Quantifying shorebird habitat in managed wetlands by modeling shallow water depth dynamics.

Over 50% of Western Hemisphere shorebird species are in decline due to ongoing habitat loss and degradation. In some regions of high wetland loss, shorebirds are heavily reliant on a core network of remaining human-managed wetlands during migration journeys in the spring and fall. While most refuges have been designed and managed to match the habitat needs of waterfowl, shorebirds typically require much shallower water (<10 cm deep). Traditional static habitat modeling approaches at relatively coarse spatial and temporal resolution are insufficient to capture dynamic changes within this narrow water depth range. Our objectives were to (1) develop a method to quantify shallow water habitat distributions in inland non-tidal wetlands, and (2) to assess how water management practices affect the amount of shorebird habitat in Sacramento National Wildlife Refuge Complex. We produced water depth distributions and modeled optimal habitat (<10 cm deep) within 23 managed wetlands using high-resolution topography and fixed-point water depth records. We also demonstrated that habitat availability, specifically suitable water depth ranges, can be tracked from satellite imagery and high-resolution topography. We found that wetlands with lower topographic roughness may have a higher potential to provide shorebird habitat and that strategically reducing water levels could increase habitat extent. Over 50% of the wetlands measured provided optimal habitat across <10% of their area at the peak of migration in early April, and most provided a brief duration of shallow water habitat. Reducing water volumes could increase the proportion of optimal habitat by 1-1,678% (mean = 294%) compared to actual volumes measured at peak spring migration in 2016. For wetlands with a high habitat potential, beginning wetland drawdown earlier and extending drawdown time could dramatically improve habitat conditions at the peak of shorebird migration. Our approach can be adapted to track dynamic hydrologic changes at broader spatial scales as additional high-resolution topographic (e.g., lidar, drone imagery photogrammetry) and optical remote sensing data (e.g., planet imagery, drone photography) become available.