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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. 

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. 

Abstract AimArtificial light at night (ALAN) and roads are known threats to nocturnally migrating birds. How associations with ALAN and roads are defined in combination for these species at the population level across the full annual cycle has not been explored. LocationWestern Hemisphere. MethodsWe estimated range‐wide exposure, predictor importance and the prevalence of positive associations with ALAN and roads at a weekly temporal resolution for 166 nocturnally migrating bird species in three orders: Passeriformes (n = 104), Anseriformes (n = 27) and Charadriiformes (n = 35). We clustered Passeriformes based on the prevalence of positive associations. ResultsPositive associations with ALAN and roads were more prevalent for Passeriformes during migration when exposure and importance were highest. Positive associations with ALAN and roads were more prevalent for Anseriformes and Charadriiformes during the breeding season when exposure was lowest. Importance was uniform for Anseriformes and highest during migration for Charadriiformes. Our cluster analysis identified three groups of Passeriformes, each having similar associations with ALAN and roads. The first occurred in eastern North America during migration where exposure, prevalence, and importance were highest. The second wintered in Mexico and Central America where exposure, prevalence and importance were highest. The third occurred throughout North America where prevalence was low, and exposure and importance were uniform. The first and second were comprised of dense habitat specialists and long‐distance migrants. The third was comprised of open habitat specialists and short distance migrants. Main conclusionsOur findings suggest ALAN and roads pose the greatest risk during migration for Passeriformes and during the breeding season for Anseriformes and Charadriiformes. Our results emphasise the close relationship between ALAN and roads, the diversity of associations dictated by taxonomy, exposure, migration strategy and habitat and the need for more informed and comprehensive mitigation strategies where ALAN and roads are treated as interconnected threats.