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Abstract MotivationTrait‐based studies remain limited by the quality and scope of the underlying trait data available. Most of the existing trait databases treat species traits as fixed across time, with any potential temporal variation in the measured traits being unavailable. This is despite the fact that many species are well known to show plasticity in their trait characteristics over the course of the year. This data paper describes a compilation of species‐specific dietary preferences and their known intra‐annual variation for over 10,000 of the world's extant bird species (SAviTraits 1.0). Information on dietary preferences was obtained from the Cornell Lab of Ornithology Birds of the World (BOW) online database. Textual descriptions of species' dietary preferences were translated into semi‐quantitative information denoting the proportion of dietary categories utilized by each species. Temporal variation in dietary attributes was captured at a monthly temporal resolution. We describe the methods for data discovery and translation and present tools for summarizing the annual variability of avian dietary preferences. Altogether, we were able to document a seasonal variability in dietary attributes for a total of 1031 species (ca. 10%). For the remaining species, the dietary attributes were either temporally stationary or the information on temporal variability of the diet was not available. Main Types of Variable ContainedTemporally‐varying dietary traits for birds. Spatial Location and GrainN/A. Time Period and GrainVariation in diet was captured at a monthly temporal resolution. Major Taxa and Level of MeasurementBirds, species level. Software Format.csv/.rds 

Abstract Understanding population changes 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, although 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. 

Abstract AimUnderstanding how ecological communities are assembled remains a grand challenge in ecology with direct implications for charting the future of biodiversity. Trait‐based methods have emerged as the leading approach for quantifying functional community structure (convergence, divergence) but their potential for inferring assembly processes rests on accurately measuring functional dissimilarity among community members. Here, we argue that trait resolution (from finest‐resolution continuous measurements to coarsest‐resolution binary categories) remains a critically overlooked methodological variable, even though categorical classification is known to mask functional variability and inflate functional redundancy among species or individuals. InnovationWe present the first detailed predictions of trait resolution biases and demonstrate, with simulations, how the distortion of signal strength by increasingly coarse‐resolution traits can fundamentally alter functional structure patterns and the interpretation of causative ecological processes (e.g. abiotic filters, biotic interactions). We show that coarser trait data impart different impacts on the signals of divergence and convergence, implying that the role of biotic interactions may be underestimated when using coarser traits. Furthermore, in some systems, coarser traits may overestimate the strength of trait convergence, leading to erroneous support for abiotic processes as the primary drivers of community assembly or change. Main conclusionsInferences of assembly processes must account for trait resolution to ensure robust conclusions, especially for broad‐scale studies of comparative community assembly and biodiversity change. Despite recent improvements in the collection and availability of trait data, great disparities continue to exist among taxa in the number and availability of continuous traits, which are more difficult to acquire for large numbers of species than coarse categorical assignments. Based on our simulations, we urge the consideration of trait resolution in the design and interpretation of community assembly studies and suggest a suite of practical solutions to address the pitfalls of trait resolution biases. 

Abstract Understanding patterns and drivers of species distribution and abundance, and thus biodiversity, is a core goal of ecology. Despite advances in recent decades, research into these patterns and processes is currently limited by a lack of standardized, high‐quality, empirical data that span large spatial scales and long time periods. The NEON fills this gap by providing freely available observational data that are generated during robust and consistent organismal sampling of several sentinel taxonomic groups within 81 sites distributed across the United States and will be collected for at least 30 years. The breadth and scope of these data provide a unique resource for advancing biodiversity research. To maximize the potential of this opportunity, however, it is critical that NEON data be maximally accessible and easily integrated into investigators' workflows and analyses. To facilitate its use for biodiversity research and synthesis, we created a workflow to process and format NEON organismal data into the ecocomDP (ecological community data design pattern) format that were available through the ecocomDP R package; we then provided the standardized data as an R data package (neonDivData). We briefly summarize sampling designs and data wrangling decisions for the major taxonomic groups included in this effort. Our workflows are open‐source so the biodiversity community may: add additional taxonomic groups; modify the workflow to produce datasets appropriate for their own analytical needs; and regularly update the data packages as more observations become available. Finally, we provide two simple examples of how the standardized data may be used for biodiversity research. By providing a standardized data package, we hope to enhance the utility of NEON organismal data in advancing biodiversity research and encourage the use of the harmonized ecocomDP data design pattern for community ecology data from other ecological observatory networks.