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Avian population sizes fluctuate and change over vast spatial scales, but the mechanistic underpinnings remain poorly understood. A key question is whether spatial and annual variation in avian population dynamics is driven primarily by variation in breeding season recruitment or by variation in overwinter survival. We present a method using large‐scale volunteer‐collected data from project eBird to develop species‐specific indices of net population change as proxies for survival and recruitment, based on twice‐annual, rangewide snapshots of relative abundance in spring and fall. We demonstrate the use of these indices by examining spatially explicit annual variation in survival and recruitment in two well‐surveyed nonmigratory North American species, Carolina wrenThryothorus ludovicianusand northern cardinalCardinalis cardinalis. We show that, while interannual variation in both survival and recruitment is slight for northern cardinal, eBird abundance data reveal strong and geographically coherent signals of interannual variation in the overwinter survival of Carolina wren. As predicted, variation in wintertime survival dominates overall interannual population fluctuations of wrens and is correlated with winter temperature and snowfall in the northeastern United States, but not the southern United States. This study demonstrates the potential of participatory science (also known as citizen science) datasets like eBird for inferring variation in demographic rates and introduces a new complementary approach towards illuminating the macrodemography of North American birds at comprehensive continental extents. 

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. 

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.