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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 Citizen and community science datasets are typically collected using flexible protocols. These protocols enable large volumes of data to be collected globally every year; however, the consequence is that these protocols typically lack the structure necessary to maintain consistent sampling across years. This can result in complex and pronounced interannual changes in the observation process, which can complicate the estimation of population trends because population changes over time are confounded with changes in the observation process.Here we describe a novel modelling approach designed to estimate spatially explicit species population trends while controlling for the interannual confounding common in citizen science data. The approach is based on Double machine learning, a statistical framework that uses machine learning (ML) methods to estimate population change and the propensity scores used to adjust for confounding discovered in the data. ML makes it possible to use large sets of features to control for confounding and to model spatial heterogeneity in trends. Additionally, we present a simulation method to identify and adjust for residual confounding missed by the propensity scores.To illustrate the approach, we estimated species trends using data from the citizen science project eBird. We used a simulation study to assess the ability of the method to estimate spatially varying trends when faced with realistic confounding and temporal correlation. Results demonstrated the ability to distinguish between spatially constant and spatially varying trends. There were low error rates on the estimated direction of population change (increasing/decreasing) at each location and high correlations on the estimated magnitude of population change.The ability to estimate spatially explicit trends while accounting for confounding inherent in citizen science data has the potential to fill important information gaps, helping to estimate population trends for species and/or regions lacking rigorous monitoring data. 

Abstract AimAnimal migration is often explained as the result of resource tracking in seasonally dynamic environments. Therefore, resource availability should influence both the distributions of migratory animals and their seasonal abundance. We examined the relationship between primary productivity and the spatio‐temporal distributions of migratory birds to assess the role of energy availability in avian migration. LocationNorth America. Time periodFull annual cycle, 2011–2016. Major taxa studiedNocturnally migrating landbirds. MethodsWe used observations of nocturnally migrating landbirds from the eBird community‐science programme to estimate weekly spatial distributions of total biomass, abundance and species richness. We related these patterns to primary productivity and seasonal productivity surplus estimated using a remotely sensed measure of vegetation greenness. ResultsAll three avian metrics showed positive spatial associations with primary productivity, and this was more pronounced with seasonal productivity surplus. Surprisingly, biomass showed a weaker association than did abundance and richness, despite being a better indicator of energetic requirements. The strength of associations varied across seasons, being the weakest during migration. During spring migration, avian biomass increased ahead of vegetation green‐up in temperate regions, a pattern also previously described for herbivorous waterfowl. In the south‐eastern USA, spring green‐up was instead associated with a net decrease in biomass, and winter biomass greatly exceeded that of summer, highlighting the region as a winter refuge for short‐distance migrants. Main conclusionsAlthough instantaneous energy availability is important in shaping the distribution of migratory birds, the stronger association of productivity with abundance and richness than with biomass suggests the role of additional drivers unrelated to energetic requirements that are nonetheless correlated with productivity. Given recent reports of widespread North American avifaunal declines, including many common species that winter in the south‐eastern USA, understanding how anthropogenic activities are impacting winter bird populations in the region should be a research priority. 

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.