Both real-time and long-term environmental data perform well in predicting shorebird distributions in managed habitat.

Highly mobile species, such as migratory birds, respond to seasonal and interannual variability in resource availability by moving to better habitats. Despite the recognized importance of resource thresholds, species-distribution models typically rely on long-term average habitat conditions, mostly because large-extent, temporally resolved, environmental data are difficult to obtain. Recent advances in remote sensing make it possible to incorporate more frequent measurements of changing landscapes; however, there is often a cost in terms of model building and processing and the added value of such efforts is unknown. Our study tests whether incorporating real-time environmental data increases the predictive ability of distribution models, relative to using long-term average data. We developed and compared distribution models for shorebirds in California's Central Valley based on high temporal resolution (every 16 days), and 17-year long-term average surface water data. Using abundance-weighted boosted regression trees, we modeled monthly shorebird occurrence as a function of surface water availability, crop type, wetland type, road density, temperature, and bird data source. Although modeling with both real-time and long-term average data provided good fit to withheld validation data (the area under the receiver operating characteristic curve, or AUC, averaged between 0.79 and 0.89 for all taxa), there were small differences in model performance. The best models incorporated long-term average conditions and spatial pattern information for real-time flooding (e.g., perimeter-area ratio of real-time water bodies). There was not a substantial difference in the performance of real-time and long-term average data models within time periods when real-time surface water differed substantially from the long-term average (specifically during drought years 2013-2016) and in intermittently flooded months or locations. Spatial predictions resulting from the models differed most in the southern region of the study area where there is lower water availability, fewer birds, and lower sampling density. Prediction uncertainty in the southern region of the study area highlights the need for increased sampling in this area. Because both sets of data performed similarly, the choice of which data to use may depend on the management context. Real-time data may ultimately be best for guiding dynamic, adaptive conservation actions, whereas models based on long-term averages may be more helpful for guiding permanent wetland protection and restoration.

Waterbird response to variable-timing of drawdown in rice fields after winter-flooding.

Wetland loss and degradation have been extensive across the world, especially in California's Central Valley where over 90% of the natural wetlands have been converted to agricultural and urban uses. In the Central Valley today, a much smaller network of managed wetlands and flooded agricultural fields supports almost five million waterfowl and half a million shorebirds. Over 50% of waterbird habitat in the Central Valley is provided by flooded agricultural land, primarily rice (Oryza sativa). Each year non-breeding waterbird habitat decreases in the late winter as flooded agricultural fields are drained after waterfowl hunting season in late-January to prepare for the next crop. This study evaluated a practice called 'variable drawdown' that involves delaying the removal of water from rice fields by 1, 2, and 3 weeks to extend the availability of flooded habitat later into February and March. We studied waterbird response to variable drawdown in 2012 and 2013 at twenty rice farms throughout the northern half of the Central Valley. The staggered drawdown created a mosaic of water depths throughout the six-week study period. The 3-week delay in drawdown supported more dabbling ducks than earlier drawdowns in the first half of the study and more shorebirds and long-legged wading birds during the second half of the study. The timing of highest use of each drawdown treatment differed for each waterbird guild; dabbling ducks, geese and swans benefited at the beginning, then long-legged wading birds, followed by shorebirds. Despite the presence of appropriate water depths for shorebirds across the treatments during the entire study period, shorebird densities were highest near the end of the study when the 3-week-delayed drawdown was providing the majority of the habitat on the landscape. This suggests that shorebirds may have concentrated in our study fields due to decreasing availability of shallow water habitat elsewhere. The practice of variable drawdown successfully extended the availability of waterbird habitat provided by post-harvest flooded rice fields later into winter.