Integration of distance, direction and habitat into a predictive migratory movement model for blue-winged teal (Anas discors)

Historically, the migration of birds has been poorly understood in comparison to other life stages during the annual cycle. The goal of our research is to present a novel approach to predict the migratory movement of birds. Using a blue-winged teal case study, our process incorporates not only constraints on habitat (temperature, precipitation, elevation, and depth to water table), but also approximates the likely bearing and distance traveled from a starting location. The method allows for movement predictions to be made from unsampled areas across large spatial scales. We used USGS’ Bird Banding Laboratory database as the source of banding and recovery locations. We used recovery locations from banding sites with multiple within-30-day recoveries were used to build core maximum entropy models. Because the core models encompass information regarding likely habitat, distance, and bearing, we used core models to project (or forecast) probability of movement from starting locations that lacked sufficient data for independent predictions. The final model for an unsampled area was based on an inverse-distance weighted averaged prediction from the three nearest core models. To illustrate this approach, three unsampled locations were selected to probabilistically predict where migratory blue-wing teals would stopover. These locations, despite having little or none data, are assumed to have populations. For the blue-winged teal case study, 104 suitable locations were identified to generate core models. These locations ranged from 20 to 228 within-30-day recoveries, and all core models had AUC scores greater than 0.80. We can infer based on model performance assessment, that our novel approach to predicting migratory movement is well-grounded and provides a reasonable approximation of migratory movement.

► We introduce a model of the probability of migration waterfowl stopover locations. ► It examines habitat constraints, including a dynamic measure of wetlands. ► It also incorporates direction and bearing traveled from specific starting point. ► Prediction can be made for location lacking sampling data yet likely has population.