False-positive occupancy models produce less-biased occupancy estimates for a rare and elusive bat species
|Authors:||Vanessa Rojas, Susan Loeb, Joy M. O’Keefe|
|Station:||Southern Research Station|
|Source:||Journal of Mammalogy|
Confirming presence and distribution of a species is necessary for effective conservation. However, obtaining robust occupancy
estimates and confidently identifying factors important to occupancy may be difficult for rare and elusive species. Further, in surveys to assess presence, false-positive detections bias results; however, false-positive occupancy models can resolve this bias and, thus, better support conservation. We assessed the performance of false-positive versus standard occupancy models and important factors predicting presence for a low-density bat population in the southern Appalachian Mountains. From May to August 2013–2015, we surveyed 35 sites for northern long-eared bats (Myotis septentrionalis) using both mist-net and acoustic methods. We compared AICc values for 13 standard occupancy models and 13 corresponding false-positive occupancy models. In our model comparison, false-positive models received more support, while none of the standard occupancy models were plausible. False-positive occupancy models produced a wider range of probability of occupancy estimates (0.004–0.998) and lower mean occupancy estimate (0.62) than standard models (0.482–0.970, mean = 0.86). Weighted parameter estimates for important predictors in two plausible false-positive occupancy models indicated the probability of occupancy for northern long-eared bats was higher at less-rugged, lower- elevation sites. In contrast, there was more ambiguity regarding the most plausible standard occupancy models and important predictors of occupancy from standard models. Due to low capture rates and the uncertainty of acoustic identifications, we recommend coupling a certain method with uncertain methods when surveying rare and elusive bat species. Applying false-positive occupancy models to our data yielded less-biased site-specific occupancy estimates and informative predictors, and, hence, more reliable predictions to inform conservation management plans.