement Learning. The simulations can include hundreds of species and millions of individuals and tracks global and local biodiversity changes resulting from natural processes of mortality, replacement and dispersal and from changes in anthropogenic pressure and climate. Simlated systems are used to train models that can be then applied to empirical data and to becnhmark the outcome of different conservation policies and targets.

Species richness

Species richness and its evolution through time. After monitoring the system for 3 iterations CAPTAIN's agent establishes protected units (outlined in black) based on a policy optimized to minimize biodiversity loss. The number of protected units is constrained by a predefined budget.

Species richness

Population density

Species rank-abundance

Phylogenetic diversity

Anthropogenic disturbance


Economic loss

Variables through time

Species A

Species B

Species C

Species D