The Roosting Behaviour of Little Brown Bats (Myotis Lucifugus) and Northern Long

Home , Communal roosting, Populus balsamifera

UNIVERSITY OF CALGARY

The Roosting Behaviour of Little Brown Bats (Myotis lucifugus) and Northern Long-

Eared Bats (Myotis septentrionalis) in the Boreal Forest of Northern Alberta

CORY R. OLSON

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF MASTER OF SCIENCE

DEPARTMENT OF BIOLOGICAL SCIENCES

CALGARY, ALBERTA

JULY, 2011

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FACULTY OF GRADUATE STUDIES

The undersigned certify that they have read, and recommend to the Faculty of Graduate

Studies for acceptance, a thesis entitled "The Roosting Behaviour of Little Brown Bats

(Myotis lucifugus) and Northern Long-Eared Bats (Myotis septentrionalis) in the Boreal

Forest of Northern Alberta" submitted by Cory Olson in partial fulfilment of the

requirements of the degree of Master of Science.

Supervisor, Dr. R. M. R. Barclay, Biological Sciences

Dr. R. V. Cartar, Biological Sciences

Dr. K. E. Ruckstuhl, Biological Sciences

Dr. M. S. M. Pavelka, Anthropology

Date

ii Abstract

Tree cavities provide critical habitat for many species as sites for raising offspring. For numerous bat species, these are also primary locations for social interactions. I used radiotelemetry to examine the roosting behaviour of maternal cavity- roosting bats, emphasizing the role of sociality and roost switching on habitat use. The size of roosting groups was positively correlated with tree diameter, indicating a relationship between social behaviour and roost use. Bats formed larger groups and selected larger roost trees near parturition, suggesting an adaptive response to maintain roost environments suitable for developing offspring. Roost-network analysis revealed preferential selection for particular roost trees, which may be important for mediating social interactions and providing optimal thermal conditions. Roost switching was not primarily the result of changes in temperature, although the frequency of roost switching increased during warmer periods. These results suggest complex patterns of habitat use, with implications for the management of bat habitat.

iii Acknowledgements

I thank the many people and organizations that provided assistance, guidance, or

funding for this project. I am forever grateful to those who endured long sleepless nights in the field to collect data for this project, especially my two field assistants, Kirk Graff and Cory Kremer. Several others also provided assistance collecting data, including

Robert Barclay, Tracy Flach, Bethany Hildebrand, Glen Flach, Richard Krikun, and

Chuck and Lisa Priestley. I also thank my committee members and lab members for their questions and advice during the course of this project. Joanna Coleman deserves special mention for teaching me the art of capturing and handling bats. I am grateful to Lawrence

Harder for his advice on statistical analysis. I thank my mother, Beverly Olson, for providing supplies, especially a field truck, used for this project. My committee members,

Ralph Cartar, Kathreen Ruckstuhl, and Mary Pavelka, provided valuable feedback on this thesis. I am tremendously thankful to my supervisor, Robert Barclay, for his assistance, guidance, and support throughout my graduate program.

I thank the Boreal Centre for Bird Conservation, and especially Patti Campsall, for providing accommodations, facilities, and a wonderful place to do research for two summers. Substantial research funding was generously provided by the Alberta

Conservation Association Grants in Biodiversity, the National Science and Engineering

Research Council of Canada, and Alberta Ingenuity (now Alberta Innovates). Field transportation was provided with support from the Alberta Cooperative Conservation

Research Unit (ACCRU). Alberta Plywood Ltd. provided GIS data that was used during the course of this project. I would also like to thank Roland Eben-Ebenau for allowing me access to the bats at the North Shore Homestead.

For Tracy

v Table of Contents

Approval Page ...... ii Abstract ...... iii Acknowledgements ...... iv Table of Contents ...... vi List of Figures ...... ix

CHAPTER 1: GENERAL INTRODUCTION ...... 1 Sociality and roost switching of bats ...... 6 Study system: tree-cavity roosting bats ...... 11 Thesis objectives ...... 13

CHAPTER 2: ROOST USE BY LITTLE BROWN BATS (MYOTIS LUCIFUGUS) AND NORTHERN MYOTIS (MYOTIS SEPTENTRIONALIS) IN THE BOREAL MIXEDWOODS OF ALBERTA, CANADA ...... 15 Introduction ...... 15 Methods ...... 19 Study area ...... 19 Capture and tracking methods ...... 19 Roost measurements ...... 20 Statistical analysis ...... 21 Results ...... 22 Discussion ...... 32 Roost characteristics ...... 32 Interspecific comparison ...... 35 Implications ...... 38

CHAPTER 3: TEMPORAL VARIATION IN ROOST SELECTION BY REPRODUCTIVE FEMALE LITTLE BROWN BATS, MYOTIS LUCIFUGUS ..40 Introduction ...... 40 Methods ...... 45 Study area ...... 45 Capture and tracking methods ...... 47 Colony association ...... 48 Roost measurements and exit counts ...... 48 Statistical analysis ...... 49 Results ...... 51 Bat captures and radiotelemetry ...... 51 Roost use ...... 52 Roosting groups ...... 53 Temporal changes in roost properties ...... 57 Discussion ...... 62 Group size-habitat association ...... 62 Temporal variation ...... 63 Management implications ...... 67

vi CHAPTER 4: ROOSTING NETWORKS AND ROOST-SWITCHING BY LITTLE BROWN BATS (MYOTIS LUCIFUGUS) ...... 68 Introduction ...... 68 Methods ...... 73 Study area ...... 73 Capturing and tracking bats ...... 73 Roosting networks ...... 74 Statistical analysis ...... 76 Results ...... 78 Radiotracking ...... 78 Network analysis ...... 82 Roost switching ...... 89 Discussion ...... 94 Roosting network ...... 94 Roost switching ...... 97 Implications ...... 102

CHAPTER 5: GENERAL CONCLUSIONS...... 103 Management implications ...... 105 Future research ...... 107

LITERATURE CITED ...... 109

APPENDIX 1: PARASITE DATA ...... 131

vii List of Tables

Table 2.1. Roost-tree characteristics of roosts located with radiotelemetry for M. septentrionalis and M. lucifugus roosting within Lesser Slave Lake Provincial Park...... 28

Table 2.2. Decay characteristics of roost trees used by M. septentrionalis and M. lucifugus in Lesser Slave Lake Provincial Park from June–August 2009 and 2010...... 29

Table 2.3. Characteristics of roost cavities used by M. septentrionalis and M. lucifugus in Lesser Slave Lake Provincial Park from June–August 2009 and 2010...... 29

Table A1. Parasite counts for individual M. lucifugus prior to radio-transmitter attachment...... 132

viii List of Figures

Figure 2.1. Study area in Lesser Slave Lake Provincial Park, Alberta, Canada showing M. lucifugus and M. septentrionalis roosting areas...... 24

Figure 2.2. Living trembling aspen (P. tremuloides) roost tree used by M. septentrionalis in Lesser Slave Lake Provincial Park during 2010, showing typical radial-longitudinal split used as roost cavity ...... 30

Figure 2.3. Dead balsam poplar (P. balsamifera) roost tree used by M. lucifugus in Lesser Slave Lake Provincial Park during 2009 and 2010...... 31

Figure 3.1 Map of study area in Lesser Slave Lake Provincial Park, Alberta showing roosting areas occupied by the northern and southern colonies of M. lucifugus...... 46

Figure 3.2. Changes in the size of M. lucifugus roosting groups during the summer breeding period, June-August 2009 and 2010...... 55

Figure 3.3. Least-squares means for the number of bats counted during emergence from aspen or balsam poplar trees (a) and from trees in different DBH categories (b)...... 56

Figure 3.4. Change in the diameter-at-breast height (DBH) of roost trees used by reproductive female M. lucifugus during the summer breeding period, June- August 2009 and 2010...... 59

Figure 3.5. Change in the ratio of balsam poplar roosts to aspen roosts used by reproductive female M. lucifugus during the summer breeding period, June– August 2009 and 2010...... 60

Figure 3.6. Change in the diameter-at-breast height (DBH) of balsam poplar roosts (a) and aspen roosts (b) during the summer breeding period, June–August, 2009 and 2010...... 61

Figure 4.1. Map showing locations of the two largest M. lucifugus roosting networks in Lesser Slave Lake Provincial Park, Alberta...... 80

Figure 4.2. Change in roost area as additional roost trees were located for two M. lucifugus colonies tracked from 2009–2010 in Lesser Slave Lake Provincial Park...... 82

Figure 4.3. Number of roost trees of different degree classes for two M. lucifugus roosting networks located in Lesser Slave Lake Provincial Park, Alberta...... 84

Figure 4.4. Roosting network of the northern M. lucifugus colony, located in Lesser Slave Lake Provincial Park, Alberta...... 85

ix Figure 4.5. Roosting network of the southern M. lucifugus colony, located in Lesser Slave Lake Provincial Park, Alberta...... 86

Figure 4.6. Proportion of M. lucifugus roost trees of different degree classes, calculated from 2009 data, which were reused at least once in 2010...... 87

Figure 4.7. Differences in tree species (a) and diameter-at-breast-height (DBH) (b) between low-degree (degree = 1-2) and high-degree (degree = 3-9) roost trees used by M. lucifugus...... 88

Figure 4.8. Proportion of reproductive female M. lucifugus that switched roosts in relation to the night-to-night change in average temperature...... 91

Figure 4.10. Least-squares mean estimates for the distance moved between successive roosts by female M. lucifugus during different reproductive periods...... 93

Figure A1. Comparison of the total number of mites counted on M. lucifugus individuals prior to attachment of a radio transmitter during 2009 and 2010...... 135

x 1

CHAPTER 1: GENERAL INTRODUCTION

How organisms use and select habitat is a central theme in ecology and is particularly relevant for conservation planning and resource management (Jones 2001;

Morris 2003). Reproductive fitness depends on the availability and quality of suitable breeding habitat (Johnson 2007). Habitat used for breeding must have sufficient resources to cover the increased metabolic demands of reproduction, provide protection from predators, and provide environmental conditions conducive to growth and development of offspring (Huey 1991; Orians and Wittenberger 1991; Johnson 2007). The particular dwellings used by organisms to incubate eggs or raise altricial young, such as nesting, denning, or roosting sites, may be particularly important for reproductive success. The ability to select and use these sites is an important adaptation for surviving and successfully reproducing in an environment (Orians and Wittenberger 1991). These structures provide concealment from predators and protection from inclement weather, but also influence the microclimatic conditions experienced by developing offspring. For birds and mammals, temperatures outside the thermoneutral zone can lower their chance of survival, increase energy expenditure, and reduce growth rate (Gilbert et al. 2007).

Eggs and neonates, having limited ability to generate their own heat, are particularly reliant on ambient conditions (Hollis and Barclay 2008; Studier and O'Farrell 1972).

However, the intrinsic quality of habitat is only one factor affecting the developmental conditions of offspring.

Organisms have a variety of behavioural and physiological adaptations for surviving and successfully reproducing in their environment. Habitat selection is not only

influenced by the intrinsic quality of the habitat, but also by the particular ways that

organisms use their habitat. Two adaptations that occur in a wide range of taxa that may

influence how organisms use habitat are 1) sociality, and 2) the ability or tendency to carry altricial offspring between dwellings (i.e. nests, dens, roosts). Sociality, as used here, refers to the propensity to form groups (including passive aggregations), and may or may not involve complex social behaviours (Kerth 2008). Sociality and roost switching appear to be especially important for bats, in which extreme forms of both can be found in several members of the order. In my study, I focus on two sympatric species of cavity- roosting bats – little brown bats (Myotis lucifugus) and northern long-eared bats (Myotis

septentrionalis) – to examine how sociality and roost switching can influence habitat use

and selection.

One of the greatest challenges faced by organisms is the need for a priori

assessment of habitat quality before fitness consequences have been realized (Orians and

Wittenberger 1991). Species that are highly invested in their choice of habitat – such as those that build nests or burrows – risk unforeseen circumstances that make their habitat inappropriate for the purpose of reproduction. Bird nests, for example, are regularly lost to such causes as severe weather, flooding, fire, trampling by other animals, disturbance by predators, intraspecific aggression, anthropogenic disturbance, or other biotic or abiotic events (Greenwood et al. 1995; Sieving and Willson 1998; Davis 2003). In

contrast to birds, many small mammals that give birth to altricial young have the ability

to move dependent young to new locations when old locations become unsuitable (or else

they carry their young continually). Mammals that cannot carry their young often give

birth to precocial offspring that are able to move themselves. This distinction between

birds and mammals is particularly evident when considering only those animals that raise their young in tree cavities. For example, sciurids, mustelids, and especially bats, are well known to carry young between tree cavities at varying frequencies (Lang 1925; Causey and Waters 1936; Carey et al. 1997; Jones et al. 1997; Hanski et al. 2000; Kunz and

Lumsden 2003). Bats often show an extreme form of roost lability, moving their young to new roosts every few days (Kunz and Lumsden 2003). In contrast, cavity-nesting birds, such as many species of woodpeckers, nuthatches, chickadees, swallows, raptors, owls, and ducks, are universally constrained to use a single cavity until the young can fly (or for a few species, walk) (Baicich and Harrison 2005).

The ability to move dependant offspring between habitats may be an important adaptation for mitigating the fitness consequences of suboptimal habitat choice. Roost (or nest) switching also allows individuals to adjust habitat as the reproductive cycle progresses. In contrast, birds must select a nesting site that is suitable throughout the

breeding cycle (prior to fledging), which may require them to select habitat that is suboptimal for a portion (or all) of the reproductive cycle. The ability to switch dwellings

may also allow animals to use structures that would be suboptimal for long-term

residency (Lewis 1995). For example, Spix’s disc winged bats (Thyroptera tricolor)

roosts within the unfurled leaves of Heliconia or Calathea plants (Vonhof et al. 2004).

As these leaves may only be suitable for a single day, being able to move offspring

between leaves is clearly a requirement to take advantage of these structures. Similarly,

animals able to move offspring may be better able to use unstable structures, such as

sloughing bark, as sites for raising offspring (Lewis 1995). Animals able to transport

offspring may be able to adjust breeding-habitat selection to suit changing weather

4 conditions, physiological requirements, food availability, threats from predators, or the physical condition of their shelter (Lewis 1995; Carey et al. 1997). Regular switching of habitat may be especially important for minimizing odours that attract predators and for avoiding parasite accumulations (Lewis 1995; Carey et al. 1997). For understanding habitat use and requirements, however, the ability to switch habitats adds temporal variability to habitat use and selection (O'Donnell and Sedgeley 1999; Garroway and

Broders 2008; Patriquin et al. 2010). Whereas habitat selection of many birds is relatively constant for much of the breeding season, breeding habitat selection of some mammals can potentially change regularly within a single life-history stage.

Group living is a common adaptation for overcoming environmental challenges, and can be observed in some form across a wide range of taxonomic groups.

Explanations for the evolution of sociality are diverse, and vary within and among species. Benefits provided by social thermoregulation, which includes huddling, appears to be critical for the survival and successful reproduction of many endotherms, especially for small animals, such as bats, and animals occupying cold climates (Ebensperger 2001;

Gilbert et al. 2007; Willis and Brigham 2007). This behaviour may result in substantial energy savings, reduced water loss, and a lower risk of cold injury or death (Gilbert et al.

2007; Gilbert et al. 2010). Social thermoregulation also accelerates growth rates by allowing individuals to devote more energy to growth rather than thermoregulation

(Gilbert et al. 2007), which may be critical for species that must raise young within short breeding seasons to insure readiness for reproduction, hibernation, or migration (Solick and Barclay 2007). However, sociality is still common in the absence of social thermoregulation, suggesting other causes are important in the evolution of sociality in

certain species (Kerth 2008). Such causes include the need to reduce the risk of predation,

resource defense, cooperative breeding, information transfer, food sharing, cooperative

hunting, kin selection, and common resource requirements (Lin and Michener 1972;

Sterck et al. 1997; Lubin and Bilde 2007; Kerth 2008; Safi 2008). For many animals,

some form of fitness benefit arises from sociality, and individuals may not be able to

survive and successfully reproduce without these benefits. In such cases, the availability

and quality of habitat used to support the entire social group, rather than the resource

requirements of individuals, is of paramount importance for whether a population will

persist (O'Donnell and Sedgeley 1999; Russo et al. 2005).

The ability to move offspring allows reproductive animals to form fission-fusion

social systems. Fission-fusion behaviour is characterised by ephemeral groups (or

subgroups) that regularly change in size and composition as a result of individuals (or

groups of individuals) breaking apart and forming new groups, or periodically and

temporarily fusing into a single social unit (Wittemyer et al. 2005; Garroway and Broders

2007). Individuals present in successive groups may be drawn from a larger closed social

unit, or may be drawn from an open unbounded population (Garroway and Broders

2007). Fission-fusion sociality is common in many animals across a wide taxonomic

range, including many primates (Symington 1990; van Schaik 1999), cetaceans (Bräger

1999), ungulates (Conradt and Roper 2000; Wittemyer et al. 2005), fish (Croft et al.

2004), bats (Kerth and Konig 1999), and carnivores (Smith et al. 2007). This behaviour

may allow individuals to adjust the size of aggregations to suite changing conditions that

affect the cost-benefit trade-off of sociality (Lehmann and Boesch 2004; Wittemyer et al.

2005). For example, periods when food is limiting may favour smaller groups to reduce

6 intraspecific competition (Chapman and Chapman 2000). Larger groups may be favoured during reproduction to reduce predation and infanticide (Packer et al. 1990; Wittemyer et al. 2005). For animals that huddle to conserve heat, larger groups may be favoured during cold conditions, or during times when elevated body temperatures are favoured, but may be disadvantageous during hot weather or when colder body temperatures are preferred

(Pretzlaff et al. 2010). Fission-fusion dynamics may be important for the evolution of sociality by allowing greater flexibility in the degree that individuals engage in social behaviours.

Sociality and roost switching of bats

Bats are an appealing group for examining how social animals use and select habitat. Most of 1145+ species of bats live and raise young in groups, which suggests that sociality was important early in the evolution of the order (Kerth 2008; IUCN 2011).

Many studies have found that bats show both inter-annual and intra-seasonal fidelity to particular roosting areas (Veilleux and Veilleux 2004; Willis and Brigham 2004; Rhodes

2007; Popa-Lisseanu et al. 2008). Geographic fidelity may facilitate long-term social relationships among individuals and the evolution of potentially beneficial social behaviours (Kerth 2008). Indeed, several complex social behaviours have been described for bats, such as food sharing, information transfer, babysitting, and allogrooming (Kerth

2008). Although in some cases aggregations are likely the result of common resource requirements (e.g. cave hibernacula), for many species, sociality appears to be a deliberate behaviour and potentially confers important fitness benefits. The size of social aggregations varies considerably both among and within species. While some bat species are primarily solitary, others form aggregations numbering in the thousands or millions of

individuals (Kunz and Lumsden 2003; Russell and McCracken 2006). Even among the

same species, some populations appear more social than others. For example, lactating

long-eared bats (Myotis evotis) living on the prairies formed maternity colonies less often than those living in the mountains (Solick and Barclay 2007). The variability in social behaviour among bats allows for intra- and interspecific comparisons that are useful for identifying important patterns of habitat use and identifying underlying mechanisms for this behaviour.

One of the most notable habits of bats is the frequent switching of roosts by individuals occupying certain types of roosts. Many bats that occupy roosts that are ephemeral and abundant (such as tree cavities), switch roosts multiple times a week

(Lewis 1995; Kunz and Lumsden 2003). This includes mothers with dependant offspring

– the (usually) single pup attaches to a nipple and the fur on the underside of their mother

while being flown between roosts. Roost switching among communally roosting bats is typically non-synchronized and frequent. On any given night, some bats switch roosts, while others reuse the roost from the previous night, and those that switch may select

different roosts than their previous roost-mates. This gives rise to a pattern of roosting

that closely resembles fission-fusion social systems found in other animals. Several

studies have now described bat roosting behaviour in the context of fission-fusion

sociality (e.g. Kerth and Konig 1999; O'Donnell 2000; Kurta et al. 2002; Vonhof et al.

2004; Willis and Brigham 2004; Russo et al. 2005; Garroway and Broders 2007; Rhodes

2007; Popa-Lisseanu et al. 2008).

Fission-fusion social behaviour for many animals results primarily from fluctuations in the size and composition of foraging groups. However, for bats, fission-

fusion social behaviour has generally focused on roosting groups, largely because roosts

are important sites for social interactions. Individual roosting groups form the subgroups

of the fission-fusion roosting system. For species with fission-fusion roosting behaviour, the colony is no longer considered to be the individual roosting aggregations (such as

those occupying the cavities of trees), but rather is a collection of interconnected

subgroups that associate over a larger geographic area (Rhodes 2007). The entire colony may never be together at the same time and place, owing to spatial constraints of roosting structures, but social relationships among individuals can be maintained through regular roost switching that allow separated individuals to ‘fuse’ into a common roosting group.

Because of frequent and non-synchronized roost switching, the size, composition and location of roosting groups of bats frequently change. Most studies of fission-fusion roosting behaviour in bats refer to populations roosting in tree cavities (Kerth and Konig

1999; O'Donnell 2000; Kurta et al. 2002; Willis and Brigham 2004; Russo et al. 2005;

Garroway and Broders 2007; Rhodes 2007; Popa-Lisseanu et al. 2008), but bats roosting

in a variety of roost types may also exhibit similar behaviour – including rock crevices

(Solick and Barclay 2006) and unfurled leaves (Vonhof et al. 2004). For bats occupying

tree cavities, individual roosting aggregations may number up to several hundred or

thousand individuals (Chapter 3; Kunz and Lumsden 2003). Traditionally, individual

roosting aggregations have been viewed as a ‘bat colony’. However, for species that roost

within fission-fusion societies, what constitutes the primary social unit or ‘colony’ is

much more difficult to define (Vonhof et al. 2004). A group of bats sharing many of the

same roosts and roosting within a well-defined geographic area is more logically

considered as the bat colony; however, it is not necessarily the case that bats roost within

closed social units and occupy the same geographic area as other members of their social group. As in the fission-fusion societies of other animals, this form of roosting behaviour results in cryptic social units and complex patterns of habitat use (van Schaik 1999; Kerth

2008).

The cause of frequent roost switching is an important question for the study of bats. Given that bats incur an energetic cost by switching roosts, some benefit exceeding this cost must result (Lewis 1995). Several researchers have argued that bats switch roosts to avoid parasites. Indeed, some bats appear able to detect parasite levels (Reckardt and Kerth 2007) and may adjust their frequency of roost switching in response to their parasite load (Lewis 1996). However, roost switching still occurs regardless of parasite levels, making it difficult to implicate parasite avoidance as the cause of roost switching.

Roost switching has also been suggested as a way to increase familiarity with available roosts, which may be important for bats occupying ephemeral structures (Lewis 1995;

Russo et al. 2005). However, as most roosts last several years, this does not explain the high frequency of roost switching and the large number of alternative roosts that bats often use (Willis et al. 2006). Adapting habitat selection to suite changes in ambient conditions, physiological requirements, and developmental state of offspring, is also a compelling explanation for roost switching. Few studies have provided convincing evidence of the influence of weather conditions on roost switching (but see: Lewis 1996;

Callahan et al. 1997; O'Donnell and Sedgeley 2006). However, bats appear able to select

roosts based on microclimatic conditions (Kerth et al. 2001; Pretzlaff et al. 2010), and

possibly use site familiarly and information transfer from conspecifics to efficiently

locate roosts that meet their individual requirements (Kerth et al. 2001; Kerth and

Reckardt 2003). Predictable changes in habitat selection, in response to changing

physiological requirements, are evident in multiple bat species that roost in rock crevices.

Big brown bats (Eptesicus fuscus), long-eared bats (Myotis evotis), and pallid bats

(Antrozous pallidus) roosting in rock crevices select shallower roosts during pregnancy and deeper roosts during lactation (Vaughan and O'Shea 1976; Lewis 1996; Chruszcz and

Barclay 2002; Lausen and Barclay 2003). Bats appear to prefer the more stable temperatures provided by deep rock crevices during lactation because it provides a suitable environment for pups, whereas the more variable temperatures characteristic of shallow roosts facilitate torpor use.

For bats that have fission-fusion roosting systems, the high roost switching frequency common among many bat populations could also be a means of maintaining social relationships with a large number of conspecifics (Carey et al. 1997; Willis and

Brigham 2004). This could increase the opportunities for finding groups to roost with and may help ensure the stability of the larger social unit for the long lifetime of bats. As mentioned, roost switching may allow bats to adjust habitat selection to suit changing conditions. However, with the added advantage of fission-fusion roosting behaviour, bats can additionally change the size of roosting aggregations, possibly resulting in a greater range in roost conditions than would be possible with roost selection alone (O'Donnell and Sedgeley 2006; Willis and Brigham 2007; Pretzlaff et al. 2010). Limited evidence for bats suggests that they can adjust their group size in response to temperature and reproductive condition (e.g. Solick and Barclay 2007; Pretzlaff et al. 2010). Roost switching may also be associated with the evolution of socially in bats by reducing costs

associated with greater parasite levels, guano accumulation, and elevated ammonia

levels, which would otherwise limit the size of roosting groups (Reckardt and Kerth

2006, 2007; Bondo 2009).

Study system: tree-cavity roosting bats

Habitat use and selection studies are particularly prevalent for species that raise

young within the cavities of trees. In forested regions, tree cavities provide critical habitat

for many bird and mammal species (Martin et al. 2004). Although a few species of birds

(e.g. woodpeckers, chickadees, nuthatches) are able to excavate their own cavities, many birds and most mammals that roost in cavities rely on previously existing structures as

roosting and nesting sites (Martin and Eadie 1999; Martin et al. 2004). These structures

may include natural structural defects, such as knot holes, cracks, and sloughing bark,

and cavities originally excavated by other animals.

Communal roosting in the cavities of trees is apparent in a wide range of taxa. For

example, pygmy nuthatches (Sitta pygmaea) regularly form aggregations during the

winter, and during exceptionally cold weather, have numbered over 100 individuals in a

single tree cavity (Knorr et al. 1957; Sydeman et al. 1988). Several other birds such as

acorn woodpeckers (Melanerpes formicivorus)(du Plessis et al. 1994), green

woodhoopoes (Phoeniculus purpureus)(du Plessis and Williams 1994), tree swallows

(Tachycineta bicolor)(Stutchbury and Robertson 1990) may also roost communally, at

least during certain conditions (e.g. during colder weather). Communal roosting in tree

cavities by mammals other than bats has also been well documented. For example, tree

squirrels, such as the northern flying squirrel (Glaucomys sabrinus), southern flying

squirrel (Glaucomys volans), and fox squirrel (Sciurus niger), may form aggregations in

tree cavities in response to cold temperatures (Koprowski 1996; Merritt et al. 2001).

Many bat species rely extensively on tree cavities as sites for raising young and

most form maternity colonies (Kunz and Lumsden 2003; Barclay and Kurta 2007). For

example, in the boreal forest of northern Alberta, maternity colonies of four of six bat species roost nearly exclusively within tree cavities (or buildings). Bats are entirely reliant on pre-existing cavities, and are unique among cavity-dwelling endotherms in that they do not actively modify cavities by adding additional insulation or nesting substrate

(such as leaves, grasses, or down), making them particularly reliant on the quality of available roosting structures. Frequent roost switching is ubiquitous for bats that roost within tree cavities, and fission-fusion roosting systems among social populations appears common (Barclay and Kurta 2007).

The availability and quality of tree cavities is highly heterogeneous over the landscape, owing to differences in decay characteristics of tree species, fire regime, forest age, forest insect and disease occurrence, and the activity of cavity-excavating birds

(Martin and Eadie 1999). Short-rotation forest harvesting affects cavity-roosting species by reducing the density of old trees and snags, which are most likely to have decay characteristics that create suitable roosting or nesting sites for wildlife (Martin and Eadie

1999; Hayes and Loeb 2007). As a result of increasing demands on forest resources, considerable attention has been given to understanding the habitat requirements of cavity- dwelling species so that they can be incorporated into forest management and conservation plans. Several studies have examined roost selection in cavity-roosting bats, typically by comparing habitat that was used to habitat that is deemed to be available

(Kalcounis-Ruppell et al. 2005; Barclay and Kurta 2007). These studies typically focus on the characteristics of individuals trees (Veilleux and Veilleux 2004). However, as

mentioned, sociality and roost switching can result in complex patterns of habitat use that

may not be reflected in simple habitat-selection studies. Studies that examine bat roosting-habitat selection using methodologies designed for nest selection by birds may misrepresent the range and diversity of roosting habitat required for bats during the

summer breeding season.

Thesis objectives

My study focused on the roosting behaviour of maternal populations of two species of cavity-roosting bats occupying the boreal forest of Northern Alberta: little

brown bats (Myotis lucifugus) and northern long-eared bats (Myotis septentrionalis).

Where my study occurred, both species roost primarily within tree cavities and have sympatric roosting and foraging areas. I chose to study only reproductive female bats

(either pregnant or lactating) because compared to males or non-reproductive females,

reproductive females face the greatest energetic demands and their decisions and

behaviours are likely to have important fitness consequences. My primary objective was

to understand how roost switching and sociality (in particular, the tendency to form

groups) influence (or are influenced by) patterns of habitat use by bats. My study has two

overarching questions: (1) within fission-fusion roosting systems, is there a predictable

association between group size and habitat selection? And (2) do individuals adjust

habitat selection to suit changing conditions and biological requirements (suggesting that

roost switching may be adaptive by allowing greater flexibility in habitat use and

reducing the constraint of a priori habitat selection)? A secondary objective of my study

was to examine habitat use and selection by M. lucifugus and M. septentrionalis within a

management or conservation context. This thesis has three chapters in which I examine different aspects of the roosting behaviour of bats.

In Chapter 2, I give a general overview of the broad patterns of habitat use by

sympatric M. lucifugus and M. septentrionalis. I examine the types of roosts used by each

species, and relate them to the decay characteristics of the trees in my study area. I

examine two hypotheses: 1) that the two species will have distinct and non-overlapping

patterns of roost use, resulting in reduced interspecific competition for common roosting resources, and 2) that different patterns of roost-use reflect differences in group size and manoeuvrability.

In Chapter 3, I focus exclusively on the habitat use of M. lucifugus to examine whether temporal variation in roost selection is present, and whether this coincides with important life-history events. I examine whether habitat selection is associated with the size of roosting aggregations, and whether roost selection parallels social behaviour.

In Chapter 4, I use network analysis to examine spatial patterns of roost use and to examine potential factors influencing the rate of roost switching. Network analysis gives an indication of the spatial extent of bat social groups and helps to identify roost trees that may be important to bat colonies. I also use it to examine resiliency of bat roosting networks to perturbations. As roost-switching behaviour may vary by geographic location and colony association, I assign bats to different roosting networks and use this association to control for potential variation in roost-switching behaviour. I examine the hypothesis that roost switching is influenced by ambient temperature, reproductive condition, and which roost an individual occupied prior to roost switching.

CHAPTER 2: ROOST USE BY LITTLE BROWN BATS (MYOTIS LUCIFUGUS)

AND NORTHERN MYOTIS (MYOTIS SEPTENTRIONALIS) IN THE BOREAL

MIXEDWOODS OF ALBERTA, CANADA

Introduction

The choice of roosting habitat has important fitness consequences for bats (Kunz and Lumsden 2003). Although many potential roosting structures may be available, they are likely to vary considerably in their microclimatic properties, protection from inclement weather, exposure to competition and predation, and suitability for communal roosting (Kerth et al. 2001; O'Donnell and Sedgeley 2006; Willis et al. 2006; Willis and

Brigham 2007). Bats appear to non-randomly select roosts that provide suitable conditions for the growth and survival of offspring (Vaughan and O'Shea 1976; Kerth et al. 2001; Chruszcz and Barclay 2002; Lausen and Barclay 2003; Pretzlaff et al. 2010).

However, as most bats can neither create nor modify roosting structures, they are constrained by pre-existing structures available on the landscape. As roosts may be limiting to bat populations, many studies have examined the types of structures used by bats in an attempt to incorporate this information into conservation plans (Kalcounis-

Ruppell et al. 2005; Barclay and Kurta 2007).

In forested regions, many bat species roost within the cavities of trees, which includes structural defects such as sloughing bark, cracks, splits, breakage, knotholes, and old bird excavations. Despite some roost-tree characteristics (e.g. large diameter) being broadly important for a variety of bat species (Kalcounis-Ruppell et al. 2005), there is often pronounced geographic variation in roosting habitat selection, even within the same

species (Rancourt et al. 2005; Barclay and Kurta 2007; Lacki et al. 2010). The need for

region-specific information on habitat use has resulted in many regional studies for species of management concern. Although such studies are potentially valuable, a greater focus on the mechanisms explaining observed patterns of habitat use may allow the results of roosting-habitat studies to be more broadly applicable, and would aid our

understanding of how perturbations are likely to affect bat populations.

A wide taxonomic range of birds and mammals in forested regions compete for tree cavities as sites for raising young, including groups such as owls, raptors, songbirds, ducks, tree-squirrels, mustelids, and bats (Martin et al. 2004; Barclay and Kurta 2007).

As a result, interspecific competition is likely to be a major factor influencing the types of cavities that individuals are able to use, and may help explain regional variation in habitat selection and the geographic distribution of certain species (Perkins 1996; Martin et al.

2004). If roost space is limiting for bats, less competitive species may be marginalized to lower quality habitat or habitat that cannot be accessed by the superior competitor

(Timpone et al. 2006; Timpone et al. 2010). Structural diversity is generally correlated with increased species diversity, possibility by allowing greater opportunities for individuals to escape interspecific competition (MacArthur et al. 1962; Humphrey 1975;

Roland 1976; Tews et al. 2004). This may be especially true when applied to tree cavities

– greater structural diversity may support a greater diversity of cavity-dwelling animals

(Monterrubio-Rico and Escalante-Pliego 2006; Remm et al. 2008). In the case of bats, there is little evidence that one species actively excludes another. However, given space limitations within roost cavities, and often cramped conditions, smaller bats may be unable to access and secure roost space, potentially resulting in competitive exclusion

regardless of whether antagonistic behaviours are present. Less dominant cavity-dwelling animals can reduce competition by specializing on different cavity types, possibly those

that they are better adapted to access, but the efficacy of this strategy depends on the

degree of heterogeneity of roosting structures available in their environment. For

continued persistence of cavity-dwelling animals within managed landscapes, it may be

necessary to ensure processes that give rise to a diversity of tree-cavities are maintained.

In the case of bats, this is hindered by a generally poor understanding of the range of

roost types used and, consequently, the processes important for the formation of roost

cavities.

The North American boreal forest is the largest forest region on the continent, and

is increasingly modified by intensifying industrial development, population growth, and

resource extraction (Cumming et al. 2010). However, relatively few studies have

examined the roosting ecology of bats in the boreal forest, potentially preventing

appropriate management for these species. Because of decay characteristics conducive to

cavity formation, trembling aspen (Populus tremuloides) is an important species for

cavity-dwelling wildlife throughout much of the boreal forest and other regions where

this species occurs (Kalcounis and Hecker 1996; Crampton and Barclay 1998; Kalcounis

and Brigham 1998; Parsons et al. 2003; Psyllakis and Brigham 2006; Vonhof and

Gwilliam 2007). Balsam poplar (Populus balsamifera) co-occurs with aspen across most

of the northern latitudes of North America, and both are highly susceptible to heart-rot

fungus, which facilitates cavity formation (Thomas et al. 1960; Parsons et al. 2003).

However, comparatively few studies have reported balsam poplar as being an important

roost tree species for bats. Few tree species occur at northern latitudes in North America,

and even fewer provide cavities suitable for wildlife. Consequently, there may be few

ways that the many species occupying tree cavities can partition roosting resources.

Understanding how these trees differ in their suitability for cavity-dwelling wildlife, and

how bats partition common roosting resources, may have important implications for how

forests are managed for wildlife conservation.

In this study, I examined roost tree use by little brown bats (Myotis lucifugus) and

northern long-eared bats (Myotis septentrionalis) in the boreal forest of Northern Alberta.

I addressed two primary questions during this study: (1) is there evidence of partitioning

of roost space, resulting in reduced interspecific competition, between these two species?

And (2) what tree defects are important as bat roosts in my study area, and what

processes are important for their formation? Myotis lucifugus and M. septentrionalis have

sympatric roosting and foraging areas in my study area and both roost in tree cavities,

potentially resulting in a high degree of competition for common roosting resources.

Because of their larger size and propensity to form larger groups within my study area,

M. lucifugus is likely better able to compete for roost space than M. septentrionalis (C.

Olson, unpublished data). However, based on its wing morphology and echolocation call

structure, M. septentrionalis is likely better able to fly in cluttered environments (Farney

and Fleharty 1969; Broders et al. 2004). Consequently, M. septentrionalis may be able to

reduce competition for roosts by roosting in areas that are more difficult to reach. They

may also be able to escape competition with M. lucifugus by roosting in smaller crevices

that are not suitable for large roosting groups typical of M. lucifugus in my study area

(see Chapter 3).

Methods

Study area

Field work occurred in Lesser Slave Lake Provincial Park (55°27’N, 114°50’W),

located in the boreal plains ecozone of Alberta, Canada (Wiken 1986). The park is

situated along the eastern shore of Lesser Slave Lake, and is comprised mostly of old- growth forest (c. 150-years old) dominated by trembling aspen and balsam popular

(Alberta Forest Service 1985). Paper birch (Betula papyrifera), white spruce (Picea glauca), black spruce (Picea mariana), and jack pine (Pinus banksiana) also occur in variable densities. Few opportunities exist within the park for bats to roost in buildings.

However, a ranch to the south of the park had a large M. lucifugus nursery colony, and

two small buildings within the park have had intermittent roosting groups (pers. obs).

Four bat species are common in the park: M. lucifugus, M. septentrionalis, silver-haired bat (Lasionycteris noctivagans), and hoary bat (Lasiurus cinerius)(pers. obs.; Vonhof and

Hobson 2000). Long-term average annual temperature is 1.7°C (Environment Canada

2010). Winters are cold and long while summers are generally warm, with long-term average temperatures of 13.6°C, 15.6°C, and 14.6°C for June, July, and August, respectively. Annual precipitation averages 50.3 cm, approximately half falling from

June–August.

Capture and tracking methods

I located roost trees using radiotelemetry. Bats were captured using mist nets or a harp trap placed along forest trails or near beaver ponds. I attached radio transmitters to reproductive female M. lucifugus from June-August 2009 and 2010, and to reproductive female M. septentrionalis from June-August 2010. After trimming the fur, I glued a radio

20 transmitter (Holohil Systems Ltd., Carp, ON, Canada) weighing 0.37–0.56 g to the inter- scapular region of the back using latex adhesive. Transmitters averaged 4.3% (range =

3.4–5.5%) of M. lucifugus mass and 4.7% (range = 4.3–5.3%) of M. septentrionalis mass.

I determined reproductive condition based on a combination of gentle palpations of the abdomen, examination of the fur around the nipples, and the ability to express milk

(Racey 2009). I located roost trees daily, until the transmitter fell off, using a hand-held telemetry receiver (R-1000, Communication Specialists Inc., Orange, CA).

Roost measurements

I measured several roost-tree characteristics, including diameter-at-breast-height

(DBH), tree height, tree species, canopy closure, canopy height, average distance to nearby trees, and decay state. To calculate average distance to nearby trees, I measured the distance to the nearest tree (>10 cm DBH) in each of four quadrants based on cardinal directions (NE,SE,SW,NW) and then averaged these values; I used this measure (a modification of the point-quarter method) as an indication of tree density. Decay state was classified as either: (1) mostly alive (2) partially alive, but substantial dead sections are present; (3) recently dead, some branches remain; and (4) dead, branches have fallen off, extensive decay. Canopy closure was measured using a concave densiometer, 10 m from the roost tree, in each of the four cardinal directions; this measure was then averaged to obtain a single measure of canopy closure for each tree. For trees where a roost cavity could be determined (based on observations of bats or guano), I measured cavity height and recorded the type of defect(s) creating the roost cavity. I used a tape measure, diameter-tape, clinometer, and compass to measure distances, DBH, heights, and directions.

Exit counts

I conducted exit counts at a subset of roost trees that contained bats with radio transmitters. An observer waited silently near a roost tree and counted the number of bats that emerged. The use of artificial light was minimized and generally not needed. To avoid double counting, I stopped counting when bats re-entered until an equal number of bats emerged. Counts began by sunset, and ended once 10 min had elapsed after the previous bat emerged, or when the rate of re-entering bats exceeded the rate of emerging bats. Counts continued at least until the transmittered bat left the roost and other bats could be observed flying (approximately 1-h following sunset).

Statistical analysis

Measures of DBH, height, and distances were transformed using the natural logarithm to improve normality. I used t-tests (assuming equal variance) for comparisons between roost-tree measurements provided assumptions of normality were met. In the case of comparisons between groups that were significantly non-normal, the Wilcoxon rank-sum test was used. Normality was tested based on visual inspection and the Shapiro-

Wilk test. Assumptions of equal variance between groups were examined using the

Levene test. Pearson’s chi-square goodness-of-fit tests were used to determine if

categorical roost cavity characteristics differed between the two bat species or between

the two tree species. For comparisons of roost-tree characteristics between bat species, I

used the Benjamini-Hochberg method for controlling false discovery rate, as multiple

measurements were assessing the same hypothesis (that the two species would have

distinct patterns of roost use)(Waite and Campbell 2006). As there were seven statistical tests (considering comparisons of characteristics of aspen roosts between bat species), the

22 lowest P-value was compared to a significance threshold of 0.007 (α=0.05/7). All other significance tests were compared to α=0.05. Although multiple roost trees were located based on the same bat, most roost trees were used by multiple bats, either concurrently or on separate days, and were therefore treated as independent for statistical purposes. All statistical tests were conducted using JMP 8.0.1 (SAS Institute Inc., Cary, NC, USA).

Results

I tracked 58 M. lucifugus (13 pregnant, 45 lactating) using radiotelemetry from 28

June–15 August 2009 (30 bats) and 07 June–14 August 2010 (28 bats), and tracked 6 M. septentrionalis (3 pregnant, 3 lactating) from 02 June–30 July 2010. I located 147 distinct

M. lucifugus roost trees and 19 M. septentrionalis roost trees. I could not confidently determine which tree was occupied for 12 roosts used by M. lucifugus, so these were excluded from the analysis, leaving 135 roost trees for which measurement were obtained. Based on guano accumulation, the presence of vocalizing bats, or observations of bats emerging, I was able to determine the cavity where bats were roosting for 79 roost trees used by M. lucifugus and 17 roost trees used by M. septentrionalis. Radiotagged individuals, of both species, were only observed roosting in trees. At least two roost trees were used by both M. lucifugus and M. septentrionalis, although not concurrently.

I located M. lucifugus roosts from two broad areas of the park, one located in the north of the park and another, larger, roosting area further south (Figure 2.1). Based on proximity of roost trees, the six M. septentrionalis likely came from two distinct colonies.

Two M. septentrionalis were captured, on separate days, along a path following the lake shore in the north of the park (immediately adjacent to the northern M. lucifugus colony),

23 and roosted 2.2 and 2.3 km from where they were captured, but within close proximity to each other. The other four bats were captured along the lake further south and roosted

443 m to 794 m from the capture location.

The southern M. septentrionalis colony had a roosting home range that was nearly entirely within the roosting home range (based on 100% minimum convex polygons) of the southern M. lucifugus colony. Only one of the 13 roost trees for the southern M. septentrionalis colony fell outside the roosting area of the southern M. lucifugus colony.

Since radiotagged M. septentrionalis had sympatric roosting areas, or foraged within the roosting area of a M. lucifugus colony, I assume that in the absence of competitive exclusion, the same roost trees were available to either species, and differences between species resulted from selection rather than differences in available habitat.

Figure 2.1. Study area in Lesser Slave Lake Provincial Park, Alberta, Canada showing M. lucifugus and M. septentrionalis roosting areas. Roosting groups located using radiotelemetry from June-August 2009 and 2010.

Myotis septentrionalis roosted primarily in trembling aspen. Of the 19 roost trees,

16 (84%) were trembling aspen, while the remaining roosts were a single balsam poplar, white birch, and white spruce (Table 2.1). The transmitter of the bats occupying the white spruce never left the tree and a cavity could not be located, making it uncertain whether it was used as a day-roost. Both species typically roosted within the understory of old- growth forest with canopy closure >75%. Due to low variability in canopy closure, I did not consider this measurement in my analysis despite it being an important component of habitat selection in other areas (Kalcounis-Ruppell et al. 2005). The majority of roosts

used by M. lucifugus were in balsam poplar, and a significantly smaller proportion of

roosts were in aspen compared with M. septentrionalis (χ2 = 14.26, d.f. = 1, P < 0.001;

Table 2.1).

Several roost-tree characteristics were significantly different between those used

by M. lucifugus and those used by M. septentrionalis (Tables 2.1,2.2,2.3). However, most

differences in roost characteristics were associated with roost-tree species, making

comparisons between other roost tree characteristics uninformative. When only aspen

roosts were compared, most tree characteristics were similar between the two bat species.

Based on alpha-adjusted statistical comparisons, roost trees used by the two bat species

had similar DBH (t = 0.31, d.f. = 75, P = 0.756), and height (Wilcoxon rank-sum; Z =

1.26, P = 0.207), and were in areas of similar tree density (average distance to nearest

tree in each quadrant; t = 0.45, d.f. = 63, P = 0.658) and canopy height (t = 2.29, d.f. =

72, P = 0.025). There was no significant difference in the type of cavity used (χ2 = 0.52, d.f. = 1, P = 0.473) or the decay state (minimal decay versus extensive decay; χ2 = 1.95, d.f. = 1, P = 0.163) of aspen roosts used by the two bat species. However, there was a

significant and substantial difference in the height of cavities used by the two bat species

(Wilcoxon rank-sum; Z = 2.86, P = 0.004). Myotis lucifugus tended to roost much higher

in aspen than M. septentrionalis (Table 2.1).

I compared characteristics of balsam poplar and aspen roosts for M. lucifugus,

which unlike M. septentrionalis, had roosts in nearly equal numbers of both tree species.

Several measures of roost-tree characteristics were strongly associated with roost-tree

species (Tables 2.1, 2.2, 2.3). Balsam poplar roost trees had significantly larger DBH (t =

10.25, d.f. = 132, P < 0.001), lower height (t = 4.04, d.f. = 132, P < 0.001), a greater

average distance to neighbouring trees (t = 3.46, d.f. = 93, P < 0.001), and were more

likely to be either dead or have major dead sections (χ2 = 15.01, d.f. = 1, P < 0.001) than

aspen roosts (Tables 1.1, 1.2). Relatively small-DBH roost trees were primarily aspen.

Fewer than 10% of balsam poplar roosts, but 60% of aspen roosts, had a DBH <40 cm.

Although 26% of aspen roosts had a DBH <30 cm, no roosts in balsam poplar had a DBH

<30 cm. Neither cavity height (t = 1.43, d.f. = 77, P = 0.158) nor canopy height (t = 1.20,

d.f. = 121, P = 0.232) differed between aspen and balsam poplar roosts used by M.

lucifugus; however, there was a non-significant tendency for roost cavities to be higher

from the ground in balsam poplar than in aspen.

The type of defect used as roost cavities differed between aspen and balsam

poplar (Table 2.3). Cavities in aspen tended to be radial-longitudinal splits consistent

with structural defects caused by frost cracks (Figure 2.2; Kubler 1983; Parsons et al.

2003; Psyllakis and Brigham 2006). Splits or cracks in balsam poplar were significantly less common than in aspen, and instead cavities were caused more often by other processes, such as knot holes, breakage, or animal excavations (χ2 = 17.39, d.f. = 1, P <

0.001). Frost cracks or other similar defects were common in live and otherwise healthy trees, whereas roost cavities created by animal excavations and breakage were generally only observed in decaying trees. Radial-longitudinal splits were also the primary defect creating roost cavities in small-DBH roost trees. Of the 37 known roost cavities in trees with a DBH <35 cm (used by either bat species), 32 (86%) were caused by radial- longitudinal splits. In contrast, of the 57 roost trees with a DBH of ≥35 cm, only 20

(35%) were caused by radial-longitudinal splits. Splits or cracks were often associated with the lower sections of the tree, whereas breakage and knot holes tended to occur higher.

Although woodpeckers were common in the study area (pers. obs.), I never observed bats roosting in a recent woodpecker cavity. For 12 roost trees, M. lucifugus roosted in old and highly decomposed woodpecker excavations. On two occasions M. septentrionalis roosted in the abandoned hole of a small primary cavity excavator (e.g. nuthatch, chickadee or small woodpecker), but due to the height, close inspection was not possible.

I conducted 10 exit counts at 8 unique M. septentrionalis roost trees from 05 June

– 26 July 2010, and 63 exit counts at 48 unique M. lucifugus roost trees from 29 June –

14 August 2009 and 07 June – 13 August, 2010 (Chapter 3). The size of M. septentrionalis roost groups (median=5.5, range = 1–29 bats, n=10) was significantly smaller than M. lucifugus roost groups (median=33, range=1–386 bats, n=63;

Wilcoxon rank-sum, z = 2.42, P = 0.016).

Table 2.1. Roost-tree characteristics of roosts located with radiotelemetry for M. septentrionalis and M. lucifugus roosting within Lesser Slave Lake Provincial Park. Roosts located and measured June-August 2009 and 2010 for M. lucifugus and June-August 2010 for M. septentrionalis. Measurement data represent mean (±SD). M. septentrionalis M. lucifugus Aspen All roosts Aspen Balsam All roosts # of roosts (n) 16 18 61 73 135 Percent of roosts 89 100 45 54 100 Roost-tree DBH (cm) 36.3 (9.7) 37.3 (10.1) 36.9 (9.5) 58.1 (14.8) 48.6 (16.3) Roost-tree height (m) 21.9 (7.7) 22.2 (7.4) 20.2 (6.8) 15.5 (6.8) 17.5 (7.4) Canopy height (m) 25.6 (2.9) 25.5 (2.9) 23.7 (2.9) 23.0 (4.2) 23.3 (3.7) Nearest tree – Avg (m) 3.8 (0.9) 3.9 (1.3) 4.1 (1.5) 5.9 (3.9) 5.0 (3.0) Nearest tree – Max (m) 5.9 (1.8) 5.9 (2.1) 6.2 (2.5) 10.1 (9.2) 8.1 (6.9) Roost cavity height (m) 4.6 (3.9) 4.7 (3.8) 8.0 (4.8) 10.0 (5.8) 8.9 (5.3) Note: One Betula papyrifera and one Populus balsamifera roost used by M. septentrionalis, and one Picea glauca roost used by M. lucifugus not shown separately.

Table 2.2. Decay characteristics of roost trees used by M. septentrionalis and M. lucifugus in Lesser Slave Lake Provincial Park from June–August 2009 and 2010. M. septentrionalis M. lucifugus Aspen All Roosts Aspen Balsam All Roosts # % # % # % # % # % Mostly Alive 10 62.5 11 61.1 28 43.1 11 15.7 39 28.7 Partially Alive, 1 6.3 1 5.6 6 9.2 18 25.7 24 17.6 Extensive dead section Dead, some branches remain 2 12.5 3 16.7 23 35.4 20 28.6 43 31.6 Dead, branches generally absent 3 18.8 3 16.7 8 12.3 21 30.0 30 22.1 Total 16 100 18 100 65 100 70 100 136 100

Table 2.3. Characteristics of roost cavities used by M. septentrionalis and M. lucifugus in Lesser Slave Lake Provincial Park from June–August 2009 and 2010. M. septentrionalis M. lucifugus Aspen All Roosts Aspen Balsam All Roosts Cavity type # % # % # % # % # % Radial-longitudinal split 13 81.3 14 82.4 31 72.1 9 25.0 40 50.6 Knot hole 1 6.3 1 5.9 5 11.6 9 25.0 14 17.7 Breakage 4 9.3 8 22.2 12 15.2 Animal excavation 2 12.5 2 11.8 1 2.3 8 22.2 9 11.4 Under bark 2 4.7 2 5.6 4 5.1 Total 16 100 17 100 43 100 36 100 79 100 Note: Roosts under bark are likely underrepresented due to the difficultly in identifying these structures. The total number of roost trees for which the roost cavity could be determined represents 94% of the 18 roost trees located for M. septentrionalis, and 59% of the 135 roost trees located for M. lucifugus. In cases where cavities were an aggregation of multiple defects, the defect deemed most important for cavity formation is presented.

Figure 2.3. Dead balsam poplar (P. balsamifera) roost tree used by M. lucifugus in Lesser Slave Lake Provincial Park during 2009 and 2010. This roost contained the largest observed roosting group (≥386 bats). Bats roosted within a vertical split, which wraps around the tree, extending 1-8 m above ground level (Photos by C. Olson).

Discussion

Roost characteristics

Trembling aspen is well known to be an important roost species for bats in many areas of North America (Kalcounis and Hecker 1996; Crampton and Barclay 1998; Willis

et al. 2003; Psyllakis and Brigham 2006). However, likely owing to the scarcity of

studies in northern regions, balsam poplar has rarely been reported as a common roost

species, and comparisons of these two roost-tree species are rare (although for general

accounts, see Parsons et al. 1986; Crampton and Barclay 1998; Vonhof and Wilkinson

1999; Psyllakis and Brigham 2006). In my study area, balsam poplar was the most

common roost tree used by M. lucifugus, consisting of more roosting days, and typically

being associated with larger roosting groups (see Chapter 3). Both aspen and balsam

poplar are susceptible to heart rot fungus, which facilitates the formation of large inner

cavities ideal for roosting (Thomas et al. 1960; Parsons et al. 2003). Despite similarities

between species, however, the characteristics of roosts used by bats in my study differed

substantially between aspen and balsam poplar roost trees.

The importance of radial-longitudinal splits for bats has been reported previously

(Crampton and Barclay 1998; Vonhof and Wilkinson 1999; Parsons et al. 2003; Psyllakis

and Brigham 2006; Willis et al. 2006), suggesting that these types of roosts may be

important features for bats in areas where they are likely to form. In my study area, bats

used these defects much more commonly in aspen than in balsam poplar, likely

explaining associated physical differences between the two tree species. Both M.

lucifugus and M. septentrionalis have been observed to make near-exclusive use of

radial-longitudinal splits in some regions (Vonhof and Wilkinson 1999; Psyllakis and

Brigham 2006).

Winter injuries, especially frost cracks, are common in northern temperate regions

and cause injuries consistent with the radial-longitudinal splits I observed being used by

bats (Parsons et al. 2003). Frost cracks result primarily from differential tension between the inner and outer sections of the tree caused by the shrinkage of wood when freezing temperatures cause moisture to migrate from the cell walls into the lumen (Kubler 1983).

Colder and fluctuating temperatures, found in northern regions, increase the frequency and magnitude of frost cracks (Kubler 1983). Secondary infection by heart-rot fungus helps enlarge cavities (Parsons et al. 2003). Another type of winter injury, sunscald,

produces milder injuries but may still provide access for fungal pathogens and subsequent

cavity formation (Karels and Boonstra 2003).

Splits or cracks were comparatively less common in balsam poplar. Instead, other

processes such as breakage, knotholes or woodpecker excavations were primarily

responsible for allowing access through the sapwood. This difference in cavity types

between the two species of trees may help explain other differences between individuals of the two species used as roosts. For example, radial-longitudinal splits were frequently used as roost cavities in relatively small-DBH trees that were typically alive and otherwise healthy. In contrast, woodpecker excavations, breakage, and sloughing bark

typically occurred on larger and more decayed trees. The relative rarity of split-type

roosts in balsam poplar may explain why few small-DBH balsam poplar were used as

roosts, and why aspen roosts, on average, were less decayed than balsam poplar.

Additionally, frost cracks tend to occur most often towards the base of the tree, as

34 damage caused by wildlife, or other factors, make the formation of frost cracks more likely (Kubler 1983). This may partially explain why cavities tended to be closer to the ground (albeit non-significantly) in aspen than in balsam poplar, despite aspen roost trees generally being taller.

Both bat species rarely roosted in woodpecker cavities, in contrast to roost selection by M. lucifugus in south-western Saskatchewan (Kalcounis and Hecker 1996).

The infrequent use of these features in my study area may be partially due to greater competition from other, larger, secondary cavity users, such as silver-haired bats

(Lasionycteris noctivagans), squirrels, and many types of birds (Martin et al. 2004;

Barclay and Kurta 2007). Alternatively, split-type roosts may accommodate more bats and potentially provide greater microclimate variability, allowing more opportunity for bats to find optimal microclimates within roosts (Willis et al. 2006). I also noted that guano appeared to be shed from split-type roosts, likely more readily than from woodpecker cavities, potentially reducing guano accumulation (and associated parasites) within roosts.

The differences in characteristics between aspen and balsam poplar trees used as roosts indicate that habitat-selection studies need to be context specific. Studies examining only aspen, for example, would find different results regarding the importance of characteristics such as DBH, tree height, decay state, and distance to the nearest tree, than studies examining balsam poplar. Although the importance of diameter has been well acknowledged, roost-tree DBH differed substantially between aspen and balsam poplar. For example, a DBH of 30 cm, while typical for aspen roosts used by M. lucifugus, may rarely provide suitable cavities in balsam poplar.

Interspecific comparison

My results show significant differences in roosting habitat between the two

species of Myotis in my study area, thereby suggesting possible mechanisms that reduce

interspecific competition for roost space. The low use of balsam poplar by M.

septentrionalis has the effect of increasing niche separation between this species and M.

lucifugus, which most often roosted in balsam poplar. The reason for the rarity of use of balsam poplar by M. septentrionalis is unclear, especially since they appear to preferentially select balsam poplar in other areas (Vonhof and Wilkinson 1999).

However, this discrepancy could be explained if M. septentrionalis was prevented from

selecting balsam poplar because of greater competition for this species by M. lucifugus in my study area. Myotis lucifugus was seemingly abundant, possibly a result of an abundant food supply provided by the large lake, and typically formed larger groups than

M. septentrionalis. Larger groups were associated with balsam poplar (see Chapter 3).

Thus, M. lucifugus may have selected balsam poplar to take advantage of social benefits

(such as social thermoregulation). Because of their larger size, balsam poplar roosts may also have had superior microclimatic conditions regardless of the effect of communal roosts, but social thermoregulation is known to exert more influence on roosting conditions than the intrinsic variation in roost characteristics alone (Willis and Brigham

2007). Thus, social benefits may have been the primary advantage of using balsam poplar as roosts. As M. septentrionalis had smaller roosting groups, they may have been able to use aspen roosts while still maintaining their optimal group size. Alternatively, the smaller M. septentrionalis roosting groups may have been a by-product of competition with M. lucifugus. If M. septentrionalis could not compete for large roost cavities, they

36 may have been prevented from forming large roosting groups. If this were the case, management practices that increase the abundance of M. lucifugus could adversely affect

M. septentrionalis populations by indirectly restricting the size of roosting groups below optimal levels.

The lower height of roost cavities used by M. septentrionalis, compared with M. lucifugus, may have been an additional way of partitioning roosting resources. Clutter increases towards the ground, as shrubs and saplings become denser. As M. septentrionalis is more clutter adapted (Farney and Fleharty 1969; Broders et al. 2004), they may have been better able to access cavities at lower heights than M. lucifugus.

Cavities closer to the ground may be especially sub-optimal for large groups, typical of

M. lucifugus colonies, as bats must also avoid collisions with each other.

Few studies have provided direct evidence of competitive exclusion for roost space by bats. Myotis lucifugus and M. septentrionalis have been observed occupying the same building roost (Timpone et al. 2010), suggesting minimal antagonistic behaviour between the two species. However, space within building roosts is often abundant, and segregation within the roost should be possible (for example, see Swift and Racey 1983).

Bats may also be able to avoid interspecific competition by exiting and entering building roosts at different times, as competition for roost access points may be more limiting than actual roost space in these structures (Swift and Racey 1983). In contrast to building roosts, space in tree cavities is more limiting, and smaller individuals may not be able to compete for optimal locations within a roost, even if interspecific antagonism is absent

(Humphrey 1975; Perkins 1996). Alternatively, competition may be indirect, such as

would occur if M. lucifugus increased guano and parasite accumulation in available

roosts, thus reducing the quality of these roosts for use by M. septentrionalis.

Differences in roosting habitat have been reported in areas where two ecologically similar species occur in sympatry (Broders and Forbes 2004; Jacobs and Barclay 2009;

Timpone et al. 2010). For example, M. septentrionalis appears to select smaller and less decayed trees and more often selects cavity or crevice roosts than Indiana bats (Myotis

sodalis) in areas where they co-occur (Foster and Kurta 1999; Carter and Feldhamer

2005; Lacki et al. 2009; Timpone et al. 2010). In contrast, M. sodalis roosts more often

under sloughing bark in larger and more decayed trees. A similar result was observed for

M. septentrionalis in my study: compared to M. lucifugus, M. septentrionalis roosted in

smaller and less decayed trees, although both roosted predominately in cavity or crevice

roosts. Myotis septentrionalis and M. sodalis did not differ in roost-cavity height, but M.

septentrionalis had greater variability in cavity height (Lacki et al. 2009), supporting my

hypothesis that M. septentrionalis is better adapted to use lower roost cavities, if required.

In a study in New Brunswick, competition for roost space between M. septentrionalis and

M. lucifugus was apparently avoided as M. lucifugus roosted primarily in buildings while

M. septentrionalis primarily roosted within tree cavities (Broders and Forbes 2004). Two

bat species in South Africa, Scotophilus dinganii and S. mhlanganii, avoided roost

competition in a similar manner; S. dinganii roosted primarily in buildings while S.

mhlanganii primarily roosted in trees (Jacobs and Barclay 2009). Male and Female M.

septentrionalis in New Brunswick were able to avoid competition for roost space by

roosting in different forest types; females used deciduous trees most often while males

most often used coniferous species (Broders and Forbes 2004).

Despite limited evidence of roost competition by bats, a few authors have suggested roost competition may limit the diversity of bat species (Timpone et al. 2006;

Timpone et al. 2010), possibly more than competition for common prey (Humphrey

1975; Perkins 1996). As evidence of the importance of roost competition, bats of different size classes that occupy similar roosting structures have been noted to occur less often in the same regions than would otherwise be expected, suggesting competitive exclusion by the dominant species (Perkins 1996). Humphrey (1975) found the number of bat species in an area was correlated with the diversity of roosting habitat. Indeed, the low bat species diversity in my study area may be explained by the relative homogeneity of the landscape, which could limit opportunities for bats to avoid interspecific competition for roost space.

Implications

Conclusions regarding roost use and the influence of competition on the roosting behaviour of M. septentrionalis should be made with caution due to the low number of observations I was able to obtain for that species. Nonetheless, my results indicate that some separation of roosting niche is occurring between the two species of Myotis in my study area. Competition between these two bat species may be one important factor affecting habitat selection, especially for M. septentrionalis, and should be considered when examining habitat selection and resource requirements of these bat species. Having both balsam poplar and aspen provides greater diversity in roosting resources and may help increase species diversity by alleviating competition for similar resources. Radial- longitudinal splits were important roosting features for both species of bats in my study; trees with these features are often living, less decayed, and may offer roost space for a

39 greater number of years than occur with snags. Special efforts to maintain these structures within managed forests may create long-term benefits for multiple species of bats.

However, to ensure a diverse community of cavity-dwelling fauna, it is necessary to maintain a diversity of tree species and trees of multiple decay states within managed landscapes. However, additional research is required to identify the extent to which M. lucifugus and M. septentrionalis compete for common resources, and how populations of

M. septentrionalis are affected by M. lucifugus. For example, can these species co-exist in forests consisting of a single tree species? Is the size of M. septentrionalis roosting groups correlated with the abundance of M. lucifugus occurring in an area? The boreal is an ideal study system for examining interspecific competition for roost space as there are few roosting options and few other cavity-dwelling bats occur in most areas.

CHAPTER 3: TEMPORAL VARIATION IN ROOST SELECTION BY

REPRODUCTIVE FEMALE LITTLE BROWN BATS, MYOTIS LUCIFUGUS

Introduction

A large proportion of small vertebrates in forested regions occupy the cracks,

crevices, and cavities of trees (Martin et al. 2004). Although a few species are able to excavate their own cavities, the majority rely on naturally occurring cavities or the abandoned excavations of other animals. Trees with suitable nesting or roosting substrate are typically old and have some degree of death, decay or structural defect (Martin et al.

2004; Barclay and Kurta 2007). These trees may be scarce resources in forested regions, many of which are managed for short rotation forestry (Hayes and Loeb 2007).

Considerable attention has thus focused on the habitat preferences of cavity-dwelling species in an attempt to optimize the retention of suitable wildlife habitat (Martin et al.

2004; Lacki et al. 2007). Unlike with birds, cavity-dwelling mammals are typically able to move offspring between trees, allowing habitat selection and group size to be regularly adjusted to suit physiological requirements and the developmental condition of offspring.

This ability may represent an important evolutionary advantage over birds, which may also compete for access to tree cavities. Although roost switching may offer important fitness benefits, few studies of cavity selection emphasize temporal variation in roosting behaviour, potentially missing important aspects of habitat selection (Garroway and

Broders 2008).

As the primary site for rearing offspring, the selection of roost cavities may have important fitness consequences for bats (Kunz 1982; Kerth et al. 2001). These sites offer

shelter, thermal benefits and protection from predators (Kunz 1982). Roost microclimate,

in particular, is critical for juvenile survival by affecting energy expenditure, growth rate

and readiness for hibernation (Kerth et al. 2001; Willis and Brigham 2007; Hoying and

Kunz 1998). For many species of bats, roosts are also a primary site of social engagement and may serve an important role in maintaining social networks (Willis and Brigham

2004).

Social thermoregulation is among the most important benefits of sociality in bats

(Willis and Brigham 2007; Kerth 2008; Pretzlaff et al. 2010), but benefits such as improved predator detection and dilution (Kerth 2008), alloparental care (Bohn et al.

2009), allogrooming (Kerth et al. 2003), information transfer (Safi and Kerth 2007) and social learning (Ratcliffe and ter Hofstede 2005), may also be important. However, large groups are not always favoured, as a greater number of individuals results in additional costs. These include greater parasite and disease transmission (Cote and Poulin 1995), increased detectability by predators, greater competition for resources (Kunz 1982), and, possibly, reduced availability of roosting structures near optimal foraging areas. The relative importance of these costs and benefits depends on numerous factors, such as weather, reproductive condition of the individual, developmental state of offspring, and food availability, all of which may exhibit systematic temporal variation (Sano 2000;

Kerth et al. 2001; Lacki and Schwierjohann 2001; Kerth 2008). Many studies examining temporal variation in roost selection only compare between broad life-history stages, such as between pregnant and lactating bats. However, costs and benefits may change continuously, and roosting behaviour and habitat selection may change more within reproductive classes than is currently recognized.

Many species of tree-roosting bats exhibit fission-fusion roosting behaviour

(Kerth and Konig 1999; O'Donnell 2000; Willis and Brigham 2004; Russo et al. 2005;

Garroway and Broders 2007; Popa-Lisseanu et al. 2008). With this system, the size and composition of roosting groups change regularly, but a stable social unit, or colony, may occur at a larger spatial and temporal scale. This may be an adaptation to maintain large social groups, despite physical constraints on the size of roosting groups occupying trees

(Willis and Brigham 2004). Fission-fusion roosting behaviour allows considerable opportunity to adjust group size and roosting structure in response to changing conditions, without compromising social cohesiveness of the larger colony (Kerth and

Konig 1999; Pretzlaff et al. 2010). A correlation between the size of roosting groups and cavity size has previously been reported (Willis et al. 2006)); however, the general importance of the association between tree size and group size is poorly understood.

Nonetheless, if the size of roosting groups is associated with the choice of roosts, then factors associated with variation in group size may similarly be associated with variation in roosting habitat.

Differences in roost selection have been observed in response to reproductive period. During gestation, temperate zone bats roosting in rock crevices use shallow roosts that fluctuate with ambient temperature, while during lactation they use deeper roosts that maintain more stable temperatures throughout the day and night (Vaughan and O'Shea

1976; Chruszcz and Barclay 2002; Lausen and Barclay 2003). Changes in roost selection in relation to reproductive stage has received little attention for tree-cavity roosting bats

(Garroway and Broders 2008). However, roost selection may differ between reproductive and non-reproductive bats (Veilleux et al. 2004), and between gestating and lactating bats

(Baker and Lacki 2006; Garroway and Broders 2008). For example, compared to

pregnant bats, lactating long-legged bats (Myotis volans) were more likely to roost in

trees that were associated with large roosting groups (≥ 50 bats); these trees were

typically larger, had greater bark retention, and were more often a thick-bark tree species

compared with roosts with smaller groups (Baker and Lacki 2006). During lactation, stable thermal environments may be critical because adults must leave pups behind while they forage, and elevated temperatures promote juvenile growth (Zahn 1999; Lausen and

Barclay 2003). In contrast, during gestation, having a roost that fluctuates with ambient temperature allows deep torpor earlier in the day followed by passive rewarming prior to evening emergence (Chruszcz and Barclay 2002). Although foetal/juvenile growth rate may be slowed, colder temperatures generally result in greater energy savings from

torpor expression, which may be important to compensate for reduced food availability

and foraging time early or late in the season and the need to time parturition for periods

of food abundance (Racey 1973; Hamilton and Barclay 1994; Kerth et al. 2001; Hollis

2004).

Tree size, especially height and DBH, is a consistent determinant of cavity

selection in bats (Kalcounis-Ruppell et al. 2005). Larger trees are generally expected to

have more stable thermal environments (Wiebe 2001), offer greater space for rearing

young, and in social animals, may be associated with larger roosting groups (Lacki and

Schwierjohann 2001; Baker and Lacki 2006). Willis and Brigham (2007) found that

microclimate of vacated roosts did not differ between roost trees, a result that appears to

contrast with rock-crevice roosts. Instead, they found that occupancy by bats substantially

increased the temperature within tree roosts, which resulted in significant energy savings

44 for normothermic bats (i.e. bats not using torpor). Moreover, roost temperature and concomitant energy savings increased with the number of bats occupying a roost.

Consistent with other studies, I hypothesized that lactating bats should select roost cavities with warm stable microclimates. Newborn bats are small, have little insulation, and may be unable to thermoregulate for several days after birth (Studier and O'Farrell

1972; Hollis and Barclay 2008). To minimize risk to pups and the time spent rewarming, roost selection should favour the warmest and most stable microclimates near the time of parturition (Sano 2000). I thus predicted that female bats should select larger trees near the time of parturition, with the importance of tree size dissipating as offspring develop.

Little brown bats (Myotis lucifugus) are one of few bat species that are prevalent across much of the northern boreal forest, where cold temperatures and the low number of frost free days pose major challenges for successful reproduction (van Zyll de Jong

1985). Both building-roosting and tree-roosting individuals raise young in maternity colonies (Crampton and Barclay 1998; Fenton and Barclay 1980), which can range up to several hundred individuals. Although roosting populations have been well studied in buildings, populations of M. lucifugus occupying natural tree cavities have received relatively little attention. This species frequently roosts in trees where available

(Kalcounis and Hecker 1996), and in much of the boreal forest where buildings and rock crevices are scarce, they almost entirely rely on tree cavities for roosting (Crampton and

Barclay 1998). The social behaviour of M. lucifugus, especially social-thermoregulation, may be one reason it is able to cope with cold northern climates. Thus, the ability of habitat to support roosting groups may be one factor affecting this species’ distribution and reproductive success.

I examined roost use by M. lucifugus and determined whether the size of roosting groups and roost-tree selection change in a systematic manner from mid–late gestation

through lactation. I examined three questions: 1) is there a relationship between the characteristics of roost trees, especially roost-tree size, and the size of roosting groups, 2) how does the size of roosting groups and characteristics of roost trees change during the

gestation and lactation period, and 3) are these changes associated with life history stages

of bats in my study area?

Methods

Study area

My study occurred from June–August 2009 and 2010 in Lesser Slave Lake

Provincial Park, Alberta, Canada (55°27’N, 114°50’W), along the eastern shore of Lesser

Slave Lake. The forest occurs within the boreal plains ecozone, and is dominated by

trembling aspen (Populus tremuloides) and balsam popular (Populus balsamifera), with

lesser quantities of white spruce (Picea glauca), paper birch (Betula papyrifera), black

spruce (Picea mariana), and jack pine (Pinus banksiana) occurring sporadically (Alberta

Forest Service 1985; Wiken 1986). Stand age is approximately 150-years throughout

most of the park, which is old relative to the short fire cycles of the boreal forest (Alberta

Forest Service 1985). Most bats in the park roost in either trembling aspen or balsam

poplar; building roosting bats are uncommon (Chapter 2).

Figure 3.1 Map of study area in Lesser Slave Lake Provincial Park, Alberta showing roosting areas occupied by the northern and southern colonies of M. lucifugus.

Capture and tracking methods

I captured bats using mist nets or a harp trap placed along trails or near beaver ponds. Nets were generally placed away from known roost trees to reduce the likelihood of altering roosting behaviour. Netting sites were moved night-to-night (within or in proximity to known colony roosting home ranges) to reduce spatial bias and to increase netting success. I trimmed the inter-scapular fur and glued a radio transmitter (Holohil

Systems Ltd., Carp, ON, Canada) weighing 0.37–0.56 g to the bat using a latex-based skin adhesive. Transmitter mass averaged 4.3% of the bat’s mass (range = 3.4–5.5%), near or below the recommended limit to avoid impairing flight performance (Aldridge and Brigham 1988). I concluded that a bat was pregnant if I could feel an obvious foetus, and that a bat was lactating if milk could be expressed from the nipples. Non- reproductive and post-lactating bats were not included in this study. To reduce the chance of capturing bats that flew in from remote areas, only bats captured within 1-hour of emergence were used for radiotelemetry. I closed mist nets once bats suitable for transmitter attachment were captured, thereby minimizing captures to avoid disturbance to the colonies. I located roost trees daily, using a hand-held telemetry receiver

(R1000, Communication Specialists Inc., Orange, CA), for every radiotagged bat until the transmitter fell off or could no longer be located. I followed guidelines of the

American Society of Mammalogists (Gannon et al. 2007) for the handling and capturing of bats, and protocols were approved by the Animal Care Committee at the University of

Calgary.

Colony association

My goal was to delineate multiple colonies of bats and use colony association as a factor variable to control for differences in forest cover in different areas of the park. I considered bats as belonging to the same colony if they belonged to a common roost network, where roost trees are nodes and linkages are formed as bats move among roost trees (Rhodes et al. 2006; Fortuna et al. 2009). This resulted in two colonies: one in the north of the park and another in the south, both along the shore of Lesser Slave Lake

(Figure 3.1; see Chapter 4). In addition, 11 bats could not be assigned to either of these colonies, likely due to a limited number of observations for these individuals. These bats were included in either the northern or southern group as they had ranges that overlapped with one of these colonies and habitat was generally similar throughout each colony’s range.

Roost measurements and exit counts

Upon locating a roost tree, I recorded tree species and measured diameter-at- breast-height (DBH) using diameter-tape (see Chapter 2). I returned to most roost trees during early August and measured tree height using a clinometer. I conducted exit counts at a subset of the roosts that contained radio-transmittered bats. One observer watched a roost tree starting at sunset and counted the number of bats that emerged. Counts ended either 10-minutes following the previous bat, or when re-entering bats outnumbered emerging bats. Counts continued until at least 1-hour after sunset, the approximate emergence time for bats in my study. When bats re-entered a roost, the count was stopped until an equal number of bats emerged. Counts therefore represent a minimum number of bats that occupied a roost, as it was not possible to determine when all bats had left.

Counts were not conducted during inclement weather. To avoid mother-pup pairs, I

considered only counts with three or more bats as comprising a roosting group, and fewer

than three were considered solitary and excluded from statistical analysis. Some roosts

were counted more than once. To avoid pseudoreplication, only the maximum count at

each roost tree was used in the statistical analysis.

Statistical analysis

I performed all statistical analyses using SAS version 9.2 (SAS Institute Inc.

2008), and used a significance criterion of P < 0.05. I analyzed continuous response

variables (i.e. DBH, height) using a general linear mixed model (Proc Mixed) that

accounted for repeated measures of individual bats. Each bat was treated as the subject of

the repeated measures, which allowed me to model the correlation of roost characteristics

selected by the same bat. I considered several covariance structures to model within-

subject correlation and selected among candidate structures using Akaike’s Information

Criterion (AIC) (Littell et al. 2006). Covariance structures that result in a lower AIC are

assumed to be superior. Based on this criteria, I deemed the first-order autoregressive

covariance structure to be the most appropriate for all general linear mixed models.

Binary response variables (e.g. aspen versus balsam poplar) were analysed using a

generalized linear mixed model (Proc Glimmix) with a binary error distribution and a

logit link function. Each bat was again treated as the subject of the repeated measure, and the first-order autoregressive covariance structure was used to model within-subject correlation. Fixed effects were modeled using type III sum of squares.

DBH was transformed using the natural logarithm to improve the normality of the residuals. Julian day (defined here as the number of days since the beginning of the year)

and Julian-squared were used to examine changes in roost use (height, DBH, species)

during the summer breeding season (June-August). For all statistical tests, Julian day was grand mean centred to reduce the correlation between Julian day and Julian day squared

(Hox 2002). I centred the data by subtracting the mean of Julian day from each observation, and then squared this number to obtain centred Julian-squared (the centred versions are used hereafter). Since roost availability may be different between colonies, I also included the colony in which a bat belonged as a fixed variable, and its interaction with Julian and Julian-squared. Year and its interaction with Julian and Julian-squared were also included to accommodate possible year effects. As colony and year effects were not of primary interest, non-significant terms were removed using backwards elimination.

I examined changes in the size of roost groups (obtained from exit counts) using a generalized linear model with a negative-binomial distribution and a log-link function

(Proc Glimmix). For all analyses, I included Julian, Julian-squared, and colony as a fixed factor, along with the interaction between colony and Julian and Julian-squared. I used a similar model to examine differences in the size of roosting groups between trees with different physical properties. Because DBH varies markedly between roost-tree species, I used separate models to examine the difference in group size between DBH classes (<40 cm, 40-50 cm, >50 cm) and between different roost-tree species. I initially included either

DBH or tree species in the model as fixed factors, plus their interaction with Julian and

Julian-squared. Non-significant interactions or variables were removed from the model using backward elimination.

Where appropriate, residuals were inspected visually and a Shapiro-Wilk test was

used to test the assumptions of normality. Group variances were calculated and compared

to detect possible departures from the assumptions of the models. For general linear mixed models, the need for modeling heterogeneous variances for different levels of a

factor was determined by comparing models assuming homogenous variance to models

that allow for heterogeneous residual variance (using the group= option in the repeated statement is SAS), and choosing based on which produced the lowest AIC score (Littell et al. 2006). For generalized linear mixed model, homogeneity of variances across groups was tested using the ‘covtest’ statement in Proc Glimmix. Additionally, for generalized linear models or generalized linear mixed models, I inspected the deviance/d.f. ratio to ensure it did not depart substantially from 1 (indicating over or under dispersion).

Results

Bat captures and radiotelemetry

I tracked bats from 28 June–15 August, 2009 and from 07 June–14 August, 2010.

The first lactating bat was captured July 5 in 2009. In 2010, a lactating bat was captured

20 June and 25 June, however, these were atypical of bats captured around this time. Of

the 21 bats captured between 20 June-04 July, 2010, 19 (90%) were still pregnant. The

majority of bats in 2010 began lactating shortly following 05 July when the third lactating

bat was captured, suggesting that the timing of parturition was similar in 2009 and 2010.

I thus considered parturition as occurring on 05 July for both years. Parturition was

synchronized, and all transmitters attached after 06 July, in either year, were on lactating

bats. Fifty-eight bats (13 pregnant, 45 lactating) were tracked in total, resulting in 373

52 roosting observations. On average, a bat was tracked for 6.1 days (range = 1–14 d), and switched roosts every 1.5 d (median = 1 d, range 1–6 d). Radiotagged bats roosted in 147 distinct roost trees. For 12 roosts, I could not confidently determine which tree was being used, which resulted in 22 roosting observations being excluded from my analysis as roost measurements were not possible.

Roost use

All but one radiotagged bat in my study area roosted exclusively in either aspen or balsam poplar. Of the 135 known roost trees, 61 (45%) were trembling aspen (comprising

140 roost days; or 40% of roost days where the roost tree could be determined) and 73

(54%) were balsam poplar (210 roost days or 60% of roost days where the roost tree could be determined). During one night, a bat roosted alone in the stump of a white spruce; this observation was excluded from further analysis. No radiotagged bats were observed roosting in any structure other than trees.

Bats showed a strong tendency to use relatively large DBH trees, and on only 18 days (5% of observations) did I observe bats using trees with a DBH <30 cm. The mean

DBH for balsam poplar roosts (58.1 ± 14.8 cm; mean ± SD) was significantly greater than for aspen roosts (36.9 ± 9.5 cm; t = 10.25, d.f. = 132, P < 0.001; Chapter 2). The mean height of aspen roost trees (20.2 ± 6.8 m; range = 5.0–30.2 m) was significantly taller than the mean height of balsam poplar roost trees (15.5 ± 6.8 m; range = 5.5–28.4; t

= 4.04, d.f. = 132, P < 0.001; Chapter 2). The average DBH of roost trees, weighted based on roost days, was 50.5 ± 16.0 cm (n = 351). In addition to their greater DBH and lower average height, balsam poplar was more likely to be dead or have decaying

sections, and was further, on average, from neighbouring trees (Chapter 2). The lower height of balsam poplar was due to their greater decay state, resulting in stem breakage.

Roosting groups

I conducted 63 exit counts at 48 different roost trees. The size of roosting groups

ranged from 1 to 386 bats. For 11 roost trees, fewer than 3 bats were counted emerging

from the roost and were excluded from the analyses. This resulted in 37 roost trees being used for analyses of group size. The largest group was in a large, recently dead, balsam poplar that had an elongated, mostly-closed split extending at least 7 m along the lower section of the tree, suggesting the presence of a large inner chamber. A generalized linear model indicated that both Julian day (F = 25.51, d.f. = 1,34, P < 0.001) and Julian day squared (F = 8.15, d.f. = 1,34 , P = 0.007 ) explained a significant portion of the variation in exit counts (Figure 3.2). Colony and its interactions were not retained in the final model as they were not significant (P > 0.05). The average size of roosting groups decreased substantially as the lactation period progressed. The maximum group size, as predicted by the model, occurred on 04 July, approximately coinciding with parturition.

Although too few data exist during the gestation period to be confident of group sizes or the overall trend during this period, bats appear to have been at or near their maximum group size at the time of parturition.

Generalized linear models with Julian day and Julian-squared as covariates indicated that both roost-tree species (F = 9.90, d.f. = 1,33 , P = 0.004 ) and roost-tree

DBH (F = 13.38, d.f. = 2,32 , P < 0.001 ) was significantly associated with variation in group size (Figure 3.3). On average, balsam poplar roosts had over twice as many bats as aspen roosts. Similarly, trees with a greater diameter generally had larger group sizes

54 than smaller trees. The 10 roost trees (3 aspen, 7 balsam poplar) with counts over 100 bats all had DBH’s of at least 45 cm, despite 43% of roosts for which exit counts were performed having a smaller DBH. Since large aspen trees also held large numbers of bats,

DBH was likely more important in determining group size than tree species per se.

Based on pairwise differences in Least-squares means (P-values adjusted using Tukey-

Kramer method), roost trees with a DBH <40cm (consisting mostly of aspen) had significantly fewer bats than either trees in the 40–50 cm range (t = 3.97, d.f. = 32 , P =

0.001 ) or trees with DBH >50 cm (t = 4.84, d.f. = 32 , P < 0.001 ). The number of bats did not differ significantly between the 40–50 cm DBH range and the >50 cm DBH range

(t = 1.05, d.f. = 32, P = 0.551). Smaller-diameter roost trees (especially <40 cm DBH) were associated with less variation in group size, possibly a result in physical constraints that prevented larger group sizes from occurring (Figure 3.3).

400

350 Parturition

300

250

200

150 Number of bats countedbats ofNumber

100

0 08-Jun 20-Jun 02-Jul 14-Jul 26-Jul 07-Aug Date of exit count

Figure 3.2. Changes in the size of M. lucifugus roosting groups during the summer breeding period, June-August 2009 and 2010. Based on exit counts at a subset of roosts containing radiotagged reproductive female bats. Solitary bats were excluded from the analysis. Dashed lines represent 95% confidence interval for the mean (solid line). Open circles are actual group sizes.

56 a) 140

120 bats) 100 of

n = 20 80 (number 60 size 40 n = 17

Group 20

0 Aspen Balsam poplar Tree species b) 180 160 140 bats)

of 120 n = 12 100

(number 80 n = 12

size 60

Group n = 13 20 0 < 40 cm 40‐50 cm > 50 cm Tree DBH category

Figure 3.3. Least-squares means for the number of bats counted during emergence from aspen or balsam poplar trees (a) and from trees in different DBH categories (b). Error bars represent 95% confidence interval.

Temporal changes in roost properties

Selection for both tree species and tree DBH varied significantly with time of year

(Figures 3.4, 3.5). DBH (log transformed) varied significantly with both Julian day (F =

15.43, d.f. = 1, 93, P < 0.001 ) and Julian-squared (F = 11.66, d.f. = 1, 94, P < 0.001 ).

Likewise, roost species varied significantly with both Julian day (F = 9.40, d.f. = 1, 80.16

, P = 0.003) and Julian-squared (F = 4.85, d.f. = 1, 81.5, P = 0.031). Colony association, year, and all interactions with these terms, were not included in either the DBH model or the roost-species model because these variables were non-significant and thus removed from the model. Based on the models, peak DBH occurred on 07 July and the peak proportion of balsam poplar occurred on 04 July, both approximately coinciding with the timing of parturition.

In contrast to roost-DBH and species, roost-height did not vary significantly with either Julian day (F = 0.11, d.f. = 1,84.5, P = 0.741) or Julian-squared (F = 0.01, d.f. =

1,85.4, P = 0.910), although there was a significant difference between the north and south colonies (F = 5.10, d.f. = 1,85.9, P = 0.026). My analysis of roost-tree height resulted in significantly non-normal residuals (Shapiro–Wilk W-test: W = 0.958, P <

0.001), which violates the assumptions of the model. Non-normality was caused mainly by the data being platykurtic (kurtosis = -1.11) as skewness was low (-0.11).

Transformations were not successful in remedying this problem, and the results should be interpreted with caution. However, given the high p-values for Julian day and Julian- squared, and the low skewness, significance is unlikely to change if this violation was absent.

Separating out the effect of diameter from the effect of roost species was complicated by the inherent larger size of balsam poplar roosts. By roosting in a balsam poplar, a bat also selected a large-DBH roost tree. A repeated measures model, including roost species as a fixed factor, showed that DBH varied significantly with roost species

(F = 37.73, d.f. = 1,94.7, P < 0.001), the interaction between roost species and colony (F

= 54.57, d.f. = 1,89, P < 0.001), Julian-squared (F = 5.80, d.f. = 1,98.4, P = 0.018), and the interaction between Julian-squared and roost species (F = 4.74, d.f. = 1,98.4, P =

0.032). DBH did not vary significantly with Julian day (F = 3.10, d.f. = 1,95, P = 0.082), the interaction between Julian day and roost species (F = 0.01, d.f. = 1,95, P = 0.930), or with colony alone (F = 0.55, d.f. = 1,89, P = 0.462), although these terms were retained in the model since higher order interactions were significant. These results indicate that some temporal variation in the DBH of roost trees is still present after controlling for inherent differences in DBH between tree species (Figure 3.6).

80 Parturition 70

50 Roost tree DBH (cm) Roost tree

20 01-Jun 16-Jun 01-Jul 16-Jul 31-Jul 15-Aug Date of roosting

Figure 3.4. Change in the diameter-at-breast height (DBH) of roost trees used by reproductive female M. lucifugus during the summer breeding period, June-August 2009 and 2010. Dashed line represents 95% confidence intervals for the mean (solid line). Open circles represent actual daily observations for each radiotagged bat.

1.0

0.8

0.6

0.4

0.2 Parturition Probability bats selected balsam poplar balsam selected bats Probability

0.0 01-Jun 16-Jun 01-Jul 16-Jul 31-Jul 15-Aug Date of roosting

Figure 3.5. Change in the ratio of balsam poplar roosts to aspen roosts used by reproductive female M. lucifugus during the summer breeding period, June–August 2009 and 2010. Dashed line represents 95% confidence intervals for the mean (solid line). Open circles represent actual daily observations for each radiotagged bat.

61 a) 100 Balsam poplar 90 80 Parturition 70 60 South 50 Colony 40 North Colony

DBH of roost tree (cm) tree roost DBHof 30 20 10 01-Jun 16-Jun 01-Jul 16-Jul 31-Jul 15-Aug Date of roosting b) 100 Trembling 90 Aspen 80 70 Parturition 60 50 40 North DBH of roost tree (cm) tree roost DBHof 30 Colony South 20 Colony 10 01-Jun 16-Jun 01-Jul 16-Jul 31-Jul 15-Aug Date of roosting Figure 3.6. Change in the diameter-at-breast height (DBH) of balsam poplar roosts (a) and aspen roosts (b) during the summer breeding period, June–August, 2009 and 2010. Dashed line represents 95% confidence intervals for the predicted mean (solid line). Open circles represent actual tree DBH’s for the southern colony and x’s are those for the northern colony. Two values for balsam poplar exceed 100 cm and are not shown.

Discussion

Group size-habitat association

The results of my study show a strong association between group size and roost

selection. The maximum size of roosting groups was positively correlated with roost-tree

DBH and varied between roost-tree species. Smaller-DBH categories also had lower variation in the size of roosting groups, likely because small-DBH roost trees cannot accommodate large-roosting groups, whereas large roost trees can accommodate a variety of group sizes. This suggests that in my study area the size of roosting groups may be constrained by the availability of large-DBH roost trees, a result with potentially important conservation implications. Although many bats appear to select large-DBH trees, few studies report a strong relationship between tree size and group size. Trees with large roosting groups have been reported to be larger, on average, than those supporting smaller aggregations or solitary bats (Lacki and Schwierjohann 2001; Baker and Lacki

2006). However, not all studies examining whether group size changes among DBH

categories report significant differences (Mattson et al. 1996; Sasse and Pekins 1996;

Callahan et al. 1997; Vonhof and Wilkinson 1999), although typically they still reported

a positive correlation between group size and roost-tree DBH (Mattson et al. 1996;

Callahan et al. 1997). Most studies only compare DBH between categories of roost

groups, such as solitary bats versus communally roosting bats (Mattson et al. 1996; Lacki

and Schwierjohann 2001) or large roosting groups versus small roosting groups (Sasse

and Pekins 1996; Callahan et al. 1997; Britzke et al. 2003; Baker and Lacki 2006),

obscuring important aspects of the relationship between DBH and group size. Small-

diameter roosts were frequent at my study site; however, the largest roosting groups were

63 in trees with a DBH of at least 45 cm. The greater size of roost trees in my study area may explain why I observed larger roosting groups of M. lucifugus than those reported by

Crampton and Barclay (1998), despite the proximity of these two study sites. The maximum group size reported in my study (386 bats) appears to be unique among studies examining temperate cavity-roosting bats in North America, possibly due to a combination of the availability of large roosts and an abundant food supply provided by the lake. My data suggest that in some regions, the lack of large roost trees could limit the size of roosting groups, potentially impacting local bat populations.

The most plausible explanation for the association between group size and DBH is that tree diameter is correlated with cavity size. Cavity size was not correlated with roost-tree DBH for big brown bats (Eptesicus fuscus), despite significant results showing a correlation between cavity size and group size (Willis et al. 2006). However, the study by Willis et al. (2006) only contained aspen roosts and there may have been insufficient variation in DBH to find a significant correlation with cavity volume. Different modes of cavity formation between their study and ours may also have accounted for this discrepancy. Alternatively, properties associated with large trees, such as greater insulative properties and more stable temperatures, may cause bats to congregate in large-

DBH roost trees irrespective of cavity size (Sedgeley 2001).

Temporal variation

Consistent with my hypothesis, bats in my study appeared to change their roosting behaviour in response to changing physiological requirements. Peak group size and peak roost-tree DBH both coincided with parturition, a time when optimal roost microclimate may be most critical (Sano 2000; Kerth et al. 2001). Microhabitat selection and group

64 size preferences around the time of parturition is generally obscured in most studies because parturition falls at the boundary of the gestation and lactation period, which are typically binned to simplify statistical analyses. If key measures of roosting behaviour, such as habitat selection or group size, also peaked around the time of parturition in other studies, binning the gestation and lactation period into categorical variables would result in misleading results. Much of the variation would be averaged, producing similar means between categories, despite important trends being present within the stages.

Although I did not measure roost temperature, the combination of large roost groups and relatively large-DBH roost trees from late gestation through early lactation suggests that bats prefer a warm roosting environment during this period. This is supported by other studies that examined roost temperature directly and found that bats selected warmer roosts during late gestation and lactation (Kerth et al. 2001; Ruczynski

2006). Kerth et al. (2001) also found that colder roost types (natural tree holes) are preferred during early gestation and avoided during late gestation and lactation, likely because of food limitation that required bats to make greater use of torpor during early gestation. Provided they have adequate energy, torpor should be minimized by reproductive bats to accelerate milk production and promote juvenile growth (Racey

1973; Wilde et al. 1999). Warm roost temperature reduces the cost to both adult females and pups of maintaining an elevated body temperature (Ruczynski 2006; Willis and

Brigham 2007; Pretzlaff et al. 2010). However, during early June in my study region, few insects are available (pers. obs) and cold night-time temperatures limit foraging time.

Under these conditions, bats may benefit from selecting colder roost temperatures to prolong gestation and conserve energy until food becomes more abundant (Hoying and

Kunz 1998; Kerth et al. 2001; Lausen and Barclay 2003). Later in the season, as parturition approaches, food is more abundant and bats may benefit from accelerated foetal growth, which would favour large groups to reduce the energetic cost of maintaining an elevated body temperature (Willis and Brigham 2007). Furthermore, bats may benefit from having their maternity roosts in place prior to parturition to avoid being unable to give birth and raise newborn offspring in a warm roost environment. Once juveniles become better able to thermoregulate on their own, smaller groups may be favoured to alleviate crowding, avoid parasite infested roosts or roost mates, or to reduce competition for food (Lacki and Schwierjohann 2001; Reckardt and Kerth 2007).

Few studies have found significant or consistent patterns for how the size of roosting groups changes throughout gestation and lactation. Myotis lucifugus and pipistrelle bats (Pipistrellus pipistrellus) roosting in buildings, both had peak emergences around the end of July, when juvenile bats were beginning to fledge (Swift 1980;

Kalcounis and Brigham 1994). However, spatial constraints and social thermoregulation are likely to be less important for bats roosting in buildings since temperatures are generally already elevated and these spaces are often large. As is the case in terms of roost switching (Lewis 1995), it may be that the roosting behaviour of bats roosting in buildings may not reflect the behaviour of bats occupying natural tree cavities.

Several tree-roosting bat species have been reported to have similar group sizes during gestation and lactation, including northern long-eared bats (Myotis septentrionalis)

(Foster and Kurta 1999; Lacki and Schwierjohann 2001; Patriquin et al. 2010), long- legged bats (Myotis volans) (Baker and Lacki 2006), and E. fuscus (Willis and Brigham

2004). In one study, M. septentrionalis had significantly larger groups during gestation

than lactation (Sasse and Pekins 1996). In contrast to my results, most of these studies

indicate that group size remains stable throughout gestation and lactation. However, as

mentioned, comparing the gestation period with the lactation period may miss important

trends if peak group size occurs during late gestation and early lactation. Pretzlaff et al.

(2010) reported that Bechstein’s bats (Myois bechsteinii) formed larger groups when

normothermia was favoured, especially during colder temperatures, and that larger group

sizes reduced the metabolic expenditure of maintaining a normothermic body

temperature. Although Pretzlaff et al. (2010) did not report substantial differences in

group size between reproductive periods, their results nonetheless indicate that larger

group sizes should be preferred during cold conditions when torpor may be detrimental,

which in my study system likely corresponded to the time of parturition.

Given that group size peaked around the time of parturition, and group size was

correlated with DBH, the peak in DBH around the time that bats were giving birth could

be explained by changes in social preferences. Few studies report changes in roost-tree

selection during the gestation and lactation period. The DBH of trees used by M.

septentrionalis significantly decreased from gestation to post-lactation in one study

(Lacki and Schwierjohann 2001), while in another study of the same species, DBH of

roost trees was nearly the same during lactation compared to the pre and post-lactation

period (Garroway and Broders 2008). Nonetheless, limited evidence indicates that changes in DBH parallel changes in group size (Lacki and Schwierjohann 2001; Baker and Lacki 2006).

Management implications

Bats roosted frequently in both aspen and balsam poplar and used trees of a variety of DBH classes; however, selection appeared to change depending on reproductive condition. This result indicates that a variety of roost trees, offering a diversity of roosting environments, may be important so that bats can adjust to changing physiological and environmental conditions (Sedgeley 2001; Russo et al. 2005;

Garroway and Broders 2008). Group size decreased sharply at a certain threshold DBH

(approximately 45 cm), indicating that the loss or absence of large roost trees could restrict the size of roost groups. Smaller roosts with fewer individuals may result in cooler roost temperatures, greater energy expenditure for thermoregulation, delayed gestation, and slower juvenile growth (Hoying and Kunz 1998; Lausen and Barclay

2006). Reproductive success may decrease if fewer juveniles have sufficient time to accumulate fat reserves needed for hibernation (Ransome 1989; Kunz et al. 1998).

Smaller roost groups will also reduce the number of social interactions that a bat has, potentially disrupting social networks, decreasing the stability of colonies, and reducing beneficial social behaviours (O'Donnell 2000; Willis and Brigham 2004; Patriquin et al.

2010). In regions with cool temperatures and short, warm seasons, such as occurs in the boreal forest, the availability of large well-insulated roost trees that permit a greater number of bats to roost together may be especially important. I suggest that forest management plans should ensure the continued availability of a variety of roost types, with high priority given to the long-term availability of large-diameter roost trees.

CHAPTER 4: ROOSTING NETWORKS AND ROOST-SWITCHING BY LITTLE

BROWN BATS (MYOTIS LUCIFUGUS)

Introduction

Frequent roost switching is pervasive among temperate cavity-roosting bats,

including reproductive females that must carry pups between successive roosts (Lewis

1995). For some bat species, roost switching is associated with fission-fusion roosting

patterns (Kerth and Konig 1999; O'Donnell 2000; Kurta et al. 2002; Vonhof et al. 2004;

Willis and Brigham 2004; Russo et al. 2005; Garroway and Broders 2007; Rhodes 2007;

Popa-Lisseanu et al. 2008). This behaviour is characterized by the regular splitting and

recombination of roosting groups, which results in both the location and composition of

roosting groups changing from night-to-night (Kerth 2008). Fission-fusion roosting

patterns facilitate the maintenance of social relationships with bats from a larger pool of

individuals, and thus may have important implications for bat social systems and our

understanding of essential habitat for cavity-roosting bats (Willis and Brigham 2004).

Several studies have shown that individuals show fidelity to specific roost areas and form

non-random associations with other bats (Kerth and Konig 1999; O'Donnell 2000; Willis

and Brigham 2004; Garroway and Broders 2007). However, because of the cryptic nature

of bat social systems, most studies of habitat use by cavity-roosting bats focus on

individuals or ephemeral roosting groups, underemphasising habitat requirements and

patterns of habitat use at higher levels of social organisation. As social benefits are likely

a requirement for successful reproduction, analyses of roosting behaviour may provide

more biologically meaningful results if the focus was at a higher spatial scale, preferably

encompassing the roosting area used to support the entire colony or other level of social

organisation.

The roosting behaviour of bats with fission-fusion roosting patterns is logically

examined using network analysis. The topology of roosting networks has important

implications for our understanding of information transfer, disease transmission, critical

habitat, and vulnerability of bats to anthropogenic disturbance (Solé and Montoya 2001;

Rhodes et al. 2006; Fortuna et al. 2009). Within a roosting network, individual roosts act

as nodes (or vertices); bats create links (or edges) between nodes as they move among

roost trees as a result of roost switching (Rhodes et al. 2006). Nodes will typically vary in

the number of links they have with other nodes; this measure, node degree (or degree centrality), is an important indication of the relative importance (or centrality) of a node within the network (Fortuna et al. 2009).

If roosts are randomly selected from a suite of potential roosts over multiple roost switches, the degree distribution in the network should follow a Poisson distribution,

where the probability of finding a roost with k-linkages can be calculated as P !

(Barabási and Albert 1999). The degree distribution of a random network decreases exponentially, so the observation of nodes with many connections is highly improbable.

However, many real-world networks, including at least one bat roosting network (Rhodes et al. 2006), have fat-tailed degree distributions characterized by an overabundance of high-degree nodes relative to expectations based on a Poisson distribution. The overabundance of high-degree nodes is an indication of preferential selection of certain roosts in the network (Barabási and Albert 1999). Although roosting networks have only

rarely been examined, some species of cavity-roosting bats seem to have non-random

preference for certain roost trees in their roosting area (Callahan et al. 1997; Baker and

Lacki 2006; Rhodes et al. 2006). Conceptually, such trees may be hubs, linking disjunct

roosts into a larger roosting network, and potentially being important for facilitating

social interactions (Rhodes et al. 2006). From a conservation prospective, identifying

hubs, or the characteristics of hubs, is important, as these resources may have disproportional influences on colony stability and individual fitness (Rhodes et al. 2006).

Roost switching is a prerequisite for fission-fusion roosting patterns.

Understanding motivations for this behaviour is important for understanding the evolution of fission-fusion roosting patterns and the formation of roosting and social networks. Roost switching also influences our understanding of critical habitat for bats, as multiple roosts appear to be necessary to maintain bat colonies (O'Donnell 2000;

Willis and Brigham 2004; Russo et al. 2005, 2007; Popa-Lisseanu et al. 2008). Bats exert energy and risk injury to offspring by switching roosts; the benefit of switching must surpass these costs (Lewis 1995; Lewis 1996). Several reasons have been suggested to explain why bats switch roosts, including the maintenance of social interactions (Willis and Brigham 2004), parasite avoidance (Lewis 1996; Bartonicka and Gaisler 2007),

seeking more suitable microclimates (Lewis 1995, 1996), predator avoidance (Barclay et al. 1982), and increasing familiarity with other roosts (Russo et al. 2005). These hypotheses are likely complementary, each helping to explain the occurrence and frequency of roost switching.

Little brown bats (Myotis lucifugus) are an ideal species for examining roosting networks. They often form large roosting groups in buildings, and show high fidelity to

these roosts (Fenton and Barclay 1980; Kalcounis and Brigham 1994; Talerico 2008). In forested areas, where building roosts are not available, they roost in trees, and switch

roosts regularly, suggesting the presence of large roosting and social networks (Kalcounis and Hecker 1996; Crampton and Barclay 1998). Few studies, however, have examined the roosting behaviour of tree-roosting populations of M. lucifugus. This species is distributed across much of North America, and is one of the few species occurring at northern latitudes (van Zyll de Jong 1985).

My study had two main objectives. The first was to use network analysis to examine how M. lucifugus uses available roost trees on the landscape. I hypothesised (1) that bats exhibit fission-fusion roosting patterns and roost within discrete roosting areas,

and (2) that roost trees differ in their importance for mediating interactions among bats

occupying a particular area, which results in heterogeneous centrality measures for roosts

in the network. My second objective was to examine roost switching within these

networks to examine factors that may mediate roost-switching behaviours, and influence

the characteristics of roosting networks.

For the second objective, I focused on three factors that I hypothesized affect

either the probability of switching roosts or the distance between successive roosts. These

are (1) ambient temperature, (2) degree centrality of roost trees, and (3) the stage of

reproduction. Roost microclimate, especially temperature, is highly dependent on

ambient weather conditions (Bondo 2009; Pretzlaff et al. 2010). Colder roosts increase

the energetic cost of remaining normothermic (i.e. not using torpor), while warmer roosts

may impede the use of torpor, limit metabolic activity, and may increase the risk of

dehydration (Genoud et al. 1990). Both extremes may be associated with a suite of

negative consequences, such as reduced juvenile growth rates, risk to pups, and

dehydration (Genoud et al. 1990; Lausen and Barclay 2006). Because of the need to

maintain an optimal roost microclimate, I predicted that the likelihood of switching roosts

would increase if the night-to-night average temperature was not stable (either warming

or cooling), as this may induce bats to switch to roosts that are more suitable for

prevailing conditions. Roosts that are more central with the roosting network are likely to

have superior physical characteristics, and because of their familiarity to the colony,

likely contain larger roosting groups. Such characteristics likely result in warmer roost

temperatures, which may result in central roost trees (or network hubs) being more preferred during colder weather. If bats are already using more central (high-degree) roost trees when the temperature decreases, they may be less likely to switch roosts. In contrast, high-degree roost trees should be least preferred during warming conditions because they derive less benefit from social thermoregulation, but would still be exposed to elevated parasite levels, guano accumulation, and conflicts with other bats if they

stayed in the more central roost trees. Therefore, for roost-switching analyses, I

anticipated an interaction between the trend in night-to-night temperature and the degree

centrality of roost trees. Higher quality roosts have previously been linked to longer

intervals between roost switches (Kurta et al. 2002; Trousdale et al. 2008). Similarly, I

predicted bats would be less likely to switch from roost trees that are more central within

the roosting network. I also predicted that roost-switching probability and distance

between successive roosts would decrease as the mass females had to carry increased,

resulting in the general pattern: post-fledging>pregnancy>early lactation>late lactation.

Methods

Study area

I conducted field work in Lesser Slave Lake Provincial Park, located in boreal mixedwood forest along the eastern shore of Lesser Slave Lake (55°27’N, 114°50’W).

Most of the landscape consists of intact old growth (c. 150-year old) forest, dominated by trembling aspen (Populus tremuloides) and balsam poplar (Populus balsamifera). Some forest cover has been altered due to oil and gas activity, power lines, roads, and recreational infrastructure. Myotis lucifugus in the park primarily roosts in the cavities of trembling aspen or balsam poplar, although there is a large maternity colony at a ranch to the south of the park (Chapter 2).

Capturing and tracking bats

I located roost trees from June to August 2009–2010. Bats were captured using mist nets or a harp trap, typically placed along trails or near wetlands. To minimize my influence on bat roosting behaviour, I avoided capturing bats near known roost trees, and minimized the number of captures. To locate roost trees, I attached a radio transmitter

(Holohil Systems Ltd., Carp, ON, Canada) to the back of reproductive female M. lucifugus, and tracked them to their diurnal roost trees, each day, for the life of the transmitter. Transmitters were attached by trimming the inter-scapular fur and attaching a transmitter to this area using a latex-based adhesive. Transmitters weighed 0.4–0.6g, equivalent to 3–5% ( = 4%) of the bat’s mass, which was below recommended limits for small bats (Aldridge and Brigham 1988). Transmitters fell off after 1–14 d ( = 6.1 d), requiring new bats to be regularly captured so that new transmitters could be attached. To increase the likelihood of capturing bats from targeted colonies, I captured bats within or

near known roost areas, and avoided attaching transmitters beyond 1-hour after bats

emerged from their roosts. Once roost trees were located, I recorded GPS coordinates

using a Garmin Colorado 300 GPS receiver, and distances were calculated using Garmin

MapSource 6.16.1. Roost trees that I could confidently locate were identified to species

and the diameter-at-breast-height (DBH) was measured using a DBH tape. For the

tracking duration, ambient temperature was recorded every 10-minutes using a temperature data-logger (HOBO H08-001-02, Onset Computer Corporation, Bourne,

MA) placed in a vented enclosure near the centre of the study area. For each night, I calculated average temperature from sunset to sunrise for use in temperature analyses.

Roosting networks

Roosting networks were constructed and analyzed for all bats tracked during the study using Netdraw 2.097 (Borgatti 2002) and Ucinet 6.289 (Borgatti et al. 2002);

ARCGIS 9.1 (ESRI Corporation, Redlands, CA) was used to create a geographic representation of roost networks. I created a unipartite network by treating individual roost trees as nodes, and placed links between nodes if they were used on consecutive days by the same bat. To avoid biases caused by unequal tracking days for individual bats, I did not create links between non-sequential roosts, as has been done in other studies (e.g. Rhodes et al. 2006; Fortuna et al. 2009). I considered bats to belong to the same network if there was at least one possible path to every other node in the network.

To reduce the occurrence of breaks in the network, data from 2009 and 2010 were combined for the purpose of network analysis.

I calculated degree centrality using a directional version of network, wherein degree is equivalent to the number of unique links a roost tree has with other roost trees.

However, to avoid underrepresenting degree centrality for roosts used on the final day of transmitter attachement, only those links involving bats switching into the roost tree are considered for degree determination. Thus, my measure of degree is more properly referred to as indegree; however, since bats always leave roosts they enter, indegree will approximately be half the measure of degree had I used an undirected version of the network. Since degree is only incremented when a bat switches into a roost from another roost, the degree measure of roosts used on the first night following transmitter attachment would be underrepresented. To minimize this bias, roost-tree degree was incremented by one when it was the first roost used by a radiotagged bat. Once calculated, I binned degree centrality into two or three categories to allow statistical comparison. Ranges used for categories were selected to provide a similar number of observations in each category. Because most trees were in low-degree classes, higher degree categories necessarily encompassed a greater range of values.

To examine the association between degree centrality and other centrality measures, I also calculated betweenness and fragmentation centrality. Betweenness is a measure of the number of shortest paths between two nodes in the network that pass through a particular node (Borgatti et al. 2002). Fragmentation centrality is the increase in the proportion of pairs of nodes in the network that cannot be reached following removal of a particular node (Borgatti et al. 2002). This measure is useful for determining how important a particular node (roost tree) is for maintaining continuity within the network. The loss of nodes with high fragmentation centrality is more likely to result in greater isolation among individuals occupying a particular geographic area than the loss of low fragmentation centrality roosts.

The degree distribution of nodes (roost trees) should follow a Poisson distribution

if connections to other nodes are established randomly (Barabási and Albert 1999;

Rhodes et al. 2006). Based on the Poisson distribution, the probability that any node has

k-links can be calculated as: P . I fit a curve based on this probability !

distribution to the actual degree-distribution data by optimizing two unknown parameters

based on minimum sum of squared error. The first parameter is λ, which is equivalent to

the mean of the Poisson distribution; the second parameter multiplies the probability

distribution to fit the actual number of observations.

Statistical analysis

Statistical analyses were performed using SAS version 9.2 (SAS Institute Inc.

2008). For all statistical tests, P-values < 0.05 indicated significance. I used Pearson’s

Chi-square goodness-of-fit test or t-tests (assuming equal variance) for general comparisons between groups. I used a generalized linear model with a binomial error distribution and logit link function to examine differences in tree species use (aspen versus balsam poplar) between degree categories (i.e. high degree versus low degree).

Analysis of variance (ANOVA) was used to examine differences in DBH between degree categories. DBH was first transformed using the natural logarithm to improve normality.

Tree species or DBH was treated as the dependant variable and degree category as an independent variable. Colony and the interaction between colony and degree was originally included in the model as fixed factors.

For analyses involving roost-switching behaviours of individual bats, it was necessary to use a repeated measures approach to account for possible correlation among

observations of the same bat. The single continuous response variable (distance between

successive roosts) was modeled using a general linear mixed model (Proc Mixed). I

modelled binary response variables (switch vs. not switch; high-degree roost vs. low-

degree roost) using a generalized linear mixed model (Proc Glimmix), with a binary error

distribution and logit link function. Individual bats were treated as the subject of the

repeated measures analyses for both general linear mixed models and generalized linear

mixed models. An autoregressive covariance [AR(1)] structure was used to model within

subject correlation of distance measures. The autoregressive structure resulted in the

lowest AIC score when compared to other candidate covariance structures, so was deemed appropriate for modeling within-subject covariance (Littell et al. 2006). An autoregressive covariance structure was also used for models of binary response variables. For the distance measures, I modelled heterogeneous variances between colonies using the group option in Proc Mixed.

To detect possible year and colony effects, I included year, colony (i.e. north or

south), and all interactions with these terms, in the model statements. Non-significant terms were removed from the model using backwards elimination. Due to an obvious non-linear relationship, I binned the change in average night-time temperature between successive nights into three categories: >1°C warmer, <1°C change, >1°C cooler. To improve the normality of residuals, the distance between roosts was transformed using the fourth-root. Temperature used as an independent variable was square transformed to improve normality. The Kenward-Roger method was used to adjust degrees-of-freedom for both general and generalized linear mixed models. Post-hoc comparisons between levels of a categorical variable were performed using the Tukey-Kramer adjustment for

78 multiple comparisons. Means, estimates, and confidence intervals reported in text or graphically were first back-transformed, if necessary, which resulted in asymmetric error bars. I anticipated that the initial capture and transmitter attachment may have strongly influenced roost-switching behaviour on the night of release. To minimize potential bias,

I thus excluded the first roost-switching decision from analyses involving the probability of roost switching, the distance between successive roosts, and the selection of high versus low-degree roosts.

Where appropriate, residuals were inspected visually to determine suitability of the model, and normality was checked using a Shapiro-Wilk test. For groups being compared using t-tests, the assumption of equal variances was checked using the Levene test. In the case of general linear mixed models, equality of variances between groups was examined by modeling separate residual variances (using the group= option in the repeated statement is SAS; Littell et al. 2006), and comparing the resultant AIC score to the model where homogenous variance was assumed. For generalized linear mixed model, homogeneity of variances across groups was tested using the ‘covtest’ statement in Proc Glimmix. Additionally, for generalized linear models or generalized linear mixed models (binary error distribution), I inspected the deviance/d.f. ratio to ensure it did not depart substantially from 1 (indicating over or under dispersion).

Results

Radiotracking

I tracked 58 bats using radiotelemetry from June–August 2009 and 2010, which resulted in 374 roosting observations and the locations of 153 distinct roost trees. Bats

were tracked an average of 6.1 d (range 1–14) before the transmitter fell off or a signal

could no longer be located. Tracking of the 58 bats resulted in 2 primary (unbroken) networks (Figure 4.1) and 11 smaller networks. The two largest networks combined

contained 42 (72%) of the radiotagged bats, and 40 and 67 roost trees were located for the north and south colonies, respectively. Unless specified otherwise, I excluded bats from the 11 smaller network segments from further analysis as I could not determine whether they belonged to the two larger networks or were part of different, but poorly defined, roost networks. The north and south networks consisted of 18 bats (2009: 10 bats, 2010: 8 bats) and 24 bats (2009: 15 bats, 2010: 9 bats), respectively. Most bats in the north and south were lactating at the time of capture (north: 3 pregnant, 15 lactating; south: 3 pregnant, 21 lactating). Based on capture records, 05 July of each year was deemed to be the time of first lactation (see Chapter 3). In 2010, two lactating bats were captured prior to this date, but were not considered representative of the majority of bats captured near known roosting areas. The first flying juvenile captured near the study was on 28 July in 2009 and 25 July in 2010. I considered 25 July as the time of fledging in both years. These dates were used to bin the reproductive period into 3 categories: pre- parturition (07 June – 04 July), pre-fledging lactation (05 – 24 July), and post fledging

(25 July – 14 August).

Figure 4.1. Map showing locations of the two largest M. lucifugus roosting networks in Lesser Slave Lake Provincial Park, Alberta. Circles represent roost tree locations for the north and south colony.

Mean residency time for individual bats averaged 1.66 d ± 0.61 d (N = 54; mean ±

SD). Individual roosting bouts in a single roost ranged from 1–6 d (median = 1 d). The average number of days between roost switches was not significantly different between

the north (1.55 ± 0.48 d, N = 18) and south (1.75 ± 0.70 d, N = 22) colony (t = 1.07, d.f. =

38, P = 0.291). Although roost-switching frequency was similar between colonies, the

size of the roosting areas, based on 95% minimum convex polygon (MCP), was

substantially larger for the north colony (3.0 km2) than for the south colony (0.2 km2).

The MCP method is likely biased by a few individuals that made longer-distance

movements; however, regardless of the size of roost areas, mean distance between

consecutive roosts was significantly greater for bats in the south colony (532 ± 389 m,

range = 119 – 1729 m, n = 23) than for bats in the north colony (198 ± 122 m, range =

50–476 m, n = 18; t = 4.40, d.f. = 39, P < 0.001). An asymptotic roost area was reached in the north colony after approximately 13 roost trees were located, suggesting a well-

defined and consistent roost area (Figure 4.2). Bats in the south colony made more

frequent long-distance movements, making it more difficult to determine if bats showed

fidelity to particular patches of forest. On 13 occasions two or more radiotagged bats

roosted in the same tree and roost locations could be determined following roost

switching; on only 1 of these occasions did bats move together to the same roost tree.

However, on four occasions, bats that separated for at least one day roosted together

again a few days later, but not in the roost tree where they originally roosted together.

0.25 North colony 3.50 South colony

3.00 0.20 2.50 ) ) 2 2 0.15 2.00

1.50 0.10 y = 0.20 (1-0.89n) y = 2.76 (1-0.94n) 2 2 Roost Area Area (km Roost R = 0.89 Area (km Roost R = 0.87 1.00 0.05 0.50

0.00 0.00 0 102030400 204060 Number of roosts located (n) Number of roosts located (n)

Figure 4.2. Change in roost area as additional roost trees were located for two M. lucifugus colonies tracked from 2009–2010 in Lesser Slave Lake Provincial Park. Based on 95% minimum convex polygon. Note different scales for each axis.

Network analysis

The majority of roosts in the network had relatively few linkages, typically having

degree class 1 or 2 (Figure 4.3). There was an overabundance of high-degree roost trees

relative to what is expected based on a Poisson distribution, indicating preferential selection of certain roost trees by bats. Visual representation of the networks also show

that a small number of roost trees contain many linkages, often surrounded by a high

density of lower-ranked roost trees (Figures 4.4, 4.5). High-ranked roost trees are most

evident in the north colony, which roosted in a relatively small area of forest, bounded by

a high-voltage power line to the northeast, a lake to the southwest and a campground to

the north. The roost tree with the greatest number of links occurred in the northern colony

(degree = 9); this tree fell over near the beginning of my 2010 field season and was not

observed to be used in 2010.

Roost trees in higher degree classes – based on 2009 data – were significantly

more likely to have observed reuse in 2010 compared to low-degree trees (Figure 4.6; χ2

= 7.93, d.f. = 2, P = 0.019). Characteristics of roost trees differed between degree categories. Higher degree roost trees were significantly more likely to have been balsam poplar than aspen (Figure 4.7a; χ2 = 6.63, d.f. = 1, P = 0.010), and had a significantly

larger DBH (Figure 4.7b; F = 6.79, d.f. = 1,94, P = 0.011). Both tree species and DBH

were similar between colonies so the two colonies were combined for comparison

purposes.

As expected, higher degree categories also had higher average betweenness.

However, in a few cases, especially in the south colony, roost trees with low-degree

centrality still had high betweenness. These appear to represent roosts that link

geographically disjunct network segments. For degree-classes 1, 2–3, and 4–9, average

betweenness values were 19.6, 56.1, and 194.0, respectively, for the north colony, and

28.4, 295.4, and 335.2 for the south colony. The extent of network fragmentation

resulting from simulated removal of roost trees ranged from 0 – 33.5%. Average

fragmentation resulting from roost removal was 1.2% (range = 0 – 30.2%), 6.0% (range =

0 – 29.9%), and 9.5% (range = 0 – 33.5%) for the low (degree = 1), middle (degree = 2–

3), and high (degree = 4–9) degree categories, respectively.

Figure 4.3. Number of roost trees of different degree classes for two M. lucifugus roosting networks located in Lesser Slave Lake Provincial Park, Alberta. Degree is a measure of the centrality of a roost tree in the roosting network. Lines represent expected values based on the Poisson distribution (fitted to actual data), which should approximately have occurred if roosts were randomly selected from a pool of suitable roost trees. Note the overabundance of high- degree roost trees.

Figure 4.4. Roosting network of the northern M. lucifugus colony, located in Lesser Slave Lake Provincial Park, Alberta. Nodes represent individual roost trees. Links are formed between roost trees used on consecutive nights by at least one radiotagged bat. The size of a node is proportional to the measure of degree centrality calculated in a one-directional version of the network.

Figure 4.5. Roosting network of the southern M. lucifugus colony, located in Lesser Slave Lake Provincial Park, Alberta. Nodes represent individual roost trees. Links are formed between roost trees used on consecutive nights by at least one radiotagged bat. The size of a node is proportional to the measure of degree centrality calculated in a one-directional version of the network.

1.0

0.9 North Colony 5 0.8 South Colony

0.7

0.6 8 rved reuse in2010 rved reuse 0.5 11

0.4

0.3

0.2 30 7

Proportion with obse with Proportion 11 0.1

0.0 123-5 2009 roost tree degree

Figure 4.6. Proportion of M. lucifugus roost trees of different degree classes, calculated from 2009 data, which were reused at least once in 2010. Numbers above bars represent the number of trees in 2009 that were used to calculate the proportion of reuse. One degree class 9 roost tree in the north colony was excluded because it fell down at the beginning of 2010.

88 a) 1.0

0.8 N=22 0.6

0.4 N=74

0.2 Proportion balsam poplar balsam Proportion

0.0 1-2 3-9 Roost tree degree category b) 70

N=22 50

N=74 40

Roost tree DBHRoost tree (cm) 30

20 1-2 3-9 Roost tree degree category

Figure 4.7. Differences in tree species (a) and diameter-at-breast-height (DBH) (b) between low-degree (degree = 1-2) and high-degree (degree = 3-9) roost trees used by M. lucifugus. High-degree roost trees share more connections with other roost trees and are assumed to be more important within the roosting network.

Roost switching

The probability of roost switching was significantly associated with the change in average night-time temperature (F = 4.60, d.f. = 2,166.1, P = 0.011) and with the centrality of the roost tree a bat used immediately prior to switching roosts (F = 4.20, d.f.

= 2,175.9 , P = 0.042). The interaction between these two variables was not significant (F

= 1.15, d.f. = 2, 175.3 , P = 0.319) and was originally excluded from the model to test the significance of the two main factor variables. However, since I was interested in this interaction, I included it for the purposes of examining interactions between factor levels of the two variables (Figure 4.8). A decrease in average night-time temperature between nights did not result in a greater probability of roost switching relative to stable temperatures (Figure 4.8). Relative to either stable or decreasing temperatures, an increase in temperature resulted in a greater probability that bats would switch roosts.

However, this is primarily for bats that were occupying high-degree roosts immediately prior to a roost-switching decision, as bats occupying low-degree roosts immediately prior to a roost-switching decision frequently switched regardless of the temperature change (Figure 4.8). Unless the temperature increased, bats were generally less likely to switch roosts if they occupied high-degree roosts prior to switching. High-degree roost trees (degree classes 3–9) were significantly more likely to be selected than low-degree roosts (degree classes 1–2) if it was colder during nights bats switched roosts (Figure 4.9;

F = 4.08, d.f. = 1,196.4, P = 0.045).

The probability of roost switching did not vary among reproductive periods (F =

0.53, d.f. = 2,35.02, P = 0.593), nor did roost switching vary with Julian day during the pre-fledging lactation period (F = 0.15, d.f. = 1,17.02, P = 0.700). The distance traveled

between successive roosts also did not vary significantly with Julian day during the pre-

fledging lactation period (F = 4.06, d.f. = 1,16, P = 0.061), but there was a significant

effect for reproductive period (F = 8.45, d.f. = 2,37.2, P < 0.001). Colony, but not

interactions with colony, were significant in the models examining distance (P<0.05), but

not with analyses that examined the probability of roost switching (P>0.05). To simplify

comparisons while still controlling for the effect of colony, I used least-squares means to

compare the effect of reproductive period on the distance traveled between successive

roosts (Figure 4.10). The distance between successive roosts during the pregnancy period

was not significantly different than during the pre-fledge lactation period (t = 0.65, d.f. =

35.1, Padj = 0.795) or the post-fledge lactation period (t = 1.61, d.f. = 35.3, Padj = 0.255);

however, this may have been due to low statistical power resulting from few observations

during the pregnancy period. In contrast, there was a significant difference between the

lactation period before juveniles fledged and during the three weeks afterwards (t = 4.10,

d.f. = 39.9, Padj = 0.0006). On average, bats moved less than half the distance after July

25th, when the first fledgling was captured, than prior to that date (Figure 4.10).

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

Probability roosts of switching Probability 0.2 Degree = 1-2 0.1 Degree = 3-9 0.0 0.5Cooling 1.5Stable 2.5Warming 3.5 (≥1.0°C decrease) (<1.0°C change) (≥1.0°C increase)

Trend in average night-time temperature

Figure 4.8. Proportion of reproductive female M. lucifugus that switched roosts in relation to the night-to-night change in average temperature. Analysed separately for bats that occupied high-degree roosts (degree = 3–9) and bats that occupied low-degree roosts (degree = 1–2). Temperature trends compare the night of potential roost switching to the previous night. High-degree roost trees are those trees with many connections within the roosting network, and likely represent particularly important roosting resources. Error bars represent 95% confidence limits.

1.0

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

Probability selected high-degree roost tree roost high-degree selected Probability 0.1

0.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 Average night-time temperature (°C)

Figure 4.9. Estimated probability of reproductive female M. lucifugus roosting in a high-degree roost tree (degree class 3–9) versus low-degree roost tree (degree class 1–2) in response to different average night-time temperatures. Temperature corresponds to the night when a bat is selecting a roost tree. Dotted lines represent 95% confidence limits. Open circles represent actual observations.

500

450

400

350 69 300 12 250

200

150 47

Distance moved between roosts (m) 100

0 Pregnant Lactation Early Pre-Fledge Post-Fledge Reproductive Period

Figure 4.10. Least-squares mean estimates for the distance moved between successive roosts by female M. lucifugus during different reproductive periods. Least-squares means used to adjust for differences in distance moved between colonies. Error bars represent 95% confidence limits. Numbers beside markers represent the number of observations in each reproductive period. Values shown have been back-transformed from natural logarithms.

Discussion

Roosting network

My study shows that Myotis lucifugus exhibit fission-fusion roosting patterns

consistent with those reported for other species of Myotis (Kerth and Konig 1999; Kurta

et al. 2002; Garroway and Broders 2007). Evidence for this behaviour includes non-

synchronous roost switching, periodic re-establishment of previous roosting associations,

and fidelity to specific roost areas. The frequent roost switching of bats in my study

allowed construction of detailed roosting networks for two areas of the park, likely

corresponding to two distinct bat colonies. Unexpectedly, these two colonies differed by

over an order of magnitude in their spatial extent. Willis and Brigham (2004) suggested

that roosting areas were analogous to building or cave roosts; bats show a similar degree

of fidelity to a specific area, only the roosting sites are dispersed over a larger geographic

area. My results generally agree with this argument; however, differences between the

north and south of my study area show that spatial fidelity of bats may be highly variable,

possibly being influenced by habitat quality (i.e. availability of suitable roost trees), and

studies in one area cannot necessarily be generalized to novel areas. The greater distance

moved between successive roosts in the south suggest there may have been a lower density of suitable roost trees. Lower roost density has been associated with reduced spatial fidelity and greater movements between roosting clusters, which may produce less discrete roosting or social networks (Chaverri 2010). Thus, greater roost density

associated with the north colony may explain why it is smaller and more discrete than the

south.

The overabundance of highly connected roost trees (nodes) observed in my study indicates that bats have non-random preference for certain trees. Similar degree distributions are frequently reported in real-world biological networks – examples include food webs (Solé and Montoya 2001), sexual contact networks (Liljeros et al. 2001), metabolic networks (Jeong et al. 2000), and social networks (Lusseau 2003; Manno

2008). In contrast to random networks, where the probability of highly connected nodes decreases exponentially, the degree distribution in these networks follows a power law, resulting in a greater number of highly connected nodes. The overabundance of high- degree nodes is the result of non-random processes, which appear ubiquitous in biological networks. In the roosting networks of bats, non-random selection may occur if certain trees have superior physical characteristics or if certain trees are known to be important social hubs by members of the colony. I am aware of only two other studies that have explicitly examined bat roosting networks and to which I can make comparisons. A similar pattern to my study has been reported for giant noctule bats

(Nyctalus lasiopterus). This species used a few trees intensively, while the majority of trees received comparatively little use (Fortuna et al. 2009). The degree distribution among roost trees was fairly homogeneous within colonies, a result that seemingly contrasts with my findings. However, the difference may be attributable to link-saturation

(within modules) in their study, which was more likely to occur since they created links between all roosts used by the same bat rather than just those used in sequence (as in my study). In a different study, the roosting network of the white-striped freetail bat

(Tadarida australis) had a single high-degree roost tree (the communal roost) that had direct connections to most other trees in the network (Rhodes et al. 2006). The targeted

removal of the communal roost in that study would result in near complete fragmentation

of the roosting network. However, the number of links with the communal roosts may have been exaggerated by only capturing bats from that roost, thus predisposing it to be central within the network. I minimized this bias in my study by not capturing bats near roosts and regularly changing netting locations. The targeted removal of the most

important roost tree in my study – based on its role in maintaining network cohesion – would result in <34% fragmentation. The removal of a typical high-degree roost tree would result in <10% fragmentation. The actual degree of fragmentation would be less since links to other trees would increase as a result of any removal. My result suggests that roosting networks may be resilient to the periodic loss of high-degree roost trees. The resiliency of roosting networks to the periodic loss of roost trees is an important property given the ephemeral nature of cavity bearing trees (Lewis 1995; Barclay and Brigham

2001).

Roost trees frequently used by colonies may represent important resources for bats. High-degree roost trees are likely to increase social interactions within colonies, and

possibly facilitate the flow of information (or disease) across the network, and thus play

an important role in maintaining colony cohesion (Fortuna et al. 2009). In my study,

these roosts were associated with high rates of reuse, longer residency time, greater use during cold weather, and physical characteristics associated with larger roosting groups

(e.g. large diameter and typically balsam poplar; see Chapter 3). Together, this suggests that important fitness benefits are gained by using these trees. Roost temperature may

influence how often they are used by bats, as bats are known to select cavities with

warmer microclimates (Kerth et al. 2001; Sedgeley 2001; Ruczynski 2006). Larger-

97 diameter trees may have superior insulative properties, and cavities in large diameter trees have more stable temperatures (Wiebe 2001; Coombs et al. 2010). However, variation in the microclimate of available roost cavities may be small relative to the increase in temperature provided by the metabolic heat of roosting aggregations (Willis and Brigham 2007). In temperate climates, social thermoregulation is an important benefit of sociality, possibly explaining the frequent use of select roost trees (Willis and

Brigham 2007; Pretzlaff et al. 2010) despite this behaviour likely resulting in greater exposure to parasites or other disease (Cote and Poulin 1995; Reckardt and Kerth 2007).

The association of high-degree roosts with larger roosting groups, and the more frequent use of high-degree roosts during cold weather both support the social thermoregulation hypothesis. A similar result has been shown for Indiana bats (Myotis sodalis), which selected primary roosts – characterised by larger roosting groups – more often than alternate roosts during colder temperatures (Callahan et al. 1997).

Roost switching

The lower rates of roost switching from high-degree roost trees provide additional evidence for the general importance of these trees within the roosting network. Longer switching intervals have been observed for bats roosting in structures that appear to be of higher quality. For example, Rafinesque’s big eared bats (Corynorhinus rafinesquii) use artificial roosts for longer periods than natural roosts (Trousdale et al. 2008). Likewise,

Myotis sodalis roosted for longer periods in crevice roosts than when they roosted under peeling bark (Kurta et al. 2002). High-degree roosts in my study were substantially larger and were most often balsam poplar, two characteristics that appear to be preferred by bats in my study area and that are associated with larger roosting groups (see Chapters 2 and

3). Although I did not measure roost microclimate, these trees may have more stable

temperatures in some areas of the roost, but may also offer a greater range of

microclimate variability if multiple roosting sites are present. Both factors may reduce

the need to switch roosts (Lewis 1995). By definition, high-degree roost trees are central

within the roost network, and thus are more likely to be locations where bats can

encounter other bats to roost with. Thus they may stay longer to take advantage of social

benefits, especially social thermoregulation. If bats prefer not to switch roosts, they can

maximize interactions with other bats by choosing roosts that are central within the

network. Building roosts, to which M. lucifugus show high fidelity (Kalcounis and

Brigham 1994; Syme et al. 2001), could be seen as an extreme high-degree roost.

Possibly the same factors that reduce roost switching in high-degree roost trees are also

responsible for the high fidelity to building roosts. In contrast to my findings, the single

‘hub’ roost reported by Rhodes et al. (2006) was typically used for shorter durations per

visit than the satellite roosts (Rhodes 2007), and communal roosts used by the New

Zealand long-tailed bat (Chalinolobus tuberculatus) were used for shorter durations than

solitary roosts (O'Donnell and Sedgeley 1999).

Contrary to my prediction, there was no evidence that bats switched roosts in

response to decreasing temperature, and thus optimizing roost selection to suit ambient

conditions is not likely a primary reason for frequent roost switching. A similar result

was reported for pallid bats (Antrozous pallidus), which did not switch more or less often

in response to changes in maximum temperature (Lewis 1996). However, unlike the A.

pallidus study, individuals in my study were more likely to change roosts during nights

when the temperature had increased relative to the previous night. One interpretation of

my results is that an increase in the roosting temperature is more detrimental than a

decrease in the roosting temperature, at least beyond some threshold level where heat

stress may become an issue. Under some conditions, the ability of bats to dissipate heat

may constrain metabolic activity more so than energy supply (Speakman and Król 2010).

Bats occupying roosts that are too warm may therefore have an incentive to move to roosts that have cooler microclimates, particularly those that contain fewer other bats.

This would fit with the observation that the responsiveness to increased night-time temperature was most pronounced for bats already occupying high-degree roosts, as these roosts are more likely to have contained large roosting groups that would have elevated roost temperature. Bats may not need to switch roosts when the temperature decreases because they can use torpor; in contrast, roost switching may be the only option to escape sub-optimally warm roost environments. Alternatively, bats may be more likely to switch when temperature increases because insect prey are more active and energetic constraints that discourage roost switching are relaxed. However, this does not explain the similar likelihood of switching when temperature decreased compared with nights when the temperature remained stable. Additional research, possibly including measurements of roost microclimate, is needed to examine these hypotheses; however, studies that only measure the temperature of unoccupied roosts will provide incomplete data, as metabolic heat produced by roosting aggregations is likely the primary factor elevating roost microclimates relative to unused roosts (Willis and Brigham 2007).

Both Lewis (1996) and Russo et al. (2005) found that bats traveled less far during lactation than during pregnancy, although this trend was not significant in the latter study.

They suggested that the additional energetic cost (Lewis 1996) or greater exposure to

100

predators (Russo et al. 1995) may have explained this result. The small sample size for

pregnant bats in my study prevented conclusions regarding differences between pregnant

and lactating bats; however, available data are inconsistent with the findings of Lewis

(1996) and Russo et al. (2005). I found no indication that bats changed their roost-

switching frequency between reproductive periods. In contrast, Russo et al. (2005)

reported that lactating bats switched significantly less often than during other

reproductive periods. I also found little indication that bats reduced either the frequency

of switching, or the distance between successive roosts during the pre-fledging lactation

period, indicating that having to transport larger pups does not affect roost-switching

behaviour. However, there was a significant difference in the distance traveled between

roosts located before versus after the time that the first volant juvenile was captured. In

contrast to these findings, Russo et al. (2005) reported that bats travelled greater average

distances between roosts during the post-lactation period than during the lactation period.

However, this discrepancy could be explained by the lower roost switching frequency during the lactation period of their study. A lower roost-switching frequency would result in a lower reported value for distance measures, as they considered days with no- switching as zeros.

The shorter distance between successive roosts in the post-lactation period may be attributable to the reduced flight proficiency of pups, which now have to move themselves instead of being carried. Juvenile bats continue to be nursed for a few days after they fledge (Kunz et al. 1995). Mothers that continue to associate with their pups after fledging may move shorter distances during the post-fledging period so that they can roost with their pups. I only tracked bats for 3-weeks after the first fledged bat was

101

captured, so site familiarity and flight performance of juveniles was likely still poor; this

effect may dissipate with a longer tracking period. If shorter distances between roosts are

favoured by newly fledged bats, then roost areas with a high density of suitable trees may

be an important habitat property during this period. The distance traveled between roosts

may also affect modularity in the network, as bats moving shorter distances may

associate with only a small part of a larger roosting area.

Frequent roost switching has been reported for many cavity or crevice-roosting

bats (Kurta et al. 2002; Baker and Lacki 2006; Timpone et al. 2006), with average roost-

switching intervals of <2 days being common in northern regions (Willis and Brigham

2004; Garroway and Broders 2007; Nixon et al. 2009). Roost switching in my study was similar between years, between colonies, and among reproductive periods. As there were differences in parasite levels (see Appendix A) and roost density between the two main colonies, the similar roost-switching rates indicate a general process may have been responsible for the high rates of roost switching. The consistency of roost-switching behaviour among bats in my study and among similar species suggests this may be an instinctual behaviour that may occur without the need for external stimulus. Factors such as habitat quality (Trousdale et al. 2008), actual parasite levels (Lewis 1996), and

weather (Lewis 1996) may influence the rate of roost switching but are not necessarily

the primary cause. Several reasons, likely working in unison, may have caused the

evolution of regular roost switching as an instinctive behaviour. These include parasite

avoidance or disrupting parasite life cycles (Reckardt and Kerth 2007), increasing

interactions with other bats (Willis and Brigham 2004), and increasing familiarly with

other roosts (Russo et al. 2005). Regular roost switching will also result in the scent

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marking of more trees, which may signal its use to other colony members or competitors

(De Fanis and Jones 1995), and may increase predator search time by misdirecting them to unoccupied roosts. Social reasons are unlikely the only cause of roost switching, as solitary bats also regularly switch roosts (e.g. Nixon et al. 2009).

Implications

Like many cavity-roosting species, M. lucifugus appears to require a diverse and abundant supply of roost trees during the summer breeding period. My results suggest that conservation efforts for M. lucifugus or other similar species are best directed at the

quality of the overall roosting area, as network structure appears resilient to the periodic

loss of individual roost trees. Nonetheless, strong behavioural biases (i.e. longer residency time, greater use and reuse) of bats towards certain roost trees indicate that a

relatively few trees may provide disproportional fitness benefits. There was no factor that would allow a priori determination of high-degree roost trees, but large diameter trees, which in my study area were often balsam poplar, were the most likely candidates and their retention within managed landscapes should be encouraged. Differences between the two main colonies in my study suggest that a lowering of habitat quality may result in bats moving greater distances between roosts, larger roosting areas, and potentially increased interactions (including competition) with adjacent groups. Thus, activities that degrade habitat, even if some potential roost trees are retained, are likely to disrupt roosting networks and negatively affect bat populations.

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CHAPTER 5: GENERAL CONCLUSIONS

During the course of this project, I examined habitat use for two sympatric species of cavity-roosting bats: the little brown bat (Myotis lucifugus) and the northern long-eared bat (Myotis septentrionalis). Focusing on M. lucifugus, I examined how roost switching affects how bats use habitat and how this may be adaptive within the context of bat sociality. My research had two fundamental questions: (1) within fission-fusion roosting systems, is there a predictable association between group size and habitat selection? And

(2) may roost switching be adaptive by allowing individuals to adjust habitat selection to suit changing conditions and biological requirements?

My results show that cavity-roosting populations of M. lucifugus have fission- fusion type roosting systems, which is consistent with expectation based on comparable studies of ecologically similar species. In answer to my first question, I found that habitat use is strongly influenced by social behaviour, in particular, the size of roosting aggregations. In Chapter 2, this association was evident when examining patterns of habitat use between the two sympatric bat species. Myotis lucifugus, which formed much larger roosting groups, made extensive use of balsam poplar, the larger of the two main roost-tree species in my study area. In contrast, M. septentrionalis, which formed comparatively small roosting groups, mainly used aspen, the smaller but more abundant roost-tree species in my study area. In Chapter 3, I showed that there is a strong association between roost-tree DBH (which is associated with tree species) and group size. Larger DBH trees (typically balsam poplar) generally had much larger roosting groups compared to smaller-DBH trees (typically aspen). Likely because of this

104 association, temporal variation in group size generally paralleled temporal variation in roost-tree selection. In Chapter 4, I showed that roost trees that were central within a bat’s roosting network were more likely to be reused between years, were more likely to be used for multiple days, and were more likely to be used during cold weather. The most likely explanation for this trend is that these are social hubs and bats can gain social benefits, such as social thermoregulation, from using these locations. For most of the evidence I presented, I cannot be certain whether the use of large-diameter roost trees resulted in large group sizes, or whether large group sizes necessitated the use of large- diameter roost trees. However, given the importance of social thermoregulation for bats and the strong influence that roosting aggregations have on roost temperature, it is likely that the large group size was the primary benefit of selecting large-diameter trees.

Regardless of the cause of the relationship between habitat use and social behaviour, it is apparent that the two are interrelated and both need to be considered when examining habitat use.

Evidence related to the second question, which asked whether roost switching is adaptive by allowing bats to adjust roosting behaviour to suit changing conditions and biological requirements, provided mixed results. In Chapter 3, I showed that the size of roosting groups and the diameter of roost trees both peaked around the time of parturition and then decreased as offspring became independent. This is consistent with my predictions and the results of other studies showing that bats select warm stable microhabitats to protect the vulnerable pups that must be left behind when females leave to forage. However, my results and the results of other researchers suggest that group size may be more important than the roost structure per se in determining roost microclimate.

105

This contrasts with studies of bats occupying rock crevices, where the thermal properties

caused by the roost structure itself appeared to be more important. In Chapter 4, I showed that bats selected more central ‘hub’ roosts during colder weather, presumably to take advantage of the warmer conditions in these roosts. My prediction that bats would switch roosts to adapt to changing weather conditions was not supported. However, I did find that bats switched from roosts when the average night-time temperature increased substantially, suggesting that roost switching may still be adaptive. My results suggest that bats may have switched to avoid excessively hot temperatures that may occur if large roosting groups coincide with warm ambient temperature.

Management implications

My results are consistent with past research showing that bats generally use large- diameter trees. However, tree size or other tree characteristics should not be assumed to be important without also considering the decay characteristics or other causes of structural defects that create roost cavities used by bats. The importance of tree height and diameter, decay class, and tree species, and possibly numerous other characteristics, are highly dependent on how roost cavities are formed. For example, at my study site, aspen used as roosts were much more likely to be alive and had smaller diameter than balsam poplar owing to the greater prevalence of radial-longitudinal splits on living and otherwise healthy trees. In contrast, roost cavities in balsam poplar were most likely to be found in dead or decaying trees, which typically is associated with large diameter trees

nearing the end of their life. If the cause of the radial-longitudinal splits (e.g. scarification by animals, weather patterns, etc.) did not occur in other regions, then a much different pattern of habitat use by bats might be observed. I therefore recommend that forest

106

managers do not arbitrarily apply results from habitat-selection studies to novel areas

without considering the biotic and abiotic processes that facilitate tree cavity formation.

Radial-longitudinal splits were the most common type of roost used by bats in my study

area. These defects are easy to identify and could be preferentially retained in managed forests. Processes that result in cavity formation could also be enhanced to ameliorate the effects of forest harvesting on bats. For example, manually scarring trees may increase the occurrence of frost cracks and facilitate introduction of heart-rot fungus, both of which would increase the likelihood of cavity formation (Kubler 1983; Parsons et al.

2003).

Viable populations of cavity-roosting bats appear to require multiple roost trees over the course of the breeding season. Bats in my study used trees of a variety of species, diameter, height and openness, and used multiple types of tree cavities.

However, trees with large diameters, especially balsam poplars, were associated with much larger roosting groups, longer residency periods, and higher inter-annual reuse.

They also served as hubs within roosting networks, possibly serving an important

function by mediating bat social interactions. Large trees were clearly selected by bats

under some conditions, and are possibly the more limiting roosting resource in my study

area. Forest managers should ensure that large diameter trees persist on the landscape.

However, M. lucifugus in my study also made extensive use of the smaller aspen trees,

and M. septentrionalis almost exclusively used aspen. Maintaining a diversity of roosting

options may thus be the better strategy than preferentially retaining large-diameter trees

on the landscape. Diverse roosting structures may be important for 1) allowing bats to

adapt their roost selection to suit their physiological requirements and prevailing weather

107 patterns, and 2) allowing different bat species to reduce interspecific competition for roost space by selecting roosts for which they are better adapted to use.

Perturbations that reduce the density of suitable roost trees may have negative fitness consequences for bats regardless of whether some suitable roost trees remain for bats to use. This may result, for example, if bats have to spend greater energy moving between roosts. Social networks could also be disturbed if bats have to spread over a larger area, possibly interacting more with neighbouring colonies. Poorer quality habitat has been implicated in lower fidelity to roosting areas, which may increase the transfer of parasites and other diseases among neighbouring colonies (Chaverri 2010). Forest management practices should therefore aim to maintain a high density of cavity bearing trees across the landscape.

Future research

My results strongly suggest that loss of some or all of the core roosting area of a group of bats could be highly disruptive to bat populations. However, empirical evidence is needed to support this hypothesis. Examining bat roosting networks in the context of forest harvesting would provide useful information on how resilient bat colonies are to perturbations. For example, how do bats respond when some or all of their roost trees are destroyed? Does the entire colony move to a new geographic area? Do they simply increase the size of their roosting area? Do they fragment into smaller groups or join neighbouring colonies? Examining the fitness consequences when roosting networks are disrupted could also be informative. One way of doing this would be to compare parturition dates, fledging dates, and possibly the proportion of bats that are reproductive in areas with and without forest harvesting treatments. I predict that in the short term, key

108 reproductive events would be delayed as the ability to find optimal microclimates for raising young would be impaired. In the long-term, I predict that if bats continue to roost within lower quality habitat (such as post-harvest treatments), they will have larger roosting areas, show lower spatial fidelity, and possibly have higher parasite levels

(because of increased interactions with neighbouring colonies). I predict if large roost trees are lost (or do not otherwise occur) then the average size of roosting groups (and possibly roosting networks) will become smaller.

109

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APPENDIX 1: PARASITE DATA

Methodology

I counted the number of external parasites on little brown bats (Myotis lucifugus) prior to radio-transmitter attachment. Parasite counts consisted of visual inspections of all external surfaces. Counts are an appropriate indication of parasite intensity and diversity

(at broad taxonomic levels), as it is unlikely that all parasites were accurately counted.

Wings and tail membranes were extended and parasites on the ventral and dorsal surfaces were recorded. Parasites obscured by fur were observed by blowing through the fur for at least 5-seconds on both the ventral and dorsal surface. Estimates were made in the event of high parasite levels. Illumination was provided by a headlamp and no other source of magnification was used. One observer (Cory Olson) did all the parasite counts to avoid observer bias. Parasites were classified into one of three groups: Bedbugs (Cimicidae),

Fleas (Siphonaptera: Ischnopsyllidae), and Mites (Acarina). Mites were broadly classified as either small (body length approximately < 0.5 mm) or large (body length approximately > 0.5 mm), as more precise classification would not have been practicable without magnification.

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Table A1. Parasite counts for individual M. lucifugus prior to radio-transmitter attachment. Colony associations based on the two main roosting networks described in Chapter 4. Q1 = 25% quartile, Q3 = 75% quartile. Date Small Big Bat ID Year Colony Captured Mites Mites Fleas Bedbugs

N0972A 2009 North 02-Jul 1 2 0 0 N0974A 2009 North 04-Jul 6 0 3 2 N0978A 2009 North 08-Jul 32 0 1 0 N09715A 2009 North 15-Jul 1 3 1 0 N09715B 2009 North 15-Jul 0 2 0 0 N09719A 2009 North 19-Jul 6 0 0 0 N09724A 2009 North 24-Jul 4 3 0 0 N09728A 2009 North 28-Jul 3 0 0 0 N09728B 2009 North 28-Jul 12 2 0 0 N09731A 2009 North 31-Jul 19 1 0 0 Q1 2 0 0 0 Median 5 2 0 0 Q3 11 2 1 0

S09628A 2009 South 28-Jun 3 4 0 0 S09630A 2009 South 30-Jun 12 5 0 0 S0975A 2009 South 05-Jul 181 1 0 0 S0976A 2009 South 06-Jul 0 3 1 0 S0979A 2009 South 09-Jul 23 9 0 0 S09712A 2009 South 12-Jul N/A N/A N/A N/A S09712B 2009 South 12-Jul 5 2 0 0 S09714A 2009 South 14-Jul 20 0 0 1 S09716A 2009 South 16-Jul 31 1 0 0 S09720A 2009 South 20-Jul 59 1 0 0 S09721A 2009 South 21-Jul 46 1 0 0 S09721B 2009 South 21-Jul 37 0 5 0 S09725A 2009 South 25-Jul 24 2 5 0 S09729A 2009 South 29-Jul 149 2 1 0 S09730A 2009 South 30-Jul 122 3 2 0 Q1 14 1 0 0 Median 28 2 0 0 Q3 56 3 1 0

133

Parasite data continued… Date Small Big Bat ID Year Colony Captured Mites Mites Fleas Bedbugs

U09627A 2009 Unclassified 27-Jun 2 0 0 0 U0977A 2009 Unclassified 07-Jul 128 1 0 1 U09714A 2009 Unclassified 14-Jul 7 0 0 0 U09722A 2009 Unclassified 22-Jul 22 0 6 2 U0983A 2009 Unclassified03-Aug 2 4 0 0 Q1 2 0 0 0 Median 7 0 0 0 Q3 22 1 0 1

N10612A 2010 North 12-Jun 0 3 1 1 N10626A 2010 North 26-Jun 11 1 0 2 N10713A 2010 North 13-Jul 13 1 0 0 N10713B 2010 North 13-Jul 27 1 1 0 N10715A 2010 North 15-Jul 48 0 1 0 N10719A 2010 North 19-Jul 47 0 0 0 N10719B 2010 North 19-Jul 37 0 1 0 N10724A 2010 North 24-Jul 109 5 0 1 Q1 13 0 0 0 Median 32 1 1 0 Q3 47 2 1 1

S1066A 2010 South 06-Jun 19 0 3 0 S1076A 2010 South 06-Jul 11 1 0 0 S1077A 2010 South 07-Jul 23 0 1 0 S1078A 2010 South 08-Jul 7 0 0 0 S10711A 2010 South 11-Jul 152 1 2 0 S10711B 2010 South 11-Jul 99 2 1 0 S10717A 2010 South 17-Jul 17 2 0 0 S10725A 2010 South 25-Jul 118 2 0 0 S1083A 2010 South 03-Aug 6 5 2 0 Q1 11 0 0 0 Median 19 1 1 0 Q3 99 2 2 0

134

Parasite data continued… Date Small Big Bat ID Year Colony Captured Mites Mites Fleas Bedbugs

U10612A 2010 Unclassified 12-Jun N/A N/A N/A N/A U10618A 2010 Unclassified 18-Jun 13 2 1 0 U10618B 2010 Unclassified 18-Jun 79 2 1 0 U10620A 2010 Unclassified 20-Jun 80 3 1 1 U10625A 2010 Unclassified 25-Jun N/A N/A N/A N/A U10628A 2010 Unclassified 28-Jun 12 0 1 0 U1071A 2010 Unclassified 01-Jul 0 2 2 0 U1079A 2010 Unclassified 09-Jul 30 0 0 0 U10717A 2010 Unclassified 17-Jul 109 0 0 0 U10721A 2010 Unclassified 21-Jul 124 3 1 1 U10727A 2010 Unclassified 27-Jul 13 2 1 0 U10728A 2010 Unclassified 28-Jul 342 0 0 0 U1088A 2010 Unclassified08-Aug 2 1 1 0 Q1 13 0 1 0 Median 30 2 1 0 Q3 95 2 1 0

135

200

180

160

140

120

100

Number ofmites 60

0 2009 - North 2009 - South 2010 - North 2010 - South Year and colony

Figure A1. Comparison of the total number of mites counted on M. lucifugus individuals prior to attachment of a radio transmitter during 2009 and 2010. Total mites equals total small mites plus total large mites that were counted on the surface of individual bats. Box plot represents minimum, 25% quartile, median, 75% quartile, and maximum. Data only shown for the north and south colony. Colony associations based on the two main roosting networks described in Chapter 4.