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1. Surveying wildlife communities provides data for informing conservation and management decisions that affect multiple species. Autonomous recording units (ARUs) can efficiently gather community data for a variety of taxa, but generally require software algorithms to classify each recorded call to a species. Species classification Surveying wildlife communities provides data for informing conservation and management decisions that affect multiple species. Autonomous recording units (ARUs) efficiently gather community data by passively recording animal vocalizations (Gibb, Browning, Glover-Kapfer, & Jones, 2019), generally for multiple time periods ('visits') at each surveyed location ('site'). These data, including counts of call recordings and corresponding species classifications, can be used to investigate various ecological questions and are applicable for surveying multiple taxa (e.g. anurans, bats, birds). However, due to the large volumes of data typically collected, most studies using acoustic surveys require classification software to identify the species of each call recording (Gibb et al., 2019). This automated process includes species classification errors that lead to both false-negative and false-positive detections. For instance, when a species is present, false-negative detections can result from successfully recording its calls but misclassifying them as alternative species. These errors are in addition to false negatives from failing to record any of its calls. False-positive detections at sites where a species is absent are often due to misclassifying recorded calls from another species. Estimating the ecological parameters of interest, while addressing these errors is an important consideration when analysing ARU data. Occupancy models (MacKenzie et al., 2002) are a natural framework for analysing ARU data when visits are summarized to detection/non-detection observations for each species (e.g. Banner et al., 2018; Rodhouse et al., 2019). Originally developed to account for false negatives, standard occupancy models assume that all false positives are removed (MacKenzie et al., 2002). Completely eliminating false positives from ARU data is generally cost prohibitive because it requires manually confirming at least one recording for every visit. False positives are an important source of errors in many
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.
Aim Bat mortality rates from white-nose syndrome and wind power development are unprecedented. Cryptic and wide-ranging behaviours of bats make them difficult to survey, and population estimation is often intractable. We advance a model-based framework for making spatially explicit predictions about summertime distributions of bats from capture and acoustic surveys. Motivated by species-energy and life-history theory, our models describe hypotheses about spatio-temporal variation in bat distributions along environmental gradients and life-history attributes, providing a statistical basis for conservation decision-making.Location Oregon and Washington, USA.Methods We developed Bayesian hierarchical models for 14 bat species from an 8-year monitoring dataset across a~430,000 km 2 study area. Models accounted for imperfect detection and were temporally dynamic. We mapped predicted occurrence probabilities and prediction uncertainties as baselines for assessing future declines.Results Forest cover, snag abundance and cliffs were important predictors for most species. Species occurrence patterns varied along elevation and precipitation gradients, suggesting a potential hump-shaped diversity-productivity relationship. Annual turnover in occurrence was generally low, and occurrence probabilities were stable among most species. We found modest evidence that turnover covaried with the relative riskiness of bat roosting and migration. The fringed myotis (Myotis thysanodes), canyon bat (Parastrellus hesperus) and pallid bat (Antrozous pallidus) were rare; fringed myotis occurrence probabilities declined over the study period. We simulated anticipated declines to demonstrate that mapped occurrence probabilities, updated over time, provide an intuitive way to assess bat conservation status for a broad audience.Main conclusions Landscape keystone structures associated with roosting habitat emerged as regionally important predictors of bat distributions. The challenges of bat monitoring have constrained previous species distribution modelling efforts to temporally static presence-only approaches. Our approach extends to broader spatial and temporal scales than has been possible in the past for bats, making a substantial increase in capacity for bat conservation.
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