Background/Question/Methods   The distributions of animal populations are not static. During regular migratory movements species exploit different habitats. This spatiotemporal variation needs to be accounted for in any modeling of species' distributions and is essential for developing conservation strategies for widespread species, and especially for migratory species. Attempts to design conservation landscapes across large regions or entire species' ranges based on models of distributions in a single season may not fully reflect the limiting factors that are driving population declines. For this talk we employ a novel species distribution modeling methodology to identify and explore dynamic patterns of species occurrence from broad-scale survey data. The SpatioTemporal Exploratory Model (STEM) is a multi-scale, semiparametric model that differentiates between local and global-scale spatiotemporal structure. A user specified species distribution model accounts for spatial and temporal patterning at the local level. These local patterns are then allowed to “scale up” via ensemble averaging to larger scales. Ensemble averaging adds essential spatiotemporal structure to existing techniques for developing species distribution models through a simple parametric structure without requiring a detailed understanding of the underlying dynamic processes. This makes STEMs especially well suited for exploring distributional dynamics arising from a variety of processes.

Results/Conclusions   Using data from eBird (http://www.ebird.org), an online citizen science bird-monitoring project and associated environmental descriptions from U.S.-wide GIS layers, we demonstrate how STEMs can be used to study broad-scale movements of bird populations both between and within years. We compare and contrast the seasonal migrations of several common bird species occurring the continental U.S., to demonstrate that our data are capable of resolving the changing distributions of birds through their migrations. Then we demonstrate how irregular year-to-year variation in distributions can be modeled for irruptive winter migrant species. Finally, we present an analysis that illustrates how seasonal variation in habitat association can be identified with our data and analysis methods. These tools provide valuable information for generating hypotheses and making inference about the processes driving dynamic distributional patterns.