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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. 

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. 

Abstract Effective solutions to conserve biodiversity require accurate community‐ and species‐level information at relevant, actionable scales and across entire species' distributions. However, data and methodological constraints have limited our ability to provide such information in robust ways. Herein we employ a Deep‐Reasoning Network implementation of the Deep Multivariate Probit Model (DMVP‐DRNets), an end‐to‐end deep neural network framework, to exploit large observational and environmental data sets together and estimate landscape‐scale species diversity and composition at continental extents. We present results from a novel year‐round analysis of North American avifauna using data from over nine million eBird checklists and 72 environmental covariates. We highlight the utility of our information by identifying critical areas of high species diversity for a single group of conservation concern, the North American wood warblers, while capturing spatiotemporal variation in species' environmental associations and interspecific interactions. In so doing, we demonstrate the type of accurate, high‐resolution information on biodiversity that deep learning approaches such as DMVP‐DRNets can provide and that is needed to inform ecological research and conservation decision‐making at multiple scales. 

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. 

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. 

Summary Bird species’ migratory patterns have typically been studied through individual observations and historical records. In recent years, the eBird citizen science project, which solicits observations from thousands of bird watchers around the world, has opened the door for a data-driven approach to understanding the large-scale geographical movements. Here, we focus on the North American tree swallow (Tachycineta bicolor) occurrence patterns throughout the eastern USA. Migratory departure dates for this species are widely believed by both ornithologists and casual observers to vary substantially across years, but the reasons for this are largely unknown. In this work, we present evidence that maximum daily temperature is predictive of tree swallow occurrence. Because it is generally understood that species occurrence is a function of many complex, high order interactions between ecological covariates, we utilize the flexible modelling approach that is offered by random forests. Making use of recent asymptotic results, we provide formal hypothesis tests for predictive significance of various covariates and also develop and implement a permutation-based approach for formally assessing interannual variations by treating the prediction surfaces that are generated by random forests as functional data. Each of these tests suggest that maximum daily temperature is important in predicting migration patterns. 

Abstract The research and conservation community has successfully harnessed the wealth of ecological knowledge found in unprecedented volumes of citizen science (CS) data world‐wide. However, few examples exist of the use of CS data to directly inform policy.Current examples of applications of CS data mainly stem from programs that are restricted in scope (e.g. defined protocols, restricted sampling time frame), and the potential use of unrestricted CS data to inform policy remains largely untapped.Here, we make a call for moving beyond questioning the reliability of CS data and present a case study of how the US Fish and Wildlife Service (USFWS) used information from an unrestricted CS program (eBird) to inform levels of exposure to collision risk for wind energy development.Policy implications. The USFWS made the technical recommendation to use eBird abundance estimates for the bald eagle as the only source of information to define low‐risk collision areas as part of the agency's wind energy permitting process. Our study contributes a clear pathway of how to realize the potential of unrestricted CS programs for generating the evidence base needed to inform policy decisions. 

Abstract Extreme weather, including heat waves, droughts, and high rainfall, is becoming more common and affecting a diversity of species and taxa. However, researchers lack a framework that can anticipate how diverse species will respond to weather extremes spanning weeks to months. Here we used high‐resolution occurrence data from eBird, a global citizen science initiative, and dynamic species distribution models to examine how 109 North American bird species ranging in migration distance, diet, body size, habitat preference, and prevalence (commonness) respond to extreme heat, drought, and rainfall across a wide range of temporal scales. Across species, temperature influenced species’ distributions more than precipitation at weekly and monthly scales, while precipitation was more important at seasonal scales. Phylogenetically controlled multivariate models revealed that migration distance was the most important factor mediating responses to extremely hot or dry weeks; residents and short‐distance migrants occurred less often following extreme heat. At monthly or seasonal scales, less common birds experienced decreases in occurrence following drought‐like conditions, while widespread species were unaffected. Spatial predictions demonstrated variation in responses to extreme weather across species’ ranges, with predicted decreases in occurrence up to 40% in parts of ranges. Our results highlight that extreme weather has variable and potentially strong implications for birds at different time scales, but these responses are mediated by life‐history characteristics. As weather once considered extreme occurs more frequently, researchers and managers require a better understanding of how diverse species respond to extreme conditions.