Bat Foraging Habitat Use: Response to Forest and Landscape Conditions

Submission to the Edna Bailey Sussman Foundation

Megan GallagherState University of New York

College of Environmental Science and ForestryBACKGROUND

Bats provide important ecosystem services including playing a significant role in arthropod suppression, seed dispersal, and pollination (Kunz et al. 2011). Bat populations across North America have declined due to white-nose syndrome (WNS; Pseudogymnoascus destructans), a cold-tolerant fungal skin pathogen that directly affects bats during hibernation (Fenton 2012) which can result in severe damage to wing tissues and membranes (Cryan et al. 2010). It has been hypothesized that physiological changes due to WNS infection may cause bats to lose fat reserves that are crucial for surviving the winter months, and may also cause them to emerge their hibernacula early increasing the chances of mortality (USFWS 2017a). Consequently since the emergence of WNS, winter hibernacula studies have been a research priority (Brownlee-Boubouli 2013). However, research on summer bat habitat requirements is equally as important, because sufficient, high quality summer habitat is necessary for fecundity and recruitment. Summer season is an important time for bats to attain sufficient food resources, and find suitable roosts for shelter and rests to optimize reproductive success and juvenile survival, supporting the future bat population.

Suitable summer habitats must have multiple elements to meet two critical life history needs, including roosting and foraging sites. These two requirements are necessary for supporting females during pregnancy, lactation, and the early weeks of young pup development (Tuttle and Stevenson 1982). Selection of suitable roosts are essential for protection from predators (Clement and Castleberry 2013), protection from weather effects (Syme et al. 2001), and for satisfying thermoregulation needs (Hamilton and Barclay 1994). It is also important to consider bats physiological needs including accessibility of water sources and prey abundance. Research has shown that with increasing proximity to water sources increases the chances that a bat will use a habitat for foraging or roosting (Kalcounis-Rüppell et al. 2005). In highly cluttered forests bats may rely on dirt roads or trails to travel to and from foraging and roosting locations, potentially for achieving a faster less energetic flight, to increase foraging success, or a way to avoid injury with it being a less cluttered environment (Grindal and Brigham 1999, Law and Chidel 2002). Some bats have been known to use night roosts that are close to suitable foraging areas (Kunz 1982). Research suggests that these night roosts act as locations for resting and digesting between feeding periods (Kunz 1982). ultimately it is important to understand the roosting and foraging requirements of bats when determining conservation goals.

Foraging style and forest type preference are also influenced in part by morphology. For example, the little brown bat (Myotis lucifugus) and the northern long-eared bat (Myotis septentrionalis) are able to use forest stands with high stem density because they are maneuverable flyers that are able to capture prey by aerial hawking and gleaning (Ratcliffe and Dawson 2003). Small species (Myotis spp.) tend to be agile and able to maneuver in forests with

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high tree densities (Ford et al. 2005). Larger, heavier bodied species such as silver-haired bats (Lasionycteris noctivagans) and hoary bats (Lasiurus cinereus) tend to be less agile and are typically better able to maneuver in less cluttered vegetation types (Owen et al. 2004). Intraspecific and interspecific interactions including niche portioning may also affect habitat use. Studies have shown that there is co-existence of morphologically similar species (Aldridge and Rautenbach 1987)

There is minimal knowledge about summer bat habitat use in the Northeast, and virtually no information on bat communities and ecology in the Adirondacks. Given that forest fragmentation, human development, and industrial forest use may affect wildlife using the forests, it is important to understand what forest features are important for bat habitat use during the summer months and how forest alterations affect them. Most hardwood forests of the Northeast have over 100 years of history with silviculture or land alteration in one form or another. Forests may be managed using a variety of methods such as clearcutting, seedtree, shelterwood, and selection cuts depending on the forest conditions and management goals. Research suggests that forest management practices that produce gaps and openings within stands allow opportunities for bats to travel and forage (Perdue and Steventon 1995, Verboom and Huitema 1997, Menzel et al. 2002, Loeb and O’Keefe 2006). Clearing most or all of a forest stand creates open space that is eventually filled in by dense undergrowth creating even-aged regrowth that lacks structural heterogeneity of multi-aged natural forests (Florence 2004). Ultimately heavy logging results in the regrowth of a dense forest which may negatively affect bats flight and foraging activities (Adams et al. 2009).

I will be focusing on several bat species, but will be giving special attention to M. septentrionalis. M. septentrionalis is a medium-sized bat that is widely distributed in the eastern United States and Canada (Caceres and Barclay 2000). M. septentrionalis was once a common bat species, but has experienced great declines due to WNS so much that M. septentrionalis hibernacula counts have declined up to 99 percent in the Northeast (USFWS 2017b). This species has been recently listed as federally threatened under the endangered species act as of April 2015, primarily based on their population decline due to WNS (USFWS 2015). This listing includes a 4(d) rule published January 14th 2016 that puts direct restrictions on forest management around known M. septentrionalis winter hibernation and known maternity roost locations. There is little information on the occurrence, and habitat use of bats, especially M. septentrionalis, within the central Adirondack region. Knowing the habitat requirements for M. septentrionalis and other bat species is crucial for understanding how forest management practices may affect their habitat use and thus for making management decisions that can effectively protect or create suitable habitat and minimize deleterious impacts. M. septentrionalis in particular is thought to exhibit high site fidelity, returning to the same forests and roosts year after year for raising young, thus identifying areas of high use by this species is especially important (Barclay and Kurta 2007).

To effectively conserve insectivorous bat populations in the northeast it is important to understand what forest conditions are necessary to support bats in the summer season, and how forest management practices may affect bat habitat use. The purpose of my proposed research is to identify bat species composition and to determine forest features associated with habitat use of foraging bats on managed landscapes within the Adirondacks. There has been no published bat

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research in this area, making this research of particular importance for informing forest management practices incorporating bat conservation and timber production in the Northeast.

METHODSStudy Area

The Adirondack park is a mountainous region approximately 6 million acres with about 2.8 million acres of forest preserve land and 3.1 million acres of private land, ultimately being a mosaic of private, public, settled, and wild lands (Jenkins 2004, Glennon and Porter 2007). My sampling points are distributed within the Adirondack park across 2 land ownerships with different management histories: Huntington Wildlife Forest (HWF) and F&W Forestry. Huntington Wildlife Forest (HWF) is a 6,000-hectare forest in the center of the Adirondack park, managed by the Adirondack Ecological Center (AEC). This property is located in Newcomb and Long Lake, NY within Essex and Hamilton counties, respectively (latitude 44° 00" N, longitude 74° 13" W). This area has been the focus of many research projects due to it being a unique transitional boreal-temperate ecotone (Beier et al. 2012). My second study location is a F&W Forestry property adjacent to HWF in Long Lake, Hamilton County. The approximately 13,300 hectares property is owned by F&W Forestry and has been a managed as an industrial forest since the early 1900s with a previous ownership of Finch Pruyn & Company Inc.

Figure 1. Location of 60 acoustic sampling points within HWF (NE) and F&W Forestry (SW).

Sampling DesignTo select potential sampling points, I used Ortho Imagery (New York State 2013), a

raster data layer with GIS and Google Earth (Google Earth 2017) to depict changes in forest structure in harvested and intact forest locations across HWF and F&W Forestry properties. A single sampling site has a radius of approximately 100m and is where I sampled for bat detections, using automated acoustic detectors. The detectors where placed at each sampling point located in the center of each 100m radius sampling site. Acoustic detectors can have a detection cone up to 100m, depending on the habitat structure, therefore, I made sure that all detector sampling points had spacing of at least 200m apart (Crampton and Barclay 1998). This insured that there was not any cross over detection from point to point, and the detection process was independent at each sampling point (MacKenzie et al. 2002). I obtained GIS polygon layers from HWF and F&W Forestry including harvested locations, rivers, waterbodies, major roads, dirt roads and trails. I used this data to determine suitable sampling point locations by first

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determining accessibility to potential locations using the major roads, dirt roads and trails polygon layers. Next, I focused on the previously harvested polygon layers and systematically choose locations within these harvest locations as potential sampling sites. The harvest dates ranged from within the past 5 years on F&W forestry property and from the 1920s through the early 2000s on HWF. Potential sampling sites that were not located in known harvest location were chosen based on accessibility first, then I used the river and waterbody layers to further choose locations that ranged in proximity to these features. My goal was to choose potential sampling sites that ranged in harvest history, current forest structure, as well as ranged in proximity to forest features including water sources. Once in the field I visually confirmed that my proposed sampling sites ranged in habitat structure including an increase and decrease in stem densities (basal area, BA), tree species composition, and that the sampling points kept a consistent distance off 200m apart from each other.

Habitat and landscape metricsI took vegetation measurements at each sampling site, which have a radius of

approximately 100m. I placed 4 equally spaced 50m transects in each cardinal direction at the center sampling point within the sampling sites. At 10m intervals along each transect, I measured canopy openness using a spherical densiometer and calculated percent open canopy at each sampling site by averaging the 21 measurements taken including the center point. Vertical vegetation stratification measurements were taken at all 21 locations by recording the amount stem and foliage obstructions in one-meter intervals for 7 height categories using a 7m pole. Stand density was determined by taking readings with a 10-factor prism at the center sampling point and at all four 50m sub-points in each cardinal direction. The following measurements were taken from all the trees observed as “in” using the prism: tree species, DBH, height, and crown ratio. All borderline tree distances were measured to determine if they were to be counted (Oderwald 1990). I used ArcMap 10.4 to calculate landscape metrics including proximity from each center sampling point to dirt roads/trails, major paved roads, pond/lakes, rivers/streams, and to determine the altitude at each center point.

Acoustic DeploymentEach sampling site was surveyed for at least two consecutive nights for two rotations to

minimize temporal variability and avoided sampling during periods of inclement weather or rain (Bender et al. 2015). The first rotation occurred during the maternity season 1 June 2017 through 10 July 2017. The second rotation was during the volant season when pups are starting to fly, 11 July 2017 through 10 August 2017. Overall there was at least four survey nights for each sampling site. I programed the SM4BAT FS and SM3BAT detectors (Wildlife Acoustics, Maynard, MA) to capture echolocation calls within the frequency range of northeastern bat species. The detectors were programmed to run nightly turning on approximately at dusk and turning off at dawn based on the solar time for the coordinate location of Newcomb NY approximately (latitude 44° 00" N, longitude 74° 13" W). Microphones were attached to the top of ½ inch 10ft tall EMT conduit metal poles that where secured into the ground about 1ft. To heighten the chances of recording the most amount of bat calls the mics were pointed in the direction with the least amount of clutter or stem obstructions and oriented towards potential bat flying routes (Weller and Zabel 2002).

Acoustic Call Classification

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I used SonoBat software (version 4.0.6 nE) to automatically append species codes to acoustic call files (Szewczak 2017). I then manually ID all of the echolocation calls based on the minimum, maximum, characteristic frequencies, and slope of calls (O’Farrell et al. 1999). A bat call sequence was identified if it has at least two discrete echolocation pulses (Burns et al. 2015). To avoid biases associated with relative abundance and relative use via number of passes or quantifying feeding activity I assigned detection (1) or non-detection (0) of a bat species or group at a site regardless of the number of calls or feeding buzzes (Ford et al. 2005). Seven bat species or species groups will be considered: Myotis - MYLU/MYSO/MYLE/MYSE (little brown bats, M. lucifugus; Indiana bat, M. sodalis; eastern small-footed bats, M. leibii; northern long-eared bat, M. septentrionalis), EPFU/LANO (big brown bat, Eptesicus fuscus; silver haired bat, Lasionycteris noctivagans), LACI (hoary bat, Lasiurus cinereus), and LABO (eastern red bat, Lasiurus borealis). I will also have separate groupings for sonobat ID species including EPFU/LANO (big brown bat, Eptesicus fuscus; silver haired bat, Lasionycteris noctivagans), EPFU (big brown bat, Eptesicus fuscus), and LANO (silver haired bat, Lasionycteri noctivagans).

For the separation of the LANO and EPFU species group I identified echolocation calls that had 2 clear pulses and were identified to species or species group using just SonoBat. These two species overlap in call structure especially when the high ƒ is below 60 kHz, which makes it nearly impossible to separate the difference between these two species. Only EPFU calls with a high ƒ above 65 kHz can be distinguished from LANO calls, which does not happen often in dense forest habiatas (Szewczak 2011). Calls from the genus Myotis were not identified to species due to their highly overlapping call structure that makes dependable species identification difficult and problematic (Broders et al. 2004).

RESULTSAcoustic Identification

Of the 8,334 call files collected, I classified 3,542 calls to species or species group. From the sonobat automatic classification and my manual vetting of calls, 7 bat species or species groups were identified Myotis - MYLU/MYSO/MYLE/MYSE (little brown bats, M. lucifugus; Indiana bat, M. sodalis; eastern small-footed bats, M. leibii; northern long-eared bat, M. septentrionalis), EPFU/LANO (big brown bat, Eptesicus fuscus; silver haired bat, Lasionycteris noctivagans), LACI (hoary bat, Lasiurus cinereus), and LABO (eastern red bat, Lasiurus borealis). I also have separate groupings for sonobat ID species including EPFU/LANO (big brown bat, Eptesicus fuscus; silver haired bat, Lasionycteris noctivagans), EPFU (big brown bat,

Eptesicus fuscus), and LANO (silver haired bat, Lasionycteri noctivagans). (Fig 2).

Figure 2. Manually vetted calls classified to species

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CONTINUIED ANALYSISMy objectives for statistical analysis are to determine the relationship of temporal and

environmental factors to the probability of detection (p), and determine how site occupancy (Ψ) varies among local and landscape covariates for seven bat species or species groups in the Adirondacks. I will fit single season occupancy models for each species or species group. Each survey night will constitute as 1 sampling event with a total of 4 per sampling location. My first approach will be to evaluate factors affecting p which will then aid in support for site occupancy models that include local and landscape effects. The probability of detection may vary by species which may be related to certain covariates. Variable names, descriptions, and hypotheses (including predicted effect) for detection, landscape, and local habitat variables that will be used as covariates in bat occupancy analysis can be found in table 3. Multiple models consisting of these covariates will be considered individually and in all additive combinations. I will use the Akaike's Information Criteria (AIC) to select the models that will best explain the empirical data.

FUTURE WORKMy second field season will take place June through August, 2018. The results of season one will directly play a role in planning for the second field season. I may survey new locations using the same techniques as season one in order to gain more statistical power with a higher number of sampling points. Ultimately the results from season one and season two will be combined and single season occupancy models will be derived.

ACKNOWLEDGEMENTSThis research would not have been possible without the financial support of the Edna

Bailey Sussman Foundation, and the Northeastern States Research Cooperative (NSRC). The Edna Bailey Sussman Foundation will be recognized in all reports, and publications that result from this research. I would also like to thank Dr. Shannon Farrell my major professor for her all of her support and guidance my graduate committee members Dr. René Germain, and Stacy McNulty for their guidance inside and outside of the field, my technicians Paige Lewandowski and Mike Loquet for all of their hard work, and F&W Forestry and Adirondack Ecological Center staff.

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