Predictive modeling of migratory waterfowl

Several factors have contributed to impeding the progress of migratory waterfowl spatial modeling, such as (1) waterfowl’s reliance on wetlands, (2) lack of understanding about shifts in distributions through time, and (3) large-scale seasonal migration. This doctoral dissertation provides an array of tools to address each of these concerns in order to better understand and conserve this group of species. The second chapter of this dissertation addresses issues of modeling species dependent on wetlands, a dynamic and often ephemeral habitat type. Correlation models of the relationships between climatic variables and species occurrence will not capture the full habitat constraints of waterfowl. This study introduces a novel data source that explicitly models the depth to water table, which is a simulated long-term measure of the point where climate and geological/topographic water fluxes balance. The inclusion of the depth to water table data contributes significantly to the ability to predict species probability of occurrence. Furthermore, this data source provides advantages over traditional proxies for wetland habitat, because it is not a static measure of wetland location, and is not biased by sampling method.
Utilizing the long-term banding bird data again, the third chapter examines the behavior of waterfowl niche selection through time. By using the methods developed in chapter two, probability of occurrence models for the 1950s and the 1990s were developed. It was then possible to detect movements in geographic and environmental space, and how movements in these two spaces are related. This type of analysis provides insight into how different bird species might respond to environment changes and potentially improve climate change forecasts. The final chapter presents a new method for predicting the migratory movement of waterfowl. The method incorporates not only the environmental constraints of stopover habitat, but also includes likely distance and bearing traveled from a source point. This approach uses the USGS’ banding bird database; more specifically, it relies on banding locations, which have multiple recoveries within short time periods. Models made from these banding locations create a framework of migration movement, and allow for predictions to be made from locations where no banding/recovery data are available.