Assessing the status and trend of bat populations across broad geographic regions with dynamic distribution models

Abstract

Bats face unprecedented threats from habitat loss, climate change, disease, and wind power development, and populations of many species are in decline. A better ability to quantify bat population status and trend is urgently needed in order to develop effective conservation strategies. We used a Bayesian autoregressive approach to develop dynamic distribution models for Myotis lucifugus, the little brown bat, across a large portion of northwestern USA, using a four-year detection history matrix obtained from a regional monitoring program. This widespread and abundant species has experienced precipitous local population declines in northeastern USA resulting from the novel disease white-nose syndrome, and is facing likely range-wide declines. Our models were temporally dynamic and accounted for imperfect detection. Drawing on species-energy theory, we included measures of net primary productivity (NPP) and forest cover in models, predicting that M. lucifugus occurrence probabilities would covary positively along those gradients. Despite its common status, M. lucifugus was only detected during -50% of the surveys in occupied sample units. The overall naive estimate for the proportion of the study region occupied by the species was 0.69, but after accounting for imperfect detection, this increased to -0.90. Our models provide evidence of an association between NPP and forest cover and M. lucifugus distribution, with implications for the projected effects of accelerated climate change in the region, which include net aridification as snowpack and stream flows decline. Annual turnover, the probability that an occupied sample unit was a newly occupied one, was estimated to be low (-0.04-0.14), resulting in flat trend estimated with relatively high precision (SD = 0.04). We mapped the variation in predicted occurrence probabilities and corresponding prediction uncertainty along the productivity gradient. Our results provide a much needed baseline against which future anticipated declines in M. lucifugus occurrence can be measured. The dynamic distribution modeling approach has broad applicability to regional bat monitoring efforts now underway in several countries and we suggest ways to improve and expand our grid-based monitoring program to gain robust insights into bat population status and trend across large portions of North America.

Evidence of region‐wide bat population decline from long‐term monitoring and Bayesian occupancy models with empirically informed priors

“…The region is divided in half by the north-south trending Cascade Range that creates a distinct rain shadow over the eastern half of the region and a west-to-east forest cover gradient that is a dominant biogeographic influence on bats ( Figure 1). The forest cover gradient in the region is strongly correlated with net primary productivity (ρ = 0.7) and moderately so with precipitation and elevation (Rodhouse et al, 2012. The little brown bat and hoary bat range widely across the region and are found in all habitat types but are associated with forested landscapes more than nonforested shrub steppe (Hayes, 2003;Kalcounis-Rüppell, Psyllakis, & Brigham, 2005;.…”

“…Rather than starting anew after each cycle of data collection, model-fitting, evaluation, and inference, Bayes theorem allows for previous modeling results, in the form of posterior probability distributions, to be used as prior probability distributions that formally represent best-available understanding about model parameters (Crome, Thomas, & Moore, 1996;Hobbs & Hooten, 2015;McCarthy & Masters, 2005). This scenario is exemplified by a bat monitoring program in an ~440,000 km 2 region of the Pacific Northwestern United States ( Figure 1) in which the occupancy modeling results from 8 years of monitoring, which ended in 2010 (Rodhouse et al, 2012, require updating with new survey data gathered during 2016-2018 for contribution to the North American Bat Monitoring Program (NABat; Loeb et al, 2015). In this way, the empirically informative Bayesian inferential paradigm, when harnessed to replicate geographically extensive large-sample encounter surveys, provides a way to "scaffold", or build upon, prior knowledge to improve conservation decision-making.…”

Improved Analysis of Long-Term Monitoring Data Demonstrates Marked Regional Declines of Bat Populations in the Eastern United States

“…For these reasons, and because of high spatial and temporal variability in local population estimates (P. de Valpine, T.E. Ingersoll & W. Rainey, unpublished), long-term, regional estimates of abundance are essential to improving understanding of bat populations [39].…”

“…Thus, although hibernacula counts have become widespread, estimates of long-term changes in regional bat abundance from these counts are rare, or where available often do not include measures of variance, confidence intervals, or account for detection probability [40]. The resulting uncertainty about regional population trends has hindered managers' ability to accurately assess bat conservation status, efficiently allocate scarce management resources to high-priority species, and develop effective management strategies [39].…”

A Habitat-based Wind-Wildlife Collision Model with Application to the Upper Great Plains Region

“…Additionally Rodhouse et al (2012) used a large-scale distribution model to assess bat population trends in the northwestern United States.…”