| Summary: | Implementation of adaptation actions to protect biodiversity is limited by uncertainty about the future. One reason for this is the fear of making the wrong decisions caused by the myriad future scenarios presented to decision-makers. We propose an adaptive management (AM) method for optimally managing a population under uncertain and changing habitat conditions. Our approach incorporates multiple future scenarios and continually learns the best management strategy from observations, even as conditions change. We demonstrate the performance of our AM approach by applying it to the spatial management of migratory shorebird habitats on the East Asian–Australasian flyway, predicted to be severely impacted by future sea-level rise. By accounting for non-stationary dynamics, our solution protects 25 000 more birds per year than the current best stationary approach. Our approach can be applied to many ecological systems that require efficient adaptation strategies for an uncertain future. gompertz_model_parametersParameters used in the Gompertz population models for each species. See supporting information S2 for further details of the model.POMDP_input_files.tarSymbolic Perseus POMDP input files for each species. See readme file for additional information.sea_level_rise_by_modelEstimated extent of sea level rise for each region and scenario with and without compensatory loss. For further details, see Iwamura, T (2012) Spatial conservation prioritisation under global threats: University of Queensland.network_capacitiesNode capacity (number of birds) for each species network model.
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