Species Assemblage and Habitat Use of Bats in a Northeastern Coastal Plain Ecosystem (Cape Cod National Seashore) Submission to the Edna Bailey Sussman Foundation
State University of New York
College of Environmental Science and Forestry
BACKGROUND Recent attention has focused on the conservation and management of bats due to the ecosystem services they provide and for their role as an indicator species (Kunz et al. 2011). Since 2007, bat populations in the Northeast and Midwest United States have been decimated by white-nose syndrome, a disease caused by the cold-loving fungus Pseudogymnoascus destructans. Decline in these species led to a recent decision by the U.S. Fish & Wildlife Service to list the northern long-eared bat (Myotis septentrionalis) as threatened under the Endangered Species Act (16 U.S.C. §§ 1531-1544) and has encouraged consideration of future protection for other species at risk. Despite these efforts, there is a lack of research addressing habitat use of forest-roosting bats (like M. septentrionalis) in the Northeast (Fenton et al. 1992, Miller et al. 2003).
We know little about bat assemblages in coastal plain ecosystems of the Northeast and up to date information is necessary for future management and conservation programs in this unique habitat. This study will be one of the few comprehensive assessments of summer bat habitat use and co-occurrence in a northeastern coastal plain ecosystem. The Outer Cape of Cape Cod is the northernmost point of the Atlantic coastal plain that stretches from Cape Cod National Seashore to the southernmost tip of Florida. Published research on bat activity and habitat use within and around the unique coastal plain ecosystem of Cape Cod National Seashore is limited. There has been no published work within Cape Cod National Seashore on bat species presence or habitat use since an observational study conducted in 1897 (Miller). In the summer, Cape Cod is a heavily populated tourist destination with most protected habitats surrounded by development, noise pollution, and human activity. For bats, these negative effects are coupled with massive population declines caused by white-nose syndrome. Modeling habitat use of bats in this ecosystem will update our historical understanding of species’ associations and distribution in Cape Cod National Seashore and enable managers to predict future distributions and focus management objectives (Morrison 1992). The goals of this study are to determine what bat species are present on Cape Cod National Seashore, analyze local and landscape conditions associated with habitat use for all bat species present, and ascertain the effects of prescribed burning on bat detection with a focus on the recently listed northern long-eared bat (Myotis septentrionalis). This information will be beneficial to natural resources managers at Cape Cod National Seashore and in other areas of the northeastern coastal plain as they design management objectives for declining bat species living within developed ecosystems.
METHODS Study Area and Field Methods
Cape Cod National Seashore is located in Barnstable County, Massachusetts (41˚57’N, 70˚ W). My study area includes the entirety of the 176.5 km2 national seashore and some adjacent residential or commercial areas. Cape Cod National Seashore is characterized by a variety of terrestrial and marine ecosystems including beaches, salt marshes, kettle ponds, and vernal pool. The forested landscape is dominated by pitch pine and scrub oak forests, but also includes heathlands, dunes, and sandplain grasslands. This area is home to some of the most popular beaches in the United States including Nauset Lighthouse Beach and Coast Guard Beach.
Figure 1. Location of 147 passive acoustic sampling sites for summers 2015-2016 within Cape Cod National Seashore (red boundary line).
I conducted acoustic sampling of bat echolocations at 86 sample sites on Cape Cod National Seashore from 1 June to 24 July 2016. Combined with my acoustic sampling from the summer of 2015 (3 June to 20 July 2015), I sampled a total of 147 sites within the National Seashore (Fig. 1). To select sample sites, I used ArcGIS to generate eight categories of varying land cover types using vegetation information provided by the National Park Service. These categories were: coastal plain pondshore/swamp, pitch pine forest, scrub oak forest, dune shrub/heathland grassland, oak/beech forest, black locust/cedar swamp, developed/disturbed, and red cedar/salt shrub forest. I placed a 300 meter x 300 meter point grid across the sampling frame to identify a set of prospective sample points and randomly selected sites. Wildlife Acoustics SM3BAT acoustic bat detectors were deployed for 2 consecutive nights at each sample site, on 2 sampling occasions during the summer that was separated by at least three weeks. I programmed each acoustic detector to automatically record echolocations within a frequency range designed to capture calls of northeastern bat species. Each acoustic detector ran nightly from sunset to sunrise for approximately 12 hours.
At each sample site, I quantified local vegetation characteristics by sampling vegetation along four 25 meter transects centered at the origin, that spread out at 90º angles to form an “X”. At 5 meter intervals along each transect, I measured understory structure in 0.5 meter height increments using a Robel pole (up to 2 meters) and percent canopy cover using a densiometer. I also recorded the species, diameter at breast height (DBH), total height, and status (alive or dead) of the nearest tree. I used ArcGIS to calculate Euclidean distance from each of the sample sites to water, main roads, and human developments such as roads, houses, and buildings.
Echolocation recordings were automatically classified to species using SonoBat version 3.2.2 software (Arcata, CA, USA). I then analyzed the acoustic data collected and manually vetted all echolocation passes to confirm species identification, reduce bias, and increase identification precision. Although acoustic detectors are an important resource, they are not without limitations. Misidentification rates can range from 5 to 30% and may never be eliminated between species with similar call structure, such as species in the genus Myotis (Armitage and Ober 2010, Britzke et al. 2011). By thoroughly manually vetting all automatic call classification, I have mitigated these concerns and removed requirement of an estimate of software uncertainty.
An occupancy framework with the strict assumption that the population of a species remain closed during the sampling period was inappropriate to address my objectives due to the addition of newly volant (flying) pups in the late summer. However, by analyzing habitat use rather than occupancy, I can model an analogous metric that accounts for imperfect detection without the constraints of the closure assumption. These single-season, single-species models were used to investigate probability of habitat use, probability of detection, and site- and landscape-level covariates influencing these parameters.
RESULTS Species Assemblage
Of the 12,493 call files collected in summer 2015, I classified 3,447 calls to species. In summer 2016, I collected 8,197 call files and classified 1,249 to species following manual vetting of the SonoBat automatic classification and detected eight of the nine species previously detected in Massachusetts (Fig. 2).
Figure 2. All full-spectrum acoustic calls manually vetted and classified to species in 2015 and 2016. MYLE: eastern small-footed bat (Myotis leibii), MYSE: northern long-eared bat (Myotis septentrionalis), MYSO: Indiana bat (Myotis sodalis), MYLU: little brown bat (Myotis lucifugus), PESU: tricolored bat (Perimyotis subflavus), LABO: eastern red bat (Lasiurus borealis), EPFU: big brown bat (Eptesicus fuscus), LANO: silver-haired bat (Lasionycteris noctivagans), and LACI: hoary bats (Lasiurus cinereus).
I used these species-specific detections in combination with site- and landscape-level information to describe factors associated with bat habitat use throughout Cape Cod National Seashore. For this report, I focused on habitat use of the recently listed northern long-eared bat (Myotis septentrionalis). Preliminary habitat use models suggest that probability of site use by M. septentrionalis was most affected by year, while probability of detection was affected by average daily humidity (%) and average daily wind speed (MPH). The best model shows that probability of habitat use by this species decreased from 0.2405 (0.0484 SE) in the summer of 2015 to 0.0911 (0.0439 SE) in 2016 potentially due to a large outbreak of gypsy moths during that year (Table 1). As expected, probability of detection was negatively correlated with average daily humidity and wind speed as bats tend to avoid foraging in high winds or during precipitation events.
Table 1. Preliminary habitat use models compared using Akaike’s Information Criterion (n = 147) for M. septentrionalis in summer of 2015 and 2016 where is the probability of site use, is the probability of detection, K is the number of model parameters, and AIC is the model weight.
(year) (humidity, wind)
Three sampling points were in areas that had undergone prescribed burning in the last 3-4 years, all three of which came from the developed or disturbed land cover category. Two species were found at all three sites: the big brown bat (Eptesicus fuscus) and the silver-haired bat (Lasionycteris noctivagans). Two other species were detected at only one sampling site: the eastern red bat (Lasiurus borealis) and the hoary bat (Lasiurus cinereus). The target species, the northern long-eared bat (Myotis septentrionalis), was not detected in any of the three sampling sites that had experienced recent prescribed burning. Due to the small number of sampling sites located within recently burned habitat, future analysis will expand to include sampling sites within 100 meters of a recently burned area.
FUTURE WORK Single-species, single-season occupancy modeling will continue for the remaining seven species detected on Cape Cod National Seashore, as well as continued analysis on the effects of prescribed burning on bat detection. Future goals of this project also include an investigation of habitat use by two co-occurring species, the big brown bat (Eptesicus fuscus) and the federally threatened northern long-eared bat (Myotis septentrionalis) using conditional two-species occupancy models as proposed by Richmond et al. (2010). The results of the above species assemblage and habitat use analysis, and the future two-species occupancy models will form my Master’s thesis and be prepared for submission to peer reviewed journals.
ACKNOWLEDGEMENTS This research would not have been possible without the financial support of the Edna Bailey Sussman Foundation and the National Park Service. Due to their generous support, the Edna Bailey Sussman Foundation will be recognized in all presentations, reports, and publications that result from this research. I would also like to thank my major professor Dr. Shannon Farrell for her insight and support and the members of my thesis committee Dr. Jacqui Frair and Dr. Brian Underwood for their guidance on this project. I am deeply grateful to my many technicians and Dr. Robert Cook as our National Park Service liaison and guide to Cape Cod National Seashore.
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