Fig. 4: Summary statistics for one simulation optimized to reduce species loss and informed through full recurrent monitoring. | Nature Sustainability

Fig. 4: Summary statistics for one simulation optimized to reduce species loss and informed through full recurrent monitoring.

From: Improving biodiversity protection through artificial intelligence

Fig. 4: Summary statistics for one simulation optimized to reduce species loss and informed through full recurrent monitoring.

a, Living (or surviving) and locally extinct species after a simulation of 30 time steps with increasing disturbance and climate change. The x and y axes show the initial range and population sizes of the species (log10 transformed), respectively. The size of the circles is proportional to the resilience of each species to anthropogenic disturbance, with smaller circles representing more sensitive species. b, Cumulative number of species encompassed in the ten protected units (5 × 5 cells) selected on the basis of a policy optimized to minimize species loss. The grey density plot shows the expected distribution from 10,000 random draws, and the purple shaded area shows the expected distribution when protected units are selected ‘naively’ (here, randomly chosen from among the top 20 most diverse units). The dashed red line indicates the number of species included in the units selected by the optimized CAPTAIN policy, which is higher than in all the random draws. The optimized policy learned to maximize the total number of species included in protected units, thus accounting for their complementarity. Note that fewer species survived (421) in this simulation compared with how many were included in protected areas (447). This discrepancy is due to the effect of climate change, in which area protection does not play a role47 (see also www.captain-project.net). c, Species richness across the 100 protection units included in the area (blue), ten of which were selected to be protected (orange). The plot shows that the protection policy does not exclusively target units with the highest diversity.

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