COS 44-2
Implications of post white-nose syndrome survival on population viability of little brown bats

Tuesday, August 12, 2014: 1:50 PM
Regency Blrm E, Hyatt Regency Hotel
Brooke Maslo, Rutgers Cooperative Extension, Rutgers University
Mick Valent, Endangered and Nongame Species Program, New Jersey Division of Fish and Wildlife, Clinton, NJ
John F. Gumbs, BATS Research Center, Shohola, PA
Winifred F. Frick, Ecology and Evolutionary Biology, University of California, Santa Cruz, Santa Cruz, CA
Background/Question/Methods

Understanding the long-term impacts of novel stressors on wildlife populations is challenging because species’ responses to these perturbations are not known. Individual vital rates may be significantly or slightly impacted, and they may degrade or stabilize over time, leading to very different long-term population trajectories. Because ecological systems are dynamic, conservation tools, such as population viability analysis (PVA) and matrix population models, should be used initially to generate testable hypotheses about the impacts of novel stressors and then adapted iteratively as empirical data are gathered. Using five years of mark-capture data on little brown bats (Myotis lucifugus) at Hibernia Mine in New Jersey after White-nose Syndrome (WNS) arrived, we present the first robust apparent annual survival estimates for bats at a WNS infected site.  We then incorporate these survival rates into a previously published demographic population model to compare predicted and actual population sizes at Hibernia Mine. 

Results/Conclusions

Post-WNS survival ranged from 0.61-0.65 from 2010 - 2014, with male survival only slightly higher than female survival in all years. While survival rates appear to stabilize within a few years following WNS emergence, observed bat abundance at Hibernia Mine is markedly lower than that predicted by our model, suggesting that WNS likely impacts additional vital rates, such as juvenile survival and adult reproduction. Further investigation of reproductive vital rates can improve understanding of WNS impacts and help direct management toward mitigating influential factors.