Strategic conservation efforts for cryptic species, especially bats, are hindered by limited understanding of distribution and population trends. Integrating long‐term encounter surveys with multi‐season occupancy models provides a solution whereby inferences about changing occupancy probabilities and latent changes in abundance can be supported. When harnessed to a Bayesian inferential paradigm, this modeling framework offers flexibility for conservation programs that need to update prior model‐based understanding about at‐risk species with new data. This scenario is exemplified by a bat monitoring program in the Pacific Northwestern United States in which results from 8 years of surveys from 2003 to 2010 require updating with new data from 2016 to 2018. The new data were collected after the arrival of bat white‐nose syndrome and expansion of wind power generation, stressors expected to cause population declines in at least two vulnerable species, little brown bat (Myotis lucifugus) and the hoary bat (Lasiurus cinereus). We used multi‐season occupancy models with empirically informed prior distributions drawn from previous occupancy results (2003–2010) to assess evidence of contemporary decline in these two species. Empirically informed priors provided the bridge across the two monitoring periods and increased precision of parameter posterior distributions, but did not alter inferences relative to use of vague priors. We found evidence of region‐wide summertime decline for the hoary bat (trueλ^ = 0.86 ± 0.10) since 2010, but no evidence of decline for the little brown bat (trueλ^ = 1.1 ± 0.10). White‐nose syndrome was documented in the region in 2016 and may not yet have caused regional impact to the little brown bat. However, our discovery of hoary bat decline is consistent with the hypothesis that the longer duration and greater geographic extent of the wind energy stressor (collision and barotrauma) have impacted the species. These hypotheses can be evaluated and updated over time within our framework of pre–post impact monitoring and modeling. Our approach provides the foundation for a strategic evidence‐based conservation system and contributes to a growing preponderance of evidence from multiple lines of inquiry that bat species are declining.

Acoustic recording units (ARUs) enable geographically extensive surveys of sensitive and elusive species. However, a hidden cost of using ARU data for modeling species occupancy is that prohibitive amounts of human verification may be required to correct species identifications made from automated software. Bat acoustic studies exemplify this challenge because large volumes of echolocation calls could be recorded and automatically classified to species. The standard occupancy model requires aggregating verified recordings to construct confirmed detection/non‐detection datasets. The multistep data processing workflow is not necessarily transparent nor consistent among studies. We share a workflow diagramming strategy that could provide coherency among practitioners. A false‐positive occupancy model is explored that accounts for misclassification errors and enables potential reduction in the number of confirmed detections. Simulations informed by real data were used to evaluate how much confirmation effort could be reduced without sacrificing site occupancy and detection error estimator bias and precision. We found even under a 50% reduction in total confirmation effort, estimator properties were reasonable for our assumed survey design, species‐specific parameter values, and desired precision. For transferability, a fully documented r package, , for implementing a false‐positive occupancy model is provided. Practitioners can apply to optimize their own study design (required sample sizes, number of visits, and confirmation scenarios) for properly implementing a false‐positive occupancy model with bat or other wildlife acoustic data. Additionally, our work highlights the importance of clearly defining research objectives and data processing strategies at the outset to align the study design with desired statistical inferences.

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Bat conservation has been impeded by a lack of basic information about species' distributions and abundances. Public participation in closing this gap via citizen (community) science has been limited, but bat species that produce low-frequency calls audible to the unaided human ear provide an overlooked opportunity for collaborative citizen science surveys. Audible bats are rare in regional faunas but occur globally and can be under-surveyed by traditional methods. During 2019-2020, we were joined by community members to conduct aural surveys and expand our knowledge of rare audible desert bats in western North America through a structured survey design broadly adaptable for practitioners across the globe where audible bats occur. Our study was integrated into a statistically robust but flexible master sample in use by the North American Bat Monitoring Program (NABat), ensuring representativeness of data contributions. We used survey results to update a Bayesian species distribution model for the rare spotted bat, Euderma maculatum, accounting for imperfect detection and including land cover occupancy predictors. Detection probability was estimated~0.7 ± 0.1. Informative priors from a previous attempt to model E. maculatum were leveraged with the new citizen science data to support spatial predictions of occurrence previously impeded by data sparsity and which reinforced the biogeographic importance of arid cliffs and canyons. Our results are preliminary but encouraging, and future surveys can scale up through the NABat design structure and Bayesian modeling framework. We encourage future surveys to use recording devices to obtain voucher calls and double-observer methods to address false-positive detection errors that arise with inexperienced volunteers. Our design and model supported approach to integrating citizen science surveys into bat conservation programs can strengthen both the scientific understanding of rare species and public engagement in conservation practices.