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

Abstract

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.