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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 Changes in leaf phenology from warming spring and autumn temperatures have lengthened the temperate zone growing “green” season and breeding window for migratory birds in North America. However, the fitness benefits of an extended breeding season will depend, in part, on whether species have sufficient dietary flexibility to accommodate seasonal changes in prey availability. We used fecal DNA metabarcoding to test the hypothesis that seasonal changes in the diets of the insectivorous, migratory black‐throated blue warbler (Setophaga caerulescens) track changes in the availability of arthropod prey at the Hubbard Brook Experimental Forest, New Hampshire, USA. We examined changes across the breeding season and along an elevation gradient encompassing a 2‐week difference in green season length. From 98 fecal samples, we identified 395 taxa from 17 arthropod orders; 242 were identified to species, withCecrita guttivitta(saddled prominent moth),Theridion frondeum(eastern long‐legged cobweaver), andPhilodromus rufus(white‐striped running crab spider) occurring at the highest frequency. We found significant differences in diet composition between survey periods and weak differences among elevation zones. Variance in diet composition was highest late in the season, and diet richness and diversity were highest early in the season. Diet composition was associated with changes in prey availability surveyed over the green season. However, several taxa occurred in diets more or less than expected relative to their frequency of occurrence from survey data, suggesting that prey selection or avoidance sometimes accompanies opportunistic foraging. This study demonstrates that black‐throated blue warblers exhibit diet flexibility and track seasonal changes in prey availability, which has implications for migratory bird responses to climate‐induced changes in insect communities with longer green seasons. 

Abstract Population size is a key metric for management and policy decisions, yet wildlife monitoring programmes are often limited by the spatial and temporal scope of surveys. In these cases, citizen science data may provide complementary information at higher resolution and greater extent.We present a case study demonstrating how data from the eBird citizen science programme can be combined with regional monitoring efforts by the US Fish and Wildlife Service to produce high‐resolution estimates of golden eagle abundance. We developed a model that uses aerial survey data from the western United States to calibrate high‐resolution annual estimates of relative abundance from eBird. Using this model, we compared regional population size estimates based on the calibrated eBird information with those based on aerial survey data alone.Population size estimates based on the calibrated eBird information had strong correspondence to estimates from aerial survey data in two out of four regions, and population trajectories based on the two approaches showed high correlations.We demonstrate how the combination of citizen science data and targeted surveys can be used to (a) increase the spatial resolution of population size estimates, (b) extend the spatial extent of inference and (c) predict population size beyond the temporal period of surveys. Findings based on this case study can be used to refine policy metrics used by the US Fish and Wildlife Service and inform permitting regulations (e.g. mortality/harm associated with wind energy development).Policy implications: Our results demonstrate the ability of citizen science data to complement targeted monitoring programmes and improve the efficacy of decision frameworks that require information on population size or trajectory. After validating citizen science data against survey‐based benchmarks, agencies can harness strengths of citizen science data to supplement information needs and increase the resolution and extent of population size predictions.