Establishing conservation baselines with dynamic distribution models for bat populations facing imminent decline

Abstract

AbstractAimBat 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.LocationOregon and Washington, USA.MethodsWe developed Bayesian hierarchical models for 14 bat species from an 8‐year monitoring dataset across a ~430,000 km2 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.ResultsForest 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 conclusionsLandscape 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.