  Bird species' migratory patterns have typically been studied through
individual observations and historical records. In recent years however, the
eBird citizen science project, which solicits observations from thousands of
bird watchers around the world, has opened the door for a data-driven approach
to understanding the large-scale geographical movements. Here, we focus on the
North American Tree Swallow (\textit{Tachycineta bicolor}) occurrence patterns
throughout the eastern United States. Migratory departure dates for this
species are widely believed by both ornithologists and casual observers to vary
substantially across years, but the reasons for this are largely unknown. In
this work, we present evidence that maximum daily temperature is a major factor
influencing Tree Swallow occurrence. Because it is generally understood that
species occurrence is a function of many complex, high-order interactions
between ecological covariates, we utilize the flexible modeling approach
offered by random forests. Making use of recent asymptotic results, we provide
formal hypothesis tests for predictive significance various covariates and also
develop and implement a permutation-based approach for formally assessing
interannual variations by treating the prediction surfaces generated by random
forests as functional data. Each of these tests suggest that maximum daily
temperature has a significant effect on migration patterns.
