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The bat fauna of northwestern Canada remains poorly known, principally owing to a lack of dedicated surveys across this vast region. To better assess the diversity of bats in the region, we compiled records from several acoustic survey projects and capture sessions whose purpose was to inventory bats in Yukon and northwestern British Columbia (BC) from 2013 to 2018. During our surveys we obtained the 1st apparent acoustic records for Yuma Myotis (Myotis yumanensis), Californian Myotis (M. californicus), Long-eared Myotis (M. evotis), Silver-haired Bat (Lasionycteris noctivagans) and Eastern Red Bat (Lasiurus borealis) from this region. We also captured 2 Long-eared Myotis in northwestern BC. Additionally, our surveys provided range extensions of Northern Myotis (M. septentrionalis), Long-legged Myotis (M. volans), and Hoary Bat (Lasiurus cinereus). Finally, by deploying bat detectors in late autumn, and in some cases throughout the winter, we provide data about seasonal patterns for bat activity in some locations, revealing that bats in northwestern BC may be active as late as 29 October and emerge as early as 30 March, a pattern similar to that observed in southern BC. By combining data from several disparate surveys, conducted for different reasons by different researchers, we were able to provide a fuller picture of the bat fauna of this vast region. Our data provides further evidence that the bat fauna of northwestern Canada is richer than previously documented. [ABSTRACT FROM AUTHOR]

The increasing complexity and pace of ecological change requires natural resource managers to consider entire species assemblages. Acoustic recording units (ARUs) require minimal cost and effort to deploy and inform relative activity, or encounter rates, for multiple species simultaneously. ARU‐based surveys require post‐processing of the recordings via software algorithms that assign a species label to each recording. The automated classification process can result in cross‐species misidentifications that should be accounted for when employing statistical modelling for conservation decision‐making.Using simulation and ARU‐based detection counts from 17 bat species in British Columbia, Canada, we investigate three strategies for adjusting statistical inference for species misclassification: (a) 'coupling' ambiguous and unambiguous detections by validating a subset of survey events post‐hoc, (b) using a calibration dataset on the software algorithm's (in)accuracy for species identification or (c) specifying informative Bayesian priors on classification probabilities. We explore the impact of different Bayesian prior specifications for the classification probabilities on posterior estimation. We then consider how the quantity of data validated post‐hoc impacts model convergence and resulting inferences for bat species relative activity as related to nightly conditions and yearly site occupancy after accounting for site‐level environmental variables.Coupled methods resulted in less bias and uncertainty when estimating relative activity and species classification probabilities relative to calibration approaches. We found that species that were difficult‐to‐detect and those that were often inaccurately identified by the software required more validation effort than more easily detected and/or identified species.Our results suggest that, when possible, acoustic surveys should rely on coupled validated detection information to account for false‐positive detections, rather than uncoupled calibration datasets. However, if the assemblage of interest contains a large number of rarely detected or less prevalent species, an intractable amount of effort may be required, suggesting there are benefits to curating a calibration dataset that is representative of the observation process. Our findings provide insights into the practical challenges associated with statistical analyses of ARU data and possible analytical solutions to support reliable and cost‐effective decision‐making for wildlife conservation/management in the face of known sources of observation errors. [ABSTRACT FROM AUTHOR]