Date of Award

2016

Degree Type

Thesis

Degree Name

Master of Science (MS)

Author's Department

Biological Sciences

First Advisor

Jill L. Deppe

Abstract

Bats have an astonishing diversity and provide vital ecosystem services in an array of different niches. In North America, most species of bats are insectivores and tend to be frequently overlooked for their important ecosystem role providing insect control. As bat populations have declined in recent years, farmers, land managers, conservationists, and bat enthusiasts have wondered what we can do to protect our local bat populations. As a first step, we need to develop methods that more effectively survey for rare species of bats. By performing inefficient surveys, we are doing a disservice to our funding agencies providing misinformation that ultimately puts populations at risk. Our results reveal the low detection probability associated with mist netting of relatively common bats, the big brown (Eptesicus fuscus) and little brown bat (Myotis lucifugus), compared to the detection probability using full spectrum recorders. These results suggest that acoustic recorders may provide the most robust information and that mist netting alone for presence-absence of species may require additional nights of sampling for accurate results.

We can also manage for bat populations through a better understanding of how they select habitat. In this study we used full spectrum acoustic detectors to sample major land cover types and analyze bat activity patterns at local and landscape scales. Our results indicate that bats in McHenry County most likely use a hierarchical approach to habitat selection and prefer forested riparian areas with large trees that also have numerous small patches of agriculture within a 1 km radius. This information can help us better manage forests for Midwestern bat populations as they hopefully recover from recent population declines.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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