Wintering birds serve as vital climate sentinels, yet they are often underrepresented in comprehensive surveys and overlooked in studies of avian diversity change. Here, we provide a continental-scale characterization of change in multiple facets of wintering avifauna and examine the effects of climate change on these dynamics. We reveal a strong functional reorganization of wintering bird communities marked by a distinct east-west gradient in functional diversity change, along with a superimposed north-south gradient in trait composition change. Assemblages in the eastern US saw an expansion of the functional space and increases in functional originality, evenness, and divergence, while the western US saw contractions of the functional space. Shifts in functional diversity were underlined by significant reshuffling in trait composition, particularly pronounced in the northern US. Finally, we find strong contributions of climate change to this functional reorganization, underscoring the importance of wintering birds in tracking climate change impacts on biodiversity.
Understanding population change across long time scales and at fine spatiotemporal resolutions is important for confronting a broad suite of conservation challenges. However, this task is hampered by a lack of quality long-term census data for multiple species collected across large geographic regions. Here, we used century-long (1919-2018) data from the Audubon Christmas Bird Count (CBC) survey to assess population changes in over 300 avian species in North America and evaluate their temporal non-stationarity. To estimate population sizes across the entire century, we employed a Bayesian hierarchical model that accounts for species detection probabilities, variable sampling effort, and missing data. We evaluated population trends using generalized additive models (GAMs) and assessed temporal non-stationarity in the rate of population change by extracting the first derivatives from the fitted GAM functions. We then summarized the population dynamics across species, space, and time using a non-parametric clustering algorithm that categorized individual population trends into four distinct trend clusters. We found that species varied widely in their population trajectories, with over 90% of species showing a considerable degree of spatial and/or temporal non-stationarity, and many showing strong shifts in the direction and magnitude of population trends throughout the past century. Species were roughly equally distributed across the four clusters of population trajectories, though grassland, forest, and desert specialists more commonly showed declining trends. Interestingly, for many species, region-wide population trends often differed from those observed at individual sites, suggesting that conservation decisions need to be tailored to fine spatial scales. Together, our results highlight the importance of considering spatial and temporal non-stationarity when assessing long-term population changes. More generally, we demonstrate the promise of novel statistical techniques for improving the utility and extending the temporal scope of existing citizen science datasets.
Each year, seasonal bird migration leads to an immense redistribution of species occurrence and abundances, with pervasive, though unclear, consequences for patterns of multi-faceted avian diversity. Here, we uncover stark disparities in spatiotemporal variation between avian taxonomic and functional diversity across the continental US. In the eastern US, the temporal patterns of taxonomic and functional diversity are diametrically opposed, with functional richness highest in winter despite seasonal loss of species, and the remaining most abundant species amassed in a few regions of the functional space that likely reflect the resources available in winter. In contrast, in the western US, both species and functional richness are high during the breeding season, and species' abundances are regularly distributed and widely spread across the functional space. We anticipate that the uncovered complexity of spatiotemporal associations among avian diversity measures will be the catalyst for adopting an explicitly temporal framework for multi-faceted biodiversity analysis.