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Abstract Autonomous recording units (ARUs) are recognized for their use in detecting vocalizing bird species to assess presence, occupancy, and density, but their potential to monitor reproductive status of individuals and reproductive rates is not well known. We investigated whether song rates derived from ARU data, when combined with the known date, can be used to predict the proportion of male songbirds in 3 breeding status classes (single, paired, and feeding young). We monitored breeding status with weekly field visits and collected daily ARU recordings at 46 olive‐sided flycatcher (Contopus cooperi) breeding territories in northwestern Canada in 2016–2017. We tested 4 variations of a hierarchical multinomial regression model that used time of day, day of year, and song rate derived from 2‐minute recordings to predict breeding status, and evaluated models using a novel, likelihood‐based approach. We found the top model correctly estimated 79% of the observed proportions of birds in each breeding status across the length of the breeding season. Although date was the primary predictor of breeding status, singing rate reduced some of the uncertainty and provided more accurate estimates for a given time. A major challenge to prediction accuracy and data interpretation was accounting for bird movement and the associated impact on detection, which we partly addressed by limiting our study to individuals who were detected on at least 30% of ARU sampling days. We demonstrate that ARUs can be used to assess breeding status in a cryptic, low‐density species at risk such as the olive‐sided flycatcher, suggesting this method could be applied to a wider range of species to better understand demographics and population dynamics, and inform management decisions, for bird species of concern. 

Abstract Identifying genetic conservation units (CUs) in threatened species is critical for the preservation of adaptive capacity and evolutionary potential in the face of climate change. However, delineating CUs in highly mobile species remains a challenge due to high rates of gene flow and genetic signatures of isolation by distance. Even when CUs are delineated in highly mobile species, the CUs often lack key biological information about what populations have the most conservation need to guide management decisions. Here we implement a framework for CU identification in the Canada Warbler (Cardellina canadensis), a migratory bird species of conservation concern, and then integrate demographic modelling and genomic offset to guide conservation decisions. We find that patterns of whole genome genetic variation in this highly mobile species are primarily driven by putative adaptive variation. Identification of CUs across the breeding range revealed that Canada Warblers fall into two evolutionarily significant units (ESU), and three putative adaptive units (AUs) in the South, East, and Northwest. Quantification of genomic offset, a metric of genetic changes necessary to maintain current gene–environment relationships, revealed significant spatial variation in climate vulnerability, with the Northwestern AU being identified as the most vulnerable to future climate change. Alternatively, quantification of past population trends within each AU revealed the steepest population declines have occurred within the Eastern AU. Overall, we illustrate that genomics‐informed CUs provide a strong foundation for identifying current and future regional threats that can be used to inform management strategies for a highly mobile species in a rapidly changing world. 

The urgency for remote, reliable and scalable biodiversity monitoring amidst mounting human pressures on ecosystems has sparked worldwide interest in Passive Acoustic Monitoring (PAM), which can track life underwater and on land. However, we lack a unified methodology to report this sampling effort and a comprehensive overview of PAM coverage to gauge its potential as a global research and monitoring tool. To address this gap, we created the Worldwide Soundscapes project, a collaborative network and growing database comprising metadata from 416 datasets across all realms (terrestrial, marine, freshwater and subterranean).