Colin Sweeney

and 5 more

Understanding how 3D habitat structure drives biodiversity patterns is key to predicting how habitat alteration and loss will affect species and community-level patterns in the future. To date, few studies have contrasted the effects of three-dimensional (3D) habitat composition with those of 3D habitat configuration on biodiversity, with existing investigations often limited to measures of taxonomic diversity (i.e., species richness). Here, we examined the influence of Light Detecting and Ranging (LiDAR)-derived 3D habitat structure–both its composition and configuration–on multiple facets of bird diversity. Specifically, we used data from the National Ecological Observatory Network (NEON) to test the associations between eleven measures of 3D habitat structure and avian species richness, functional and trait diversity, and phylogenetic diversity. We found that 3D habitat structure was the most consistent predictor of avian functional and trait diversity, with little to no effect on species richness or phylogenetic diversity. Functional diversity and individual trait characteristics were strongly associated with both 3D habitat composition and configuration, but the magnitude and the direction of the effects varied across the canopy, subcanopy, midstory, and understory vertical strata. Our findings suggest that 3D habitat structure influences avian diversity through its effects on traits. By examining the effects of multiple aspects of habitat structure on multiple facets of avian diversity, we provide a broader framework for future investigations on habitat structure.

Stephen Murphy

and 1 more

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.