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ABSTRACT AimHalting widespread biodiversity loss will require detailed information on species' trends and the habitat conditions correlated with population declines. However, constraints on conventional monitoring programs and commonplace approaches for trend estimation can make it difficult to obtain such information across species' ranges. Here, we demonstrate how recent developments in machine learning and model interpretation, combined with data sources derived from participatory science, enable landscape‐scale inferences on the habitat correlates of population trends across broad spatial extents. LocationWorldwide, with a case study in the western United States. MethodsWe used interpretable machine learning to understand the relationships between land cover and spatially explicit bird population trends. Using a case study with three passerine birds in the western U.S. and spatially explicit trends derived from eBird data, we explore the potential impacts of simulated land cover modification while evaluating potential co‐benefits among species. ResultsOur analysis revealed complex, non‐linear relationships between land cover variables and species' population trends as well as substantial interspecific variation in those relationships. Areas with the most positive impacts from a simulated land cover modification overlapped for two species, but these changes had little effect on the third species. Main ConclusionsThis framework can help conservation practitioners identify important relationships between species trends and habitat while also highlighting areas where potential modifications to the landscape could bring the biggest benefits. The analysis is transferable to hundreds of species worldwide with spatially explicit trend estimates, allowing inference across multiple species at scales that are tractable for management to combat species declines. 

ABSTRACT Assemblages in seasonal ecosystems undergo striking changes in species composition and diversity across the annual cycle. Despite a long‐standing recognition that seasonality structures biogeographic gradients in taxonomic diversity (e.g., species richness), our understanding of how seasonality structures other aspects of biodiversity (e.g., functional diversity) has lagged. Integrating seasonal species distributions with comprehensive data on key morphological traits for bird assemblages across North America, we find that seasonal turnover in functional diversity increases with the magnitude and predictability of seasonality. Furthermore, seasonal increases in bird species richness led to a denser packing of functional trait space, but functional expansion was important, especially in regions with higher seasonality. Our results suggest that the magnitude and predictability of seasonality and total productivity can explain the geography of changes in functional diversity with broader implications for understanding species redistribution, community assembly and ecosystem functioning. 

Responses of wildlife to climate change are typically quantified at the species level, but physiological evidence suggests significant intraspecific variation in thermal sensitivity given adaptation to local environments and plasticity required to adjust to seasonal environments. Spatial and temporal variation in thermal responses may carry important implications for climate change vulnerability; for instance, sensitivity to extreme weather may increase in specific regions or seasons. Here, we leverage high-resolution observational data from eBird to understand regional and seasonal variation in thermal sensitivity for 21 bird species. Across their ranges, most birds demonstrated regional and seasonal variation in both thermal peak and range, or the temperature and range of temperatures when observations peaked. Some birds demonstrated constant thermal peaks or ranges across their geographical distributions, while others varied according to local and current environmental conditions. Across species, birds typically demonstrated either geographical or seasonal adaptation to climate. Local adaptation and phenotypic plasticity are likely important but neglected aspects of organismal responses to climate change. 

The conversion of forest to agriculture is considered one of the greatest threats to avian biodiversity, yet how species respond to habitat modification throughout the annual cycle remains unknown. We examined whether forest bird associations with agricultural habitats vary throughout the year, and if species traits influence these relationships. Using data from the eBird community‐science program, we investigated associations between agriculturally‐modified land cover and the occurrence of 238 forest bird species based on three sets of avian traits: migratory strategy, dietary guild, and foraging strategy. We found that the influence of agriculturally‐modified land cover on species distributions varied widely across periods and trait groups but highlighting several broad findings. First, migratory species showed strong seasonal differences in their response to agricultural land cover while resident species did not. Second, there was a migratory strategy by season interaction; Neotropical migrants were most negatively influenced by agricultural land cover during the breeding period while short‐distance migrants were most negatively influenced during the non‐breeding period. Third, regardless of season, some dietary (e.g. insectivores) and foraging guilds (e.g. bark foragers) consistently responded more negatively to agricultural land cover than others (e.g. omnivores and ground foragers, respectively). Fourth, there were greater differences among dietary guilds in their responses to agricultural land cover during the breeding period than during the non‐breeding period, perhaps reflecting how different habitat and ecological requirements enhance the susceptibility of some guilds during reproduction. These results suggest that management efforts across the annual cycle may be oversimplified and thus ineffective when based on broad ecological generalisations that are static in space and time.