Optimizing wetland restoration and management for avian communities using a mixed integer programming approach

Conservation planning and management decisions often present trade-offs among habitats and species, generating uncertainty about the composition and configuration of habitat that will best meet management goals. The public acquisition of 5471 ha of salt ponds in San Francisco Bay for tidal-marsh restoration presents just such a challenge. Because the existing ponds support large numbers of waterbirds, restoring the entire area to tidal marsh could cause undesirable local declines for many species. To identify management strategies that simultaneously maximize abundances of marsh- and pond-associated species, we applied an integer programming approach to maximize avian abundance, comparing across two objectives, two models, and five species weightings (20 runs total). For each pond, we asked: should it be restored to a tidal marsh or kept as a managed pond, and with what salinity and depth? We used habitat relationship models as inputs to non-linear integer programs to find optimal or near-optimal solutions. We found that a simple linear objective, based on maximizing a weighted sum of standardized species’ abundance, led to homogeneous solutions (all-pond or all-marsh). Maximizing a log-linear objective yielded more heterogeneous configurations that benefit more species. Including landscape terms in the models resulted in slightly greater habitat aggregation, but generally favored pond-associated species. It also led to the placement of certain habitats near the bay’s edge. Using the log-linear objective, optimal restoration configurations ranged from 9% to 60% tidal marsh, depending on the species weighting, highlighting the importance of thoughtful a priori consideration of priority species.