Landscape features associated with the roosting habitat of Indiana bats and northern long-eared bats
Bat conservation in the eastern United States faces threats from white nose syndrome, wind energy, and fragmentation of habitat. To mitigate population declines, the habitat requirements of species of concern must be established. Assessments that predict habitat quality based upon landscape features can aid species management over large areas. Roosts are critical habitat for many bat species including the endangered Indiana bat (Myotis sodalis) and the threatened northern long-eared bat (M. septentrionalis).
While much is known about the microhabitat requirements of roosts, translating such knowledge into landscape-level management is difficult. Our goal was to determine the landscape-scale environmental variables necessary to predict roost occupancy for both species.
Using MaxLike, a presence-only occupancy modeling approach, with known roost sites, we identified factors associated with roosting habitat. Spatially independent roost locations were particularly limited for northern long-eared bats resulting in differences in study areas and sample sizes between the two species.
Occupancy of Indiana bat roosts was greatest in areas with >80 % local forest cover within broader landscapes (1 km) with <40 % forest, <1 km of perennial streams but >1 km from intermittent streams and in areas with poor foraging habitat. Northern long-eared roost occupancy was greatest in areas with >80 % regional but fragmented forest cover with greater forest edge approximately 4 km from the nearest major road.
Landscape features associated with roost occupancy differed greatly between species suggesting disparate roosting needs at the landscape scale, which may require independent management of roost habitat for each species.
KeywordsHabitat Landscape MaxLike Myotis septentrionalis Myotis sodalis Occupancy Presence-only model Roost
This research was funded by the Indiana Department of Natural Resources, Division of Forestry. We would like to thank RA King and MG Hohmann for providing much of the roosting data used in this research. RK Swihart, G Shao, S Fei, DW Sparks, and LE D’Acunto provided valuable feedback on earlier drafts of this manuscript.
- Badin HA (2014) Habitat selection and roosting ranges of northern long-eared bats (Myotis septentrionalis) in an experimental hardwood forest system. Thesis, Ball State UniversityGoogle Scholar
- Carter TC, Carroll SK, Hofmann JE, Gardner JE, Feldhamer GA (2002) Landscape analysis of roosting habitat in Illinois. In: Kurta A, Kennedy J (eds) The Indiana bat, biology and management of an endangered species. Bat Conservation International, Austin, pp 160–164Google Scholar
- Diggle P, Ribeiro PJ Jr (2007) Model-based geostatistics. Springer, New YorkGoogle Scholar
- Duchamp JE, Arnett EB, Larson MA, Swihart RK (2007) Ecological considerations for landscape-level management of bats. In: Lacki M, Hayes J, Kurta A (eds) Bats in forests: conservation and management. Johns Hopkins University Press, Baltimore, pp 237–261Google Scholar
- Fitzpatrick MC, Gotelli NJ, Ellison AM (2013) MaxEnt versus MaxLike: empirical comparisons with ant species distributions. Ecosphere 4:art55Google Scholar
- Gumbert MW, O’Keefe JM, MacGregor JR (2002) Roost fidelity in Kentucky. In: Kurta A, Kennedy J (eds) The Indiana bat, biology and management of an endangered species. Bat Conservation International, Austin, pp 143–152Google Scholar
- Kramer-Schadt S, Niedballa J, Pilgrim JD, Schröder B, Lindenborn J, Reinfelder V, Stillfried M, Heckmann I, Scharf AK, Augeri DM, Cheyne SM, Hearn AJ, Ross J, Macdonald DW, Mathai J, Eaton J, Marshall AJ, Semiadi G, Rustam R, Bernard H, Alfred R, Samejima H, Duckworth JW, Breitenmoser-Wuersten C, Belant JL, Hofer H, Wilting A (2013) The importance of correcting for sampling bias in MaxEnt species distribution models. Divers Distrib 19:1366–1379CrossRefGoogle Scholar
- Kunz TH, Lumsden LF (2003) Ecology of cavity and foliage roosting bats. In: Kunz TH, Fenton MB (eds) Bat ecology. University of Chicago Press, Chicago, pp 3–89Google Scholar
- Kurta A, Murray SW, Miller DH (2002) Roost selection and movements across the summer landscape. In: Kurta A, Kennedy J (eds) The Indiana bat, biology and management of an endangered species. Bat Conservation International, Austin, pp 118–129Google Scholar
- Leisch F, Tibshirani R (2013) Bootstrap: functions for the book “An Introduction to the Bootstrap.” CRAN package repository. Available from http://cran.r-project.org. Accessed January 2015
- McGarigal K, Cushman SA, Ene E (2012) FRAGSTATS v4: Spatial pattern analysis program for categorical and continuous maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. http://www.umass.edu/landeco/research/fragstats/fragstats.html
- Menzel MA, Menzel JM, Carter TC, Ford WM, Edwards JW (2001) Review of the forest habitat relationships of the Indiana bat (Myotis sodalis). USDA Forest Service, Newtown Square, Pennsylvania, General Technical Report NE-284Google Scholar
- Miller NE, Drobney RD, Clawson RL, Callahan EV (2002) Summer habitat in northern Missouri. In: Kurta A, Kennedy J (eds) The Indiana bat, biology and management of an endangered species. Bat Conservation International, Austin, pp 165–171Google Scholar
- O’Shea TJ, Clark DR Jr (2002) An overview of contaminants and bats, with special reference to insecticides and the Indiana bat. In: Kurta A, Kennedy J (eds) The Indiana bat, biology and management of an endangered species. Bat Conservation International, Austin, pp 237–253Google Scholar
- Pauli BP (2014) Nocturnal and diurnal habitat of Indiana and northern long-eared bats, and the simulated effect of timber harvest on habitat suitability. Dissertation, Purdue UniversityGoogle Scholar
- Pauli BP, Zollner PA, Haulton GS, Shao G, Shao G (2015) The simulated effects of timber harvest on suitable habitat for Indiana and northern long-eared bats. Ecosphere 6:art58Google Scholar
- R Core Team (2012) R: A language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
- Ribeiro PJ Jr, Diggle PJ (2001) geoR: a package for geostatistical analysis. R News 1:14–18Google Scholar
- Shao G (2012) Indiana forest cover mapping based on multi-stage integrated classification using satellite and in situ forest inventory data. Thesis, Purdue UniversityGoogle Scholar
- Thogmartin WE, Sanders-Reed CA, Szymanski JA, McKann PC, Pruitt L, King RA, Runge MC, Russell RE (2013) White-nose syndrome is likely to extirpate the endangered Indiana bat over large parts of its range. Biol Conserv 160:162–172Google Scholar
- Turner GG, Reeder D, Coleman JT (2011) A five-year assessment of mortality and geographic spread of white-nose syndrome in North American bats and a look to the future. Bat Res News 52:13–27Google Scholar
- U.S. Fish and Wildlife Service (2007) Indiana bat (Myotis sodalis) draft recovery plan: first revision. Fort Snelling, MinnesotaGoogle Scholar
- U.S. Fish and Wildlife Service (2012) North American bat death toll exceeds 5.5 million from white-nose syndrome. Arlington, VirginiaGoogle Scholar
- U.S. Fish and Wildlife Service (2015) Endangered and threatened wildlife and plants; threatened species status for the northern long-eared bat with 4(d) rule. Fed Regist 80:17974–18033Google Scholar
- Weber TC, Sparks DW (2013) Summer habitat identification of an endangered bat, Myotis sodalis, across its eastern range of the USA. J Conserv Plan 9:53–68Google Scholar
- Whitaker JO Jr, Brack V Jr (2002) Distribution and summer ecology in Indiana. In: Kurta A, Kennedy J (eds) The Indiana bat, biology and management of an endangered species. Bat Conservation International, Austin, pp 48–54Google Scholar
- Whitaker JO Jr, Brack V Jr, Sparks DW, Cope JB, Johnson S (2007) Bats of Indiana. Indiana State University Center for North American Bat Research and Conservation, Terre HauteGoogle Scholar
- Yackulic CB, Chandler R, Zipkin EF, Royle JA, Nichols JD, Campbell Grant EH, Veran S (2013) Presence-only modelling using MAXENT: when can we trust the inferences? Methods Ecol Evol 4:236–243Google Scholar