Rodhouse, Thomas; et al.
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 (λ trend = 0.86 ± 0.10) since 2010, but no evidence of decline for the little brown bat (λ trend = 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.
Thomas J. Rodhouse, National Park Service and Human and Ecosystem Resiliency and Sustainability Lab, Oregon State University—Cascades, Bend
Rogelio M. Rodriguez, Human and Ecosystem Resiliency and Sustainability Lab, Oregon State University—Cascades, Bend
Katharine M. Banner, Department of Mathematical Sciences, Montana State University, Bozeman
Patricia C. Ormsbee, Willamette National Forest, Springfield, Oregon
Jenny Barnett, Mid‐Columbia River National Wildlife Refuge Complex, U.S. Fish and Wildlife Service, Burbank, Washington
Kathryn M. Irvine, Northern Rocky Mountain Science Center, U.S. Geological Survey, Bozeman, Montana
Ecology and Evolution. 2019;00:1–11.
First published: 11 September 2019
doi: 10.1002/ece3.5612 
Download original document: “Evidence of region‐wide bat population decline from long‐term monitoring and bayesian occupancy models with empirically informed priors ”
URL to article: https://www.wind-watch.org/documents/evidence-of-region-wide-bat-population-decline-from-long-term-monitoring-and-bayesian-occupancy-models-with-empirically-informed-priors/
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 doi: 10.1002/ece3.5612: https://doi.org/10.1002/ece3.5612
 Evidence of region‐wide bat population decline from long‐term monitoring and bayesian occupancy models with empirically informed priors: https://docs.wind-watch.org/Rodhouse_et_al-2019-Ecology_and_Evolution.pdf
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