How to select networks of marine protected areas for multiple species with different dispersal strategies
To develop and test theory based on connectivity to identify optimal networks of marine protected areas (MPAs) that protect multiple species with a range of dispersal strategies.
The eastern North Sea in the Atlantic Ocean.
Theory of finding optimal MPA network is based on eigenvalue perturbation theory applied to population connectivity. Previous theory is here extended to the persistence of multiple species by solving a maximization problem with constraints, which identifies an optimal consensus network of MPAs. The theory is applied to two test cases within a 120,000 km2 area in the North Sea where connectivity was estimated with a biophysical model. In a realistic case, the theory is applied to the protection of rocky-reef habitats, where the biophysical model is parameterized with realistic dispersal traits for key species. Theoretical predictions of optimal networks were validated with a simple metapopulation model. Persistence of optimal consensus MPA networks is compared to randomly selected networks as well as to the existing MPA network.
Despite few overlapping MPA sites for the optimal networks based on single dispersal strategies, the consensus network for multiple dispersal strategies performed well for 3 of 4 contrasting strategies even without user-defined constraints. In the test with five realistic dispersal strategies, representing a community on threatened rocky reefs, the consensus network performed equally well compared to solutions for single species. Different dispersal strategies were also protected jointly across the MPA network (93% of sites), in contrast to simulations of the existing MPA network (2% of sites). Consensus networks based on connectivity were significantly more efficient compared to existing MPAs.
Our findings suggest that the new theoretic framework can identify a consensus MPA network that protects a whole community containing species with multiple dispersal strategies.
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