HABITAT SELECTION AND ROOSTING RANGES OF NORTHERN LONG-EARED BATS

(MYOTIS SEPTENTRIONALIS ) IN AN EXPERIMENTAL HARDWOOD FOREST SYSTEM

A THESIS

SUBMITTED TO THE GRADUATE SCHOOL

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR A MASTER OF SCIENCE DEGREE

BY:

HOLLY A. BADIN

DEPARTMENT OF BIOLOGY

BALL STATE UNIVERSITY

MUNCIE, INDIANA

ADVISOR: DR. TIMOTHY C. CARTER

MAY 2014

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THESIS PROJECT: Habitat selection and roosting ranges of northern long-eared bats ( Myotis septentrionalis ) in an experimental hardwood forest system

STUDENT: Holly Badin

DEGREE: Master of Science

COLLEGE: Science and Humanities

DATE: May 2014

PAGES: 90

This study presents the findings of a field study examining roost tree selection and roosting ranges of the northern long-eared bat ( Myotis septentrionalis ) in an experimental ecosystem of two southern Indiana state forests comprised of differing timber harvesting treatments. The northern long-eared bat is anticipated to be added to the Endangered Species list in the fall of 2014, so understanding its habitat selection is important to aid in minimizing their population decline. Northern long-eared bats were captured in Morgan-Monroe and

Yellowwood state forests, and females were fitted with transmitters. We tracked these bats to their maternity roost trees during the day, and measured vegetation characteristics around those trees. Roost tree locations were plotted in ArcMap (ArcGIS 10.2) to find roosting ranges, and the roosting range size for this species was found to average 5.4 ha. Bats roosted in the unharvested forest more often than in trees within the harvested areas, and selected areas containing more vegetation obstruction, or clutter, in both areas. However, northern long-eared bats are roost generalists when compared to other species for many vegetative characteristics, and may tolerate smaller forest harvests as long as adequate roost trees remain available on the landscape.

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ACKNOWLEDGMENTS

Many people helped me complete this project through their guidance and support, and I wish to thank those who have made this project possible. First, I thank my family for their constant emotional and mental support. My mother, Mindy Badin, my father, Joseph Badin, my sister Jill Badin, and my brother Brett Badin have not only always been there for me but also helped spawn my love of nature through exposing me to parks, zoos, nature hikes, and the great outdoors when young. I truly would not be where I am today if it was not for you.

I would also like to thank my thesis committee. My major advisor and mentor, Dr. Tim

Carter, has given me the encouragement, support, and guidance needed to complete my thesis, and I have learned much in the two years under his supervision. I cannot thank him enough for his patience with me, and for teaching me much of the skills and knowledge I possess today. Dr.

Kamal Islam and Dr. David LeBlanc have also challenged me, helping to form my thesis into what it is today with their expertise and insights.

Other lab members, graduate students, faculty members, and field technicians have spent numerous hours helping me both in the field and with my data, and I cannot thank them enough.

I would especially like to thank Dustin Owen for our brain-storming sessions and the hours we spent talking statistics. This project would have not gone as smoothly without his dedication. I am also indebted to Scott Bergeson, who worked closely with me in the field and taught me much of what needed to be done with my project. I would also like to thank my fellow graduate student and lab member Katherine Caldwell for closely working with me in the field, lending me her bat books, and keeping me optimistic with her upbeat attitude. I am incredibly thankful to all of the field technicians and those who helped with data entry, who put countless hours into the project: Marissa Thalken, Kyle G. George, Kristi Confortin, Valerie Mitchell, Abbi Hirschy, 3

Chad Argabright, and Aaron Hinton. I would also like to thank Dr. Randall Bernot, Dr. Mark

Pyron, Dr. Steven Jacquemin, and Jason Doll for taking the time out of their busy schedules to sit down with me to help me with statistics, and Kevin Barnes and Ben Lockwood for patiently explaining the complexities of GIS with me.

This project would not have gotten off of the ground without the proper funding. I am grateful for the Indiana Department of Natural Resources (IDNR) through Purdue University and assistance from Ball State University for supporting me and this project for the last two years.

Additionally, the guidance and assistance with understanding the HEE and its various quirks from Scott Haulton, Andy Meier, Jeff Riegel, and Rebecca Kalb has been greatly appreciated.

Finally, I would like to thank my other friends and lab mates here in Indiana and back in

Maryland for supporting me throughout my time at Ball State University earning my graduate degree.

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TABLE OF CONTENTS

ACKNOWLEDGMENTS ...... 3

TABLE OF CONTENTS ...... 5

CHAPTER ONE: LITERATURE REVIEW ...... 7

LITERATURE CITED ...... 22

CHAPTER TWO: ROOST TREE SELECTION OF THE NORTHERN LONG-EARED BAT

(MYOTIS SEPTENTRIONALIS ) IN SOUTHERN INDIANA ...... 33

ABSTRACT ...... 34

INTRODUCTION ...... 35

STUDY AREA ...... 37

METHODS ...... 38

RESULTS ...... 40

DISCUSSION ...... 44

MANAGEMENT IMPLICATIONS ...... 48

ACKNOWLEDGMENTS ...... 48

LITERATURE CITED ...... 49

TABLES ...... 58

FIGURES ...... 59

APPENDICES ...... 61

CHAPTER THREE: ROOSTING RANGES OF THE NORTHERN LONG-EARED BAT

(MYOTIS SEPTENTRIONALIS ) IN SOUTHERN INDIANA ...... 68

ABSTRACT ...... 69

INTRODUCTION ...... 70 5

MATERIALS AND METHODS ...... 71

RESULTS ...... 75

DISCUSSION ...... 76

ACKNOWLEDGMENTS ...... 78

REFERENCES ...... 79

TABLES ...... 85

FIGURES ...... 87

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CHAPTER ONE

LITERATURE REVIEW

Bats and Their Importance

Bats (Order Chiroptera) constitute about 20% of all mammalian species, with only the

Rodentia being more speciose (Reid 2006). Bats have a large range across many habitats, and inhabit every continent with exception of Antarctica (Willig et al. 2003). Bats are the only mammals capable of powered flight, and this capability has undoubtedly influenced their wide distribution and diversity (Kunz and Lumsden 2003). There are over 1,000 species of bats worldwide, with 45 species that inhabit the United States, all of which belonging to the Suborder

Microchiroptera (Reid 2006). Of this Suborder, the Family Vespertilionidae makes up the largest contribution to the North American species (Reid 2006).

The northern long-eared bat ( Myotis septentrionalis ) is a common forest bat ranging from central to eastern United States and into Canada, reaching as far south as Florida and as far west as Alberta, British Columbia, Montana, and Wyoming (Caceres and Barclay 2000). Although this species hibernates in caves during the winter (Caire et al. 1979), M. septentrionalis resides in forests over the summer where they depend on trees for roosting during the day (Kunz and

Lumsden 2003). The substrate of roosts for M. septentrionalis consists of exfoliating bark of dead trees, or cavities or crevices in either live or dead trees (Foster and Kurta 1999). M. septentrionalis have been found to occasionally roost in anthropogenic structures as well (Sasse and Pekins 1996, Foster and Kurta 1999, Timpone et al. 2010).

Roosts provide areas for rearing young and protection from predators and weather

(Vonhof and Barclay 1996). From early May into August, female M. septentrionalis form 7

maternity colonies to facilitate the growth of their young while males tend to roost alone or in small groups (Godin 1977). Roost trees provide a thermally stable environment for the development of pups, and the maternity colonies can cluster together to reduce thermoregulation costs (Vonhof and Barclay 1996). This is particularly important for female maternity colonies, since lower temperatures can slow fetal and juvenile development (Racey 1973). Bats switch roost trees every few days (Lewis 1995), potentially because of factors like temperature, predator pressure, parasitism, social interactions, and maintaining knowledge of alternate roost sites

(Carter and Feldhamer 2005). Lactating females may switch roosts less often than nonlactating females, possibly due to the energy expenditure required for moving young or the limited availability of suitable roost trees for developing young (Kurta et al. 1996). Distance between roost trees is often short, suggesting that bats have a fidelity to a group of trees or forest area

(Vonhof and Barclay 1996).

M. septentrionalis populations have seen declines throughout their range over the last few years, and the species is currently anticipated to be added to the Endangered Species List in the fall of 2014 (Department of the Interior 2013). Bats as a group currently face many threats such as habitat disturbance or timber harvesting impacting roosting and foraging areas, impacts on prey abundance and toxicity from pesticides, climate change, wind turbines, and the fungal disease White-nose syndrome (WNS; Thomas 1988, Arnett et al. 2008, Blehert et al. 2009,

Stahlschmidt and Bruhl 2012, Sherwin et al. 2012, Ingersoll et al. 2013). Many hibernating species of bats in eastern North America are declining due to these threats, with current regional relative abundance decline estimates as high as 71% for some species (Ingersoll et al. 2013).

Due to bat’s long generation time and low reproductive rates of commonly one pup a year,

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combined with their sensitivity to environmental stress, declines of this magnitude are

particularly alarming (Godin 1977, Jones et al. 2009).

The most profound threat to bats is WNS, which has killed over 5.7 million bats and has

caused cave population declines as much as 100% (USFWS 2014). It is caused by the cryophilic

fungus Pseudogymnoascus destructans (formerly Geomyces destructans), which colonizes bats in their winter hibernacula and causes them to awake from their hibernation, where they eventually metabolize their fat reserves and starve to death (Blehert 2012, Minnis and Lindner

2013). WNS is more difficult than many other diseases to eradicate, since fungal spores are incredibly resilient and spread easily (Castle 2011). WNS was first discovered in New York in

2006 and has since spread as far west as western Missouri as of February of 2014 (Blehert et al.

2009, USFWS 2014). Despite the fact that certain bats species were common before WNS, their numbers have been declining recently due to this disease, and many species are predicted to become threatened or endangered (Frick et al. 2010, Langwig et al. 2012). Studying populations before a more drastic decline will help biologists manage smaller populations in the future.

Comparison of these data to post WNS populations in the future will shed light on how the disease affects bat populations, and may allow for more effective mitigation.

Bat conservation efforts are receiving much attention as many species decline, especially since bats have many important environmental functions such as their role in nutrient cycling and their economic importance (Fenolino et al. 2006, Boyles et al. 2011). All but four of the bat species found in the United State feed solely on insects often eating close to or above their own weight in insects per night and therefore aiding in pest suppression. Those bats that feed on other food sources such as pollen, nectar, and fruit play important roles in pollination and seed dispersal (Taylor 2006, Kunz et al. 2011). Bats can eat a wide variety of crop pests, adding an 9

important biological control to the pest populations, and therefore preventing losses of crops

(Kunz et al. 2011). The service they provide for agriculture is valued at over $22.9 billion per year, and the loss of bats could lead to agricultural losses between $3.7-53 billion per year

(Boyles et al. 2011). When extrapolated, it is estimated that there are between 660-1320 tons of insects currently in WNS affected areas that would have been consumed if WNS had not reduced bat populations and that number will only continue to increase in the future (Boyles et al. 2011).

This will likely cause the agricultural industry to use more pesticides. Increased amounts of pesticides can more easily enter the ecosystem and possibly harm other organisms. With decreased predation from bats, insects can more quickly develop resistances to pesticides. It is currently unknown what the long term effect of this increase of insects will be to forests (Boyles et al. 2011). In forests, bats play an important role in nutrient cycling with their high nitrogen and phosphorus guano, and its wide deposition distribution at night can potentially redistribute nutrients to nutrient-poor areas (Kunz et al. 2011). Bats are keystone species in subterranean habitats as well. Their guano provides a large quantity of nutrients for many organisms that live in caves, including endangered species that may inhabit only one particular cave, such as some cave salamanders, fish, and invertebrate and fungi species (Fenolino et al. 2006, Bureau of Land

Management 2009, Nieves-Rivera et al. 2009). These bat-dependent species are likely to become extinct if bats disappear or greatly decline (Bureau of Land Management 2009). This will be a significant loss in diversity in an already scarce and fragile ecosystem. In addition, bats are an important bioindicator species for environmental quality and ecosystem functioning, so their declines could represent not only a deteriorating environment but also problems that are arising in other taxa (Jones et al. 2009).

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Timber Harvesting

Timber harvesting practices are known to change the composition of forest stands, and often traditional clear-cuts can alter large portions of the stand (Thomas 1988). As little as three decades ago it was common practice to maximize timber harvesting through clear-cutting then planting more fast-growing, profitable tree species (Guldin et al. 2007). Recently, silvicultural practices have evolved to maximize harvests while attaining other goals, such as forest health, diversity, productivity, sustainability, and conserving wildlife habitat, as well as maintaining the forest’s multiple uses, including recreation, water, wildlife, and others (Guldin et al. 2007).

Silviculture has been defined as the science of manipulating trees in a stand towards a desired future condition (Guldin et al. 2007). Harvests can be characterized into two broad categories: even-aged harvesting and uneven-aged harvesting. Even-aged harvesting involves the complete or near-complete removal of the forest stand, and the replacement stand is comprised of trees that are roughly the same age (Taylor 2006). This includes clear-cutting, which clears all of the trees in an area, and shelterwood harvests, which removes most trees in a stand in various stages, with a few years in between stages, in order to leave trees in the stand for shade and seeds (Taylor

2006). During the initial phases of the shelterwood cut, the vertical strata volume of the forest is decreased, which reduces the understory and midcanopy. Shelterwood harvests then remove all

overstory trees once regeneration has established. Uneven-aged harvesting creates stands of

different age classes and sizes by removing either single trees (single tree selection) or groups of

trees (group selection; Taylor 2006). This method creates smaller canopy gaps that mimic

natural disturbances better than even-aged harvesting. There is little documentation on how

forest harvesting directly affects bats. This may be due to infrequency of direct impact,

anecdotal occurrences that do not warrant publication, or direct effects are difficult to detect and 11

therefore not easily studied (Hayes and Loeb 2007). However, since forest harvesting affects forest structure directly by modified habitat characteristics and ecological processes, likely affects bats at least indirectly.

Timber harvesting can have both positive and negative effects on bats through altering the abundance and status of roosts trees and by creating openings and edge habitats that bat species may use for foraging. M. septentrionalis have a small size and are more maneuverable than many other bat species, and often prefer to roost and forage within interior cluttered forest stands (Carroll et al. 2002, Patriquin and Barclay 2003, Broders et al. 2006). Their feeding ecology of primarily gleaning insect prey off vegetation is well adapted to the interior forest

(Faure et al. 1993). Some have suggested that M. septentrionalis does not often forage in intensively managed stands (Patriquin and Barclay 2003, Ford et al. 2005, Sheets et al. 2013).

The creation of edge habitats and open areas through harvesting may reduce optimal roosting and foraging habitat for M. septentrionalis. In northern portions of their range, this species generally prefers old-growth or mature forests to younger forests for roosting (Krusic et al. 1996, Sasse and Pekins 1996, Caceres and Pybus 1997, Jung et al. 1999). Old-growth forests often provide more vertical complexity that different bat species can partition (Kalcounis et al. 1999), and M. septentrionalis have been found in higher abundance in stands with a higher diversity of tree species (Lacki and Schwierjohann 2001). Old growth forests can provide good roosting habitat as they provide a multi-layered vertical structure from the differing tree ages, a high number of stems, tree-fall gaps, snags with more exfoliating bark, and woody debris for bats (Center for

Biological Diversity 2010). Timber harvesting often targets older trees, potentially removing valuable M. septentrionalis habitat. However, this species may benefit from the creation of

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snags with exfoliating bark, which harvesting can produce (Sasse and Pekins 1996, Lacki and

Schwierjohann 2001).

Studying Bats

Sampling bat populations is essential for determining population trends, demographics, fluctuations, and establishing and reaching recovery goals. However the cryptic nature of bats makes them difficult to sample. Mist nets are the primary tools to capture bats and it is the most common method to sample bat populations when bats are needed in hand. This method allows for the collection of individual specific data such as age, gender, reproductive status, body measurements, weight, as well as marking individuals for recapture, and collecting tissue samples and parasites, thus giving scientists valuable data that cannot be derived from acoustic or visual surveys alone (Dunn and Ralph 2004). However, mist netting does have some inherent disadvantages. Data can be biased due to size and placement of nets, net check frequency, net nights per site, composition of the netting material, duration of the net night, environmental conditions, and the specific habitat (Carroll et al. 2002, MacCarthey et al. 2006, Larsen et al.

2007). Many bats learn avoidance behaviors and often do not fly into a net a second time, making mark-recapture studies for bats to monitor population fluctuations nearly impossible

(Larsen et al. 2007). Some bats, such as the hoary bat ( Lasiurus cinereus ) are high fliers and therefore are not captured in nets often, which affect abundance estimations. Studies have found ways to improve the efficiency of mist netting, such as decreasing the time between net checks and placing the nets in strategic areas or multiple habitat types depending on the survey objective

(Carroll et al. 2002, MacCarthey et al. 2006). Even small changes in netting protocol can lead to significant differences in species presence and abundance, and bat capture protocols are being improved regularly (Winhold and Kurta 2008). Additionally, it is important to use the same 13

protocol over different nights and sites within the same study (Winhold and Kurta 2008). While mist netting is not without its flaws, it is still the best technique to capture bats and for study objectives that are focused on monitoring a populations. Mist netting is the primary method to catch bats for radio telemetry based studies.

Radio telemetry is defined as “the sensing and measuring of information at some remote location and then transmitting that information to a central or host location” (Allen 2012). Radio telemetry does this by using a radio transmission. The equipment consists of two parts: the transmitting system and the receiving system. The transmitting system is the unit placed on the animal that sends the radio frequency, and it consists of the transmitter, battery, antenna, and casting. The transmitter emits radio waves, and these are picked up by a receiver through an antenna usually attached to the receiver. The size of the transmitters and the battery inside can influence animal activity or survivorship. It is important to have the transmitter weigh ≤5% of the animal’s weight so as to minimally burden the animal while still having a battery with a useful lifespan (Aldridge and Brigham 1988). Since many Myotis bats often weigh less than

seven grams, it is very important to minimize the transmitter’s weight so as not to interfere with

the bat’s maneuverability or behavior (Aldridge and Brigham 1988). The smallest transmitter

available is about 0.2g (Model LB-2X, Holohil Systems Ltd., Ontario Canada; Model A2405,

Advanced Telemetry Systems, Isanti, Minnesota). This can only be attached to a bat greater than

four grams. A heavier transmitter can have a longer battery life, but that may involve sacrificing

sample size if not every bat caught could be equipped with a transmitter. Important

considerations for radio telemetry studies include the random selection of animals to tag, the

number of animals, and the number of locations per animal (Amelon et al. 2009). Stratification

by age or gender is recommended to reduce variation (Amelon et al. 2009). After the necessary 14

considerations, telemetry is a powerful tool that researchers can use to locate animals and gather valuable data on their home ranges and movement patterns (Amelon et al. 2009).

Home range estimation is often an important part of habitat studies. Burt (1943) defined home range as an area an animal regularly travels for activities such as foraging, mating, and caring for young. There are two main types of home range calculation models that use radio telemetry data to analyze home ranges: minimum convex polygon (MCP) and kernels. The MCP method consists of connecting lines around the outermost data points collected for an individual animal and forming a polygon (Hayne 1949). This method is conservative since the animal was likely within those lines in order to get to the outermost data points. Opponents argue that the home range of an animal likely goes outside of the polygon, and therefore this method is not very accurate with much unpredictable bias (Hayne 1949, Borger et al. 2006). If some points fall beyond the animal’s main activity area, the range would be strongly influenced by those outermost points and include much area that the animal did not use extensively. Lavar and Kelly

(2008) argue that, mainly due to common improper study design, MCP results may be biased due to its sensitivity not only to the number of location estimates but also sampling duration and serial autocorrelations, the varying levels of robustness in the analysis, and the varied treatment of outliers . MCP also cannot indicate home range use intensity for different areas within the polygon (Harris et al. 1990). However, the MCP method is used most often to compare home range results of different studies both between different species and among the same species, partly due to the large number of studies using this method. Nilson et al. (2008) found that MCP is not any more biased than other home range methods assuming both were conducted appropriately. The MCP method is often touted as the only comparable technique between studies, and Harris et al. (1990) claimed that researchers should include it as one of two or more 15

methods when calculating home range, if the researchers opt for additional techniques beyond just MCP. Additionally, MCP is more robust than other home range techniques when the number of location points is low (Harris et al. 1990).

The kernel density estimator (KDE) method was introduced for home range studies by

Worton (1989) and has been widely used since. This computer based method consists of using the density of animal location points to estimate home range (Worton 1989). A higher concentration of location points will contribute more to the estimate than points farther away

(Seaman and Powell 1996). This method measures the probability of habitat use across the animal’s home range, with a higher concentration of location points corresponding to a higher usage area (Gitzen et al. 2006). Smoothing parameters, or bandwidth values, help generate kernel models, and are responsible for determining the level of influence one location point has on the concentration of points around it (Gitzen et al. 2006). Narrower widths can show more small-scale details but wider widths are better at showing the general shape of the range (Seaman and Powell 1996). Narrower widths potentially ungroup clusters of points, leading to underestimation, while wider widths potentially overestimate home ranges by grouping widely dispersed points together (Gitzen et al. 2006). Researchers need to look at the scope of the study, as well as their specific research question to find a smoothing parameter that works best for their study. There are two types of smoothing parameters available: fixed and adaptive. Fixed kernels use the same size parameter over the study area, and adaptive kernels are of differing sizes depending on intensity of use within the home range (Worton 1989). For kernel-based estimates it is suggested that an absolute minimum of 30 locations per animal be used, with 50 being ideal, so as not to underestimate home range (Amelon et al. 2009). MCPs do not have a suggested

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minimum number of locations for analysis, but a higher number would lead to greater accuracy

(Amelon et al. 2009).

Many studies determine animal habitat preference by comparing usage to availability data. “Preferred” habitats are identified when usage exceeds availability, and “avoided” habitats are identified when usage falls below availability, but these designations are often dependent on what the researcher deems available to the animal (Johnson 1980). Johnson (1980) recognized that habitat selection is a hierarchical process, and defined four level of selection. First order selection is the geographical range of a species, second order selection is the home range of an individual or group, third order selection is refers to the usage within that home range, such as through specific habitat selection, and fourth order selection refers to particular resource selection within the third order (Johnson 1980). A researcher may erroneously view that an animal “avoids” a certain type of habitat or habitat component if it’s available in the environment at a higher percentage than its use, when in actuality the animal could have chosen that site because that component was abundant there yet only needed in small quantities. This

“avoidance” conclusion does not reflect the component’s importance to the animal. Johnson

(1980) defines “preference” as the likelihood of a particular component being chosen if it is offered on an equal basis with others and “selection” as the process in which the animal chooses the component. It is important to note that an animal spending time in a habitat is not synonymous to the animal selecting the habitat. Researchers can rank the available habitat components to determine preference and then rank the usage of these components, and the differences can be compared to find significant use or avoidance by animals (Johnson 1980).

Using the most precise methods to understand habitat selection will lead to more effective management decisions when conserving animal habitat. 17

In a seminal paper, Aebischer et al. (1993) identified four shortcomings of many habitat analysis studies that are based on radiotracking data. The first problem was an inappropriate sampling and insufficient sample size (Aebischer et al. 1993). This can come about from using the number of sample locations collected from multiple animals as individual data points, causing non-independence and pseudoreplication. This will increase the statistical Type 1 error rate. This problem can be fixed by using the individual animal as the unit of data instead of each sample point across multiple animals (Aebischer et al. 1993). The second problem is one of non- independence of proportions (Aebischer et al. 1993). When researchers use proportions to describe habitat use, this links the usage so that an avoidance of one habitat will suggest preference for another. The third problem arises when researchers fail to take into account differential habitat use by differing groups of animals within the same population, as defined by age, sex, or size differences (Aebischer et al. 1993). Aebischer et al. (1993) suggests using statistical tests which separate out the animals for each group, or only focusing on one subset of the population, such as only adults, and extrapolating the results to only that group. The fourth problem deals with the arbitrary definition of habitat availability and boundaries for animals during a study (Aebischer et al. 1993). Not every area may be open to an animal within a study area, and animals do not heed arbitrarily defined boundaries such as park property or county lines. Aarts et al. (2008) identified additional problems associated with habitat studies based on telemetry data. The first was that environmental data may not coincide with usage data when environmental data is collected at a different time than the telemetry data (Aarts et al. 2008).

Researchers should collect environmental and telemetry concurrently in order to avoid differences caused by seasons, weather, etc. Another problem is failure to identify and account for correlations between environmental variables (Aarts et al. 2008). These correlations may be 18

strong enough to alter animal usage and not reflect the relationship between a single variable and usage. Finally, animals may not be equally detectable in different habitats (Aarts et al. 2008).

Data points may be affected by the animal’s behavior, the environment, or equipment performance (Aarts et al. 2008). To minimize bias, the sample points should be obtained at a constant rate, so that the number of points in each habitat type in an unbiased estimate on the amount of time spent there. The severity of this problem depends on the animal’s ecology and the detection technique used, and for some studies this may not be an issue.

Since bats fly long distances, they are likely to perceive habitat in the hierarchical manner defined by Johnson (1980; Limpet et al. 2007). Most studies of the relationship between bats and their summer habitat use have focused on either the stand level such as how bats respond to different forest types, ages, or harvesting practices, or on the microhabitat level such as specific tree characteristics of bat roost selection (Loeb and O’Keefe 2006). I plan to study bat habitat selection at both the stand and microhabitat levels to more fully understand the habitat selection of M. septentrionalis . The wide range of roost tree species used by M. septentrionalis suggests

that the stand level may play a greater role in bat habitat selection than previously realized, and

understanding both level of selection are essential for M. septentrionalis conservation (Silvis et

al. 2012). Furthermore, management for bats is often focused on the conservation of summer

maternity roosts, which are possible limiting factors that are threatened by timber harvesting

(Kunz and Lumsden 2003). Due do the variations in conclusions from multiple studies on M. septentrionalis roosting in different conditions and types of managed forests, such as tree condition and species preference (Sasse and Pekins 1996, Lacki and Schwierjohann 2001, Owen et al. 2002, Silvis et al. 2012), this type of study is instrumental in managing for M. septentrionalis in the wake of their decline. 19

Study Site

A consortium of state agencies and universities created the Hardwood Ecosystem

Experiment (HEE), a 100-year large-scale study launched in 2006, located in Morgan-Monroe

State Forest (MMSF) and Yellowwood State Forest (YSF; Kalb and Mycroft 2013). YSF is located in Brown County, Indiana, and comprises 9,439 hecatres (ha), and MMSF is located in both Morgan and Monroe counties, Indiana and comprises 9,712 ha (HEE 2010). The landscape is mainly dry ridges with steep ravines overlaying siltstone, shale, and sandstone bedrock (Kalb and Mycroft 2013). The forest consists of primarily mixed upland hardwoods, and on both properties an oak-hickory forest landscape dominates at 43%, with conifer or mixed forest types making up <5% (Haulton 2013). The HEE consists of 9 management units: 3 even-aged units of clear-cut and shelterwood harvests, 3 uneven-aged units of single tree selection or patch-cuts, and 3 control units where no harvesting took place. The even-aged and uneven-aged units were harvested in the fall of 2008 (Kalb and Mycroft 2013).

The 9 management units, consisting of a research core surrounded by a buffer zone, were randomly drawn from Department of Forestry management tracts, with each core comprising 2 to 3 management tracts and totaling 78 – 110 hectares (Kalb and Mycroft 2013). Buffer zones were designated as the tracts immediately adjacent to the research core. The management units range from 303 – 483 ha in size (Kalb and Mycroft 2013). Each of the 9 units was randomly assigned to a treatment consisting of 3 replicates each: control, even-aged, or uneven aged. In

MMSF, there were 2 control units, 1 uneven age treatment, and 1 even age treatment. In YSF, there was 1 control unit, 2 uneven age units, and 2 even age units (HEE 2010). The 3 units assigned to control will receive no harvest or burn treatments for the duration of the study, and will be used to monitor plant and animal populations in the absence of silviculture treatment 20

(HEE 2010). Even-aged management units consisted of four 4-hectare (10-acre) openings per unit; 2 shelterwood and 2 clear-cut. (HEE 2010). The shelterwood stands are currently in the first stage of the preparatory cut with only the understory removed, and the forest canopy remains intact. In the clear-cut stands, only trees greater than 30.5cm DBH were removed initially, but since then post-harvest timber stand improvement was implemented to remove most material greater than 2.5cm DBH, although some large snags were left. Uneven-aged management treatment replicated the current silvicultural practices used at Morgan-Monroe and

Yellowwood state forests. Uneven-aged management units consisted of four 0.4 ha (1.0 ac), two

1.2 ha (3.0 ac), and two 2 ha (5.0 ac) patch-cuts, which resembled clear-cuts in appearance (HEE

2010). In the remaining area of the research core, single tree selection occurred. Single-tree selection harvests and some small patch-cuts also take place in the MMSF and YSF lands outside of the HEE units.

Objectives

My broad objective is to describe M. septentrionalis habitat and maternity roost selection at the microhabitat and stand scales and how differing silviculture treatments affect their roosting ecology and roosting ranges.

1. Microhabitat level: I will determine what environmental characteristics female M. septentrionalis select for when choosing their maternity roosts. This will be accomplished through analyzing different vegetative and environmental characteristics around known M. septentrionalis maternity roost trees, as well as around randomly selected trees in the surrounding forest. I hypothesize that M. septentrionalis do select for specific environmental characteristics, different from the general forest, when selecting their roost trees.

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2. Stand level: I will determine how forest management affects the roosting ranges of M. septentrionalis . Roosting ranges are the entire area that a single bat uses for its numerous roost

trees. M. septentrionalis are known to be interior forest species for roosting (Carroll et al. 2002,

Broders 2006), possibly due to their feeding ecology, which mainly consists of gleaning insect prey off of vegetation (Faure et al. 1993, Foster and Kurta 1999). Their small size and maneuverability, wing morphology, flight behavior, and short duration echolocation call design helps M. septentrionalis fly through cluttered interior forests well and feed by gleaning

(Patriquin and Barclay 2003, Faure et al. 1993). Therefore, bats are likely not randomly choosing the location of their roosts trees within the stand and would prefer to roost closer to where they feed, further in the intact forest. I hypothesize that M. septentrionalis roosts will be farther from harvests when compared to randomly selected points within the study area. I further hypothesize that M. septentrionalis roosts will be closer to key resources that the bat needs, such

as water for drinking and roads and trails for flight corridors, when compared to randomly

selected points within the study area.

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32

CHAPTER TWO: ROOST TREE SELECTION OF NORTHERN LONG-EARED BATS

IN AN EXPERIMENTAL HARDWOOD FOREST SYSTEM

Badin, Holly A., and Timothy C. Carter.

Prepared for The Journal of Wildlife Management

33

ABSTRACT

Populations of northern long-eared bats ( Myotis septentrionalis ) have been declining since the detection of white-nose syndrome in 2006 and its effects on hibernating bats. This has led to the anticipation that the northern long-eared bat will be added to the federally endangered species list in 2014. Because of this decline, the relationship between forest management practices and bats has become increasingly important, especially the conservation of summer maternity sites.

Northern long-eared bats roosting preferences are poorly understood and are now of increasingly higher conservation value. We attempt to identify the environmental factors important in the roost selection for northern long-eared bat roosts. We examined how those factors differed between undisturbed and disturbed forests since 2008 in the Hardwood Ecosystem Experiment in

Morgan-Monroe and Yellowwood state forests of southern Indiana. This large scale project consists of 9 management units of different forest harvest regimes: 3 even-aged, 3 uneven-aged, and 3 control units. Bats were captured by mist-nets in 2 designated areas per management unit as well as 3 additional ponds. Female northern long-eared bats were fitted with radio- transmitters and 23 were tracked via radiotelemetry to their roost trees. Microhabitat characteristics were measured at each roost tree, and at randomly chosen trees. Roost trees tended to have greater solar exposure than random trees in the intact forest, and when bats roosted in disturbed forest areas they selected for characteristics related to greater vegetative obstruction that more closely resembled the undisturbed forest. Roosts were also more likely to be found on northwest facing slopes, and in weakly decayed dead trees. This species also selected sassafras more often than other tree species as a roost tree. Bats were found to roost in the undisturbed forest more often than the disturbed harvested forest, but northern long-eared

34

bats may tolerate smaller forest harvests as long as adequate roost trees remain available on the landscape.

KEY WORDS habitat, Indiana, Myotis , maternity roost, northern long-eared bat, septentrionalis , timber harvesting

INTRODUCTION

Many common Myotis bat species of the Midwest, including the northern long-eared bat

(Myotis septentrionalis ) reside in forests over the summer and depend on trees for roosting

during the day (Kunz 1982). The substrate of roosts consists of exfoliating bark of dead trees, or

cavities or crevices in either live or dead trees (Foster and Kurta 1999). Roosts provide areas for

rearing young and protection from predators and weather (Vonhof and Barclay 1996). Bats

switch roost trees every few days, potentially because of factors like temperature, predator

pressure, parasitism, social interactions, and maintaining knowledge of alternate roost sites

(Lewis 1995, Carter and Feldhamer 2005). Summer maternity roosts are assumed critical, and

are often considered a limiting factor for bats (Kunz and Lumsden 2003).

Greater bat activity is often found in intact mature forests rather than younger forests

(Humes et al. 1999). Northern long-eared bats generally prefer mature forests to younger forests

for roosting (Sasse and Pekins 1996, Caceres and Pybus 1997, Broders and Forbes 2004, Perry et

al. 2008, O’Keefe 2009) and foraging (Krusic et al. 1996, Jung et al. 1999, Loeb and O’Keefe

2006). Mature forests often provide more vertical complexity that different bat species can

partition (Kalcounis et al. 1999), and northern long-eared bats have been found in higher

abundance in stands with a higher diversity of tree species (Lacki and Schwierjohann 2001).

Northern long-eared bats are often considered a clutter-adapted species, preferring areas with 35

more clutter, or vegetative obstruction (Jung et al. 1999, Carroll et al. 2002, Patriquin and

Barclay 2003, Broders and Forbes 2004, Broders et al. 2006). Northern long-eared bats are adapted morphologically to clutter by having shorter, broader wings and small body sizes, allowing this species to be more maneuverability than many other forest bats (Norberg and

Rayner 1987). Additionally, northern long-eared bats primarily glean their prey from vegetative surfaces (Faure et al. 1993).

Timber harvesting practices are known to change the composition of forest stands, and often traditional even-age management techniques can alter large portions of the stand simultaneously (Thomas 1988). Indiana was the leading producer of oak and walnut prior to the

20 th century, but due to slow rates of secondary growth and increased demand, hardwood forests

in the region began to decline, leading the state government to implement better forest

management practices through increased protection, legislature, and public education (Carman

2013). Efforts have recently increased to encourage forest sustainability and management in

response to a decline in populations of native wildlife, including bats.

Timber harvesting often targets older trees, potentially taking away valuable northern

long-eared bat habitat. The impact timber harvesting plays on northern long-eared bat ecology is

unclear. Northern long-eared bat occupancy has been found to be inversely related to edge

habitat, which harvests create on the landscape (Yates and Mazika 2006), and in some cases this

species has been found to roost in younger stands as well (Sasse and Pekins 1996, Lacki and

Schwierjohann 2001, Menzel et al. 2002). In addition, the structural changes in forests brought

about by certain types of timber harvesting can create differing habitat structures that bats can

partition effectively (Humes et al. 1999). Researchers generally believe that bat species and

forest management can coexist as long as the harvesting is done in a manner that takes into 36

account bat roosting and foraging ecology, such as potentially employing alternative forms of timber harvesting instead of large scale even-age management such as clear-cut practices. The

Hardwood Ecosystem Experiment (HEE) in southern Indiana provides a framework to study long-term effects of forest management on different wildlife and plant communities.

In the wake of population declines, the conservation of maternity roosts for northern long-eared bats is of increased importance, especially with continual demand for timber harvesting. However, northern long-eared bat roosting preferences are poorly understood. A few studies have tried to quantify their roosting preferences, but there are often conflicting results, depending on region and between studies. Our study attempts to better understand the maternity roost characteristics selected by female northern long-eared bats in hardwood forests of southern Indiana. Based on prior research, we predicted that northern long-eared bats would roost in the unharvested forest more often, and select for vegetative characteristics that are characteristic of more cluttered environments both in the intact forest and when roosting in a disturbed forested area.

STUDY AREA

A consortium of state agencies and universities developed the Hardwood Ecosystem

Experiment (HEE), a 100-year large-scale study launched in 2006, located in Morgan-Monroe

State Forest (MMSF; Figure 1) and Yellowwood State Forest (YSF; Figure 2; Kalb and Mycroft

2013). YSF is located in Brown County, Indiana, and comprises 9,439 hectares (ha), and MMSF is located in both Morgan and Monroe counties, Indiana and comprises 9,712 ha (HEE 2010).

The area is made up of dry ridges with steep ravines overlaying siltstone, shale, and sandstone bedrock (Kalb and Mycroft 2013). The forest is characterized by mixed upland hardwoods 37

(Haulton 2013). Both properties together consist of 43% oak-hickory forest (Haulton 2013).

The HEE consists of 9 management units ranging from 303 – 483 ha in size: 3 even-aged units of clear-cut and shelterwood harvests, 3 uneven-aged units of single tree selection or patch-cuts, and 3 control units where no harvesting has taken place (Kalb and Mycroft 2013). Even-aged management units consist of 4-hectare (10-acre) openings per unit; 2 shelterwood and 2 clear- cut. (HEE 2010). The shelterwood stands were in the first stage of the preparatory cut with only the understory removed, and the forest canopy remained intact. In the clear-cut stands, most material greater than 2.5cm DBH was removed, although some large snags were left in the harvest. Uneven-aged management treatment replicates the current silvicultural practices used at

MMSF and YSF. Uneven-aged management units consisted of four 0.4 ha (1.0 ac), two 1.2 ha

(3.0 ac), and two 2 ha (5.0 ac) patch-cut openings, which resembled clear-cuts in appearance

(HEE 2010). In the remaining area of the research core, single tree selection occurred. Single- tree selection harvests and some small patch cuts also take place in the MMSF and YSF lands outside of the HEE units.

METHODS

Data were collected in the summer of 2012 and 2013 from mid May to late July. We captured bats in 2 designated sites per management unit, as well as around 3 small ponds. These sites were chosen based on the likelihood of bat presence, such as road and trail corridors where bats forage. We captured bats using high mist net systems (Gardner et al. 1989). Upon capture, bats were identified, sexed, reproductive condition determined, aged, and weighed. We attached transmitters (model LB-2N Holohil Systems, Ltd., Ontario, Canada) to female northern long- eared bats. Transmitters weighed ≤ 0.42 g to keep the transmitter below 5% of the animal’s total 38

weight (Aldridge and Brigham 1988). Either Perma-Type, (Perma-Type Company, Inc.,

Plainville, Connecticut) or Skin-Bond (Smith and Nephew, Inc., Largo, Florida) was used to attach the transmitter to the dorsal surface of the bat between its scapulae. We tracked radio tagged bats starting the day after they were fitted with a transmitter until that transmitter fell off or its battery died. We initially tracked bats using a car-mounted 5-element Yagi antenna, and once the animals’ general location was discovered they were tracked on foot with a 3-element

Yagi antenna and telemetry receiver (model TRX-1000WR Wildlife Materials, Inc.,

Murphysboro, Illinois or model R2000 Advanced Telemetry Systems, Inc., Isanti, Minnesota).

A handheld GPS unit was used to record the location of each roost tree.

We measured microhabitat variables within a 0.04 ha circular plot (hereafter called

“vegetation plots”) as described by Carter and Feldhamer (2005) at the tree level (see Appendix

A) and at the plot level (see Appendix B). All trees in a plot were identified to species, with their

DBH and decay class recorded (Makinen et al. 2006). We used a random compass bearing and a random distance from 30 to 100 m from each known roost and centered a second plot (random) on the closest tree with DBH ≥ 10 cm to compare to the roost trees by measuring the same

microhabitat variables. Each plot was classified as either undisturbed or disturbed habitat.

Undisturbed habitats were defined as habitats where no forest management was taking place

(control units, the matrix of the even-aged units, and forested areas outside the state forest lands)

or areas of light single-tree selection comprising most areas of the state forests and the matrix of

the uneven-aged units. Disturbed habitats were defined as clear-cut, patch-cut, or shelterwood

harvests. Exit counts (Kunz et al. 2009) were conducted on as many trees as possible.

39

Data Analysis

We analyzed roost trees as independent observations. A multivariate analysis of variance

(MANOVA) (Haase and Ellis 1987, Minitab 16.0) was used to test for differences in quantitative variables for roost plots found in the undisturbed forest (undisturbed roosts), the disturbed forest

(disturbed roosts), random plots found in the undisturbed forest (undisturbed randoms), and disturbed forest (disturbed randoms). Following significant multivariate results, variables were examined separately using one-way ANOVAs with Tukey’s HSD tests for significant variables to see where the differences lay. Non-normal data was appropriately transformed to achieve normality, including the arcsine transformation for canopy cover (Prasad 2008). A chi-square goodness of fit test (Neu et al. 1974) was used to determine the dissimilarities in categorical variables (roost tree species, plot aspect, decay class, density of understory, measurement of understory, and live vs. dead status of trees). All roost plots were compared to all random plots, and if significant results were found the variable was analyzed again for the undisturbed and disturbed forest if sample size would allow.

We used a false discovery rate test for all p-values to account for accumulation of Type 1 error, and all p-values given are adjusted (Pike 2011). All statistical tests were performed in

Minitab Statistical Software Version 16 at α = 0.05, with the exception of the false discovery rate test, which was performed on an online calculator for multiple comparisons procedures (Pike

2011).

RESULTS

Between the 2 summers there were a total of 59 nights of mist netting conducted. We

captured 111 northern long-eared bats. We attached transmitters to and tracked 23 female 40

northern long-eared bats comprising 19 adults and 4 juveniles. Of the adult bats, 5 were pregnant, 5 were lactating, 2 were post-lactating, and 7 were non-reproductive at the time of capture and tracking. Bats were tracked for an average of 5.3 days (range 1 - 11) and switched roosts an average of 3.1 times (range 0 - 8), using an average of 3.7 trees (range 1 - 9) over their tracking period. The longest time that a bat used the same roost tree consecutively was for 5 days. We tracked bats to 71 roost trees, with 91.5% of roost trees found in the undisturbed forest

(65 of 71) and 8.5% of roost trees found were in the disturbed forest (6 of 71). Of these roosts

29 were found in Yellowwood State Forest and 42 were found in Morgan-Monroe State Forest.

Yellowwood State Forest contained 3 disturbed roost trees (2 in a shelterwood harvest and 1 in a

HEE 5-acre patch-cut) with 26 roost trees found in the undisturbed forest. Morgan-Monroe State

Forest contained 3 disturbed roost trees (all in a 2-acre patch-cut outside of the HEE area) and 39 roost trees in the undisturbed forest. Of the roost trees, 34 were living, 33 were dead, and 4 were partially dead. No live roost trees were selected in disturbed areas. We conducted random tree vegetation plots around 109 random trees: 29 were sampled in the disturbed forest (2 were in a shelterwood cut, 2 were in a patch-cut, and 25 were in clear-cuts) and 80 were sampled in the undisturbed forest.

The most common trees in the forest as found through our vegetation plots were sugar maple ( Acer saccharum; 16.8%), chestnut oak ( Quercus prinus ; 15.1%), sassafras ( Sassafras albidum ; 13.9%), red maple ( Acer rubrum ; 8.2%), white oak ( Quercus alba ; 6.5%), red oak

(Quercus rubra; 6.4%), and tulip poplar ( Liriodendron tulipifera ; 5.6%). All other tree species,

including unidentifiable snags, constituted <5% each. Thirty-four species of trees were recorded

in the vegetation plots, in addition to 4 categories identified down to genus. Northern long-eared

bats used sassafras (n = 22), red oak (n = 10), chestnut oak (n = 9), black oak (n = 6), red maple 41

(n = 4), shagbark hickory (n = 3), tulip poplar (n = 3), pignut hickory ( Carya glabra ; n = 2), white oak (n = 2) and sugar maple (n = 2) for roosting, with 1 use each in beech ( Fagus grandifolia ), red bud ( Cercis canadensis ), black gum ( Nyssa sylvatica ), an unidentified hickory species, and an unidentified oak species. Three roosts were found in trees unidentifiable from decay. Northern long-eared bats did select certain trees species more than expected (χ2 =

84.7404, df = 37, p < 0.01), with the greatest contributions seen in sassafras (18.2%), shagbark hickory (12.6%), sugar maple (9.5%) and red oak (8.1%). Bats roosted under exfoliating bark

(26.8% of roosts; n = 19), in cavities (39.4% of roosts; n = 28), and crevices (25.3% of roosts; n

= 18), with 8.5% (n = 6) of roosts either unseen due to height or indistinguishable from cavity or crevice. Roost height averaged 8.4 m (range 1.2 – 27.4 m) off of the ground, and on average

53% of tree height (range 18.4 – 100%).

Exit counts were conducted on 40 trees, and 23 of those trees had maternity colonies ≥2 bats. Thirty-nine trees had 1 exit count each and 1 tree had 2 exit counts on 2 consecutive days.

The average colony size was 8.1 bats for all emergence counts (range 0 - 44) and 14 bats for trees with ≥2 bats (range 2 - 44).

The MANOVA indicated significant differences between quantitative variables for roost plots and random plots within undisturbed and disturbed areas of the forest (Wilks’ λ, F 21, 474 =

14.827, p < 0.01; Table 1). In the disturbed forests, bats selected for a greater number of trees per plot by about threefold (F 3,171 = 93.61, p < 0.01), while bats did not differentiate in the undisturbed forest. Disturbed plots overall contained about half as many trees as undisturbed plots overall (F 3,171 = 93.61, p < 0.01). Undisturbed random trees were taller than disturbed

random trees by about 50% (F 3,171 = 4.11, p = 0.01), but the bat roosts in the undisturbed and disturbed forests did not differ significantly in height. Roost trees in the undisturbed forest had 42

on average 11% lower canopy cover than random trees in the undisturbed forest (F 3,174 = 33.27, p

< 0.01). The disturbed random forest plots were about 50% shorter for crown depth of the tallest tree than roost trees found in both forest types (F3,174 = 11.36, p < 0.01), while all roost trees did not significantly differ from the undisturbed forest. For this variable, bats were selecting roost trees within both forest types that more closely resembled the undisturbed forest. The disturbed random plots had about a 50% shorter height of tallest understory tree than the undisturbed random plots, and while bats roosting in the undisturbed forest roosted in areas with similar understory depth as the undisturbed random plots, the bats roosting in the disturbed areas chose plots with similar understory depth not significantly different from either the undisturbed forest or the disturbed forest (F 3,174 = 11.09, p < 0.01).

Using a Chi-square analyses to see if bats selected certain characteristics at a higher frequency than what was available revealed that bats roosted in dead trees more often than what was expected based on the higher frequency of live trees recorded (χ2 = 53.61, df = 1, p < 0.01).

This same pattern held true in the undisturbed forest ( χ2 = 39.1, df = 1, p < 0.01), and in the disturbed forest ( χ2 = 12.65, df = 1, p < 0.01). The proportion of roost trees in decay classes was

compared to the proportion of all trees in vegetation plots in the same decay classes with separate

analyses for random plots and roost plots. Bats roosted in trees with a decay class of “2” more

often than expected (χ2 = 132.26, df = 4, p < 0.01). Trees in this class have loose bark in which

the outer wood has started to soften while the inner wood is still fairly hard. This pattern was

also seen from comparison of roost trees to the random trees in the undisturbed forest alone (χ2 =

95.38, df = 4, p < 0.01). Comparison of roost tree decay class to the decay class counts of all trees found in the roost plot surrounding the tree gave similar results of a preference for decay class of “2” as well (χ2 = 106.94, df = 4, p < 0.01), and again when analyzing just the undisturbed 43

forest (χ2 = 78.65, df = 4, p < 0.01). Bats roosted on northwest (NW) facing slope aspects more than what was expected ( χ2 = 51.56, df = 8, p < 0.01). Northeast (NE) was the second most

selected, although bats did not roost on NE facing slopes as much as expected. When aspect was

analyzed again for only the undisturbed forest (χ2 = 19.89, df = 8, p = 0.017), NW facing slopes were still selected more than expected, while NE and east facing slopes were used less than expected from their availability.

DISCUSSION

Bats selected roost trees with lower canopy cover and thus more solar exposure than

random trees in the undisturbed forest, similar to Foster and Kurta (1999) and O’Keefe (2009),

although many other studies have not identified higher solar exposure as important for this

species (Sasse and Pekins 1996, Menzel et al. 2002, Carter and Feldhamer 2005, Perry and Thill

2007). Roosts did not differ from random trees in disturbed areas for canopy cover, as most of

the area of the disturbed forest is open without much vegetation obstruction reducing solar

exposure. Solar exposure is typically considered particularly important for female maternity

colonies, since lower temperatures can slow fetal and juvenile development (Racey 1973) and

warmer roosts can help with the energy demands of reproduction (Garroway and Broders 2008).

Although roost trees did have lower canopy cover than random in the undisturbed forest, they

still had on average 55% canopy cover. Timpone et al. (2010) found a similar canopy cover

average of 56% for this species, although their finding wasn’t significantly different than that of

random trees. Much lower mean canopy cover percentages have been reported for other species

that have been found to select for high solar exposure, such as the Indiana bat (Callahan et al.

1997, Foster and Kurta 1999, Britke et al. 2003, Timpone et al. 2010). As a clutter-adapted 44

species, northern long-eared bats may be facing a trade-off between areas with more vegetative obstruction and receiving adequate solar exposure on their roost trees, leading them to select trees with less canopy cover that still have more access to solar exposure than what is typically available on average in the undisturbed forest.

The undisturbed forest differed from the disturbed forest in number of trees per plot, tree height, crown depth of the tallest tree, and height of the tallest understory tree. More trees providing more vegetation, taller trees with many branches and roosting substrate, and the greater vertical depth in a forest can all provide additional clutter to plots. Bats did not need to select for plots with more trees in the undisturbed forest, as the means for roost plots and random plots there were so similar. However, in the disturbed forest, bats selected for more trees in their plots compared to random disturbed plots, similar to the findings of Cryan et al. (2001), Owen et al. (2002), and O’Keefe (2009).

Bats were found more often than expected on NW-facing slopes. Northern slopes are often wetter than southern slopes due to less solar radiation driving evaporation, and plants often experience less heat stress (Parker 1991). The wetter, cooler north-facing slopes may allow for greater plant growth for substrate for bats to glean from. Their preference for potentially more clutter on these NW-facing slopes may override their maximization of solar exposure, similar to how the bats did select trees with less canopy cover than random but still at 55% canopy cover on average. However, the extrapolation of this finding may be limited since bats did not select roost trees more often than expected on north- or northeast-facing slopes, and the temperature and moisture extremes on either side were not measured. Silvis et al. (2012) and Grindal (1998) noticed their northern long-eared bat roosts were primarily on south-facing slopes; however,

45

their results were not significant. Silvis et al. (2012) suggests their result to be more an artifact of snag availability towards the top of hills on their study site.

Consistent with other studies on this species, northern long-eared bats roosted in a great variety of trees. Sassafras trees were selected most often by bats. This tree species often has plentiful cavities and crevices for roosting, similar to what Silvis et al. (2012) found. This trees’ small size is conducive for the typically smaller maternity colony size. In some studies of managed forests, northern long-eared bats preferred the black locust ( Robinia psuedoacacia )

tree, which has a similar ecology to the sassafras (Menzel et al. 2002, O’Keefe 2009, Johnson et

al. 2009). Sassafras is an early successional tree species, so it is often common in managed

forests undergoing early succession. Sassafras wood is also very decay resistant (Scheffer and

Cowling 1966), so this tree species likely persists as a snag longer than other similarly sized trees

on the landscape. However, the great variety of tree species used, both in this study and other

studies, suggests that northern long-eared bats are not dependent on a single tree species for

roosting, and that most tree species with the appropriate characteristics could be used.

Preference for dead trees was consistent with other studies on this species (Sasse and

Pekins 1996, Lacki and Schwierjohann 2001, Perry and Thill 2007, Johnson et al. 2009,

Timpone et al. 2010), but also consistent is the bats frequently using live trees as roosts as well.

Although the bats roosted in dead trees more often than expected based on availability, the roost

trees themselves were roughly equal in number of live and dead trees. Six of the 7 roosts with an

exit count greater than 20 bats were in dead trees, and all were counted early in the maternity

season from late May to mid June. With the exception of 1 dead tree crevice, all of these large

colonies were under exfoliating bark. Large maternity colonies may prefer to roost in the

exfoliating bark of dead trees, also reported by Lacki and Schwierjohann (2001), since that 46

substrate may be able to hold a larger maternity colony. Sasse and Pekins (1996), Cryan et al.

(2001), and Owen et al. (2002) found large maternity colonies in a managed forest as well. In our study, 6 of the 7 trees with a high exit count were in the undisturbed forest. When a bat chose to roost in a dead tree in our study, it often selected a weakly decayed tree. The selection for weakly decayed trees for maternity colonies of northern long-eared bats was also seen in

Broders and Forbes (2004), Lacki et al. (2009), and O’Keefe (2009). A weakly decayed tree more likely to have more exfoliating bark, and cavity presence is also positively correlated with decay class (Smith et al. 2008). It is possible that a weakly decayed tree may provide more roosting substrate for bats than a recently dead tree with little decay or exfoliating bark, while still maintaining structural integrity.

Although other researchers have found less northern long-eared bat activity in harvested patches as compared to the interior forest (Patriquin and Barclay 2003, Ford et al. 2005, Sheets et al. 2013), northern long-eared bats may be adaptable to managed forests, often roosting in or near areas of partial harvesting in other studies as well (Menzel et al. 2002, Perry and Thill 2007,

O’Keefe 2009). After a disturbance, bats have been known to readily use the forest (Johnson et al. 2009, Lacki et al. 2009), and tolerate areas adjacent to their preferred habitat for roosting

(O’Keefe 2009, Russo 2010), seemingly tolerant to the disturbance. A recent study found that the roosting behavior for this species is robust to a random loss of < 20% of roost trees, supporting this species’ tolerance to minor roost loss (Silvis et al. 2014). Bats of this species often roost in close proximity to roads, trails, or water (Grindal 1998, Perry et al. 2008, O’Keefe

2009, Johnson et al. 2013), so the harvests may also provide accessibility as travel corridors to other areas of the forest or serve as a landmark.

47

MANAGEMENT IMPLICATIONS

Results presented in this study suggest that northern long-eared bats are not choosing to

roost in proximity to the harvest areas, although they are close to the harvests at some point

during their night movements. There is a general trend of movement away from disturbed areas;

although the bats were capable of roosting in the disturbed forest, when they do they typically

select more cluttered areas within that habitat. Northern long-eared bats select for the

undisturbed forest more often than standing trees left in the disturbed forest, and select the

cluttered areas of the disturbed forest that are more similar to the undisturbed forest. The impact

of shelterwood harvests in our study remains unclear, as only 2 roost trees were found in that

harvest type. Titchenell et al. (2011) found no difference in northern long-eared bat foraging

between different stages of shelterwood harvests or between shelterwood harvests and the intact

forest. Thinning forests, as the first few stages of a shelterwood accomplish, often leads to a

lower snag abundance and a reduction in certain tree species used by bats, such as sassafras

(Graves et al. 2000). The northern long-eared bat selections for dead trees and clutter would

indicate that shelterwood harvests would not be beneficial for this bat species, but more studies

targeting this species in shelterwood harvests are needed. Forest management is not single-

species focused and needs to account for multiple bat species with varying habitat preferences,

so maintaining a mosaic of habitat types in the area would be best to accommodate for various

bat species and their needs.

ACKNOWLEDGMENTS

Funding for this project was provided by Indiana Department of Natural Resources (IDNR)

through Purdue University. We thank S. Bergeson for project oversight, M. Thalken, K. George, 48

K. Confortin, V. Mitchell, K. Caldwell, A. Hirschy, C. Argabright, and A. Hinton for their help with field work and data entry, D. Owen, K. Barnes, Dr. M. Pyron, Dr. R. Bernot, Dr. S.

Jacquemin, and J. Doll for their help with statistics, and the HEE and IDNR staff members A.

Meier, S. Haulton, J. Riegel, and R. Kalb.

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treatments for oak regeneration. Pages 203-217 in R. K. Swihart, M. R. Saunders, R. A.

Kalb, G. S. Haulton, and C. H. Michler, editors. The Hardwood Ecosystem Experiment: a

framework to studying responses to forest management. United States Department of

Agriculture Forest Service. General Technical Report NRS-P-108. Newton Square,

Pennsylvania, USA.

Silvis, A., W. M. Ford, E. R. Britzke, N. R. Beane, and J. B. Johnson. 2012. Forest succession

and maternity day roost selection by Myotis septentrionalis in a mesophytic hardwood

forest. International Journal of Forest Research 2012:1-8.

Silvis, A., W. M. Ford, E. R. Britzke, and J. B. Johnson. 2014. Association, roost use and

simulated disruption of Myotis septentrionalis maternity colonies. Behavioural Processes

103:283-290.

Smith, C. Y., I. G. Warkentin, and M. T. Moroni. 2008. Snag availability for cavity nesters

across a chronosequence of post-harvest landscapes in western Newfoundland. Forest

Ecology and Management 256:641-647.

Thomas, D. W. 1988. The distribution of bats in different ages of douglas-fir forests. Journal of

Wildlife Management 52:619-626.

Timpone, J. C., J. G. Boyles, K. L. Murray, D. P. Aubrey, and L. W. Robbins. 2010. Overlap in

roosting habits of Indiana bats ( Myotis sodalis ) and northern bats (Myotis

septentrionalis ). American Midland Naturalist 163:115-123. 56

Titchenell, M. A., R. A. Williams, and S. D. Gehrt. 2011. Bat response to shelterwood harvests

and forest structure in oak-hickory forests. Forest Ecology and Management 262:980-

988.

Vonhof, M. J., and R. M. R. Barclay. 1996. Roost-site selection and roosting ecology of forest-

dwelling bats in southern British Columbia. Canadian Journal of Zoology 74:1797-1805.

Yates, M. D., and R. M. Muzika. 2006. Effect of forest structure and fragmentation on site

occupancy of bat species in Missouri Ozark forests. Journal of Wildlife Management

70:1238-1248.

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Table 1: Averages ± standard error and F and p-values from ANOVAs examining vegetative characteristics measured in undisturbed roost plots, disturbed roost plots, undisturbed random plots, and disturbed random plots for northern long-eared bats tracked in

Yellowwood and Morgan-Monroe state forests in southern Indiana, 2012-2013.

Undisturbed Disturbed Roost Random Roost Random Response Variable (n=65) (n=80) (n=6) (n=29) F-value p-value

Number of trees/plot 22 ± 1.3 11 ± 3.8 20 ± 1.2 3 ± 0.9 F3,171 = 93.61 < 0.01

Crown depth of tallest tree (m) 19 ± 0.8 21 ± 4.5 19 ± 0.3 11 ± 1.5 F3,171 = 11.36 < 0.01

Average canopy cover (%) 55 ± 3.3 24 ± 13.6 66 ± 2.6 14 ± 3.7 F3,171 = 33.27 < 0.01

Height of tallest understory tree (m) 7 ± 0.4 8 ± 3.4 7 ± 1.1 4 ± 0.3 F3,171 = 11.09 < 0.01

Tree height (m) 19 ± 1.5 13 ± 2.8 22 ± 1.2 15 ± 1.4 F3,171 = 4.11 0.01

Slope (degrees) 12 ± 0.9 15 ± 2.6 13 ± 0.8 15 ± 1.3 F3,171 = 1.13 0.41

DBH (cm) 30 ± 2.1 35 ± 5.3 27 ± 1.7 25 ± 1.9 F3,171 = 1.02 0.45

58

Figure 1: The Morgan-Monroe State Forest study area for the Hardwood Ecosystem Experiment comprising Morgan and Monroe counties of southern Indiana and consisting of one even-aged unit, one uneven-aged unit, and two control units. 59

Figure 2: The Yellowwood State Forest study area for the Hardwood Ecosystem Experiment comprising Brown County of southern Indiana and consisting of two even-aged units, two uneven-aged units, and one control unit.

60

Appendix A: Microhabitat variables measured at roost tress of Myotis septentrionalis in southern Indiana.

Tree species: Species of the tree, with identification help from Jackson (2003).

Height of roost (m) : Estimated with telemetry by visually locating suitable roosting substrate on roost tree, and often confirmed by an exit count. Actual height was determined by using a clinometer (Suunto, Helsinki, Finland).

Height of roost tree (m) : Determined using a clinometer.

Diameter of roost tree (cm) : Diameter at breast height (DBH). Determined using a diameter tape (Forestry Suppliers Inc., Jackson MS).

Substrate of roost : Exfoliating bark, cavity, or crevice on tree. Determined visually and with radiotelemetry, and often confirmed by exit count the same evening as bats found.

Decay class : Rated between 0 (live) to 5 (almost decomposed) from categories described by Makinen et al. (2006).

Canopy cover (%) : Estimated visually from bottom of tree for above the roost from the four cardinal directions. Observer visualized a 45° cone from roost to estimate percentage.

Aspect : Determined by compass for which way the slope faced of the hill the tree was on (N, S, E, W, NE, SE, NW, SW)

Slope : Degree slope of the hill, determined using a clinometer.

Appendix B: Measurements from a 0.04 ha (11.3 m) radius circular plot around roost trees and random trees in southern Indiana.

Measurement of overstory : Species, DBH, and decay class of all trees with DBH ≥ 10 cm. DBH (cm) was measured using a diameter tape.

Crown depth of tallest overstory tree in the plot (m) : Height range from the top of the tallest live tree to the height of the lowest live major branches of that tree.

Measurement of understory : Number of stems of woody species with DBH > 1.5 and < 10 cm recorded in categories of 0-9, 10-19, 20-29, and ≥30.

Height of tallest understory tree in the plot (m): Height of the tallest understory tree (< 10 cm DBH).

61

Density of understory : Visually determined by same observer as open, light, moderate, moderate- heavy, and heavy, taking into account both vertical and horizontal densities.

Appendix C: Contingency Tables for Chi Squared Analyses

Table 2: Tree species observed through vegetation plots in Yellowwood and Morgan-Monroe state forests in the summers of 2012 and 2013.

Observed Bat Roost Expected Tree Tree Species Count Count Totals Basswood 0 16 16 Beech 1 158 158 Black Oak 6 133 139 Hickory spp. 1 10 11 Oak spp. 1 8 9 Pignut Hickory 2 142 144 Red Bud 1 2 3 Red Maple 4 267 271 Red Oak 10 208 218 Chestnut Oak 9 491 500 Sassafras 22 455 477 Shagbark Hickory 3 26 29 Sugar Maple 2 548 550 Tulip Poplar 3 184 187 White Oak 2 213 215 Bigtooth Aspen 0 27 27 Bitternut Hickory 0 55 55 Black Cherry 0 19 19 Black Gum 1 19 20 Black Locust 0 7 7 Black Walnut 0 5 5 Blackjack Oak 0 1 1 Bur Oak 0 3 3 Cedar spp. 0 3 3 Chinkapin Oak 0 1 1 E. Red Cedar 0 2 2 Flowering Dogwood 0 53 53 Hop Hornbeam 0 14 14 Kentucky coffeetree 0 1 1 62

Mockernut Hickory 0 57 57 Musclewood 0 2 2 Persimmon 0 14 14 Pin Oak 0 1 1 Pine spp. 0 1 1 Red Hickory 0 18 18 Scarlet Oak 0 5 5 Sycamore 0 3 3 Swamp White Oak 0 1 1 Totals 68 3173 3241

Table 3: Aspect of the hill comparing roost trees to all random trees in Yellowwood and Morgan-Monroe state forests in the summers of 2012 and 2013.

All Roosts All Randoms Aspect Count Count Totals E 3 11 14 Flat 8 10 18 N 7 17 24 NE 2 17 19 NW 19 8 27 S 9 11 20 SE 7 6 13 SW 5 10 15 W 11 19 30 Totals 71 109 180

Table 4: Aspect of the hill comparing undisturbed roost trees to all undisturbed random trees in Yellowwood and Morgan-Monroe state forests in the summers of 2012 and 2013.

Undisturbed Roosts Undisturbed Randoms Aspect Count Count Totals E 2 8 10 Flat 8 9 17 63

N 7 9 16 NE 2 10 12 NW 15 8 23 S 9 11 20 SE 7 6 13 SW 5 6 11 W 10 13 23 Totals 65 80 145

Table 5: Measurement of understory of roost trees to all random trees in Yellowwood and Morgan-Monroe state forests in the summers of 2012 and 2013.

Measurement of All Randoms Understory All Roosts Count Count Totals Zero to Nine 5 4 9 Ten to Nineteen 6 9 15 Twenty to Twentynine 1 9 10 Over Thirty 57 87 144 Totals 69 109 178

Table 6: Status (alive or dead) of roost trees to all random trees in Yellowwood and Morgan- Monroe state forests in the summers of 2012 and 2013. Not included were partially dead trees.

All Roost Tree All Non-Roost Tree Status Count Count Totals Alive 34 2757 2791 Dead 33 535 568 Totals 67 3292 3359

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Table 7: Status (alive or dead) of roost trees to random trees in the undisturbed forest in Yellowwood and Morgan-Monroe state forests in the summers of 2012 and 2013. Not included were partially dead trees.

All Undisturbed Roost Tree All Undisturbed Non-Roost Tree Status Count Count Totals Alive 34 2641 2675 Dead 27 480 507 Totals 61 3121 3182

Table 8: Status (alive or dead) of roost trees to random trees in the disturbed forest in Yellowwood and Morgan-Monroe state forests in the summers of 2012 and 2013. Not included were partially dead trees.

All Disturbed Roost Tree All Disturbed Non-Roost Tree Status Count Count Totals Alive 0 116 116 Dead 6 55 61 Totals 6 171 177

Table 9: Density of understory of roost plots to random trees in the undisturbed forest in Yellowwood and Morgan-Monroe state forests in the summers of 2012 and 2013.

Density of All Roosts All Randoms Understory Count Count Totals Light 16 22 38 Moderate 31 47 78 Moderate-Heavy 18 21 39 Heavy 4 19 23 Totals 69 109 178

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Table 10: Decay class of roost trees to all trees in a random plot in Yellowwood and Morgan- Monroe state forests in the summers of 2012 and 2013.

Decay All Roosts All Random Plot Trees Class Count Count Totals Zero 34 1494 1528 One 8 110 118 Two 20 79 99 Three 6 77 83 Four 1 1 2 Totals 69 1761 1830

Table 11: Decay class of undisturbed roost trees to all trees in an undisturbed random plot in Yellowwood and Morgan-Monroe state forests in the summers of 2012 and 2013.

Decay Undisturbed Roosts Undisturbed Random Plot Trees Class Count Count Totals Zero 34 1421 1455 One 8 94 102 Two 17 67 84 Three 4 62 66 Four 0 1 1 Totals 63 1645 1708

Table 12: Decay class of roost trees to all other trees in roost plots in Yellowwood and Morgan- Monroe state forests in the summers of 2012 and 2013.

Decay All Roosts All Roost Plot Non-Roost Trees Class Count Count Totals Zero 34 1233 1267 One 8 73 81 Two 20 70 90 Three 6 44 50 Four 1 6 7

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Totals 69 1426 1495

Table 13: Decay class of undisturbed roost trees to all other trees in undisturbed roost plots in Yellowwood and Morgan-Monroe state forests in the summers of 2012 and 2013.

Decay All Undisturbed Roosts All Undisturbed Roost Plot Non-Roost Trees Class Count Count Totals Zero 34 1180 1214 One 8 67 75 Two 17 68 85 Three 4 43 47 Four 0 3 3 Totals 63 1361 1424

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CHAPTER THREE: ROOSTING RANGES OF NORTHERN LONG-EARED BATS

(MYOTIS SEPTENTRIONALIS ) IN AN EXPERIMENTAL HARDWOOD FOREST

SYSTEM

Badin, Holly A., and Timothy C. Carter.

Prepared for Forest Ecology and Management 68

ABSTRACT

We examined roosting range size of northern long-eared bats ( Myotis septentrionalis ) in an experimental hardwood forest of southern Indiana, with the goal of assessing the impacts of different timber harvesting methods on bat roosting ranges. We also examined the proximity of roost trees to key features such as water sources, roads and trails for travel corridors, and the harvests treatments. We tracked 23 bats via radio-telemetry and were able to create roosting ranges using the 100% minimum convex polygon method for 15 bats with 3 or more recorded roost tree locations. The average roosting range for the 7 bats with 5 or more roost locations was

5.4 ha. The distances of the centroid of roosting ranges to water sources, roads, trail, and all harvest types was not different to the distances of randomly selected points to these key features.

Keywords: bats, Indiana, Myotis septentrionalis , northern long-eared bat, roosting range, timber harvesting

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1.0 INTRODUCTION

Many studies have demonstrated the importance of the stand level attributes for bat summer roosting ecology, as bats are capable of flying long distances nightly and perceive the environment on multiple spatial scales (Limpert et al. 2007). These studies often examine the relationship between bats and their summer habitat use such as how bats respond to different forest types, ages, or harvesting practices (Loeb and O’Keefe 2006). Bats are likely affected by different forest management practices, and as managers increase their attention on maintaining a forest’s biodiversity while simultaneously harvesting timber resources, the importance of fully understanding the impact of timber harvesting becomes seminal.

Northern long-eared bats ( Myotis septentrionalis ) are forest-dwelling bats residing in the eastern United States and Canada that depend on trees for roosting during the day (Caceres and

Barclay 2000). This species switches roosts often, and maternity roosts are assumed critical and a possible limiting factor for bat roosting (Kunz and Lumsden 2003, Lewis 1995). Northern long-eared bats tend to prefer older forests, which are often the targets of timber harvesting

(Caceres and Pybus 1997, Jung et al. 1999, Krusic et al. 1996, Sasse and Pekins 1996).

Additionally, this species has a high wing-loading and aspect ratio, allowing them to be more maneuverable (Norberg and Raynor 1987), and their feeding ecology consists of primarily gleaning insect prey off of surfaces within the forest (Faure et al. 1993). In feeding studies, the northern long-eared bat has been found to ingest a higher amount of non-flying prey, such as those of the Orders Araneae and Orthoptera when compared to other bat species, supporting the notion of this gleaning behavior (Feldhamer et al. 2009, Lee and McCracken 2004).

The northern long-eared bat is of special concern as its populations have been steadily declining since the discovery of white-nose syndrome, leading to the anticipation that this 70

species is to be added to the Federal Endangered Species list in the fall of 2014 (Department of the Interior 2013, Francl et al. 2012, Ingersoll et al. 2013, Johnson et al. 2013). Few studies have quantified specific roost-tree preferences of northern long-eared bats, but even fewer have estimated the area needed to support the roosting resources for the species. Knowing the size of the roosting area used by this species in a managed forest can aid forest managers greatly when deciding the area of land to be harvested, and how harvesting might impact this species. Since northern long-eared bats are interior forest species for roosting and foraging, they are likely to roost in harvested areas less often than what is expected compared to other bat species. Northern long-eared bats are known to have a smaller home ranges than other common forest bats of the eastern United States, such as the big brown bat ( Eptesicus fuscus ) or red bat ( Lasiurus borealis ;

Elmore et al. 2005, Menzel et al. 2001, Owen et al. 2003) so the relatively small size of certain

types of harvests are likely to have a greater influence on northern long-eared bats than other

species of forest bats. We will provide an estimate of roosting range size for northern long-eared

bats, and quantify the spatial preferences of this species to timber harvests, as well as other key

resources.

2.0 MATERIALS AND METHODS

2.1 Study Site

The Hardwood Ecosystem Experiment (HEE) is a 100-year large-scale study launched in

2006, located in Morgan-Monroe State Forest (MMSF; Figure 1) of Morgan and Monroe counties, Indiana, and Yellowwood State Forest (YSF; Figure 2) of Brown County, Indiana with the goal of quantifying the effects of long-term even-aged and uneven-aged silvicultural practices on various taxa (Kalb and Mycroft 2013). Even-aged management consists of clear-cut 71

or shelterwood harvests, and uneven-aged management consists of patch-cut or single-tree selection harvests. The forests of the HEE consist of primarily mixed upland hardwoods on dry ridges and steep ravines (Haulton 2013, Kalb and Mycroft 2013). An oak-hickory forest landscape dominates at 43%, with conifer or mixed forest types making up <5% (Haulton 2013).

The HEE consists of 9 management units: 3 even-aged units consisting of clear-cut and shelterwood harvests, 3 uneven-aged units consisting of single tree selection or patch-cuts, and 3 control units where no harvesting will take place. The even-aged and uneven-aged units were harvested in the fall of 2008 (Kalb and Mycroft 2013).

The 9 management units, each consisting of a research core surrounded by a buffer zone, were randomly drawn from Department of Forestry management tracts, with each core totaling about 78 – 110 hectares and each buffer as the tract immediately adjacent (Kalb and Mycroft

2013). The management units of the core and buffer range from 303 – 483 ha in size (Kalb and

Mycroft 2013). The 3 units assigned to control treatment received no timber management for the duration of the study, and plan to be used to monitor plant and animal populations in the absence of silvicultural treatment (HEE 2010). Even-aged management units consist of four 4-hectare

(10-acre) openings per unit: 2 shelterwood and 2 clear-cut. (HEE 2010). The shelterwood stands are in the first stage of the preparatory cut with only the understory removed in 2008. Uneven- aged management units consist of four 0.4 ha (1.0 ac), two 1.2 ha (3.0 ac), and two 2 ha (5.0 ac) patch-cuts (HEE 2010). The rest of the research core consists of single tree selection (HEE

2010).

2.2 Methodology

We collected data in the summers of 2012 and 2013 from mid-May to late July. There were 2 designated capture sites per management unit, established in 2006, that had been 72

historically used for bat capture studies pre- and post-harvest which we used to net bats. These nets were often placed across road and trail corridors or streams to target bats flying through.

Bats were captured using high mist net systems (Gardner et al. 1989). Upon capture, bats were identified, sexed, reproductive condition determined, aged, weighed, forearm length measured, and wing condition scored. Transmitters (model LB-2N Holohil Systems, Ltd., Ontario, Canada) was attached to the backs of healthy female bats in between their scapulae, if the transmitter weighed no more than 5% of the animal’s weight (Aldridge and Brigham 1988), using Perma-

Type, (Perma-Type Company, Inc., Plainville, Connecticut) or Skin-Bond (Smith and Nephew,

Inc., Largo, Florida). Bats with transmitters were tracked starting the day after the attachment until that transmitter fell off or its battery died. Both a car-mounted 5-element Yagi antenna and a handheld 3-element Yagi antenna were used with a telemetry receiver (model TRX-1000WR

Wildlife Materials, Inc., Murphysboro, Illinois or model R2000 Advanced Telemetry Systems,

Inc., Isanti, Minnesota) to track bats. A handheld GPS unit was used to record the location of each roost tree. If a roost tree occurred on private property and permission could not be obtained, its location was approximated through triangulation (Amelon et al. 2009).

2.3 Data Analysis

Locate III (Pacer Computing, Tatamagouche, NS, Canada) was used to generate location points from triangulation data. Roost tree location coordinates were loaded onto ArcMap

(ArcGIS 10.2, ESRI, Redlands, CA). To generate 100% minimum convex polygon roosting ranges for each bat, the Minimum Bounding Geometry tool was used in ArcMap, with the convex hull shape selected. Only bats with 3 or more roost locations were included in this analysis. The average roosting range estimate only includes the roosting ranges of bats that had

73

an adequate number of roost tree locations, as determined as where a graph of roosting range size versus number of roost locations found plateaued as described in Harris et al. (1990).

A roosting availability analysis was conducted for all tracked bats to determine what percentage of harvest occurred in an area deemed available for each bat. We created a circle centered on the individual bat’s capture location with the radius as the furthest roost tree distance found per bat using the Buffer Tool, and recorded the area. The Clip tool was then used to find the area of harvests, defined as a clear-cut, shelterwood, patch-cut, or non-HEE opening, within each availability area. From this we were able to find the percent harvest per each bat’s availability roosting area.

To determine if bats roosted closer or farther from key resources (roads, trails, water sources) as well as shelterwoods or openings (clear-cuts, patch-cuts, or non-HEE openings), the

Near tool was used to find the closest of each feature to each roosting range centroid, as well as to 100 random points. Because of the small travel distances of northern long-eared bats, the placement of random points was limited to the areas available to our captured bats. Therefore all random locations were within twelve overlapping circles around the capture locations with all circles having the same radius of the average maximum travel distance found for all bats tracked.

Because of limited GIS habitat data, this area was further limited by removing any points that were located outside state forest boundaries. Non-normal data were appropriately transformed to achieve normality. Two sample t-tests were used to compare distances from roost trees and randomly selected points to the selected features.

A false discovery rate test was used for all p-values to account for accumulation of Type

1 error, and all p-values given were adjusted (Pike 2011). All statistical tests were performed in

Minitab Statistical Software Version 16 at α = 0.05, with the exception of the false discovery rate 74

test, which was performed on an online calculator for multiple comparisons procedures (Pike

2011).

3.0 RESULTS

Bats were caught during the summer maternity seasons in 2012 (May 15 - July 24) and

2013 (May 18 – July 23) with a total of 59 nights of mist netting conducted. A total of 111 northern long-eared bats were captured. We attached transmitters and tracked 23 female northern long-eared bats. The 23 bats comprised of 19 adults and 4 juveniles, and the adult bats were either pregnant (n = 5), lactating (n = 5), post-lactating (n = 2), or non-reproductive (n = 7) at the time of capture and tracking. Bats were tracked for an average of 5.3 days (range 1-11).

The longest that a bat used the same tree consecutively was for 5 days. We tracked bats to 71 roost trees and used triangulation to estimate locations on 10 additional roost trees.

Although 23 bats were tracked, only bats with 3 or more roost trees were used in the roosting range estimation (n = 15). Only bats with 5 or more roost tree locations were included in the average roosting range calculations (n = 7 bats, 45 roost trees). The average roosting range was 5.4 ha (range 1 – 9.3 ha, S.E. = 1.1) (Table 1). Out of all roosting ranges, only 7% (1

range) had all roost trees within a harvest, which was a 2-acre non-HEE patch-cut opening.

Thirteen percent of roosting ranges (2 ranges) had some roost trees within a harvest, with other

roost trees in the intact forest. One bat used 1 tree in a shelterwood out of a total of 3 trees, and

another bat used 2 trees in a shelterwood area out of the total 6 trees. One of the trees found in

the shelterwood area was the same roost tree shared by both bats. The remaining 80% of bats

had all locations within the intact forest. Only 1 other bat not included in the roosting range

analysis roosted in a HEE 5-acre patch-cut once and the undisturbed forest once. The only bat 75

whose range was entirely in a harvest had the smallest roosting range found at 0.02 ha (Figure 3), while the size of the largest roosting range found at 9.3 ha (Figure 4) (Table 1).

The average distance of the farthest bat roost from its capture location was 1000 m (range

223 m – 2649 m), and roost trees were an average of 218 m away from each other within a single bat’s roosting range (range 5 m – 778 m; Table 1). The average roosting availability area for all bats was 489.9 ha, and the proportion of the area containing a clear-cut, patch-cut, or shelterwood harvest within the availability plots for all bats was 4.4% (Table 1).

The centroid of all bat roosting ranges were used in the analysis of distance to features (n

= 15), regardless of their use in the average roosting range estimate. Bat roosts were not significantly closer to water sources ( t = -1.04, df = 18, p = 0.39), roads ( t = -0.84, df = 22, p =

0.45), trails ( t = -0.69, df = 24, p = 0.53), shelterwood harvests ( t = 0.55, df = 18, p = 0.61), or clear-cuts, patch-cuts, or non-HEE openings ( t = -0.52, df = 20, p = 0.61) than were random locations (Table 2).

4.0 DISCUSSION

Our average roosting range estimate for female northern long-eared bats of 5.4 ha is consistent with the small roosting ranges typically found for this species. All other studies looking at the roosting ranges of female northern long-eared bats have used the 100% MCP method as well. O’Keefe (2009) found a similar roosting range size for female northern long- eared bats in the southern Appalachian Mountains of North Carolina of 5.23 ha for 18 female bats and 52 roost trees. Broders et al. (2006) found an average roosting range size of 8.6 ha in a hardwood forest of Canada for 16 female northern long-eared bats in a total of 55 roost trees

(captures reported in Broders and Forbes 2004). Henderson and Broders (2008) estimated the 76

minimum roosting area size for one female northern long-eared bat study site at 0.3 ha (9 roosts) and 4.13 ha (21 roosts) for another study site, for 21 bats utilizing both sites, and that the foraging area was only about six times larger than the roosting area for this species. Perry and

Thill (2007) informally estimated their individual bat roost aggregates for this species to be within about 2 ha within a mixed pine-hardwood forest for 23 female northern long-eared bats in a total of 49 roost trees. Roosting range estimates are likely underestimates due to the relatively small number of roost trees per bat and short tracking period seen across multiple studies. Bats in our study with a higher number of roost trees found tended to have larger roosting ranges, but overall bats showed fidelity to particular areas despite harvesting, similar to what other studies have found (Sasse and Pekins 1996, Cryan et al. 2001, O’Keefe 2009).

Northern long-eared bats are known to be an interior forest species, obtaining most of their insect prey by gleaning off of vegetative substrate (Broders et al. 2006, Carroll et al. 2002,

Faure et al. 1993, Patriquin and Barclay 2003). Yellowwood and Morgan-Monroe state forests both contain more intact interior forest than harvests, and finding the vast majority of the roost trees in the intact forest reflects this, containing more vegetative clutter than the harvested forest does. The area available to our bats for roosting had an average of 4.4% harvest within them, and out of 23 bats used in this analysis, only 4 had any roosts within a harvest (Table 1). The bat with its entire range of 3 trees within a harvest was tracked over the course of 4 days, so the small sample of data may be influencing the result. While only 4 bats selected trees in harvests, a general pattern is that the more harvested area a bat has in its area of availability, the more likely that bat is to select a roost tree in a harvest, while bats select most of their roosts in the unharvested, intact forest. This suggests a tolerance of this species’ roost tree selection to limited harvesting on the landscape. However, the small sample of roost trees found in harvests 77

is not a strong indicator of bat preference. It is also important to note that no roosts were found in clear-cuts. Fukui et al. (2011) had found that in clutter adapted species of bats, occurrence was inversely proportional to gap size. Northern long-eared bat roosting behavior in this study support this finding, with roosting occurrences seen at greater abundances in the undisturbed forest (n = 65), with a few roosts in patch-cuts (n = 4), and none in larger clear-cuts.

All distances to key resources and harvests were not significant from the distance analysis. A few other studies have found that northern long-eared bats do prefer to roost closer to water sources and roads (Grindal 1998, Johnson et al. 2013, Perry et al. 2008, O’Keefe 2009).

Water sources serve as important drinking and foraging sites for bats (Grindal et al. 1999), and roads and trails have been postulated as flight corridors. Our capture locations were intentionally close to these features as well as harvests, and the relatively small roosting range for this species often caused bats to not travel far from capture. This species likely roosts close to where they forage to minimize commuting costs, since their short, broad wings are not adapted well to long distance flights (O’Keefe 2009, Ethier and Fahrig 2011). While the bats in this study did not show selection at the stand level we examined, our study was not designed to examine landscape level selection. It is possible that our bats intentionally selected these locations on the landscape

because of their proximity to landscape level features.

ACKNOWLEDGMENTS

Funding for this project was provided by Indiana Department of Natural Resources (IDNR)

through Purdue University. We thank Scott Bergeson for project oversight, Marissa Thalken,

Kyle G. George, Kristi Confortin, Valerie Mitchell, Katherine Caldwell, Abbi Hirschy, Chad

Argabright, and Aaron Hinton for their help with field work and data entry, Dustin Owen, Kevin 78

Barnes, Ben Lockwood, Dr. Mark Pyron, Dr. Randall Bernot, Dr. Stephen Jacquemin, and Jason

Doll for their help with statistics and GIS, and the HEE and IDNR staff members Andy Meier,

Scott Haulton, Jeff Riegel, and Rebecca Kalb.

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Table 1: Summaries of the home range (n = 7), farthest roost from capture (n = 23), percent of roosts in harvests (n = 23), and the percent of harvests taking up the availability roosting area (n = 23) in the Hardwood Ecosystem Experiment study area 2012-2013.

Home Range (100% Farthest Roost From Percent Roosts in Percent Harvest in MCP) (ha) Capture (m) Harvest Availability Area Average 5.4 1000 8.5 4.4 Minimum 1 223 0 0 Maximum 9.3 2649 100 20.3 SE 1.1 159 4.9 1.2

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Table 2: The average distances in meters of the center of roosting ranges (n = 15) and random points (n = 100) in the Hardwood

Ecosystem Experiment study area 2012-2013 to key features, presented as “average (standard error).”

Shelterwood Clear-cut / Patch-cut Water Distance Trail Distance Road Distance Distance Distance

Roosting Range Centroids (n = 15) 436 (98) 145 (23) 224 (47) 2223 (541) 303 (63)

Random Points (n = 100) 545 (39) 164 (13) 294 (24) 1906 (200) 343 (28)

p – value 0.39 0.53 0.45 0.61 0.61

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Figure 1: The Morgan-Monroe State Forest study area for the Hardwood Ecosystem Experiment comprising Morgan and Monroe counties of southern Indiana and consisting of one even-aged unit, one uneven-aged unit, and two control units. 87

Figure 2: The Yellowwood State Forest study area for the Hardwood Ecosystem Experiment comprising Brown County of southern Indiana and consisting of two even-aged units, two uneven-aged units, and one control unit.

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Figure 3: The roosting range of Bat 189, the smallest roosting range found of 0.02 ha.

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Figure 4: The roosting range of Bat 147, the largest roosting range found of 9.4 ha

90