Clemson University TigerPrints All Theses Theses 12-2017 North American Bat Monitoring Program (NABat) in South Carolina: Acoustic Detection and Landscape Occupancy of Bats Benjamin D. Neece Clemson University Follow this and additional works at: https://tigerprints.clemson.edu/all_theses Recommended Citation Neece, Benjamin D., "North American Bat Monitoring Program (NABat) in South Carolina: Acoustic Detection and Landscape Occupancy of Bats" (2017). All Theses. 2780. https://tigerprints.clemson.edu/all_theses/2780 This Thesis is brought to you for free and open access by the Theses at TigerPrints. It has been accepted for inclusion in All Theses by an authorized administrator of TigerPrints. For more information, please contact [email protected]. NORTH AMERICAN BAT MONITORING PROGRAM (NABAT) IN SOUTH CAROLINA: ACOUSTIC DETECTION AND LANDSCAPE OCCUPANCY OF BATS A Thesis Presented to the Graduate School of Clemson University In Partial Fulfillment of the Requirements for the Degree Master of Science Wildlife and Fisheries Biology by Benjamin D. Neece December 2017 Accepted by: David S. Jachowski, Committee Chair Catherine M. Bodinof Jachowski Yoichiro Kanno Susan C. Loeb 1 ABSTRACT Bats are under threat from habitat loss, energy development, and the disease white-nose syndrome. The North American Bat Monitoring Program (NABat) suggests standardized, large scale monitoring to benefit ecologists and managers. Our first objective was to determine the efficacy of NABat in South Carolina. Detection probabilities differ within and among species and among survey conditions. Thus, our second objective was to determine factors affecting detection probabilities. Finally, effective management strategies addressing large scale threats require landscape scale analyses. Thus, our third objective was to conduct state-wide assessments of environmental factors influencing landscape occupancy and generate predicted distributions. We conducted NABat acoustic surveys across South Carolina from mid-May through July 2015 and 2016. To determine the efficacy of NABat, we compared species detections to known distributions based on historical records, and to predicted distributions based on environmental occupancy models. We detected some species throughout their ranges and others in ≤ 50% of cells within their ranges, and detected some species outside their ranges. Thus, NABat monitoring may be suitable for many species but may not be suitable for species with echolocation calls that are difficult to detect or identify, and may also reveal new information about species distributions. To determine factors that affected detection, we evaluated support for detection models. We found that detection covariates greatly varied among species, but most species had higher detection probabilities at stationary points than mobile transects. Our ii results suggested that effects of factors on detection probabilities were based on biological and behavioral characteristics of species, which indicated the importance of monitoring survey variables and accounting for them in analyses. To assess effects of environmental factors on occupancy, we evaluated temporally dynamic occupancy models. Occupancy probability differed among ecoregions for northern yellow bats (Dasypterus intermedius) and Myotis species. Hoary bats (Lasiurus cinereus) were negatively associated with forest edge density. We found no significant effects of habitat conditions for five species. Thus, for some species, site-use analyses of NABat data may be more appropriate than grid-based occupancy analyses. However, predicted distributions closely matched species habitat associations. Our findings can improve future monitoring efforts and inform conservation priorities. iii ACKNOWLEDGMENTS First, I would like to thank my advisors, Dr. Susan Loeb and Dr. David Jachowski, for offering the opportunity to further my career in wildlife ecology. They have been excellent mentors, essential to my professional development, and supportive of my career goals. I would like to acknowledge my committee members, Dr. Catherine Bodonif Jachowski and Dr. Yoichiro Kanno, who provided great feedback and substantially improved my analytical abilities. I also want to thank Mary Bunch for her collaborative efforts which helped initialize and support this research. This research would not have been possible without funding from a U.S. Fish and Wildlife Service Competitive State Wildlife Grant (SC-U2-F14AP00958). I am thankful for the support of the South Carolina Department of Natural Resources, U.S. Forest Service, U.S. Fish and Wildlife Service, U.S. Army Corps of Engineers, South Carolina Forestry Commission, South Carolina Department of Parks, Recreation, and Tourism, Folk Land Management, Naturaland Trust, Greenville Water, and other agencies, organizations, and volunteers. I would especially like to thank Johnny Stowe, Rob Harrison, Donald Cockman, Daniel Belken, Daniel Jones, and Ashley Watford for devoting their time collecting data. I would also like to thank my fellow graduate students and friends for their feedback and camaraderie. Finally, I want to thank my parents and family for always supporting my interests and helping develop my love of nature. iv TABLE OF CONTENTS Page TITLE PAGE .................................................................................................................... i ABSTRACT ..................................................................................................................... ii ACKNOWLEDGMENTS .............................................................................................. iv LIST OF TABLES .......................................................................................................... vi LIST OF FIGURES ....................................................................................................... vii CHAPTER I. AN ASSESSMENT OF THE NORTH AMERICAN BAT MONITORING PROGRAM IN SOUTH CAROLINA .......................... 1 Methods.................................................................................................... 3 Results .................................................................................................... 14 Discussion .............................................................................................. 31 Management Recommendations ............................................................ 39 II. EFFECTS OF ENVIRONMENTAL FACTORS ON LANDSCAPE SCALE BAT SPECIES OCCUPANCY ................................................ 41 Methods.................................................................................................. 43 Results .................................................................................................... 55 Discussion .............................................................................................. 63 Management Recommendations ............................................................ 70 APPENDICES ............................................................................................................... 71 A: Tables ........................................................................................................... 72 REFERENCES .............................................................................................................. 79 v LIST OF TABLES Table Page 1.1 Hypothesized effects of covariates on detection probabilities............................................................................................ 12 1.2 Detection probability models ranked higher than the null for each species ............................................................................... 22 1.3 Effects and statistical significance of covariates in top ranked detection models ........................................................................ 30 1.4 Estimated detection probability of each species .......................................... 31 2.1 Predicted and observed effects of environmental covariates on occupancy probabilities ................................................... 50 2.2 A priori reasoning for occupancy models tested .......................................... 54 2.3 Occupancy probability models ranked higher than the null for each species ............................................................................... 56 2.4 Estimated turnover rate of each species ....................................................... 62 A-1 Correlation of detection covariates .............................................................. 72 A-2 Detailed cell detection history of each species ............................................ 73 A-3 Estimates for detection covariates ............................................................... 74 A-4 Correlation of occupancy covariates ............................................................ 75 A-5 Predictive performance of top ranked detection models.............................. 77 A-6 Estimates for occupancy covariates ............................................................. 78 vi LIST OF FIGURES Figure Page 1.1 Map of cells surveyed and survey methods within each cell for each year .................................................................................... 17 1.2 Detection histories and known summer range of each species .................................................................................................... 20 1.3 Predicted distributions and detection histories ............................................ 21 1.4 Effects of survey duration on detection