Science, Irving K. Barber Faculty of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Rensel, Leah J.
Master of Science - MSc
University of British Columbia
Wild animals often congregate in social groups to enhance individual and group survival. Social cooperation allows animals to share limited resources among conspecifics without incurring the costs of competition. Many reproductive bats demonstrate social cooperation by congregating in maternity colony roosts, which are critical but limited habitat components for North American bat populations. Female myotis bat species may select artificial structures, such as bat boxes and buildings to form their colonies, and may move among multiple roosts throughout the season. Female little brown bats (Myotis lucifugus) and Yuma bats (M. yumanesis), for example, form maternity colonies during the summer to rear their young. I studied two colonies in artificial structures near Vancouver, British Columbia, to assess social behavior and understand what conditions influenced day roost selection. During summer 2019, I used Passive Integrated Technology (PIT-tags and scanners) to track individuals’ daily roost selection. I monitored temperature and humidity at all known roosts and used radio telemetry to find previously unknown roosts. Yuma myotis and little brown myotis used at least 5 roosts at each site and switched locations frequently between roosts. Bats selected roosts within specific temperature ranges likely corresponding to their thermoneutral zone. Bats were less likely to switch during lactation when the pups were vulnerable and most likely to leave a roost during post-lactation when the pups were volant. Yuma myotis and little brown myotis formed strong, close associations with conspecifics during pregnancy and lactation, which then fragmented during post lactation when adults could roost independently with volant pups. Little brown myotis left the maternity site in August, while Yuma myotis persisted until September. During this time, Yuma myotis mixed with conspecifics from nearby maternity colonies in a previously unoccupied roost. My findings suggest land managers should ensure that multiple nearby roosts are available to maternity colonies, as bats in a maternity colony require a range of temperatures and roost types during the summer. Yuma myotis and little brown myotis may have similar roost requirements, but different social behavior, suggesting that these two species have different life history patterns.
Roost Selection and Social Organization of Myotis in Maternity Colonies by Leah J. Rensel B.Sc., Linfield College, 2013 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) June 2021 © Leah J. Rensel, 2021 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Roost Selection and Social Organization of Myotis in Maternity Colonies Submitted by Leah J. Rensel in partial fulfillment of the requirements of the degree of Master of Science. Examining Committee: Dr. Karen E. Hodges, Biology Department, Irving K. Barber Faculty of Sciences Supervisor Dr. Cori Lausen, Wildlife Conservation Society Canada Supervisory Committee Member Dr. Adam Ford, Biology Department, Irving K. Barber Faculty of Sciences Supervisory Committee Member Dr. Katie Marshall, Biology Department, Vancouver Faculty of Sciences University Examiner iii Abstract Wild animals often congregate in social groups to enhance individual and group survival. Social cooperation allows animals to share limited resources among conspecifics without incurring the costs of competition. Many reproductive bats demonstrate social cooperation by congregating in maternity colony roosts, which are critical but limited habitat components for North American bat populations. Female myotis bat species may select artificial structures, such as bat boxes and buildings to form their colonies, and may move among multiple roosts throughout the season. Female little brown bats (Myotis lucifugus) and Yuma bats (M. yumanesis), for example, form maternity colonies during the summer to rear their young. I studied two colonies in artificial structures near Vancouver, British Columbia, to assess social behavior and understand what conditions influenced day roost selection. During summer 2019, I used Passive Integrated Technology (PIT-tags and scanners) to track individuals’ daily roost selection. I monitored temperature and humidity at all known roosts and used radio telemetry to find previously unknown roosts. Yuma myotis and little brown myotis used at least 5 roosts at each site and switched locations frequently between roosts. Bats selected roosts within specific temperature ranges likely corresponding to their thermoneutral zone. Bats were less likely to switch during lactation when the pups were vulnerable and most likely to leave a roost during post-lactation when the pups were volant. Yuma myotis and little brown myotis formed strong, close associations with conspecifics during pregnancy and lactation, which then fragmented during post lactation when adults could roost independently with volant pups. Little brown myotis left the maternity site in August, while Yuma myotis persisted until September. During this time, Yuma myotis mixed with conspecifics from nearby maternity colonies in a previously unoccupied roost. iv My findings suggest land managers should ensure that multiple nearby roosts are available to maternity colonies, as bats in a maternity colony require a range of temperatures and roost types during the summer. Yuma myotis and little brown myotis may have similar roost requirements, but different social behavior, suggesting that these two species have different life history patterns. v Lay Summary Yuma and little brown bats often form groups called maternity colonies during the summer. I studied two bat maternity colonies in buildings and bat boxes near Vancouver, British Columbia, to understand what social or environmental factors influenced roost selection. During the summer of 2019, I placed tiny chips in adult female bats (PIT-tagging; Passive Integrated Technology). When the chipped bat flew past the scanner on a roost, the scanner recorded the time and date the bat arrived at the roost. Using these data, I discovered that bats used at least 5 artificial roosts per site and frequently switched between roosts. Bats seek out roosts that will allow them to save energy. Bats form tighter associations with other bats during pregnancy and lactation, when the pup survival is dependent on group warmth. Little brown bats leave the maternity site first but Yuma bats occupy roosts with other bats from nearby colonies. vi Preface I was responsible for designing the study, selecting study sites, organizing and executing the field work, analyzing the data, and writing this thesis. Dr. Lausen and Dr. Hodges guided and provided suggestions for the entire process. Dr. Lausen supervised my field seasons and provided the bulk of the funding, equipment and supplies I needed to be successful, and Dr. Hodges oversaw my thesis milestones and academic development. Dr. Ford provided input and guidance for my thesis proposal, and helped me select the appropriate modeling analysis for Chapter 2. Nick Hindley and Suhani Srihari assisted me as my field technicians in 2018 and 2019, respectively. Laura Kaupas, Aimee Mitchell, Chris Currie and Patrick Burke taught me the finer points of bat capture, processing and radiotelemetry, in addition to assisting my fieldwork. John Saremba graciously shared site information, exit count data and roost temperature data with me. Brian Slough shared information about designing home-made harp traps, which I used to fashion my own. I intend to publish two papers in peer-reviewed journals from this thesis and both will be authored by myself, Dr. Hodges and Dr. Lausen. All bat capture, handling, and radio telemetry were approved by the University of British Columbia Animal Care Committee (Certificate # 718-0031), SARA (Permit # SARA-PYR-2018-00444), and the British Columbia Government in accordance with the Wildlife Act under Dr. Cori Lausen (Permit # MRCB15-163558). Land access was approved to Minnekhada and Colony Farm Regional Parks was approved by Metro Vancouver Regional Parks (2018 Permit # LRMINCOL2018; 2019 Permit # LRCOL2019), Alice Lake Park (BC Parks File # 98700-20), and Alaksen NWA (Environment Canada Permit # BC-18-0043). vii Table of Contents Abstract ................................................................................................................................................... iii Lay Summary .......................................................................................................................................... v Preface ..................................................................................................................................................... vi Table of Contents .................................................................................................................................. vii List of Figures ......................................................................................................................................... ix List of Tables ........................................................................................................................................ xiii Acknowledgements ............................................................................................................................... xv Dedication ............................................................................................................................................ xvii Chapter 1: Introduction ......................................................................................................................... 1 1.1 Pressures of Reproduction ......................................................................................................... 2 1.2 The Benefits and Consequences of Maternity Colonies ............................................................ 4 1.3 Life History of Small Insectivorous Bats ................................................................................... 7 1.4 Bat Conservation in British Columbia ....................................................................................... 8 1.5 Research Objectives ................................................................................................................... 9 Chapter 2: Myotis Roost Selection is Influenced by Seasonal Thermal Needs ............................... 10 2.1 Background ................................................................................................................................... 10 2.2 Methods ......................................................................................................................................... 16 2.2.1 Data Analysis ........................................................................................................................ 18 2.3 Results ........................................................................................................................................... 21 viii 2.3.1 Roost Use Results ................................................................................................................. 21 2.3.2 Model Results ....................................................................................................................... 23 2.4 Discussion ..................................................................................................................................... 25 Chapter 3: Social Organization of Myotis in Maternity Roosts ....................................................... 45 3.1 Background ................................................................................................................................... 45 3.2 Methods ......................................................................................................................................... 50 3.2.1 Network Construction ........................................................................................................... 53 3.2.2 Network Analysis.................................................................................................................. 54 3.3 Results ........................................................................................................................................... 56 3.3.1 Capture Summary ................................................................................................................. 56 3.3.2 Network Analysis.................................................................................................................. 57 3.4 Discussion ..................................................................................................................................... 60 Chapter 4: Conclusion .............................................................................................................................. 72 Bibliography .............................................................................................................................................. 77 Appendices ................................................................................................................................................. 99 ix List of Figures Figure 2.1: Daily number of bats in each PIT-tag monitored roost by A) Stave Lake and B) Alice Lake. Roost monitoring ended at Stave Lake on August 15th, when the Stave Building 1 PIT-tag reader failed; a similar systems malfunction occurred at the same roost on July 18th, which accounts for the lack of bats recorded on that date. ...................................................................... 32 Figure 2.2:A comparison of A) Daily maximum temperature and B) humidity percentage readings at all monitored roosts during the summer of 2019. Stave Lake roosts are grey, Alice Lake Roosts are yellow. The lines represent the top and bottom quartiles, the shaded boxes represent the second and third quartiles, and the black lines represent the mean readings per roost. Outliers are represented by dots. ......................................................................................... 33 Figure 2.3: Seasonal temperature (˚C) patterns at Stave Lake (A) and Alice Lake (B) by roost. Temperatures were measured every 15 minutes by all data loggers. Roof refers to the Alice Building at Alice Lake, where two groups of bats roosted under the corrugated tin roofing. Conditions were recorded starting July 12th, when the bats roosting in the roof were discovered........................................................................................................................................................ 34 Figure 2.4: The probability of a bat switching roosts at Stave Lake: panels show the predictions of A) maximum daily roost temperature (˚C), B) maximum daily humidity percentage, C) daily ambient temperature at sunrise and D) seasonal (date) variation. Black lines show the mean, with shading denoting the 95% confidence intervals. The original binary response data are shown at the top (switch) and bottom (stay) of each panel. Blue lines and letters delineate reproductive periods: pregnancy, lactation and post-lactation. .......................................................................... 38 Figure 2.5: The probability of bats switching roosts at Alice Lake: panels show the predictions of the significant covariates in the model including A) maximum daily roost temperature, B) x maximum daily humidity percentage, and C) seasonal variation (Date). Black lines show the mean, with shading denoting the 95% confidence intervals. Blue lines and letters delineate reproductive periods: pregnancy, lactation, post-lactation. The tick marks at the top and bottom of the panels represent the distribution of the original binary response data. ............................... 39 Figure 2.6: Alice Lake Count Model effects of maximum daily Roost Temp (TR), Difference between maximum daily roost temperature and maximum daily ambient temperature (Tdiff) and Date on bat abundance. Panels A and B show effects for Count Model 1 (Tdiff + Date); Panels C and D show predictions for Count Model 3 (Tr + Date). Panel E shows effects of Model 2 (Tdiff) and Panel F shows effects of Model 4 (Tr). All effects are significant, with the exception of Panel A. Black lines show the mean, with shading denoting the 95% confidence intervals while the tick marks delineate original dataset distribution. ............................................................................... 40 Figure 2.7: Stave Lake Count Model predictions for maximum daily Roost Temp (TR), Difference between maximum daily roost temperature and maximum daily ambient temperature (Tdiff) and Date on bat abundance. Panels A and B show effects for Count Model 1 (Tdiff + Date); Panels C show predictions Date covariate for Count Model 3 (Tr + Date); Tr was not significant and not pictured. Panel D shows effects of Model 2 (Tdiff) and Panel E shows effects of Model 4 (Tr). Tick marks show original dataset distribution while the black line denotes mean and shading denotes 95% confidence intervals ................................................................................... 41 Figure 2.8: Bat Abundance at individual Roosts at Stave Lake for Count Model 4 (Tr): panels show predictions of roost temperature (˚C) at Stave Building 1 (A) and Stave Bat Box 1 (B). Black lines show the mean and grey shading shows the 95% confidence intervals. Tick marks at the top and bottom of the panels represent distribution of original dataset. ................................. 42 xi Figure 2.9: Maximum Daily Roost Temperature (˚C) plotted against Date at Stave Building 1 for the summer of 2019. ..................................................................................................................... 43 Figure 2.10: Daily relative humidity percentages at monitored bat boxes at both Alice Lake and Stave Lake. Panels include A) Stave Bat Box 1, B) Alice Bat Box 1 and C) Alice Bat Box 2. The x-axis refers to the number of tagged bats occupying the bat box and y-axis is the maximum daily humidity reading from that box. .......................................................................................... 44 Figure 3.1: Bat social network visualization by season and location. Stave Lake A) Pregnancy, B) Lactation and C) Post-Lactation networks and Alice Lake: D) Pregnancy, E) Lactation and F) Post-Lactation networks. Nodes represent individual bats; edges represent simple ratio index associations. Bats that are spatially closer together have stronger associations with each other than bats that are spatially separate. .............................................................................................. 68 Figure 3.2: Network Visualization of post-lactation period at Stave Lake by A) species identity and B) community assignment by Infomap. Species identity was assigned by 90% probability using Weller et al. 2001; MYLU refers to M. lucifugus, MYYU refers to M. yumanensis, and YULU refers to uncertain species identification. ......................................................................... 69 Figure 3.3: Individual tagged bat roost location by date, separated by species. The top panel includes only bats identified as Myotis yumanesis, middle panel contains bats with uncertain species identity, and the bottom panel includes only bats identified as Myotis lucifugus. Species identity was assigned by 90% probability using Weller et al. 2001. Roost location is denoted by color, and position along the x-axis denotes date. If a dot is missing, the bat was not located at any monitored roost that day ......................................................................................................... 70 Figure 3.4: Stave Lake bats ranked according to degree-centrality (measurement of how many bats are connected to another individual bat within a network) for the entire summer of 2019. A) xii Average degree-centrality for binned deciles of bats. B) Histogram of degree-centrality measurements for bats. C) Comparison of deciles by percent of reproductive and non-reproductive bats. D) Comparison of deciles by percent of bats by date of original capture. ...... 71 xiii List of Tables Table 2.2: Effects of Switch models at both Alice and Stave Lake. Wald Type II Chi-Square tests were used to infer from general additive mixed effect models. Reproductive status was a categorical term, covering two states (reproductive or not), while all other terms were continuous. Ambient temperature was measured as the daily temperature at sunrise, while roost temperature and humidity were the maximum values in the roost from the previous day. Significant effects are bolded. ....................................................................................................... 35 Table 2.3: Effects of Count Models at Stave Lake. Wald Type II Chi-Square tests were used to infer from zero inflated negative binomial models. Tdiff represents the difference between daily maximum roost and ambient temperature (˚C) and Tr represents the daily maximum temperature (˚C). Significant effects are bolded. .............................................................................................. 36 Table 2.4: Effects of Count Models at Alice Lake. Wald Type II Chi-Square tests were used for inference from zero inflated negative binomial models. Tr was calculated using the daily maximum temperature (˚C) and Tdiff was the difference between maximum daily roost and ambient temperature (˚C). Significant effects are bolded. ............................................................ 37 Table 3.1: 2019 dates used to construct each seasonal network by site. Data collection ceased prior to the seasonal disbanding of the colonies. ……………………………………………67 Table 3.2: Summary of capture efforts at Stave Lake and Alice Lake in 2019. Only bats caught in 2019 were PIT-tagged and only bats with >4 observations were included in the social network analysis. ......................................................................................................................................... 67 Table 3.3: Comparison of social network metrics by site and time period. Average SRI for all dyads were calculated as a measure of association strength across the colony. Each bat has a xiv degree-centrality, indicating how many other bats they associated with during the time period. Density and modularity are network level measurements. ........................................................... 67 Table A.1: Parameter estimates for non-parametric reproductive status covariate in generalized additive Switch Models by site in 2019. Reproductive female bats were labeled as “Repro Yes”, non-reproductive bats as “Repro No”, and indeterminate reproductive status was labeled as “Repro Unk”. All covariates were compared to non-reproductive bats as the control…….. 101 Table A.2: Approximate significance of smoother terms for continuous covariates in generalized additive Switch Models by site in 2019. Ambient temperature was measured as the daily temperature at sunrise, while roost temperature and humidity were the maximum values in the roost from the previous day. ....................................................................................................... 101 Table A 3: Parameter estimates for Count Models. Date represents the Julian Date in 2019. Tr represents the maximum daily roost temperature. Tdiff represented the maximum daily roost temperature minus the maximum daily ambient temperature. Significant covariates are bolded...................................................................................................................................................... 102 xv Acknowledgements I want to thank my family for their unending patience and love. I specifically want to thank my unofficial third advisers, Ruth Milner and Jack Rensel, for sharing the encouragement and advice I needed to succeed. My sister, Caroline Rensel, deserves accolades for her emotional support of her sleep deprived sibling. I want to thank Mike Olson for being my adventure buddy during this degree, and his willingness to “Ride the Coq” to see me. My success in this degree is due in large part to the wonderful people I had the luck to meet when I moved to Canada. As ever, I am grateful for the support and hard work of Rockstar Team of field assistants and biologists—Nick Hindley, Suhani Srihari, Aimee Mitchell and Chris Currie. I also want to thank my incredible volunteers, without whom this project would not be possible: Kirk Miles, Lara Tessaro, Cynthia Coewen, Jennifer Barrett, AJ Fedoruk, Riley Kennedy, Greg Michalowski, Milena Wilson, Kirsti Owen, Joanna Burgar, Alison Wald, Spencer Fitschen, John Saremba, Taylor Knutzley, Robyn Worcester, Linda Qian, Deanna MacTavish, Julie Fournier, Matt Jones, Melodie Lioret, Sarah McLaughlin, Stephanie Lee, Thea Warren, and Timothy Silva. Your enthusiasm, encouragement and dedication meant the world to me and I am incredibly grateful for all the time you graciously donated to help the bats. I am also grateful to Jacquelyn Wang, Tony and Maria Addante, as well as Sarah and Susan Pinkus (and the pups), for being my family in a new country. Your kindness and friendships are one of the dearest gifts I received over this degree. I want to thank the Dave Brittell Memorial Scholarship Fund, Stanwood Chapter of the American Association of University Women, Stillaguamish Tribe (Ester Ross Memorial Scholarship), and United Steelworkers Local 12-591 (Community Grant) for their support of my education. I also want to thank the funders of my research: the National Fish and Wildlife xvi Foundation, MITACS, Habitat Conservation Trust Foundation, Fish and Wildlife Compensation Program, Fight WNWS and the Forest Enhancement Society of BC. I also want to thank my lab mates for their help, encouragement and friendship during these years: Jen Meineke, Briana All, Angie Kelly and Logan Volkmann. Your amazing accomplishments inspired me to push outside my comfort zone and I am proud to have worked beside you. And finally, I want to thank Dr. Karen Hodges Dr. Cori Lausen. I always knew, beyond a shadow of a doubt, that I could rely on you to be in my corner as I pushed myself beyond my comfort zone. The skills, perspectives and knowledge I gained from you both will be with me for the rest of my career as a biologist. From your example, I learned what it meant to be a leader and a mentor, and it inspired me to find ways to return the favor. Thanks for taking a chance on me—this experience was one of the most wild, fun things I’ve done to date. xvii Dedication To Jack Rensel and Ruth Milner, in whose footsteps I am proud to walk. 1 Chapter 1: Introduction Bats are one of the most diverse clades of mammals and are critical components of most major ecosystems in the world (Altringham 2011). For example, bats are the major predators of many forest and agricultural pests, and loss of bats could severely disrupt essential functions of ecological and human systems (Boyles et al. 2011; Kunz et al. 2011). By fulfilling important ecological roles like pollination, seed dispersal, and pest services, bats are estimated to provide an annual $4 billion economic benefit to the US agricultural industry (Boyles et al. 2011). Considering their critical function in many ecosystems, bats also serve as indicators of environmental health and biodiversity. Several species have been designated as keystone species (Jones et al. 2009). Unfortunately, over 200 bat species in 60 countries are threatened because of habitat loss, invasive species, climate change and hunting (Hutson and Mickleburgh 2001; Weller et al. 2009). For example, urbanization in most parts of the world have removed valuable natural roost sites and degraded foraging habitat (Hutson et al. 2001; Racey and Entwistle 2003). Negative local perception of bats as pests and carriers of pestilence have hardened public opinion against education campaigns to stop extermination efforts and bushmeat consumption (Knight 2008). In North America, imported diseases like Pseudogymnoascus destructans (Pd), the fungus that causes white-nose syndrome (WNS), have decimated populations of North American bat species (Frick et al. 2015). As a result, conservation research is required to help imperiled bat species and, for more common species, maintain robust populations to prevent the collapse of important economic and biological systems. Conservation efforts for bats frequently focus on the breeding season. Bats are long-lived animals with slow reproduction and delayed senescence (Kunz and Fenton 2003). Generally, bats 2 produce only one offspring per year and juveniles are altricial, requiring extensive maternal care until they are able to fly (Altringham 2011). If reproductive conditions are poor or if a pup is born too late in the summer, pre-volancy juvenile mortality can approach 100% (Frick et al. 2010). Even if a pup survives to volancy, yearly recruitment for most bat species is low compared to other small mammals (Barclay and Harder 2003). Therefore, targeting reproductive periods to boost juvenile survival and recruitment represents one of the most tangible targets for bat conservation efforts. 1.1 Pressures of Reproduction Reproduction is an energetically demanding process for bats. A single bat pup may weigh 20% of the mother’s mass at birth; the large size of offspring at birth compounds the energetically taxing performance of flight and foraging during pregnancy (Milligan 1993; Kunz and Robson 1995; Hoying and Kunz 1998). After birth, a bat pup depends on its mother for food and warmth (Studier and O’Farrell, 1972; Kurta and Kunz 1987; Kurta et al. 1989). Approximately 32% of a lactating little brown myotis’ (Myotis lucifugus) daily energy expenditure is devoted to milk production; pups are exclusively milk fed for the first few weeks of life (Kurta et al. 1989). As a result, pregnancy and lactation are stressful for these small mammals that must spend energy on flight for foraging and deal with variable food availability (Hill and Smith 1984). To combat energetic deficits, some species of bats may use the heterothermic strategy of torpor to conserve energy (Wang and Wolowyk 1988). When a bat enters torpor, the animal’s metabolic rate and body temperature are temporarily lowered to conserve fat stores and water loss (Herreid and Schmidt-Nielsen 1966; Studier 1981). However, bats cannot use extensive torpor during pregnancy and lactation without negatively impacting the development and 3 survival of offspring (Pretzlaff et al 2010). As a result, breeding bats have developed two major strategies to cope with additional pressures of reproduction: roost selection and coloniality. When reproductive bats need to avoid deep bouts of torpor, they must select a roost with microclimates withintheir thermal neutral zone (TNZ), a range of ambient temperatures under which they can maintain body internal temperatures without expending too much energy (Altringham 1996). Torpor during pregnancy and lactation delays parturition and milk supply and may decrease offspring fitness and adult female reproductive success (Racey 1973; McNab 1982; Lewis 1993). Roost selection may, therefore, reflect temperature preference during the breeding season (Brittingham and Williams 2000; Lourenco and Palmeirim 2004; Ruczyński 2006). Colder roosts may increase bats’ energy expenditure as they attempt to maintain homeothermy, whereas overly warm roosts above the upper thermal limit of the TNZ may disrupt metabolic activity and increase the risk of dehydration (Altringham 1996). Therefore, selecting an optimal roost during the day can both decrease energy expenditure and favor adult and offspring survival (Hoying and Kunz 1998; Pretzlaff et al. 2010). In addition to careful roost selection, reproductive bats may cluster together in a single roost to reduce energetic costs and increase offspring survival and development rates. Roosting together benefits both mother and offspring by reducing the amount of energy required to maintain internal body temperatures; this behavior is called social thermoregulation (Trune and Slobodchikoff 1978; Kurta and Kunz 1988). Social thermoregulation decreases both water and heat loss by reducing the amount of exposed surface area on a bat, thereby serving as a buffer against fluctuations in ambient temperature (Kerth 2008; Boratynski et al. 2015). Clustered reproductive female bats typically have higher body temperatures than solitary reproductive females (Kurta and Kunz 1988). For pups, which are born blind and furless, social 4 thermoregulation is vital for staying warm when their mothers are foraging away from the roost; as a result, some bats preferentially allow juveniles to roost in the center of the maternity cluster (Trune and Slobodchikoff 1978). 1.2 The Benefits and Consequences of Maternity Colonies Many species of bat preferentially roost together throughout the breeding season to take advantage of optimal roosts and social thermoregulation; these seasonal and temporary aggregations are called maternity colonies, and the roosts containing pups are referred to as maternity or nursery roosts (Neubaum et al. 2007, 2017). In temperate climates, maternity colonies form when the bats emerge from hibernation in the spring and persist until the pups are volant in late summer (Altringham 2006). Members of a maternity colony are almost always reproductive female bats, although non-reproductive females and a minority of adult males have been recorded roosting alongside breeding females (Davis et al. 1965). The colonies can range in size from only a few bats to several thousands, and can be found in a variety of natural and artificial structures (Hill and Smith 1984). In addition to social thermoregulation, roosting together in maternity colonies facilitates social and cooperative interactions that may aid survival during a vulnerable period of a bat’s reproductive cycle (Wilkinson 1982, 1986; Kerth et al. 2003b; Ortega and Maldonado 2006). Clustering together in high quality roosts with adequate protection from predators and optimal microclimates enhances the fitness of offspring by allowing multiple individuals to access limited resources without competing with conspecifics (Lewis 1996; Sedgley and O’Donnell 2004). Cooperative behavior such as babysitting and food sharing occur in colonies in some bat species (Wilkinson 1988; Lewis 1996). While altruism is often limited to kin, bats can extend 5 mutualistic social behavior to non-related colony members (Rossiter et al. 2002; Metheny et al. 2008; Wilkinson et al. 2019). However, roosting in maternity colonies also has negative consequences for reproductive bats. Parasite accumulation and disease transmission, which reduces milk production and overall offspring fitness, increases with coloniality (McCracken and Gustin 1991). Predators frequently target maternity colony roosts to maximize hunting success (Speakman et al. 1995). Therefore, colonies of breeding bats typically spread out across several roosts and individuals frequently switch roosts to avoid predation and excess parasitism (Russo et al. 2005; Bartonička and Gaisler 2007; Reckardt and Kerth 2007). While bats evolved to roost in natural structures (caves, trees, cavities, and crevices), some bat species are capable of exploiting human-made structures (buildings, bridges, and bat boxes) (Russo and Ancillotto 2015). This change in roost preference in some populations may reflect habitat loss, a consequence of increased anthropogenic development across the world (Kunz 1982). Unlike other animals, bats do not create roosts and are thus constrained to pre-existing structures found on the landscape (Kerth 2008). However, natural roosts take decades to develop. For example, cavity development in trees occurs slowly as the result of fire, decomposition, insects, or wildlife (Kunz and Fenton 2003). Bats frequently prefer roosting in trees or snags with large diameters and an abundance of foliage, bark, and cavities, because these features provide physical and thermal protection (Boyles 2007). These characteristics are found mostly in old-growth forests, an increasingly limited habitat type in North America (Kunz and Fenton 2003). As a result, scarcity and slow recruitment of natural roosts may be a limiting factor for bat population growth. 6 Artificial structures offer many advantages over natural roosts. For example, bats may select artificial structures because they provide more predator protection, space, and thermal insulation than natural roosts (Kunz 1982; Kerth et al. 2001; Lausen and Barclay 2006). Bats in buildings may advance the timing of reproduction, thereby improving offspring growth and eventual body size (Lausen and Barclay 2006). Anthropogenic development and expansion are thought to be the reason for increased abundance and expanded range of some bat species in Europe (Russo and Ancillotto 2015). Artificial structures specifically designed for bat use represent a compelling area of conservation, but require additional investigation to assess their impact on bat survival and behavior. Bat boxes and bat condos are frequently erected where land managers wish to encourage bat activity (Brittingham and Williams 2000; Zeale et al 2016; Crawford 2020). Characteristics of artificial structures that are associated with bat abundance have been thoroughly investigated (Mering and Chambers 2014; Rueegger 2016), but very little research has been dedicated to understanding how artificial roosts may impact reproductive success, behavior and long-term population growth. Some research suggests that artificial structures change social organization, leaving bats vulnerable to infection and disturbance (Silvis et al. 2014; Webber et al. 2016; Bondo et al. 2019). Others have suggested that bat boxes, in particular, can be ecological death traps to bats due to overheating (Flaquer et al. 2014; Bideguren et al. 2019). Research about roosting patterns of bats in maternity colonies in human-made roosts cover only some bat species in North America and it may be erroneous to apply these conclusions to other species with divergent life history strategies (Keely and Tuttle 1999; Brittingham and Williams 2000; Chambers et al. 2002; Ritzi et al. 2005; Mering and Chambers 2012; Wilheim 2012). 7 1.3 Life History of Small Insectivorous Bats Yuma myotis (Myotis yumanensis) and little brown myotis are two common, small bat species whose ranges overlap in western North America (Nagorsen and Brigham 1993). They are similar in appearance as they are small brown bats weighing ~5 g (Nagorsen and Brigham 1993). Both are insectivorous bats that prefer to hunt over fresh water and both produce one pup per year (Fenton and Bell 1979). They also form maternity colonies that may exceed several thousand individuals, which may be found in both natural and artificial roosts (Adams 2004). Yuma myotis and little brown myotis have been recorded roosting together, although often spatially segregated within the roost (Studier et al. 1970; Wilson and Ruff 1999). Like many temperate bat species, little brown myotis follow an annual pattern of behavior dictated by the changing seasons (Nagorsen and Brigham 1993). For example, little brown myotis segregate by sex during the summer; the males roost independently and the reproductive females congregate in maternity colonies (Neilson and Fenton 1994). After the pups are independent, juveniles and adults travel to specific spots to mate during a period called swarming (Thomas et al. 1979). After the swarming season, the bats migrate to hibernacula in late fall to spend the winter in torpor to avoid the stressors of reduced prey availability during cold periods (Norquay et al. 2013). When insects become more abundant with the warmer spring conditions, bats emerge from their hibernacula to repeat the cycle of foraging and reproduction (Czenze and Willis 2015). Little brown myotis in maternity colonies have high natal philopatry, as well as high roost and foraging area fidelity (Reynolds 1999; Frick et al. 2010; Slough and Jung 2020). As a result, they tend to have low rates of juvenile dispersal, and groups of bats in maternity colonies may be genetically distinct units (Humphrey and Cope 1976; Lausen et al. 2008). Adult females 8 use multiple roosts during the maternity period regardless of roost type, and frequently switch among roosts up to 6.1 km away (Olson and Barclay 2013; Slough and Jung 2020). Yuma myotis are presumed to follow similar life history patterns as little brown myotis, but scant research has been directed at understanding the specifics of Yuma myotis’ maternity habitat selection or social organization (Betss 1997; Evelyn et al. 2004). It is difficult to make many recommendations for Yuma myotis conservation without concrete research identifying vulnerable aspects of their life history, so land managers often focus on generalities that can also be applied to little brown myotis (Hayes and Willis 2013). However, it may be erroneous to assume that these species face similar reproductive and ecological pressures. 1.4 Bat Conservation in British Columbia The discovery of white-nose syndrome (WNS) in Washington in 2016 represents a critical threat to western bat species, including little brown myotis and Yuma myotis (Lorch 2016). While both species are common in Washington and British Columbia, little brown myotis population declines in eastern North America are dire portents of the future of local western populations (Dzal et al. 2011). White-nose syndrome has not yet been detected in British Columbia, but considering the earlier spread across much of North America, we can reasonably expect it to arrive soon. As a result, research on healthy breeding bats is valuable to establish baseline data prior to the arrival of a major disturbance. To date, no one has investigated how Yuma myotis and little brown myotis in maternity colonies use artificial roosts in British Columbia. My thesis expands upon previous investigation of bat maternity roost selection (Lausen and Barclay 2003; Evelyn et al. 2004; Olson 2011; Randall and Barclay 2014; Webber and Willis 2018; Slough 2009, 2020), specifically, habitat use of little brown myotis and Yuma myotis. Little effort has been dedicated to understanding 9 roost microclimates in artificial roosts, despite the fact that roost temperature is a vital factor for maternity colony formation, population persistence, and reproductive success. It is clear that additional research is needed to understand what drives maternity roost selection and switching in artificial structures, so that we can optimize artificial roosting habitat to support healthy bat populations. 1.5 Research Objectives The purpose of my research was to investigate the seasonal dynamics of Yuma myotis and little brown myotis maternity colonies in artificial roosts. In chapter 2, I describe patterns of roost use throughout the summer and investigate how specific factors influence bat roost selection and switching in artificial roosts. Specifically, I ask whether bats in maternity colonies cluster when roost conditions are cool, perhaps because of social thermoregulation. I also quantify how seasonal changes in roost microclimate, ambient conditions, and individual bat characteristics influence a bat’s decision to switch between roosts. In chapter 3, I describe the seasonal changes in social organization of little brown and Yuma myotis within maternity colonies by comparing bat social networks during critical reproductive phases. I adapt principles of social network analysis in a novel context to understand how bats in maternity colonies use key roosts, like bat boxes and buildings. I also describe how individual bat characteristics, like species and reproductive status, influence social behavior. In chapter 4, I summarize my major findings and describe how my research can be used to support bat conservation efforts in North America. 10 Chapter 2: Myotis Roost Selection is Influenced by Seasonal Thermal Needs 2.1 Background Understanding habitat selection requires probing the complex behavioral and environmental processes that influence patterns of animal habitat use and distribution. Animals must reconcile conflicting demands as they prioritize certain actions or habitat features to meet a goal (Roever et al. 2014). Priorities often change seasonally as animals move through key life history phases—for example, high quality habitat often looks different to animals during reproduction compared to winter survival. Habitat features that are highly desirable in one phase may carry additional consequences in another phase. As a result, seasonal shifts in behavior are often evident in temporal changes in habitat use (Orians and Wittenberger 1991). Bats in temperate regions are a prime example of the relationship between behavioral priorities and habitat selection. Bats must make many decisions about roosting and foraging areas throughout a single year as they move from winter hibernacula to summer maternity grounds (Altringham 2011). Bats spend the majority of their lives in roosts and many of their physiological cycles are dictated by roost conditions (Kunz and Pierson 1994; Penuela-Salgado and Perez-Torres 2015). Some roosts are used to raise offspring throughout the summer, some only temporarily during the night between bouts of foraging, and still others are only occasionally used by groups of adult and volant juvenile bats (Altringham 2011). As they cannot create their own roosts, bats must select from pre-existing structures on the landscape (Altringham 2011). It is also thought that selection of high-quality roosts can enhance long-term survival of both adult bats and their offspring (Lewis 1995; Crampton and Barclay 1998; Pourshoushtari et al. 2018; Rueegger et al 2018). 11 Choice of roost is critical to adult bat survival as roosts offer protection from weather and predators, and can aid energy conservation. Bats often seek out roosts with microclimates within their thermoneutral zone (TNZ), a range of ambient temperatures under which they can maintain active body temperatures without expending energy for heat generation (Boyles 2007). Bats also use torpor, where the animal’s metabolic rate and body temperature are temporarily lowered to conserve energy and reduce water loss (Herreid and Schmidt-Nielsen 1966; Studier 1981). However, a bat can only use torpor when ambient temperatures are cooler than the bat’s thermoneutral zone, so the temperature of a day roost dictates whether a bat can use torpor. Colder roosts may increase bats’ energy expenditure as they attempt to maintain homeothermy, and hotter roosts above the upper critical limit of the TNZ may limit metabolic activity and increase the risk of dehydration (Genoud et al. 1990). Therefore, bats often select roosts that fall within their thermoneutral zone and this exact temperature range is species-dependent (Bell et al. 1986; Speakman and Thomas 2005; Wilcox and Willis 2016). During the breeding season, roost selection influences both reproductive success and offspring survival (Racey 1973; Hoying and Kunz 1998; Wilde et al. 1999). Entering torpor as an energy conservation strategy during gestation and lactation causes delays in parturition and reduces milk supply (Studier and O’Farrell 1972; Racey 1973; McNab 1982). As a result, female bats avoid deep, prolonged bouts of torpor during reproductively critical stages, which means they depend on thermally insulated roosts to conserve energy (Dzal and Brigham 2013). Low roost temperatures can cause small birth weight and delay juvenile development, which in turn can reduce survival of offspring (Hoying and Kunz 1998; Hood et al. 2002; Reiter, 2004; Zahn 2007). For example, long-tailed bat (Chalinolobus tuberculatus) colonies that occupied more 12 exposed and cooler roosts had lower reproductive success than colonies roosting in warmer, consistent temperatures (Sedgeley and O’Donnell 2004). Some North American bat species form maternity colonies, where reproductive females congregate during the summer to raise pups to take advantage of limited, appropriately insulated roosts (Altringham 2011). This colonial breeding strategy allows for many bats to use optimal roosts to enhance their reproductive success and survival while avoiding potential costs of competition from conspecifics for limited roosting resources (Kerth 2008). Roosting together also allows for mutualist interactions like social thermoregulation—where bats cluster together to minimize energy expenditure and reduce heat loss (Willis and Brigham 2007; Russo et al. 2017; Webber et al. 2018). Bats can avoid predation at emergence by exiting a roost together at the same time (Altringham 2011). Roosting together also allows for information transfer and maintenance of social relationships, which can improve foraging and reproductive success (Kerth 2008). However, communal roosting increases parasitism and disease risk, which may negatively impact offspring development and survival (Bartonička and Gaisler 2007; Reckardt and Kerth 2007). As a result, bats in maternity colonies are not restricted to a single roost, and may switch between multiple roosts throughout a single summer to offset the costs of coloniality (Whitaker 1998). For example, one little brown myotis (Myotis lucifugus) maternity colony in the forest of northern Alberta, Canada, occupied 67 roosts and, within that colony, individual bats switched roosts on average every 1.5 days (Olson 2011). Several factors affect rates of switching between roosts during the breeding season, including internal roost microclimate, ambient temperature fluctuations, and seasonal reproductive stages. For example, ambient weather patterns may impact rates of bats switching 13 roosts. Vonhof and Barclay (1996) noted that cool, wet environmental conditions may cause bats to remain in roosts, as prey may not be as abundant during these conditions. Similarly, bats are likely to leave roosts that do not provide an optimal microclimate, but bats in different reproductive stages have different physiological requirements (Turbill and Geiser 2005). For example, non-reproductive bats and post-lactating bats with volant offspring are capable of using torpor without negative fitness consequences, and can therefore exploit a wider range of roost and microclimates, than pregnant or lactating bats (Dietz and Kalko 2006; Dzal and Brigham 2013). As a result, bats are less likely to move roosts during late pregnancy and lactation and more likely to switch once pups are independent (Pretzlaff et al. 2010; Naďo et al. 2017; Ruczyński and Bartoń 2020). Roost type—natural or artificial—may also influence rates of switching between roosts in maternity colonies. Many temperate bats prefer to roost in large trees or deep crevices, which provides a buffer against fluctuations on ambient temperature; however, human activities have reduced the number of natural roosts on the landscape (Kunz 1982; Boyles 2007). Anthropogenic structures mimic many of the desirable traits of natural roosts, but may have increased thermal stability or warmer microclimates than trees or crevices (Lausen and Barclay 2006). Additionally, anthropogenic roosts like buildings or bridges offer protection from predators and space to support larger maternity colonies (Russo and Ancillotto 2015; Rueegger 2016). Therefore, maternity colony organization in artificial roosts tends to be centralized around a few roosts, with decreased rates of switching between roosts compared to natural roosts (Godhino et al. 2015; Webber et al. 2016). Artificial roosts appear at first glance to be excellent habitat for reproductive bats and may be placed deliberately by land managers to bolster bat activity or as a replacement for lost 14 roosts (Brittingham and Williams 2000; Crawford 2020). Bats in maternity colonies occupying buildings have increased reproductive success, better body condition, and faster juvenile development than do bats in natural roosts (Zahn 1999; Lausen and Barclay 2006; Allen et al. 2009). Reproductive female bats select artificial structures over less thermally stable and protected natural roosts like rocks or trees (Johnson et al. 2019). However, while natural roosting bat populations have been researched extensively (Bogan et al. 2003; Boyles 2007), only a few studies quantify the impacts of artificial roosts on bat populations. Bat boxes may be a trap for overheating, as land managers often paint these structures black and place them in exposed areas to support passive rewarming (Flaquer et al. 2014; Bidenguren et al. 2018). If bats in maternity colonies use fewer roosts and switch less frequently in artificial structures, then these populations may be less resilient in the face of roost removal (Silvis et al. 2014; Godhino 2015; Reimer 2015; Webber et al. 2016). Yuma myotis, Myotis yumanesis, and little brown myotis, M. lucifugus, are two bat species ideal for studying the effects of artificial structures as maternity roosts. Both are small insectivorous bats endemic to North America that form large maternity colonies in buildings, bridges, and bat boxes (Fenton and Bell 1979; Neilson and Fenton 1994; Duff and Morrell 2007). Both bat species are also vulnerable to white-nose syndrome, a deadly fungal disease caused by Pseudogymnoascus destructans, so conservation strategies that involve breeding habitat enhancement using artificial structures may be effective to combat population declines due to this disease (Lorch et al. 2016). To date, very little research has been devoted to understanding how little brown myotis and Yuma myotis use artificial structures and what environmental cues influence roost selection and switching. The thermoneutral zone of Yuma myotis and little brown myotis lies somewhere 15 between 35-41˚C (Licht and Leitner, 1967; Kurta et al. 1987), but this range has not been confirmed in artificial roosts. Tree-roosting populations of little brown myotis may frequently switch among over 60 roosts (Olson 2011), but to date no one has investigated if maternity colonies in artificial structures require a similar number of available roosts. Furthermore, Yuma myotis has scarcely been studied at all, so understanding this species’ habitat selection would significantly add to the conservation science of this vulnerable bat. To address the gaps in our understanding about artificial roosts, I surveyed little brown myotis and Yuma myotis maternity colonies in coastal British Columbia (BC), Canada. I developed 2 main hypotheses to investigate Yuma myotis and little brown myotis roost selection and switching in artificial roosts during the breeding season: 1) Bats in artificially-roosting maternity colonies switch between roosts to balance the tradeoffs of coloniality. 2) Bats will be more abundant in cool roosts than hot roosts, and more abundant when the difference between ambient and roost temperature is large. I expect reproductive female bats to switch roosts when the roost temperatures fall outside of their thermoneutral zone. Similarly, I expect bats will switch roosts when high humidity and high temperatures coincide, as these conditions may impede evaporative water loss and increase the risk of hyperthermia. I predict that reproductive bats will switch roosts less frequently than non-reproductive bats, who can exploit a wider range of roosts and microclimates using torpor without negatively impacting reproductive success. I also expect reproductive bats to congregate, presumably to use social thermoregulation, when roosts are cool. If thermoregulation drives colony formation, then I expect that when roosts are hot, bats will not be as abundant because social thermoregulation would not be required to maintain a stable body temperature. If suitable warm roosts are limiting, I expect bats may 16 congregate to take advantage of optimal roost microclimates that are within their thermal neutral zones, especially when ambient temperatures are well below that of the roost. 2.2 Methods I monitored 2 maternity colony sites from May to August 2019 in the lower mainland of British Columbia, Canada. At both sites, little brown myotis and Yuma myotis roosted in multiple artificial structures during the breeding season. A collection of buildings near Alice Lake in Squamish, BC, had 3 bat boxes (15.3 x 43.2 x 10.1 cm). Two boxes (Alice Lake Boxes 2 and 3) were mounted together at the end of one building, and a single box (Alice Lake Box 1) 20 m away. The boxes were installed during 2016 as a mitigation effort for maternity colonies using other buildings nearby. A predominately Yuma myotis colony used the bat boxes to raise young but bats have also been observed in the corrugated tin roof of the nearby buildings. One other maternity colony was found in the roof of the largest building, hereafter referred to as Alice Building. The second site consists of buildings and bat boxes near Stave Lake near Mission, BC. The main maternity roost was located in the boiler room a wooden building (Stave Building 1) built in the 1970’s and infrequently used by people. Bats exited from the boiler room through the siding of the western wall, underneath a wooden staircase. There were also two bat boxes (Stave Bat Box 2 and 3; 15.3 x 43.2 x 10.1 cm) on the corner of the southwestern wall that were occasionally used by a small number of bats. I km south of Stave Building 1, there was a single bat box (Stave Box 1) mounted on the outside of the southern wall of a storage building. The dates of the bat box installation are unknown, but these boxes were monitored through exit counts by the BC Bat Community Program from 2016 onwards. A third maternity site was 17 discovered 0.5 km from Stave Bat Box 1 in a private residence in July 2019, called Stave Building 2. At Alice Lake, I placed two Biomark IS1001 PIT-tag systems with bat-box antennas on the boxes—one covering the single bat box (Alice Lake Bat Box 1) and the other covering the double bat boxes (Alice Lake Bat Box 2 and 3). I installed 3 similar Biomark PIT-tag systems at Stave Lake: one cable antenna covering the wall where bats exited the boiler of the Stave Building 1, the second bat box antenna covering Stave Bat Box 1, and the third bat box antenna covering both Stave Bat Boxes 2 and 3. I installed Hobo MX2302A temperature and humidity loggers at all roosts in April 2019, recording roost conditions every 20 minutes. I installed ambient temperature loggers at both sites, but the Alice Lake ambient temperature logger failed. As a result, I used Environmental Canada’s historical climate data from the Squamish Airport weather station, approximately 140 m lower elevation than Alice Lake site (Government of Canada, 2020). During the summers of 2018 and 2019, I captured bats at both sites using mist nets and homemade harp traps. Capture events were at least 2 weeks apart to minimize disturbance to the bat colonies, and I did not trap bats during the parturition period. I trapped 3 locations at Stave Lake—Stave Building 1, Stave Bat Box 1 and Stave Building 2—over 10 capture sessions. At Alice Lake, I trapped at the bat boxes other nearby buildings with bats roosting in them over 7 capture sessions. All bat capture, handling and radiotracking conformed to the standards of the Canadian Council on Animal Care, permitted by the government of British Columbia in accordance with the Wildlife Act under Dr. Cori Lausen (Permit # MRCB15-163558) and approved by the University of British Columbia Animal Care Committee (Certificate # 718-0031). 18 My 2018 fieldwork was a pilot season; I collected roost temperature data at known roosts while captured bats were marked with split-ring lipped aluminum bands (Porzana, UK). Data from 2018 are not included in this thesis, except to note individuals that were recaptured in 2018 and 2019 and general bat behavior observed at maternity sites. During 2019, I inserted 9 mm (Biomark HPT9) RFID under the skin and between the scapulae of each healthy adult female bat; male and juvenile bats < 4.0g were banded instead. I collected information about each bat captured, including sex, mass, and forearm length. All bats were identified as M. lucifugus (MYLU), M. yumanesis (MYYU) or indeterminate (YULU) using the method proposed in Weller et al. (2007), which assigns a species identification probability based on forearm length and call frequency. Only species identification greater than 90% probability was used and all other bats, including juveniles, were assigned YULU. I also evaluated reproductive status as a binary state—presence of reproductive characteristics or not—for all adult bats and classified age—either adult or juvenile—based on whether the phalangeal epiphyses were fused (Racey 1974). I also used radio telemetry to find alternative roosts at both sites during late June and early July. Two or three bats at each site were outfitted with radio transmitters that did not exceed more than 5% of the animal’s body mass and bats were tracked for 1-2 weeks. I found one new roost at Stave Lake—a private residence, Stave Building 2, that was 500 m from the Stave Bat Box 1. I also found bats roosting in almost all buildings at the Alice Lake site. Where possible, I trapped the additional roosts to identify species and to look for previously tagged bats. 2.2.1 Data Analysis I constructed habitat selection models separately for each of the two study sites because species composition, roost type, and bat behavior were so disparate between Alice Lake and 19 Stave Lake. For each site, I constructed one model that predicted the probability that an individual bat would leave from a monitored roost (Switch Model) and another model that predicted the abundance of bats in roosts (Count Model). For the Switch Models, I estimated the effects of reproductive status, day of the year, ambient temperature, roost temperature, and roost humidity on the probability that a bat in a recorded roost would leave the following day. For roost temperature and humidity, I analyzed the maximum daily value for each measurement recorded in each roost from the previous day (when the bat is inside the box). As bat box roost conditions are highly correlated with ambient conditions, I chose the average ambient temperature during the hour before sunrise to avoid collinearity in my results, and because the decision to move to a new roost or return to the previously used roost must occur by the end of the night. This covariate was calculate on the day after the bat was originally recorded in the bat box. I constructed general additive mixed models with a binomial error distribution (logit-link function) and used a binary response variable to indicate whether an individual bat would switch from or select a monitored roost. Switch Models included individual level random effects; using R package “mgcv”, simple independent random effects can be coded using a smoother (Wood 2017). This approach is superior to using R packages ‘gamm’ or ‘gamm4’, as ‘gamm’ fits models using PQL (a poor choice for binary models) and ‘gamm4’ is not as reliable as ‘mgcv’ (Wood 2017). I selected a generalized additive model over a generalized linear model because I hypothesized that all covariates, with the exception of reproductive status, had a non-linear relationship with switching. As a result, I used the default penalized thin plate smoother to model non-linear effects for all continuous variables as smoothers allow for a range of non-parametric curves. Approximately 50% of bats at each site were not positively identified as either Yuma myotis or 20 little brown myotis and almost all bats tagged were adults, so small sample sizes precluded including species or age as covariates in any models. The temperature loggers at the Stave Bat Boxes 2 and 3 malfunctioned and, as a result, these roosts were not included in my analysis. For the Count Models, I created generalized linear mixed effect models to predict the number of bats in an artificial roost based on abiotic factors by modeling bat count data recorded daily at each monitored roost. Many daily counts at roosts were zero, and I had overdispersion in my non-zero count data, so I fitted my generalized mixed effect models with a zero inflated negative binomial (ZINB) model. All models were created using the R package ‘glmmTMB’ for generalized linear model fitting (Magnusson 2020). I tried several different combinations of covariates, including maximum daily Roost Temperature (Tr), the Difference between Tr and daily maximum Ambient Temperature (Tdiff), and Date. Both temperature variables were highly correlated with each other and thus could not be included in the same model. I also created models without a Date covariate, as I would expect bat abundance to increase throughout the summer as more tagged animals increase the population count. Therefore, I had 4 combinations of predictors for my models per site. Count Model 1 included Tdiff and Date, Count Model 2 included only Tdiff, Count Model 3 included Tr and Date, and Count Model 4 included Tr. Where appropriate, I also investigated the impacts of roost type on the Stave Count models by creating submodels of individual roosts. All analyses were performed using R software, version 4.1.0 (R Core Team 2020). Packages ‘ggplot2‘, ‘effects’ and ‘visreg’ were used to plot model effects (Wickam 2005; Breheny 2020; Fox 2020). 21 2.3 Results I captured 1107 bats from May to August 2019, 622 at Stave Lake and 485 at Alice Lake. Of the bats I captured, 75.7% were adult females, of which 70.2% were reproductive. Species composition differed between sites; 63.2 % and 15.4% bats captured at Alice Lake were Yuma myotis and little brown myotis respectively, while Stave Lake was 22.3 %. Yuma myotis and 38.7% little brown myotis (the remainder of bats were not positively identified as either species). The Stave Lake PIT-tag systems ran for 72 days, during which I recorded 342 individual bats and 4636 day-roost observations. A single day-roost observation is one diurnal day where a bat was recorded in a roost. I recorded 3274 day-roost observation from 242 individual bats over 89 days at Alice Lake. Of the total tagged bats, 18.0% of the Stave Lake bats and 13.4% of the Alice Lake bats were recorded on a daily basis. Approximately 20-25% of all PIT-tagged bats (163 at Stave Lake and 99 at Alice Lake) were never recorded again after their initial capture. The majority of these missing bats were captured early in the season or at roosts that did not have PIT-tag scanners attached to monitor bat behavior. 2.3.1 Roost Use Results Tagged bats at both the Alice Lake and Stave Lake maternity colonies used multiple roosts throughout the season. At Stave Lake, I discovered one additional maternity roost (Stave Building 2) and 2 non-maternity day roosts in nearby buildings. The Stave Bat Boxes 2 and 3 were night roosts, temporary roosts for bats before they moved to the Stave Building 1 to day roost. At the Alice Lake site, I observed bats day roosting in almost all corrugated-tin roofed buildings, in addition to the monitored bat boxes. While some of the roof roosts were inconsistently occupied and non-maternity day roosts, Alice Building was a maternity roost. Bats roosted in two distinct areas of the roof of Alice Lake Building from their discovery in July until 22 the end of the summer. Colony exit surveys confirmed that one group of ~150 adults occupied the western end of the building. Another ~200 adults occupied the eastern end of the building. Throughout the summer, bats switched between monitored and unmonitored roosts. At Alice Lake, bats switched to a new roost every 1.4 ± 0.6 (mean ± SD) days, compared to Stave Lake where they switched every 2.2 ± 1.6 days. The average bat had a 78.2 ± 21.6% daily probability of roost switching at Alice Lake, compared to 61.9 ± 28.8% at Stave Lake. For most days during summer 2019, monitored bat roosts were warmer during the day than ambient temperatures (Figure 2.3). At Stave Lake, maximum daily mean ambient temperature (20.5 ± 3.5˚C) was colder than either the Stave Building 1 (28.1 ± 1.2˚C) or Stave Bat Box 1 (30.2 ± 6.5 ˚C) (Figure 2.2). The Stave Building 1 had minimal fluctuations in daily roost temperature (28.1 ± 1.2 ˚C) or humidity (87.7 ± 6.5%) while the Stave Bat Box 1 conditions reflected changes in ambient conditions (Figure 2.5). The Stave Building 1, on average, had less humid conditions than ambient (Stave Building 1 maximum daily mean humidity 42.9 ± 1.2%; Stave Lake ambient maximum mean humidity 93.6 ± 3.5%). At Alice Lake, Bat Box 1 was much warmer than any other roost (daily mean maximum temperature 34.3 ± 10.0˚C), followed by the Alice Building roost (daily mean maximum temperature 34.9 ± 6.3˚C) The Alice Bat Box 2 and 3 had almost identical microclimates to each other (daily mean maximum temperature west box 26.0 ± 6.3˚C, east box 25.8 ± 6.7˚C) but bats preferred to roost almost exclusively in Alice Bat Box 2 after mid-May 2019. The humidity in the Alice Building (mean daily maximum humidity 57.8 ± 10.0%) roof was much lower than ambient conditions (mean daily maximum humidity 72.7 ± 11.5%) (Figure 2.2). The stark difference in humidity readings between buildings and bat boxes may be due to the proximity of 23 bats to the data logger sensors; the sensors were close to respirating bats in the bat boxes, while the sensors in buildings were located at least a meter away from bats. 2.3.2 Model Results The Stave Lake Switch Probability model (Figure 2.4) explained 17.5% of deviance, and included non-linear effects of date, sunrise ambient temperature, maximum roost temperature, and maximum roost humidity. Bats were more likely to switch when roost temperatures were below 25˚C, and slightly less likely to switch as ambient temperatures increased (Figure 2.4). Bats were less likely to switch roosts during lactation, June and July, and most likely to switch during post-lactation in August. Non-reproductive bats did not switch roosts more often than their reproductive counterparts. The Alice Lake Switch Probability model (Figure 2.5) explained 18.6% of deviance and included nonlinear effects of maximum roost temperature and humidity, in addition to ambient temperature and date. The model predicted an almost quadratic relationship for roost temperature, with bats more likely to switch in cool (<30˚C) or hot (>43˚C) conditions, but less likely to switch between these extremes. Bats switched the most during early May, early June, and late August. Reproductive status and ambient temperature at sunrise were not significant predictors of switching (p = 0.98 and p=0.33, respectively). While roost humidity was a significant predictor of individual bat switching behavior at both Stave and Alice Lake, an interaction between roost temperature and roost humidity did not exist for either site; switch Models that included this interaction failed to converge. When the number of tagged bats in the roost exceeded 20 individuals, the humidity percentage was almost always 100% (Figure 2.10). 24 At Stave Lake, all Count Models had at least one significant covariate but large 95% confidence intervals (Table 2.3; Figure 2.7). For example, Count Model 1 predicted that Tdiff had a weakly significant (p = 0.06), positive effect on bat abundance while Date had a significant (p <0.01) positive relationship; however, the effect size of Count Model 1 was small (about 15 bats). Count Model 2 predicted that Tdiff had a significant (p= 0.02) positive relationship with bat abundance, but a similarly small effect size. Count Model 3 also predicted a significant (p= <0.01) positive Date effect, although Tr was not significant (p= 0.12), and Count Model 4 found Tr had a significant (p <0.01) negative effect on bat abundance. The large model confidence intervals at Stave Lake may reflect disparate bat behavior observed between Stave Building 1 and Stave Bat Box 1. When data were analyzed in individual roosts submodels (Figure 2.8; Table 2.5) using the Count 4 model formula (Tr), bats were more likely to roost in Stave Bat Box 1 when roost temperature was warm (<40˚C), although the effect size was small (18 bats). However, Stave Building 1 sub model showed the opposite effect—bats roosted there when temperatures were cool (≤27˚C) and did not roost there when roost temperature was warm (≥31˚C). It is important to note the range of temperatures predicted by the Count Model 4 (Tr) is limited to only 6˚C. Temperatures in Stave Building 1 increased incrementally as the summer continued (Figure 2.9). The lowest Stave Building 1 Tr occurred in May and June, when bats were pregnant and most likely to roost together. The highest Tr occurred in August, when bats would be post lactating and less likely to roost in larger groups. Although Alice Lake Count Models (Figure 2.7) had more reasonable confidence intervals and predicted significant relationships between covariates and bat abundance (Table 2.4), the effect sizes of significant covariates in all models were small (10-25 bats). Date had a 25 significant (p <0.01) positive effect on bat abundance in Count Model 1, although Tdiff was not significant (p=0.37). Count Model 2, on the other hand, predicted that bat abundance would decrease as Tdiff shrank (p=0.02). Count Model 3 predicted a small, positive relationship with bat abundance for both Date and Tr (p <0.01 for both). Like Count Model 3, Count model 4 also predicted a slight positive relationship between Tr and bat abundance (p=0.02). 2.4 Discussion I found support for my first hypothesis that bats use and switch between multiple artificial roosts to maximize the benefits of coloniality, like social thermoregulation and optimal roost microclimate. Specifically, I observed that bats were less likely to switch between roosts during lactation, when coloniality was most beneficial, and more likely to switch after the pups were volant, when other energy saving tactics were available. Bats preferred specific roost temperatures (30-42˚C) and were more likely to leave when roost temperatures fell outside of that range. However, I found limited support for my second hypothesis that little brown myotis and Yuma myotis in maternity colonies cluster when roosts are colder or when Tdiff is large. Further, non-reproductive bats were not more likely to switch roosts than reproductive bats. Humidity was likely an indicator of occupancy in bat boxes, as many bats breathing in a small space raised the humidity to 100%. Little brown myotis and Yuma myotis in human-made structures switched between multiple roosts throughout the maternity season. I observed that bats switched on average every 1.4 days at Alice Lake and 2.2 days at Stave Lake, and bats at both sites used at least 5 roosts. My results, in comparison with other similar studies, does suggest that bats in artificial structures use fewer roosts and switch less frequently than do colonies relying on natural structures. For example, Olson (2011) found that reproductive little brown myotis in forests used as least 60 26 roosts and switched every 1.5 days. Similarly, Reimer (2015) found that artificially roosting little brown myotis used 1.2 roosts per tracked individual, while tree-roosting bats used 2.4 roosts per tracked individual. While I was not able to identify every roost used by the bat colonies at each site, it is clear that these animals do not statically reside in a single maternity roost, but instead switch between a number of different nursery roosts throughout the summer. It is important to note that some roosts were not used by bats as maternity roosts to rear pups. While the more consistently occupied roosts—the Stave Building 1 and the Alice Bat Box 2—did have pups, the other roosts were used more often as temporary day roosts or night roosts while foraging. I also observed other short term roosts at both Alice and Stave Lake, where bats would day roost for short periods in the roofs of buildings, primarily at the beginning and the end of the summer. Short term roost use suggests that breeding bats use artificial structures as roosts for more than just nursery roosts. I also observed several events when many bats moved from one occupied roost to another previously unoccupied roost. For example, at least 30 bats switched from the Alice Bat Box 2 to Alice Bat Box 1 during August 7th to 15th, similar to the exodus of bats from the Stave Building 1 to Stave Bat Box 1 at Stave Lake in both timing and numbers. While these en masse movements between roosts may be a reaction to human-related disturbance, I observed that these en masse movements also occurred at times where we were not onsite; however, I was unable to document any potential disturbance that might have occurred during my absence. Furthermore, I observed the same pattern of en masse switching during August 2018 at both sites, suggesting that this behavior may be seasonal. Large number of bats moving together may reflect an effort to maintain the benefits of social thermoregulation, which is vital to reproductive bats’ survival and reproductive success (Trune and Slobodchikoff 1978; Willis and 27 Brigham 2007). However, the late summer timing occurs when pups are volant and thus less reliant on social thermoregulation for growth. It also could be that bats form tight social bonds with certain individuals and selected roosts where preferred conspecifics reside; patterns of roost selection, therefore, may reflect the social organization of a colony consisting of smaller subunits of preferred associates (Chapter 3, Godhino et al. 2020). Additional research is required to establish whether these massive movements between roosts reflect annual patterns of behavior and investigate other factors that may influence bat behavior. My research suggests that bat abundance is related to more than just the roost temperature. While most of my Count models had significant covariates, the effect size was often minimal. In the case of Alice Lake Count Models, bat abundance only increased marginally as temperature, Tdiff , or Date increased (Figure 2.8). It could be that the increase in bat abundance could be related to additional trapping efforts artificially inflating the abundance in bat boxes, or the arrival of new bats from outside the maternity colony as will be discussed in Chapter 3. At Stave Lake, the confidence intervals were larger than would be considered credible for all models, likely reflecting the disparate behavior between the two colonies: bats occupied Stave Building 1 consistently for most of the summer, while bats used Stave Bat Box 1 only briefly at the end of the season (Figure 2.1). Roost temperature at Stave Bat Box 1, like Alice Lake boxes, also has small explanatory power for bat abundance (Figure 2.8). The Stave Building 1 had a minimal change in temperature (6˚C for the entire summer) so the variation in bat abundance is likely related to changes in reproductive behavior that happened to correspond with seasonal temperature changes (Figure 2.9). For example, lower roost temperatures in June correspond to when bats tend to cluster during pregnancy. 28 Seasonal changes associated with reproductive period—pregnancy, lactation and post-lactation—clearly influenced whether a bat switched from a roost. Bats in maternity colonies had the lowest probability of leaving a monitored roost during lactation. The lactation period is energetically expensive to bats, so both adults and juveniles may benefit from clustering for social thermoregulation and energy conservation (Studier and O’Farrell 1972; Kurta and Kunz 1987; Kurta et al.1989). Considering that bat pups are born at up to a third of their mother’s mass, additional post-natal mass gain would make moving pups difficult (Altringham 2011) I observed increases in the probability that a bat would switch roosts after pup volancy at both sites. Little brown myotis use torpor more often during post-lactation than pregnancy or lactation, so are less reliant on warm roosts or conspecifics (Bartonička and Gaisler 2007; Dzal and Brigham 2013). Roosting solitarily or in smaller groups during post-lactation allows for increased use of torpor to aid fat accumulation, and therefore winter survival, by decreasing the costs of body heating (Dietz and Kalko 2006; Pretzlaff et al. 2010; McGuire et al. 2014) Despite the extra energetic demands, reproductive bats were not less likely to switch from roosts than were non-reproductive bats at either site. This outcome may be the result of sampling bias, as approximately 70% of adult bats at Alice and Stave Lake were reproductive in 2019. It is possible that non-reproductive bats may choose to roost apart from the colony entirely, and thus were not recorded. It is also possible that the non-reproductive female bats in the colony are last year’s young returning to their natal roosts. Bats reach sexual maturity within 1-2 years of their birth; however, the exact age of female sexual maturity in little brown myotis and Yuma myotis has yet to be determined (Entwhistle et al. 2006; Altringham 2011). Recapture in 2019 of 4 male and 4 female adult bats banded as juveniles in 2018 suggests that last year’s young may return to 29 roost with their mothers. Further research about matrilineal influences on roost selection and fidelity in Yuma myotis and little brown myotis colonies would be needed to test this hypothesis. My results indicate that bats at Alice Lake were more likely to stay in a monitored roost between 35-42˚C, while bats at Stave Lake preferred 28-42˚C. This wide thermal range was surprising, particularly because temperatures of 44 ˚C and above are thought to be lethal (Licht and Lietner 1967). Either Yuma myotis and little brown myotis have a greater thermoneutral zone than expected or, more likely, they altered their behavior to thermoregulate in a wider range of temperatures. Many bats roosting together can raise the temperature of a roost (Trune and Slobodchikoff 1978; Burnett and August 1981). Bats can also move around in a roost, particularly in larger roosts like buildings, to seek a better microclimate (Brittingham and Williams 2000). Bat boxes and buildings may conduct enough airflow through vents or windows to allow bats to avoid hyperthermia through behavioral thermoregulation, such as panting or wing spreading or urinating on themselves. My selection of the maximum daily roost temperature of a covariate may also artificially inflate the presumed TNZ of myotis, as I did not quantify how long a roost remained at or above potentially lethal temperatures. I suspect, given the lack of deaths due to overheating, that it was a short period of time. While both Switch Models indicated that humidity influenced whether a bat switched from a roost, these results likely do not have biological significance (Figure 2.4 and 2.6). The Stave Lake humidity percentage data were divided by site—Stave Building 1 had low humidity compared to the Stave Bat Box 1—and the difference between the two roosts is reflected in the bimodal curve of switch probability. The Alice Lake Switch model is not well supported under 80% humidity and the likelihood of switching above 80% humidity fluctuated inconsistently. 30 Instead, it appears that the number of bats in a bat box influences the roost humidity percentage—100% humidity was observed when the number of tagged bats exceeded 20 in a single box (Figure 2.10). It is likely that there were untagged bats in the bat boxes so additional investigation would be needed to confirm the relationship between occupancy and humidity percentages. Humidity in a bat box may increase as bats cluster due to respiration, but humidity alone is an inconsistent predictor of bat occupancy in bat boxes (Bartonička and Řehák 2007). I expected an interaction between roost humidity and temperature to affect switching, but neither Switch model supported this prediction. My results indicate that an interaction between heat and humidity did not exist at my sites. However, I expect that maternity colonies in bat boxes with constant sunlight exposure may be at risk of heat stress due to high humidity and high heat in combination, because high humidity inhibits evaporative cooling, and may increase the risk of hyperthermia for bats as has been observed in avian species (Lasiewski et al. 1966; Webster and King 1987; Powers 1992). Roost conditions that regularly exceed 40˚C in poorly ventilated boxes have been cited as a reason for mass mortalities of bats (Flaquer et al. 2014). Additional research is required to investigate if densely occupied bat boxes, particularly during the vulnerable lactation periods, represent a serious threat to the reproductive success and survival of bats in artificial roosts. Based on my research, I recommend installing multiple roosts for a maternity colony to provide for enough roosting habitat to support all stages of reproduction. My results suggest that Yuma myotis and little brown myotis require at least 5 roosts to support a large colony (>200 bats). Single or space-limited artificial roosts may not be enough to support large maternity colonies, who use roosts for reasons other than just rearing pups. 31 It is also important that land managers include a wide array of artificial roost microclimates, in addition to multiple roosts. I also suggest that the maximum internal temperatures of the bat boxes should be between 30 and 42˚C during the day, as reproductive bats preferred to stay in boxes that achieved those temperatures daily. Roosts that fall consistently below these ranges may not be suitable for reproductive bats and their offspring during the summer; cooler roost temperatures may inhibit fetal growth, milk production and pup growth (Studier and O’Farrell, 1972; Racey 1973; McNab 1982). At the end of the season, pups are fully grown and all bats are likely to use torpor to accumulate fat prior to hibernation. Cooler roosts will thus be important at the end of the season to facilitate torpor. The inclusion of many roosts with a range of microclimates can provide long term roosting habitat in the event of environmental shifts, like climate change or growing vegetation that blocks sunlight, that may change the intended microclimates. In conclusion, my research establishes that Yuma myotis and little brown myotis have complex patterns of habitat use during the breeding season that should be carefully considered. Conservationists can bolster bat breeding populations by providing a dynamic landscape of roosting habitat to support offspring development and survival. 32 Figure 2.1: Daily number of bats in each PIT-tag monitored roost at: A) Stave Lake and B) Alice Lake. Roost monitoring ended at Stave Lake on August 15th, when the Stave Building 1 PIT-tag reader failed; a similar system malfunction occurred at the same roost on July 18th, which accounts for the lack of bats recorded on that date. A) Stave Lake B) Alice Lake 33 Figure 2.2: Daily maximum temperature (A) and humidity percentage (B) at all monitored roosts during the summer of 2019. Stave Lake roosts are grey, Alice Lake Roosts are yellow. The vertical lines represent the top and bottom quartiles, the shaded boxes represent the second and third quartiles, and the black horizontal lines represent the mean values per roost. Outliers are represented by dots. 1020304050Alice Bat Box 1Alice Bat Box 2Alice Bat Box 3Alice BuildingStave Building 1Stave Bat Box 1RoostMaximum Daily Temperature ??C Per Roost406080100Alice Bat Box 1Alice Bat Box 2Alice Bat Box 3Alice BuildingStave Building 1Stave Bat Box 1RoostMean Maximum Daily Humidity % Per RoostA B 34 Figure 2.3: Seasonal temperature (˚C) patterns at Stave Lake (A) and Alice Lake (B) by roost. Temperatures were measured every 20 minutes by all data loggers. Roof refers to the Alice Building at Alice Lake, where two groups of bats roosted under the corrugated tin roofing. Conditions were recorded starting July 12th, when the bats roosting in the roof were discovered. A) Stave Lake B) Alice Lake 35 Table 2.1: Effects of Switch models at both Alice and Stave Lake on the probability a bat will leave a roost. Wald Type II Chi-Square tests were used to infer significance from general additive mixed effect models. Reproductive status was a categorical term, covering two states (reproductive or not), while all other terms were continuous. Ambient temperature was measured as the daily average temperature in the hour before sunrise, while roost temperature and humidity were the maximum values in the roost from the previous day. Significant effects are bolded. Covariate DF Chi-Square p Stave Lake Max Roost Temperature (°C) 5 17.9 <0.01 Max Roost Humidity % 6 41.3 <0.01 Ambient Temperature (°C) 3 16.7 <0.01 Reproductive Status 1 1.8 0.40 Date 6 84.6 <0.01 Alice Lake Max Roost Temperature (°C) 6 50.8 <0.01 Max Roost Humidity % 8 23.4 0.03 Ambient Temperature (°C) 6 38.4 0.38 Reproductive Status 2 0.1 0.98 Date 9 110.2 <0.01 36 Table 2.2: Effects of Count Models at Stave Lake on bat abundance. Wald Type II Chi-Square tests were used to infer significance from zero inflated negative binomial models. Tdiff represents the difference between daily maximum roost and ambient temperature (˚C) and Tr represents the daily maximum temperature (˚C). Significant effects are bolded. Covariate DF Chi-Square p Count 1 Tdiff (˚C) 1 3.59 0.06 Date 1 18.37 <0.01 Count 2 Tdiff (˚C) 1 5.18 0.02 Count 3 Tr (˚C) 1 2.39 0.12 Date 1 16.54 <0.01 Count 4 Tr (˚C) 1 134.08 0.04 Stave Building 1 Tr 1 76.30 <0.01 Stave Bat Box 1 Tr 1 5.05 0.02 37 Table 2.3: Effects of Count Models at Alice Lake. Wald Type II Chi-Square tests were used for inference from zero inflated negative binomial models. Tr was calculated using the daily maximum temperature (˚C) and Tdiff was the difference between maximum daily roost and ambient temperature (˚C). Significant effects are bolded. Covariate DF Chi-Square p Count 1 Tdiff 1 0.79 0.37 Date 1 34.57 <0.01 Count 2 Tdiff 1 5.58 0.02 Count 3 Tr 1 9.00 <0.01 Date 1 38.22 <0.01 Count 4 Tr 1 5.34 0.02 38 Figure 2.4: The probability of a bat switching roosts at Stave Lake. Panels show the effects of A) maximum daily roost temperature (˚C), B) maximum daily humidity percentage, C) daily ambient temperature at sunrise and D) seasonal (date) variation. Black lines show the mean, with shading denoting the 95% confidence intervals. The original binary response data are shown at the top (switch) and bottom (stay) of each panel. Blue lines and letters delineate reproductive periods: pregnancy, lactation and post-lactation. 0.000.250.500.751.0020 25 30 35 40Max Roost Temperature (°C)0.000.250.500.751.0040 60 80 100Max Roost Humidity %0.000.250.500.751.0010 12 14 16 18Ambient Temperature at Sunrise (°C)0.000.250.500.751.00June July AugustDay of the Year (2019)Probability of SwitchingA B C D P L PLL 39 Figure 2.5: The probability of bats switching roosts at Alice Lake. Panels show the effects of the significant covariates in the model including A) maximum daily roost temperature, B) maximum daily humidity percentage, and C) seasonal variation (Date). Black lines show the mean, with shading denoting the 95% confidence intervals. Blue lines and letters delineate reproductive periods: pregnancy, lactation, post-lactation. The tick marks at the top and bottom of the panels represent the distribution of the original binary response data. 0.250.500.751.0020 30 40 50Max Roost Temperature (°C)80 90 100Max Roost Humidity %0.000.250.500.751.00May June July July August SeptemberDay of the Year (2019)Probability of SwitchingC B A P L PL 40 Figure 2.6: Significant Alice Lake Count Model effects. Covariates include maximum daily Roost Temp (Tr), Difference between maximum daily roost temperature and maximum daily ambient temperature (Tdiff) and Date on bat abundance. Panels A and B show effects for Count Model 1 (Tdiff + Date); Panels C and D show predictions for Count Model 3 (Tr + Date). Panel E shows effects of Model 2 (Tdiff) and Panel F shows effects of Model 4 (Tr). All effects are significant, with the exception of Panel A. Black lines show the mean, with shading denoting the 95% confidence intervals while the tick marks delineate original dataset distribution. 41 Figure 2.7: Significant Stave Lake Count Model predictions. Covariates include maximum daily Roost Temp (TR), Difference between maximum daily roost temperature and maximum daily ambient temperature (Tdiff) and Date on bat abundance. Panels A and B show effects for Count Model 1 (Tdiff + Date); Panels C show predictions Date covariate for Count Model 3 (Tr + Date); Tr was not significant and not pictured. Panel D shows effects of Model 2 (Tdiff) and Panel E shows effects of Model 4 (Tr). Tick marks show original dataset distribution while the black line denotes mean and shading denotes 95% confidence intervals 42 Figure 2.8: Bat abundance at individual roosts at Stave Lake for Count Model 4 (Tr). Panels show predictions of roost temperature (˚C) at Stave Building 1 (A) and Stave Bat Box 1 (B). Black lines show the mean and grey shading shows the 95% confidence intervals. Tick marks at the top and bottom of the panels represent distribution of original dataset. A B 43 Figure 2.9: Maximum Daily Roost Temperature (˚C) at Stave Building 1 for the summer 2019. 44 Figure 2.10: Daily relative humidity percentages at monitored bat boxes at both Alice Lake and Stave Lake. Panels are A) Stave Bat Box 1 B) Alice Bat Box 1 and C) Alice Bat Box 2. The x-axis refers to the number of tagged bats occupying the bat box and y-axis is the maximum daily humidity reading from that box. B A C 45 Chapter 3: Social Organization of Myotis in Maternity Roosts 3.1 Background Many animals form groups with conspecifics throughout their lives or during key life history stages (Krause et al. 2002). Systematic group organization arises when the benefits of close associations with conspecifics—such as access to limited resources, defense from predation, or participation in other mutualistic and cooperative behavior—exceed the costs (Van der Post et al. 2015; Grueter et al. 2020). Cooperative living during breeding seasons can influence survival and reproduction (Silk 2007; Staton and Mann 2012). Reproductive success in females may be influenced by individual social bonds within a group, or by conspecifics characteristics or genetic relatedness (Pusenius et al. 1998; Armitage and Schwartz 2000; McComb et al. 2001; Silk et al. 2003). Therefore, studying the underlying structure of animal societies may offer a better understanding of influences on reproductive outcomes. Social network analysis provides powerful tools to assess individual preferences within a group and evaluate group social behavior as a whole. Most animal interactions are defined using “Gambit of the Group” theory, where in the absence of observed behavior indicating a relationship, it is assumed that animals are interacting with proximal neighbors (Whitehead and Dufault 1999; Franks et al. 2010). Animal relationships are often characterized using association indices, which calculate the estimated proportion of time a dyad spends together (Cairns and Schwager 1987; Johnson et al. 2013). The strength of associations between pairs is used to map of the group’s relationships (Farine and Whitehead 2015). The social network can then be probed for patterns in individual and group behavior using social network metrics (Croft et al. 2011). The metrics can predict or explain how groups of animals react to behavioral or environmental triggers. For example, the measure of connectivity between individuals, known as network density, often affects the rate at which information or disease spreads throughout a 46 group (Otte and Rousseau 2002; Krause et al. 2007; Vital and Martins 2009). Similarly, identification and monitoring of persistent substructures within networks, called modularity, can provide clues to larger trends, such as population decline or fragmentation (Otte and Rousseau 2002; Parsons et al. 2009). Most networks have highly connected individuals, measured by degree centrality, and whose removal can disrupt the entire social structure (Krause et al. 2007; Kasper and Voelkl 2009). Bats are excellent candidates for social network analysis—they are long-lived and gregarious animals, and in many species, individuals live communally for part or all of their lives (Altringham 2011). Temperate bats congregate in reproductive and female-dominated groups called maternity colonies to raise offspring in the same roost during the summer (Hill and Smith 1984). Bats in maternity colonies display annual fidelity to long term structures, like trees and buildings, and some colonies contain 1000 bats or more using 50 or more roosts throughout the reproductive season (Lewis 1995; Willis and Brigham 2004; Zeale et al. 2016). As a result, female bats residing together in maternity colonies can form strong social connections with other individuals that may persist for years (Patriquin et al. 2010; Kerth et al. 2011; Zues et al. 2019) Maternity colony organization is tied to reproductive status (Patriquin et al. 2010; Ruczyński and Bartoń 2019). Group size typically peaks around parturition, when the unfurred pups and energetically-stressed mothers are most dependent on roost microclimates and social thermoregulation for survival (Trune and Slobodchikoff 1978; Sedgley et al. 2001; Ruczyński and Bartoń 2019). As a result, strong social bonds during pregnancy and lactation, which allows for mutual warming and decreases competition for high-quality roosts, may enhance survival (Kerth 2008; Willis and Brigham 2004; Patriquin et al. 2010; Nad’o et al. 2017). Patterns of frequent associations between specific dyads during pregnancy and lactation can persist for years 47 and withstand major colony disruptions (Kerth et al. 2011; Baigger et al. 2013; Zues et al. 2019). Colonies typically disperse and association patterns of individuals break down once the pups are volant and capable of foraging because coloniality offers fewer benefits over the greater costs (Bartonička and Gaisler 2007; Reckardt and Kerth 2007). It is important to include a temporal component to any maternity colony social network analysis to encompass these changes in breeding behavior. When compared to natural roosting populations, maternity colonies in artificial structures are more vulnerable to disturbance from disease and roost removal. For example, Webber et al. (2016) found less centralized social patterns in tree-roosting big brown bats (Eptesicus fuscus) compared to populations that used buildings as roosts. Buildings tend to be warm and stable, with greater choice of intra-roost microclimates, so roost switching occurs less frequently (Webber et al. 2016). Maternity colonies are vulnerable to infection from disease and parasites because of the close and prolonged proximity of bats within a single roost (Luis et al. 2015; Sah et al. 2017). For example, social organizations of Indiana bats (Myotis sodalis) roosting in trees are robust enough to maintain cohesion despite roost removal because the colony is spread throughout 20+ roosts but the same might not be true in artificial roosting colonies occupying only a handful of roosts (Silvis et al. 2015). Due to expanding anthropogenic development, available tree roosts are shrinking while artificial structures become abundant on the landscape so understanding how bats use human-made structures is imperative to conserving these bat populations. Yuma myotis, Myotis yumanensis, and little brown myotis, M. lucifugus, form maternity colonies during the breeding season in North America (Neilson and Fenton 1994). These two bat species are remarkably similar in life history and appearance—both are small insectivorous bats 48 that forage around water and each female produces one pup per year on average (Fenton and Bell 1979; Duff and Morrell 2007). Breeding female Yuma myotis and little brown myotis frequently select artificial structures for maternity colony roosts (Nagorsen and Brigham 1993; Mering and Chambers 2014). Bats of both species may roost together in the same maternity roost; therefore, heterospecific and conspecific social dynamics, observed in other colonial bat species, may also exist between Yuma myotis and little brown myotis (Zues et al. 2017). Applying social network theory to bats is rife with problems not present in other study systems focused on large, easily observed animals. Bat social interactions–for example, preferred relationships, grooming, information transfer and mutual warming–are restricted to other bats within the same roost (Altringham et al. 2005; Johnson et al. 2013). Applying Gambit of the Group theory, therefore, requires tracking bat roosting sites as well. Bats are cryptic and their roosts are often difficult to detect even with advanced technology, requiring a large investment of time and energy to obtain daily roosting location and dyadic associations of even small (<30 bats) colonies. Using social network analysis to investigate large Yuma myotis and little brown myotis maternity colony organization presents additional challenges. The ideal social network analysis requires perfect identification of all group-members at many roosting locations to determine relationships over time and space (Whitehead 2018). Silk et al. (2015) estimates that a subgroup of 30% of a group should be tracked daily for accurate social network description. However, tracking even 30% of a colony of 300 bats is a difficult proposition and will still inevitably result in an imperfect analysis; while methods exist to correct for missing animals or imperfect identification, these solutions are largely arbitrary (Ginsberg and Young 1992). It is still possible to use the tenets of social network analysis to study large colonies of little brown myotis and 49 Yuma myotis by focusing on how bats in a large colony use a few key roosting features. While this approach will not yield a perfect representation of colony relationships, social network analysis can provide details of colony organization and temporal patterns at specific habitat sites—all of which can be critical information for understanding how bat populations use artificial structures. To date, very little research has been devoted to understanding maternity colony organization of little brown myotis and Yuma myotis. By studying bat maternity roosts using social network analysis, I can quantify how individuals and groups of bats within a colony use artificial roosts throughout the summer. This information about bat behavior can inform land managers about how to design bat management and mitigation strategies to address disease, such as artificial roost alterations and eviction guidance. My research represents the first exploration of social structure of little brown and Yuma myotis breeding populations in British Columbia (BC). In addition, I provide evidence that social network analysis techniques can be effective for studying large populations of bats by monitoring several key roosting structures. I studied individual associations of little brown and Yuma bats within maternity colonies in lower mainland BC, Canada, to: 1. determine if bats preferentially associate with each other more than expected if associations are random; 2. determine if species identity influences association patterns; 3. describe the social network structure of the maternity colony and how it changes throughout the reproductive period; 4. identify and describe influential bats within the network. 50 3.2 Methods In 2018 and 2019 I monitored 2 maternity colony sites in the lower British Columbia mainland—both located in human-built structures and in less urbanized areas near the Metro Vancouver. The first colony was located in a collection of buildings near Alice Lake in Squamish, BC. This area is primarily forested and is close to Howe Sound and the Squamish River. There is a fence around several buildings and lean-tos with corrugated tin roofing. A predominantly Yuma myotis maternity colony resides in roofs of the buildings and three bat boxes (15.2 x 43.2 x 10.2 cm; 4 chambers each) attached to two structures inside the fenced area. The three bat boxes are located in the southwest corner of the compound: two are along the south fence line (Alice Bat Box 2 and 3) and the other is along the west fence line (Alice Bat Box 1). All bat boxes were installed in 2016. My second site was located near Stave Lake, Mission, BC. This area is mostly forested or rural residential area. The bats primarily use the Stave Building 1 in the basement of the 50-year-old wooden building. Bats also roost in two bat boxes placed on the Building’s southwest corner (15.2 x 43.2 x 10.2 cm; 4 chambers each). There is a third occupied bat box of the same size and style on the west-facing outside wall of a storage building ~1 km from the Stave Building 1, and referred to hereafter as the Stave Bat Box 1. The date of installation of the bat boxes is unknown, but all 3 roosts have been monitored since 2018. I monitored both colonies using exit counts, where bats emerging from roosts were tallied. I performed 8 exit counts at 3-5 roosts at Alice Lake and 6 at 1-4 roosts Stave Lake, spread throughout the summer. Individual roost counts varied seasonally as bats moved around and pups began to fly, but based on counts performed before the juveniles were volant, 51 approximate colony counts ranged from 600-900 adult bats at Stave Lake and 400-600 adult bats at Alice Lake. I captured bats at both sites during the summers of 2018 and 2019. I targeted bats roosting in artificial structures. I used mist nets and homemade harp traps. I reduced trapping during parturition to avoid undue stress to bats during this critical time. I waited 2 weeks between repeat capture events to avoid the potential risk of roost abandonment. I implanted passive RFID chips (Biomark, Boise, ID; HPT8 and HPT9) under the skin between the scapulae of healthy, adult females who weighed >4.0 g and sealed the incision sites with Vetbond. I banded adult females, males, and juveniles weighing 3.5 – 4.0 g with aluminum bands engraved with a unique identifier. I collected mass and forearm measurements for species identification and body condition. All bat capture, handling and radiotracking conformed to the standards of the Canadian Council on Animal Care, permitted by the government of British Columbia in accordance with the Wildlife Act under Dr. Cori Lausen (Permit # MRCB15-163558) and approved by the University of British Columbia Animal Care Committee (Certificate # 718-0031). Following the recommendations of Weller et al. (2007), I classified bats to species based on forearm length and acoustic call frequencies. Bat species identity with probability ≥ 90% were used and all others were assigned the ambiguous identity of YULU—either Yuma or little brown myotis. All juveniles were assigned YULU. Reproductive females were classified by palpating the abdomen and examining teats, and assigned as pregnant (P), lactating (LAC), or post-lactating (PL) (Kunz and Parsons 1988). Females with some hair missing from around the nipples but no indication of recent nursing were classified as parous (PAR) meaning they had reproduced in the past, but did not have a pup in 52 the current summer. If phalangeal epiphyses of a bat were not yet fused together, then the bat was classed as juvenile (Racey 1974). All juvenile females were classed as nulliparous (NULL) and all juvenile males were classed as non-reproductive (NR). Adult females with difficult-to-find nipples (no worn hair around them) were classed as nulliparous, indicating they had never reproduced despite being mature. During June 2019, I installed Biomark PIT-tag readers to monitor bats tagged with RFID chips and recorded bat roosting behavior until late August 2019. I placed two PIT-tag systems at Alice Lake: one system monitored Alice Bat Box 1, and the other monitored Alice Bat Box 2 and 3 so that any bats entering either box would be recorded. Bats only used Alice Bat Box 2 after installation. I placed three PIT-tag systems at 4 known roosts at Stave Lake: one system at the exit to the Stave Building 1, one system to cover both bat boxes on the building (Stave Bat Boxes 2 and 3), and the final system for the bat box at the Stave Bat Box 1. With the exception of the Stave Building 1 exit, which used a 25 ft cable antenna powered using RV batteries, all PIT-tag systems used a single or double bat bar antenna to read RFID chips in bats approaching within 3 inches of the antenna as they exited or entered the roost. While bats continued to use artificial structures at both sites after September 1st, I did not include data beyond this date. I installed HOBO MX2302A microclimate dataloggers to monitor roost conditions, specifically temperature and humidity, at all known roosts at both sites. I installed ambient temperature loggers at both sites but the Alice Lake temperature logger failed. As a result, I used Environmental Canada’s archived climate data from the Squamish Airport weather station (Government of Canada 2020), which is ~ 4.6 km from the study site. I searched for unknown alternative roosts by radiotracking g2-3 bats at each site. I glued small radio transmitters—less than 5% of the bats’ body mass (Bradbury et al., 1979; Aldridge 53 and Brigham, 1988; Manger and Nelson 2001)—on their backs to follow their movements for 1-2 weeks. I trapped newly located roosts at least once to establish species identification and to look for previously tagged bats. 3.2.1 Network Construction I monitored the roosts for 72 days at Stave Lake and 89 days at Alice Lake. I used the automated PIT-tag reader data of daily individual PIT-tag records to calculate the Simple Ratio Index (SRI), an index of association in which the number of days each dyad (2 individuals) was detected roosting together, divided by the total number of days that at least one bat per dyad was located (Ginsberg and Young 1992). SRI-calculated associations of dyads range between 0 (never observed together) to 1 (always observed together). I excluded bats that were observed ≤4 times to remove animals that possibly died or were only peripherally associated with the colony (Bejder et al. 1998; Whitehead 2008). Social networks are visualized using nodes and edges (Krause et al. 2009). Nodes are individual bats in a network and edges are the connections between individuals. Edges can be weighted or unweighted (Krause et al. 2009). Unweighted networks have edges that are assigned a binary value—the dyads either associated at some point in the observation period (1) or they did not (0). It is also possible to weight the edges between a dyad with their SRI value, which provides a more robust network than using unweighted networks (Croft et al. 2011). I used dyad SRI values to weight the edges of my social networks. I created a series of time-aggregated networks to test for differences during Pregnancy, Lactation and Post-Lactation. These time periods varied between sites—Stave Lake bats were at each stage 2-3 weeks earlier than Alice Lake, likely due to the latter site’s higher elevation (Table 3.1). I then compared networks constructed from the shorter time periods to one 54 constructed for the entire summer. All networks were created in R Studio version 1.3.1093, using the R package ‘igraph’ and network edges were weighted using R package ‘asnipe’ (Csardi and Nepusz 2006; Farine 2013; R Core Team, 2021). 3.2.2 Network Analysis To assess whether the observed SRI values between bats were non-random, indicating specific colony structure, I compared all observed association matrices to random permutated networks in the R package ‘asnipe’ (Farine 2013). I constructed randomized networks by swapping group membership within observed association matrices (Manly 1995; Bejder 1998). I created 100,000 association permutations with one swap per permutation and calculated a new association matrix after 100 swaps; in total, I created 1000 random networks for comparison to the observed network, as recommended by Whitehead (2005). The p value was calculated from the proportion of random networks with a coefficient of variation larger than the observed network; I considered p<0.05 significant (Manly 1997). I performed separate network analyses for each reproductive period. I compared network structure among periods by calculating modularity (subgroup formation, Q), degree-centrality (the number of nodes connected to one node) and graph density (the measure of how frequent and strong the edges between dyads are in a network) for each network (Newman 2002a, 2003). The more associations a bat is recorded with during a network period, the higher the degree-centrality. The more connections between bats in a network, the higher the graph density score. I also measured modularity to assess subgroup partitioning, which is a technique to find subgroup formation that does not use weighted edges but instead considers the overall structure of the network (Newman and Girvan 2004, Newman 2006). Highly modular networks are 55 characterized by dense connections within subgroup nodes but are less tightly connected between subgroups. Values of Q greater than 0.3 indicate the presence of subgroup formation within the network. If community structure is present in the network, each bat is assigned to one distinct subgroup using this algorithm. I used the infomap algorithm in R package ‘igraph’ (Rosvall and Bergstrom 2007), for community detection and modularity calculation. As both colonies contained little brown myotis and Yuma myotis, I tested for assortative mixing by species in the colonies to see if bats were more likely to associate with or avoid their own species (Newman 2002a). Newman’s assortative coefficient ranges from -1 to 1; positive values indicate preference for individuals with the same attributes, negative values indicate avoidance and 0 reflects no preference. I used the ‘assortnet’ package in R (Shizuka and Farine 2016) to calculate the weighted assortative coefficient for bats identified to species with >90% probability. I compared the observed assortativity coefficient against the assortativity expected in random networks by creating weighted randomized networks using the previously described node-based network permutation method (Farine 2014). I then compared assortativity coefficient values with significance greater than 0.05 between reproductive sampling periods to assess how species affinity changed throughout the summer (Newman 2002a, 2003). For each site, I calculated the degree-centrality for each bat over the entire summer of 2019 (Figure 3.4b). I then ranked the degree-centrality measurements and used that value to break the dataset into even deciles (Figure 3.4a), and calculated the average degree-centrality measurement. I then compared the percent of bats among deciles based on three characteristics: reproductive versus nonreproductive (Figure 3.4c), original capture date (Figure 3.4d), and species (not shown). 56 3.3 Results 3.3.1 Capture Summary I captured 1107 bats from the two sites over 16 capture sessions (Table 3.2) with 622 bats captured at Stave Lake and 481 bats at Alice Lake. The percentage of juveniles captured at Stave Lake and Alice Lake were 6.4% and 25.1%, respectively. At Stave Lake and Alice Lake respectively, 70.4% and 47.1% of adult female bats were reproductive. I captured and radio-tracked bats at 3 major roosts in the Stave Lake Area—Stave Building 1, Stave Bat Box 1, and the Stave Building 2. I located the previously unknown Stave Building 2 roost site by radiotracking 3 bats in late June; I then captured 89 bats at the roost in late July. Based on an early July colony count, >200 adults occupied the Stave Building 2. At Stave Lake, I recorded roost occupancy for 342 bats over 72 days, resulting in 4636 bat-day-roost observations. The average Stave Lake bat was recorded 18.1 ± 11.5 (mean ± SD) times throughout the season, and an average of 18.7% of the tagged bats were observed daily at one of the 3 monitored roosts. PIT-tag roost monitoring ended on August 15, 2019 when the Stave Building 1 reader failed. Bats left Stave Building 1 shortly after that, but bats continued roosting in Stave Bat Box 1 until mid-September. At Alice Lake, I monitored 3 bat boxes for 89 consecutive days, obtaining 3274 bat locations; I recorded 242 individual bats, 167 of which were recorded more than 4 times and included in the analysis. Alice Lake bats were recorded on average 14.7 ± 8.6 times in the boxes throughout the season and, on average, 13.2 % of the tagged bats were recorded in a monitored roost each day of the study. I discovered the bats roosting in the tin roof of the Alice Building after radiotracking two bats in June, and I subsequently trapped bats emerging from two locations in the roof on five occasions. Colony counts indicate that ~300 adult bats roosted in the 57 roof of the Alice Building. Bats continued roosting in the PIT-tag monitored bat boxes until late September. 3.3.2 Network Analysis Bat social behavior within a maternity colony varied by reproductive period at both sites. At both Alice Lake and Stave Lake, there were strong individual associations between colony members during pregnancy and lactation. Average SRI values were 0.15 ± 0.16 (mean ± SD) at Alice Lake and 0.23 ± 0.18 at Stave Lake during pregnancy, and 0.16 ± 0.19 at Alice Lake and 0.20 ± 0.19 at Stave Lake during lactation, indicating that most bats had strong associations with their colony members (Table 3.3; Figure 3.1). During pregnancy and lactation, female bats were tightly associated with each other, as network density remained above 60% at both sites. There was little ingress or egress of individuals; 181 out of 189 bats were present in each of the pregnancy and lactation networks at Stave Lake, while 92 out of 96 bats were at Alice Lake during these periods. Bat associations within the maternity colony weakened and colony organization fragmented during post-lactation, a trend reflected in the decrease in some measures of network connectedness at both sites (Table 3.3; Figure 3.1). Daily associations of individual bats decreased at Stave Lake, as the SRI values (0.07 ± 0.13), mean degree-centrality (60 ± 28) and overall network density (0.36) all dropped compared to previous periods at Stave Lake. Fifty previously tagged individuals appeared for the first time in the post-lactation network, unlike the previous periods that were largely composed of the same individual bats. These new bats were tagged at the beginning of the season or at Stave Building 2 in July. While the average SRI values dropped at Alice Lake (0.08 ± 0.09) during post-lactation, the mean degree-centrality increased (101 ± 32) and the network density (0.63) remained the same as previous periods. Like Stave Lake, the change in network values during post-lactation 58 may reflect ingress of additional individuals; 32 previously tagged bats that had not been recorded prior to this period suddenly arrived at the monitored roosts at Alice Lake. These new bats were tagged before the PIT-tag systems were installed at Alice Lake, or during captures at the Alice Building. At Stave Lake, the majority of the colony roosted together in the Stave Building 1 during pregnancy and lactation, before splitting between Stave Bat Box 1 and the Stave Building 1 during post-lactation. This migration contributed to the formation of two distinct communities within the Stave Lake colony: the network modularity score, which had previously been 0 during pregnancy and lactation, rose to 0.39 during post-lactation, indicating subunit formation. The Infomap algorithm assigned bats in this period to 3 different communities (Figure 3.2). Community C was very small, consisting of only 2 bats. Each bat was observed only once or twice, indicating that these bats did not frequently roost in the targeted artificial roosts. Bats in communities A and B were originally captured in different locations and chose to roost separately during post-lactation. The majority of bats (91) in Community A were originally captured at the Stave Building 1 and preferred to roost there during the summer. Community B bats were originally captured at the Stave Bat Box 1 (24 bats), the Stave Building 1 (24 Bats) and the unmonitored Stave Building 2 (14 bats). Bats in community B included individuals that roosted in Stave Bat Box 1 starting August 8th; prior to this date, these bats either roosted in Stave Building 1 or at another unknown and unmonitored roost. Bats of both species at Stave Lake roosted together during the early and middle part of the summer, then segregated by species at the end of the season as they split into different communities. While the assortativity coefficient was very low during pregnancy (0.02) and lactation (0.00), there was an increase during post lactation (0.06); this value suggests that bats 59 had higher affinity for conspecific bats as young became volant. All assortative coefficients were significantly different from randomly permuted networks (p< 0.01). Only 129 bats with confirmed species identification were included in the assortative analysis. Whitehead (2016) does express caution when interpreting biological significance from coefficient values close to zero, but preferential assortment by species was also observed in PIT-tag observations and the post-lactation period social network (Figure 3.2). Community A contained 97 bats, of which 40 were little brown myotis, while the rest were Yuma myotis (28) or not positively identified as either species (29). In contrast, almost half (30) of the 63 bats in community B were Yuma myotis, while the rest were not positively identified to either species. Little brown myotis left the study site first at Stave Lake—bats identified as M. lucifugus were rarely detected after the first week of August (Figure 3.3). Yuma myotis moved to the Stave Bat Box 1 around this time and persisted until September. In contrast, assortative species mixing did not occur at Alice Lake, likely because the colony was mostly Yuma myotis. In total, 70.8% percent of bats included in the analysis were positively identified Yuma myotis while only 7.2% were little brown myotis; the rest were not positively identified as either species. Assortative coefficient values for all other periods were functionally zero or not different from the random expectation. I considered whether bat connectivity were associated with specific characteristics—like reproductive status, original capture date and species (Figure 3.6). There were no discernable patterns of highly connected bats at Alice Lake; only 51 bats were captured and banded in 2018, compared to 110 at Stave Lake, and many Alice Lake bats had indeterminate reproductive status because they were caught in early May before pregnancy would have been visible. 60 However, at Stave Lake, a higher percentage of bats (45.5 and 27.3) were nonreproductive in the top two deciles (averaging 215.2 and 205.5 respectively) than any other decile. Similarly, 31.8% of bats captured in both 2018 and 2019 were found in the same top two deciles. There were no discernable patterns when comparing percentage of species per decile, as many of the bats were not positively identified as either Yuma myotis or little brown myotis. 3.4 Discussion I found strong social structure within two mixed-species colonies of bats roosting in artificial structures. During pregnancy and lactation, bats had stronger associations with each other than during post-lactation. Despite heterospecific roosts, species identity did not influence associative behavior until post-lactation; this result was observed at Stave Lake, where a more even mix of each bat species segregated into species after weaning of young. At Stave Lake, I identified 15 highly connected bats that may be socially influential. By focusing my social network analysis on a few, key roosts, I found a unique opportunity to directly link specific artificial roosts with social behavior. Similar PIT-tag studies, where colonies with less than 100 individuals were monitored as they moved across 50 or more artificial roosts, had stronger average dyadic associations than observed in my colonies (Kerth et al. 2011; Webber et al. 2016; Zues et al. 2018). Fewer than 20% of tagged bats were observed on a daily basis, compared to 50% or greater in the referenced studies. It is possible my average association index values may be lower than reality as bats may have associated with other colony members in roosts I did not observe. To counteract this bias, I chose to use metrics that relied on network topography (degree-centrality, density, modularity) and focused on comparisons among temporal periods. 61 Maternity colony organization of little brown myotis and Yuma myotis was seasonal, with marked differences in social network composition for bats during pregnancy, lactation, and post-lactation. During pregnancy and lactation, associations were strong between colony members at monitored roosts; the colonies’ social networks were dense, highly connected, and centralized. The seasonal associations between bats weakened during post-lactation, when the pups were independent and flying. Seasonal changes in behavior are well established in little brown myotis—the group size of a breeding little brown myotis in tree roosts peaks during parturition, when social thermoregulation most benefits the unfurred pups, and colony organization begins to fragment once the pups are volant (Olson and Barclay, 2013). Reproductive female northern myotis (M. septentrionalis) and big brown bats show stronger dyadic associations during pregnancy than lactation, as gestation occurs during the earlier, cooler period of summer when social thermoregulation would be most advantageous (Patriquin 2010; Webber et al. 2016). The lack of species segregation at Stave Lake roosts during pregnancy and lactation suggest that Yuma and little brown myotis associated, likely due to comparable reproductive pressures, preferred roost characteristics, and advantages offered by clustering of reproductive females together. High quality roosts, linked to increased offspring fitness, are not uniformly found on the landscape, so Yuma myotis and little brown myotis roosting together in a single roost maximizes mutualistic benefits and avoids the costs of interspecific competition (Sedgley and O’Donnell 2004; Salinas-Ramos et al. 2019). Interspecific cooperation allows for mutualistic behavior that benefit both Yuma myotis and little brown myotis, such as social thermoregulation, pup-guarding, babysitting, food sharing and information transfer (Kerth 2008; Papadatou et al. 2008, Ancillotto et al. 2015). Pups experimentally reared in mixed species groups developed 62 social bonds with members of both species that may persist into adulthood, thereby perpetuating mixed-species maternity colony composition (Ancillotto et al. 2015). Heterospecific roost sharing has been rarely documented outside of caves and large stone monasteries, but up to 10 different species have been recorded in a single cave (Hill and Smith 1984; Kusuminda et al. 2013). While two colonies of different species composition can roost together in a single building, they are often segregated spatially as observed by Rensel (2018) and Swift and Racey (1983). My association results, in the context of heterospecific roosts of little brown myotis and Yuma myotis, suggests what could be social dynamics between these two species. Additional research is required to ascertain if Yuma myotis and little brown myotis ats are still segregated spatially by species within individual roosts. During post-lactation, colony organization changes drastically as the volant pups do not depend on the group for warmth and can hunt independently (Renyold and Kunz 2000; Olson 2011). At the end of summer, juveniles and adults are both fattening for hibernation, and thus torpor is used more frequently; roosting together in large groups is no longer necessary for survival (Pretzlaff et al. 2010; Dzal and Brigham 2013). Weaker post-lactation associations at my sites, therefore, could be explained by increased roost switching and smaller group size, as has been observed in noctule bats (Nyctalus noctule) and Leisler’s bats (N. leisleri) (Ruczyński and Bartoń 2020). While bat associations are high regardless of species identity during pregnancy and lactation, species-specific social behavior emerges during post-lactation. Little brown myotis roosting in the Stave Building 1 left in mid-August, while Yuma myotis roosting in the Stave Bat Box 1 persisted until early September. Similarly, the predominantly Yuma myotis colony at Alice Lake roosted in the bat boxes until the last week of September. This is not a unique 63 observation: other maternity sites in BC have observed similar species-specific behavior, where little brown bats have departed before Yuma bats at maternity sites (Lausen et al. 2021). The location and timing of Yuma myotis and little brown myotis hibernation in western North America remains enigmatic; a review of hibernacula in the western United States found only small groups of hibernating myotis species scattered across the landscape in the west (Weller et al. 2018). Acoustic monitoring across western Washington indicated that while some Yuma myotis can be found throughout the winter, little brown myotis rarely appear after October (Tobin and Anderson, unpublished data; Lausen, unpublished data). The temporal differences and wintertime detection differences suggest that Yuma myotis and little brown myotis may hibernate in different habitats, which would explain why these two species leave summer breeding habitat at different times. The unusual end-of-season social behavior observed at Stave Bat Box 1 hints at differences in behavior between Yuma myotis and little brown myotis. The arrival of primarily Yuma myotis at the Stave Bat Box 1, with bats from at least two different maternity roosts (Stave Building 2 and Stave Building 1), is remarkably similar to swarming. During swarming, bats from all over gather in specific spots for a brief time to mate; however, the Stave Lake end-of-summer mixing occurs only locally and includes only adult females and offspring, so it is distinct from swarming behavior. End-of-year mixing between other adult bats and juveniles could be mother-offspring pairs visiting alternative roosts and foraging locations prior to hibernation, as has been observed in other bat species (Brigham and Brigham 1989; Rossiter et al. 2002). Yuma myotis could be engaging in information transfer with nearby or potentially related bats, as knowledge from successful conspecifics may enhance their foraging success and, thus, survival during winter 64 (Gager 2018). The maternal guidance hypothesis (Sachteleben 1991) suggests offspring learn foraging, roost locations, and migration information from their mothers (Willis and Brigham 2007, Ripperger et al. 2019). This hypothesis has been discounted in little brown myotis at swarming sites (Burns and Broders 2015), but maternal information transfer may occur before migration at maternity colonies. It could also be that highly social females who successfully rear offspring each year mediate end-of-season mixing and share information for foraging and survival before migration. I found that reproductive bats had a wide range of degree-centrality measurements, suggesting that bats who successfully reared pups to volancy are present in both the central social organization and the periphery. Willis and Brigham (2004) found that reproductive bat dyads had stronger associations than occurred between non-reproductive bats, suggesting that bats with offspring shape social organization and movement. I found that reproductive bats caught in both 2018 and 2019 tended to have higher degree-centrality measurements, indicating they were highly connected within the Stave Lake colony. These socially influential bats may be older and reproductively successful individuals, so their departure to a new roost, like Stave Bat Box 1, could signal other colony members to follow. For example, older Bechstein’s (Myotis bechsteinii) bats are central to colony social organization by visiting many spatially separate roosts and linking distinct subgroups (Kerth et al. 2011). It is possible that Yuma myotis and little brown myotis have similar patterns of socially driven movement. I suspect that the highly connected bats identified at Stave Lake may be related. At Stave Lake, 27.8% of bats in the top two degree-centrality deciles at were non-reproductive, showing that non-reproductive bats are highly connected and integral to social organization. The age of maturity for little brown myotis and Yuma myotis is currently unknown, but I posit that many of 65 those non-reproductive bats are adult offspring that return to the maternity colony. Other researchers have speculated that maternity colonies are founded by females that share a matriline in many temperate bat species (Lausen 2007; Methany et al. 2008; Kerth and Van Schaik 2012; Patriquin et al. 2013). In temperate maternity colonies, strong female philopatry and indiscriminate mating systems during swarming create low levels of nuclear genetic relatedness between individual bats but strong overall mitochondrial relatedness within the colony (Kerth and Mayer 2002; Lausen 2007). Previous research studying mtDNA haplotypes indicated that crevice roosting maternity colonies of little brown myotis were genetically distinct from nearby colonies (Lausen 2007). Similarly, Miller-Butterworth et al. (2014) found that hibernating groups of little brown myotis were mitochondrially distinct from bats at other nearby hibernacula. Therefore, matrilines may drive social organization at maternity colonies, and those associations that potentially extend into hibernation. Genetic testing at my sites or at similar colonies could explore this idea in greater detail. Human-made roosts are increasingly common habitat for bat maternity colonies and thus represent important targets for protection and conservation. During pregnancy and lactation, temperate bats in maternity colonies develop strong associations with other colony members in artificial roosts (Willis and Brigham 2004; Garroway and Broders 2007, 2008; Patriquin 2010). If artificial roosts facilitate more centralized social networks than natural roosts, disturbance of bats at an occupied artificial roost during these critical reproductive periods would be devastating to a large portion of the bat colony (Webber et al. 2016). For example, a forest-dwelling colony can lose up to 20% of their roosts before the colony fragments or abandons the area entirely (Silvis et al. 2014), but more centralized colonies in human-made structures may be more 66 sensitive to roost loss if more colony members use a few, key roosts (Silvis et al. 2014; Bondo et al.2019). While my study presents strong evidence that bat maternity colonies in artificial roosts have distinct social structure, future studies are still required to quantify the entire colony organization. A more expansive investigation into associations at all roosts at Stave Lake and Alice Lake including unidentified ‘satellite’ roosts would help determine if the patterns I detected represent the greater colony structure. Research over years, rather than a single season, would highlight which patterns observed were due to seasonal variation and help identify abiotic factors that affect associations. Research on bats using anthropogenic structures represents a new frontier in bat biology. If we continue to provide habitat for bats, inadvertently or purposefully, we must also understand how we might be influencing bat behavior and, in turn, how those behavioral changes influence population survival of these cryptic species already facing multiple threats. 67 Table 3.1: 2019 dates used to construct each seasonal network by site. Data collection ceased prior to the seasonal disbanding of the colonies. Site Pregnancy Lactation Post-Lactation Alice Lake June 6- July 4 July 5-28 July 29-August 28 Stave Lake June 6-June 26 June 27-July 22 July 23-August 15 Table 3.2: Summary of capture efforts at Stave Lake and Alice Lake in 2019. Only tagged bats with >4 observations were included in the social network analysis. Alice Lake Stave Lake Bats Captured in 2019 481 713 # 2019 Capture Sessions 7 10 # Bats Tagged 338 583 # Tagged Bats included in Analysis 167 218 Table 3.3: Comparison of social network metrics by site and time period. Average SRI for all dyads were calculated as a measure of association strength across the colony. Each bat has a degree-centrality, indicating how many other bats they associated with during the time period. Density and modularity are network level measurements. Pregnancy Lactation Post-Lactation Stave Lake # Bats in Analysis 172 198 147 Mean SRI Values ± SD 0.23 ± 0.18 0.20 ± 0.19 0.07 ± 0.13 Mean Degree-Centrality ± SD 153 ± 27 155 ± 43 60 ± 28 Density 0.86 0.76 0.36 Modularity Score 0 0 0.39 Alice Lake # Bats in Analysis 90 96 161 Mean SRI Values ± SD 0.15 ± 0.16 0.16 ± 0.19 0.08 ± 0.09 Mean Degree-centrality ± SD 55 ± 26 62 ± 21 101 ± 32 Density 0.62 0.66 0.63 Modularity Score 0 0 0 68 Figure 3.1: Bat social network visualization by season and location. Stave Lake A) Pregnancy, B) Lactation and C) Post-Lactation networks and Alice Lake: D) Pregnancy, E) Lactation and F) Post-Lactation networks. Nodes represent individual bats; edges represent simple ratio index associations. Bats that are spatially closer together have stronger associations with each other than bats that are spatially separate. 69 Figure 3.2: Network Visualization of post-lactation period at Stave Lake by A) species identity and B) community assignment by Infomap. Species identity was assigned by 90% probability using Weller et al. 2001; MYLU refers to M. lucifugus, MYYU refers to M. yumanensis, and YULU refers to uncertain species identification. 70 Figure 3.3: Individual tagged bat roost location by date, separated by species. The top panel includes only bats identified as Myotis yumanesis, middle panel contains bats with uncertain species identity, and the bottom panel includes only bats identified as Myotis lucifugus. Species identity was assigned by 90% probability using Weller et al. 2001. Roost location is denoted by color, and position along the x-axis denotes date. If a dot is missing, the bat was not located at any monitored roost that day. 71 Figure 3.4: Stave Lake bats ranked according to degree-centrality (measurement of how many bats are connected to another individual bat within a network) for the entire summer of 2019. A) Average degree-centrality for binned deciles of bats. B) Histogram of degree-centrality measurements for bats. C) Comparison of deciles by percent of reproductive and non-reproductive bats. D) Comparison of deciles by percent of bats by date of original capture. 0501001502002502.5 5.0 7.5 10.0Ranked DecileAverage Degree Centrality Measurement020406050 100 150 200Calculated Degree of CentralityNumber of Tagged BatsA) B) C) 0.000.250.500.751.002 4 6Ranked Decile2019 Reproductive StatusNonreproductiveReproductive0.000.250.500.751.002 4 6Ranked DecileOriginal Capture Date20192018Percent of Bats in QuartileD) 72 Chapter 4: Conclusion In my thesis, I investigated how little brown myotis and Yuma myotis in maternity colonies used artificial roosts during the breeding season in British Columbia. Specifically, I described two aspects of roost use: habitat selection and social organization. My research is the first to investigate Yuma myotis maternity colonies and greatly expands our knowledge about this understudied species of bat. Similarly, my research is unique in sample size—I was able to include ~850 adult female bats between two sites, one of the larger maternity roost PIT-tag bat studies in published literature (Kerth and Reckardt 2003 (17 bats); Nuebaum et al. 2005 (55 bats); Wimsatt et al. 2005 (452 bats); Ellison et al. 2007 (987 bats); Garroway and Broders 2007 (43 bats); Hayes and Adams 2008 (24 bats); O’Shea et al. 2010 (2,968 bats); Fischer et al. 2016 (653 bats) Webber et al. 2016 (31 bats); Zues et al. 2018 (80 bats); Harten et al. 2019 (2966 bats); Hoyt et al. 2019 (89 bats)). In Chapter 2, I described patterns of roost use at two maternity colony sites in British Columbia, Canada, throughout summer 2019. Like Slough and Jung (2020), I discovered that bats in a colony used many artificial roosts throughout the breeding season, and for several purposes outside of nursery roosts. Bats switched among roosts frequently, similar to populations of little brown myotis roosting in trees (Olson 2011; Slough and Jung 2020). I confirmed that Yuma myotis and little brown myotis switch more frequently when roost temperatures fall outside their putative thermoneutral zone. Bats are more likely to cluster in artificial roosts during the lactation period, when pups depend on their mothers for food and warmth. Humidity was likely a rough indicator of occupancy in bat boxes, as 20 or more tagged bats in a bat box coincided with 100% humidity. My results suggest that Yuma myotis and little brown myotis 73 have similar roost characteristic preferences, but due to the low number of bats with confirmed species identity, additional research is needed to confirm these findings. In Chapter 3, I studied how the social organization of bats in maternity colonies changed throughout the summer. I analyzed and compared social networks during distinct reproductive periods. I discovered that during pregnancy and lactation, bats in maternity colonies form highly connected and insular groups in one or two roosts until post-lactation, when association patterns between individual bats weaken as pups become volant. I suspect bats formed stronger associations during pregnancy and lactation to offset the energetic cost of reproduction through social thermoregulation, information transfer, and cooperative use of optimal roosts. Changing association patterns in response to reproductive period is not unique to Yuma myotis and little brown myotis and can be found in several other temperate bat species (Bartonička and Gaisler 2007 (Pipistrellus pygmaeus); Barclay and Lausen 2003 (Eptesicus fuscus); Reckardt and Kerth 2007 (Myotis bechsteiini); Ruczyński and Bartoń 2020 (Nyctalus noctule, N. leisleri). My research indicates that Yuma myotis and little brown myotis have different life history patterns. I discovered that during post-lactation, little brown myotis left the roosts first in mid-August, while Yuma myotis stayed through September and aggregated with individuals likely from local maternity colonies in nearby roosts. My observations fall in line with growing evidence that there is a significant difference in life history patterns between Yuma myotis and little brown myotis. For example, other sources have noted that little brown myotis disappeared from acoustic recordings in the fall (suggesting hibernation) before Yuma myotis (Rensel 2018; Tobin and Anderson 2018; Lausen et al. In press). Elsewhere in BC, little brown myotis maternity colonies dispersed before Yuma myotis colonies and both female and male Yuma bats were observed in these late-summer aggregations (Lausen et al. 2021). These discoveries suggest 74 that, unlike little brown myotis that appear to directly disperse from maternity colonies to swarming sites or potential hibernacula, Yuma myotis depart for hibernation late and interact with nearby conspecifics from other roosts. While the purpose of the end-of-season Yuma myotis mixing is unclear, I hypothesize that this behavior may be information transfer about resources to aid survival, but additional research is required to confirm this idea. I also discovered that reproductive bats that had been caught in both 2018 and 2019 were more likely to be highly connected within social networks; this result aligns with previous research that suggests that older, experienced female bats mediate social organization in maternity colonies (Kerth et al. 2011). It may be that Yuma myotis and little brown myotis colony formation is influenced by matrilineal associations, as discovered in other bat species, but genetic comparison of association patterns would be required to explore this idea (Burland et al. 2001; Kerth et al. 2002; Rossiter et al. 2002). My research also represents a unique application of social network analysis that would be beneficial for study of large maternity colonies of bats. Because bats are cryptic and difficult to locate in their roosts, focusing on a few key roosts can allow researchers to study large maternity colonies of >300 bats. While these social networks are unlikely to represent the entire colony, comparison between temporal networks illuminates patterns in bat associations, connectivity, and subgroup formation. Artificial roosts are visible, accessible, and can be closely monitored, making them ideal candidates for this research approach. My results show using social network analysis is valuable and is a viable method of studying bat behavior so that we might gain an understanding of how roosting structures influence colony organization. In conclusion, I confirmed that Yuma myotis and little brown myotis use multiple artificial roosts during the summer, and that patterns in roost switching and social organization 75 depend on reproductive period. My results can be directly applied to bat conservation and mitigation efforts in British Columbia. Specifically, I determined that bats actively seek out a certain range of temperature (30-44˚C) in maternity roosts, so future bat structure installations should include enough insulation and ventilation to maintain these conditions. Similarly, land managers should install many structures scattered in a localized area to ensure that bats in a colony have multiple roosts to move among during the summer. And finally, researchers or community bat programs using colony counts as a method of quantifying bat population patterns should not assume that the bats counted at multiple times during the summer are the same individuals. Colony counts should be used as an index of bat occupancy, rather than a measure of colony abundance or individual behavior. My research was funded and supported by the WNS probiotic vaccine program, led by Dr. Cori Lausen (WCSC). Based on the observations and results from my field seasons, I highly recommend that researchers applying a probiotic compound as a prophylaxis for WNS consider the timing of application. Considering that Yuma myotis at both of my sites moved to different roosts during post-lactation, application of probiotic materials to a roost before the bats move in would be more successful than applying them to an occupied roost shortly before their exodus. And finally, I would suggest considering an early spring application cycle, followed by a second application in late summer during post lactation. In my opinion, application prior to bat’s arrival at the roost may be the least disruptive and most successful route, if it does not pose significant risk to pup development and adult reproductive success. Finally, I feel quite strongly that it is critical that we invest in community education about the importance of human-made roosts to bat populations. As natural roosts become increasingly scarce, artificial maternity roosts represent one of the best tools in our arsenal for the 76 conservation of threatened breeding bat populations. Compared to natural cavities, human-made roosts are easy to locate, monitor, and protect, as most are accessible by roads. Community-level conservation efforts like the Alberta (www.albertabats.ca) and British Columbia Community Bat (www.bcbats.ca) programs focus on educating private landowners who have bats in human-made structures, which provides valuable opportunities to monitor breeding bat populations for potential declines associated with white-nose syndrome and habitat fragmentation. Local community support and involvement is critical to protecting bats breeding in human-made structures, and infrastructures like the BC community bat program provide a solid framework that other provinces and countries can follow. Local landowners with bat colonies can be powerful allies for bat populations and conservation in the long term. 77 Bibliography Adams, R. A. 2003. Bats of the Rocky Mountain West: natural history, ecology, and conservation. University Press of Colorado, Boulder, CO. Adams, R. A., and M. A. Hayes. 2008. Water availability and successful lactation by bats as related to climate change in arid regions of western North America. Journal of Animal Ecology 77:1115–1121. Allen, L., C. Richardson, G. McCracken, and T. Kunz. 2010. Birth size and postnatal growth in cave‐and bridge‐roosting Brazilian free‐tailed bats. Journal of Zoology 280:8–16. Altringham, J. D. 2011. Bats: from evolution to conservation. Oxford University Press, New York, NY. Ancillotto, L., C. Allegrini, M. T. Serangeli, G. Jones, and D. Russo. 2015. Sociality across species: spatial proximity of newborn bats promotes heterospecific social bonding. Behavioral Ecology 26:293–299. Armitage, K. B., and O. A. Schwartz. 2000. Social enhancement of fitness in yellow-bellied marmots. Proceedings of the National Academy of Sciences 97:12149–12152. Baigger, A., N. Perony, M. Reuter, V. Leinert, M. Melber, S. Grünberger, D. Fleischmann, and G. Kerth. 2013. Bechstein’s bats maintain individual social links despite a complete reorganisation of their colony structure. Naturwissenschaften 100:895–898. Barclay, R. M., L. D. Harder, T. Kunz, and M. Fenton. 2003. Life histories of bats: life in the slow lane. Bat Ecology 209:253. Bartonička, T., and J. Gaisler. 2007. Seasonal dynamics in the numbers of parasitic bugs (Heteroptera, Cimicidae): a possible cause of roost switching in bats (Chiroptera, Vespertilionidae). Parasitology Research 100:1323–1330. 78 Bejder, L., D. Fletcher, and S. Bräger. 1998. A method for testing association patterns of social animals. Animal Behavior 56:719–725. Bell, G. P., G. A. Bartholomew, and K. A. Nagy. 1986. The roles of energetics, water economy, foraging behavior, and geothermal refugia in the distribution of the bat, Macrotus californicus. Journal of Comparative Physiology B 156:441–450. Betts, B. J. 1997. Microclimate in Hell’s Canyon mines used by maternity colonies of Myotis yumanensis. Journal of Mammalogy 78:1240–1250. Bideguren, G. M., A. López-Baucells, X. Puig-Montserrat, M. Mas, X. Porres, and C. Flaquer. 2019. Bat boxes and climate change: testing the risk of over-heating in the Mediterranean region. Biodiversity and Conservation 28:21–35. Bogan, M. A., P. M. Cryan, E. W. Valdez, L. E. Ellison, and T. J. O’Shea. 2003. Western crevice and cavity-roosting bats. Pages 69–77 In O’Shea, T. J., M. A. Bogan, and L. E. Ellison. 2003. Monitoring trends in bat populations of the United States and territories: status of the science and recommendations for the future. US Geological Survey, Reston, VA. Bondo, K. J., C. K. Willis, J. D. Metheny, R. J. Kilgour, E. H. Gillam, M. C. Kalcounis‐Rueppell, and R. Brigham. 2019. Bats relocate maternity colony after the natural loss of roost trees. Journal of Wildlife Management 83:1753–1761. Boratyński, J. S., C. K. Willis, M. Jefimow, and M. S. Wojciechowski. 2015. Huddling reduces evaporative water loss in torpid Natterer’s bats, Myotis nattereri. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology 179:125–132. Boyles, J. G. 2007. Describing roosts used by forest bats: the importance of microclimate. Acta Chiropterologica 9:297–303. 79 Boyles, J. G., P. M. Cryan, G. F. McCracken, and T. H. Kunz. 2011. Economic importance of bats in agriculture. Science 332:41–42. Breheny, P., and W. Burchett. 2017. Visualization of regression models using visreg. R J. 9:56. Brigham, R. M., and M. B. Fenton. 1986. The influence of roost closure on the roosting and foraging behaviour of Eptesicus fuscus (Chiroptera: Vespertilionidae). Canadian Journal of Zoology 64:1128–1133. Brigham, R., and A. Brigham. 1989. Evidence for association between a mother bat and its young during and after foraging. American Midland Naturalist 121:205–207. Brittingham, M. C., and L. M. Williams. 2000. Bat boxes as alternative roosts for displaced bat maternity colonies. Wildlife Society Bulletin 28:197–207. Brooks, M. E., K. Kristensen, K. J. Van Benthem, A. Magnusson, C. W. Berg, A. Nielsen, H. J. Skaug, M. Machler, and B. M. Bolker. 2017. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R journal 9:378–400. Burland, T., E. Barratt, R. Nichols, and P. A. Racey. 2001. Mating patterns, relatedness and the basis of natal philopatry in the brown long‐eared bat, Plecotus auritus. Molecular Ecology 10:1309–1321. Burnett, C. D., and P. V. August. 1981. Time and energy budgets for dayroosting in a maternity colony of Myotis lucifugus. Journal of Mammalogy 62:758–766. Burns, L. E., and H. G. Broders. 2015. Who swarms with whom? Group dynamics of Myotis bats during autumn swarming. Behavioral Ecology 26:866–876. Cairns, S. J., and S. J. Schwager. 1987. A comparison of association indices. Animal Behaviour 35:1454–1469. 80 Chambers, C. L., V. Alm, M. S. Siders, and M. J. Rabe. 2002. Use of artificial roosts by forest-dwelling bats in northern Arizona. Wildlife Society Bulletin 30:1085–1091. Constantine, D. G. 2003. Geographic translocation of bats: known and potential problems. Emerging Infectious Diseases 9:17. Coulson, T., T. Benton, P. Lundberg, S. Dall, B. Kendall, and J.M. Gaillard. 2006. Estimating individual contributions to population growth: evolutionary fitness in ecological time. Proceedings of the Royal Society B: Biological Sciences 273:547–555. Crampton, L. H., and R. M. Barclay. 1998. Selection of roosting and foraging habitat by bats in different‐aged aspen mixed wood stands. Conservation Biology 12:1347–1358. Crawford, R. D. 2020. Bat Boxes as Mitigation Tools: Factors Impacting Microclimate and Myotis Sodalis Roost Selection. Thesis, Eastern Kentucky University, Richmond, Kentucky, USA. Croft, D. P., J. R. Madden, D. W. Franks, and R. James. 2011. Hypothesis testing in animal social networks. Trends in Ecology and Evolution 26:502–507. Csardi, G., and T. Nepusz. 2006. The igraph software package for complex network research. InterJournal, complex systems 1695:1–9. Czenze, Z. J., and C. K. Willis. 2015. Warming up and shipping out: arousal and emergence timing in hibernating little brown bats (Myotis lucifugus). Journal of Comparative Physiology B 185:575–586. Davis, W. H., M. D. Hassell, and M. J. Harvey. 1965. Maternity colonies of the bat Myotis I. lucifugus in Kentucky. American Midland Naturalist 73:161–165. Dietz, M., and E. K. Kalko. 2006. Seasonal changes in daily torpor patterns of free-ranging female and male Daubenton’s bats (Myotis daubentonii). Journal of Comparative Physiology B 176:223–231. 81 Duff, A. A., and T. E. Morrell. 2007. Predictive occurrence models for bat species in California. Journal of Wildlife Management 71:693–700. Dzal, Y. A., and R. M. Brigham. 2013. The tradeoff between torpor use and reproduction in little brown bats (Myotis lucifugus). Journal of Comparative Physiology B 183:279–288. Dzal, Y., L. P. McGuire, N. Veselka, and M. B. Fenton. 2011. Going, going, gone: the impact of white-nose syndrome on the summer activity of the little brown bat (Myotis lucifugus). Biology Letters 7:392–394. Ellison, L. E., T. J. O’Shea, D. J. Neubaum, and R. A. Bowen. 2007. Factors influencing movement probabilities of big brown bats (Eptesicus fuscus) in buildings. Ecological Applications 17:620–627. Evelyn, M. J., D. A. Stiles, and R. A. Young. 2004. Conservation of bats in suburban landscapes: roost selection by Myotis yumanensis in a residential area in California. Biological Conservation 115:463–473. Farine, D. R. 2013. Animal social network inference and permutations for ecologists in R using asnipe. Methods in Ecology and Evolution 4:1187–1194. Farine, D. R. 2014. Measuring phenotypic assortment in animal social networks: weighted associations are more robust than binary edges. Animal Behaviour 89:141–153. Farine, D. R., and H. Whitehead. 2015. Constructing, conducting and interpreting animal social network analysis. Journal of Animal Ecology 84:1144–1163. Fenton, M. B., and G. P. Bell. 1979. Echolocation and feeding behaviour in four species of Myotis (Chiroptera). Canadian Journal of Zoology 57:1271–1277. 82 Fischer, K., V. Zeus, L. Kwasnitschka, G. Kerth, M. Haase, M. H. Groschup, and A. Balkema-Buschmann. 2016. Insectivorous bats carry host specific astroviruses and coronaviruses across different regions in Germany. Infection, Genetics and Evolution 37:108–116. Flanders, J., G. Jones, P. Benda, C. Dietz, S. Zhang, G. Li, M. Sharifi, and S. J. Rossiter. 2009. Phylogeography of the greater horseshoe bat, Rhinolophus ferrumequinum: contrasting results from mitochondrial and microsatellite data. Molecular Ecology 18:306–318. Flaquer, C., X. Puig, A. López-Baucells, I. Torre, L. Freixas, M. Mas, X. Porres, and A. Arrizabalaga. 2014. Could overheating turn bat boxes into death traps. Barbastella 7:46–53. Fox, J. 2003. Effect displays in R for generalised linear models. Journal of Statistical Software 8:1–27. Franklin, A. B., D. R. Anderson, R. Gutiérrez, and K. P. Burnham. 2000. Climate, habitat quality, and fitness in northern spotted owl populations in northwestern California. Ecological Monographs 70:539–590. Franks, D. W., G. D. Ruxton, and R. James. 2010. Sampling animal association networks with the gambit of the group. Behavioral Ecology and Sociobiology 64:493–503. Frick, W. F., D. S. Reynolds, and T. H. Kunz. 2010. Influence of climate and reproductive timing on demography of little brown myotis Myotis lucifugus. Journal of Animal Ecology 79:128–136. Frick, W. F., S. J. Puechmaille, J. R. Hoyt, B. A. Nickel, K. E. Langwig, J. T. Foster, K. E. Barlow, T. Bartonička, D. Feller, and A. Haarsma. 2015. Disease alters macroecological patterns of north American bats. Global Ecology and Biogeography 24:741–749. Gager, Y. 2019. Information transfer about food as a reason for sociality in bats. Mammal Review 49:113–120. 83 Garroway, C. J., and H. G. Broders. 2007. Nonrandom association patterns at northern long-eared bat maternity roosts. Canadian Journal of Zoology 85:956–964. Genoud, M., F. J. Bonaccorso, and A. Anends. 1990. Rate of metabolism and temperature regulation in two small tropical insectivorous bats (Peropteryx macrotis and Natalus tumidirostris). Comparative Biochemistry and Physiology Part A: Physiology 97:229–234. Ginsberg, J. R., and T. P. Young. 1992. Measuring association between individuals or groups in behavioural studies. Animal Behaviour 44:377–379. Girvan, M., and M. E. Newman. 2002. Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99:7821–7826. Godinho, L. N., L. F. Lumsden, G. Coulson, and S. R. Griffiths. 2015. Network analysis reveals cryptic seasonal patterns of association in Gould’s wattled bats (Chalinolobus gouldii) roosting in bat-boxes. Behaviour 152:2079–2105. Government of Canada, 2020. Weather, Climate and Hazards. https://www.canada.ca/en/services/environment/weather.html. Accessed 08/02/2020. Grueter, C., X. Qi, D. Zinner, T. Bergman, M. Li, Z. Xiang, P. Zhu, A. Migliano, A. Miller, and M. Krutzen. 2020. Multilevel organization of animal sociality. Trends in Ecology and Evolution 35:834–847. Hayes, G., and G. J. Wiles. 2013. Bat Conservation Plan. Washington Department of Fish and Wildlife, Olympia, Washington. Herreid, C., and K. Schmidt-Nielsen. 1966a. Oxygen consumption, temperature, and water loss in bats from different environments. American Journal of Physiology 211:1108–1112. Hill, J. E., and J. D. Smith. 1984. Bats: a natural history. University of Texas Press, Austin, Texas. 84 Hood, W. R., J. Bloss, and T. H. Kunz. 2002. Intrinsic and extrinsic sources of variation in size at birth and rates of postnatal growth in the big brown bat Eptesicus fuscus (Chiroptera: Vespertilionidae). Journal of Zoology 258:355–363. Hoying, K., and T. Kunz. 1998. Variation in size at birth and post‐natal growth in the insectivorous bat Pipistrellus subflavus (Chiroptera: Vespertilionidae). Journal of Zoology 245:15–27. Hoyt, J. R., K. E. Langwig, J. P. White, H. M. Kaarakka, J. A. Redell, K. L. Parise, W. F. Frick, J. T. Foster, and A. M. Kilpatrick. 2019. Field trial of a probiotic bacteria to protect bats from white-nose syndrome. Scientific Reports 9:1–9. Hutson, A. M., and S. P. Mickleburgh. 2001. Microchiropteran bats: global status survey and conservation action plan. IUCN, Oxford, UK. Johnson, J. S., J. J. Treanor, A. C. Slusher, and M. J. Lacki. 2019. Buildings provide vital habitat for little brown myotis (Myotis lucifugus) in a high‐elevation landscape. Ecosphere 10:e02925. Jones, G., D. S. Jacobs, T. H. Kunz, M. R. Willig, and P. A. Racey. 2009. Carpe noctem: the importance of bats as bioindicators. Endangered Species Research 8:93–115. Kerth, G. 2008. Causes and consequences of sociality in bats. Bioscience 58:737–746. Kerth, G., and J. van Schaik. 2012. Causes and consequences of living in closed societies: lessons from a long‐term socio‐genetic study on Bechstein’s bats. Molecular Ecology 21:633–646. Kerth, G., and K. Reckardt. 2003. Information transfer about roosts in female Bechstein’s bats: an experimental field study. Proceedings of the Royal Society of London. Series B: Biological Sciences 270:511–515. Kerth, G., F. Mayer, and E. Petit. 2002. Extreme sex‐biased dispersal in the communally breeding, nonmigratory Bechstein’s bat (Myotis bechsteinii). Molecular Ecology 11:1491–1498. 85 Kerth, G., K. Weissmann, and B. König. 2001. Day roost selection in female Bechstein’s bats (Myotis bechsteinii): a field experiment to determine the influence of roost temperature. Oecologia 126:1–9. Kerth, G., N. Perony, and F. Schweitzer. 2011. Bats are able to maintain long-term social relationships despite the high fission–fusion dynamics of their groups. Proceedings of the Royal Society B: Biological Sciences 278:2761–2767. Knight, A. J. 2008. “Bats, snakes and spiders, Oh my!” How aesthetic and negativistic attitudes, and other concepts predict support for species protection. Journal of Environmental Psychology 28:94–103. Krause, J., D. P. Croft, and R. James. 2007. Social network theory in the behavioural sciences: potential applications. Behavioral Ecology and Sociobiology 62:15–27. Krause, J., G. D. Ruxton, G. Ruxton, and I. G. Ruxton. 2002. Living in groups. Oxford University Press, Oxford, UK. Kunz, T. H. 1982. Roosting Ecology of Bats. Pages 1–55 in T. H. Kunz, editor. 1982. Ecology of Bats. Springer US, Boston, MA. Kunz, T. H., and A. Kurta. 1987. Size of bats at birth and maternal investment during pregnancy. Symposia of the Zoological Society of London, 57:79–106. Kunz, T. H., and M. B. Fenton. 2003. Bat ecology. University of Chicago Press, Chicago, IL. Kunz, T. H., and S. K. Robson. 1995. Postnatal growth and development in the Mexican free-tailed bat (Tadarida brasiliensis mexicana): birth size, growth rates, and age estimation. Journal of Mammalogy 76:769–783. Kunz, T. H., E. B. de Torrez, D. Bauer, T. Lobova, and T. H. Fleming. 2011. Ecosystem services provided by bats. Annals of the New York Academy of Sciences 1223:1–38. 86 Kurta, A., G. P. Bell, K. A. Nagy, and T. H. Kunz. 1989. Energetics of pregnancy and lactation in freeranging little brown bats (Myotis lucifugus). Physiological Zoology 62:804–818. Kurta, A., K. A. Johnson, and T. H. Kunz. 1987. Oxygen consumption and body temperature of female little brown bats (Myotis lucifugus) under simulated roost conditions. Physiological Zoology 60:386–397. Kusuminda, T., G. Edirisinghe, R. P. Nanayakkara, and N. Vishvanath. 2013. Diversity and Population status of Bats in Pilikuttuwa ancient cave temple in the Gampaha District, Sri Lanka. Asian Journal of Conservation Biology 2:136-143. Lausen, C. L., and R. M. Barclay. 2003. Thermoregulation and roost selection by reproductive female big brown bats (Eptesicus fuscus) roosting in rock crevices. Journal of Zoology 260:235–244. Lausen, C. L., and R. M. Barclay. 2006. Benefits of living in a building: big brown bats (Eptesicus fuscus) in rocks versus buildings. Journal of Mammalogy 87:362–370. Lausen, C., D. W. Nagorsen, R. M. Brigham, and J. Hobbs. 2021. Bats of British Columbia. 2nd edition, in press. Royal British Columbia Museum, Victoria, B.C. Lausen, C., I. Delisle, R. Barclay, and C. Strobeck. 2008. Beyond mtDNA: nuclear gene flow suggests taxonomic oversplitting in the little brown bat (Myotis lucifugus). Canadian Journal of Zoology 86:700–713. Lewis, S. E. 1993. Effect of climatic variation on reproduction by pallid bats (Antrozous pallidus). Canadian Journal of Zoology 71:1429–1433. Lewis, S. E. 1995. Roost fidelity of bats: a review. Journal of Mammalogy 76:481–496. Lewis, S. E. 1996. Low roost-site fidelity in pallid bats: associated factors and effect on group stability. Behavioral Ecology and Sociobiology 39:335–344. 87 Licht, P., and P. Leitner. 1967. Behavioral responses to high temperatures in three species of California bats. Journal of Mammalogy 48:52–61. Lorch, J. M., J. M. Palmer, D. L. Lindner, A. E. Ballmann, K. G. George, K. Griffin, S. Knowles, J. R. Huckabee, K. H. Haman, C. D. Anderson, P. A. Becker, J. B. Buchanan, J. T. Foster, and D. S. Blehert. 2016. First detection of bat white-nose syndrome in western North America. mSphere 1:e00148-16. Lourenço, S. I., and J. M. Palmeirim. 2004. Influence of temperature in roost selection by Pipistrellus pygmaeus (Chiroptera): relevance for the design of bat boxes. Biological Conservation 119:237–243. Luis, A. D., T. J. O’Shea, D. T. Hayman, J. L. Wood, A. A. Cunningham, A. T. Gilbert, J. N. Mills, and C. T. Webb. 2015. Network analysis of host–virus communities in bats and rodents reveals determinants of cross‐species transmission. Ecology Letters 18:1153–1162. Manly, B. F. 1995. A note on the analysis of species co‐occurrences. Ecology 76:1109–1115. McComb, K., C. Moss, S. M. Durant, L. Baker, and S. Sayialel. 2001. Matriarchs as repositories of social knowledge in African elephants. Science 292:491–494. McCracken, G. F., and M. K. Gustin. 1991. Nursing behavior in Mexican free‐tailed bat maternity colonies. Ethology 89:305–321. McGuire, L. P., K. A. Jonasson, and C. G. Guglielmo. 2014. Bats on a budget: torpor-assisted migration saves time and energy. PLoS One 9:e115724. Mering, E. D., and C. L. Chambers. 2012. Artificial roosts for tree‐roosting bats in northern Arizona. Wildlife Society Bulletin 36:765–772. Mering, E. D., and C. L. Chambers. 2014. Thinking outside the box: a review of artificial roosts for bats. Wildlife Society Bulletin 38:741–751. 88 Metheny, J. D., M. C. Kalcounis-Rueppell, C. K. Willis, K. A. Kolar, and R. M. Brigham. 2008. Genetic relationships between roost-mates in a fission–fusion society of tree-roosting big brown bats (Eptesicus fuscus). Behavioral Ecology and Sociobiology 62:1043–1051. Miller-Butterworth, C. M., M. J. Vonhof, J. Rosenstern, G. G. Turner, and A. L. Russell. 2014. Genetic structure of little brown bats (Myotis lucifugus) corresponds with spread of white-nose syndrome among hibernacula. Journal of Heredity 105:354–364. Milligan, B. N. 1993. Carrying of young by female Yuma bats (Myotis yumanensis). Northwestern Naturalist 74:55–56. Nad’o, L., R. Chromá, and P. Kaňuch. 2017. Structural, temporal and genetic properties of social groups in the short-lived migratory bat Nyctalus leisleri. Behaviour 154:785–807. Nagorsen, D. W., and R. M. Brigham. 1993. Bats of British Columbia. UBC press, Vancouver, BC. Neilson, A. L., and M. B. Fenton. 1994. Responses of little brown myotis to exclusion and to bat houses. Wildlife Society Bulletin 22:8–14. Neubaum, D. J., K. R. Wilson, and T. J. O’shea. 2007. Urban maternity‐roost selection by big brown bats in Colorado. Journal of Wildlife Management 71:728–736. Neubaum, D. J., K. W. Navo, and J. L. Siemers. 2017. Guidelines for defining biologically important bat roosts: a case study from Colorado. Journal of Fish and Wildlife Management 8:272–282. Neubaum, D. J., M. A. Neubaum, L. E. Ellison, and T. J. O’Shea. 2005. Survival and condition of big brown bats (Eptesicus fuscus) after radiotagging. Journal of Mammalogy 86:95–98. Newman, M. E. 2002. Assortative mixing in networks. Physical Review Letters 89:208701. Newman, M. E. 2003. The structure and function of complex networks. SIAM Review 45:167–256. Newman, M. E. 2006. Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103:8577–8582. 89 Newman, M. E., and M. Girvan. 2004. Finding and evaluating community structure in networks. Physical Review E 69:026113. Norquay, K. J., F. Martinez-Nuñez, J. E. Dubois, K. M. Monson, and C. K. Willis. 2013. Long-distance movements of little brown bats (Myotis lucifugus). Journal of Mammalogy 94:506–515. O’Shea, T. J., L. E. Ellison, D. J. Neubaum, M. A. Neubaum, C. A. Reynolds, and R. A. Bowen. 2010. Recruitment in a Colorado population of big brown bats: breeding probabilities, litter size, and first-year survival. Journal of Mammalogy 91:418–428. Olson, C. R., and R. M. Barclay. 2013. Concurrent changes in group size and roost use by reproductive female little brown bats (Myotis lucifugus). Canadian Journal of Zoology 91:149–155. Olson, C.R. 2011. The Roosting Behaviour of Little Brown Bats (Myotis lucifugus) and Northern Long-Eared Bats (Myotis septentrionalis) in the Boreal Forest of Northern Alberta. Thesis, University of Calgary, Calgary, AB. Orians, G. H. and J. F. Wittenberger. 1991. Spatial and temporal scales in habitat selection. American Naturalist 137: S29–S49 Ortega, J., and J. E. Maldonado. 2006. Female interactions in harem groups of the Jamaican fruit-eating bat, Artibeus jamaicensis (Chiroptera: Phyllostomidae). Acta Chiropterologica 8:485–495. Otte, E., and R. Rousseau. 2002. Social network analysis: a powerful strategy, also for the information sciences. Journal of Information Science 28:441–453. Papadatou, E., R. K. Butlin, and J. D. Altringham. 2008. Seasonal roosting habits and population structure of the long-fingered bat Myotis capaccinii in Greece. Journal of Mammalogy 89:503–512. 90 Parsons, K., K. Balcomb I, J. Ford, and J. Durban. 2009. The social dynamics of southern resident killer whales and conservation implications for this endangered population. Animal Behaviour 77:963–971. Patriquin, K. J., F. Palstra, M. L. Leonard, and H. G. Broders. 2013. Female northern myotis (Myotis septentrionalis) that roost together are related. Behavioral Ecology 24:949–954. Patriquin, K. J., M. L. Leonard, H. G. Broders, and C. J. Garroway. 2010. Do social networks of female northern long-eared bats vary with reproductive period and age? Behavioral Ecology and Sociobiology 64:899–913. Penuela-Salgado, M., and J. Perez-Torres. 2015. Environmental and spatial characteristics that affect roost use by Seba’s short-tailed bat. Journal of Cave and Karst Studies 77:160–4. Pourshoushtari, R. D., B. P. Pauli, P. A. Zollner, and G. S. Haulton. 2018. Road and habitat interact to influence selection and avoidance behavior of bats in Indiana. Northeastern Naturalist 25:236–247. Powers, D. R. 1992. Effect of temperature and humidity on evaporative water loss in Anna’s hummingbird (Calypte anna). Journal of Comparative Physiology B 162:74–84. Pretzlaff, I., G. Kerth, and K. H. Dausmann. 2010. Communally breeding bats use physiological and behavioural adjustments to optimise daily energy expenditure. Naturwissenschaften 97:353–363. R Core Team. 2021. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Racey, P. 1973. Environmental factors affecting the length of gestation in heterothermic bats. Journal of Reproduction and Fertility 19:175–189. 91 Racey, P. A., and A. C. Entwistle. 2000. 9 - Life-history and Reproductive Strategies of Bats. Pages 363–414 in E. G. Crichton and P. H. Krutzsch, editors. Reproductive Biology of Bats. Academic Press, London, UK. Racey, P., and A. Entwistle. 2003. Conservation ecology of bats. Pages 680-744 In Kunz, T.H., and M.B. Fenton. 2003. Bat Ecology. University of Chicago Press, Chicago, IL. Randall, L. A. 2014. Roost-site selection and movements of little brown myotis (Myotis lucifugus) in southwestern Yukon. Northwestern Naturalist 95:312–317. Reckardt, K., and G. Kerth. 2007. Roost selection and roost switching of female Bechstein’s bats (Myotis bechsteinii) as a strategy of parasite avoidance. Oecologia 154:581–588. Reimer, J. 2021, March 5. Natural tree roost-use by Myotis lucifugus maternity colonies in Interior Alaska. Northern Bat Working Group Annual Meeting, Alaska. Reiter, G. 2004. Postnatal growth and reproductive biology of Rhinolophus hipposideros (Chiroptera: Rhinolophidae). Journal of Zoology 262:231–241. Reynolds, S., and T. H. Kunz. 2000. Changes in body composition during reproduction and postnatal growth in the little brown bat, Myotis lucifugus (Chiroptera: Vespertilionidae). Ecoscience 7:10–17. Ripperger, S., L. Günther, H. Wieser, N. Duda, M. Hierold, B. Cassens, R. Kapitza, A. Koelpin, and F. Mayer. 2019. Proximity sensors on common noctule bats reveal evidence that mothers guide juveniles to roosts but not food. Biology Letters 15:20180884–20180884. Ritzi, C. M., B. L. Everson, and J. O. Whitaker. 2005. Use of bat boxes by a maternity colony of Indiana myotis (Myotis sodalis). Northeastern Naturalist 12:217–220. Roever, C. L., H. L. Beyer, M. J. Chase, and R. J. van Aarde. 2014. The pitfalls of ignoring behaviour when quantifying habitat selection. Diversity and Distributions 20:322–333. 92 Rossiter, S. J., G. Jones, R. D. Ransome, and E. M. Barratt. 2002. Relatedness structure and kin-biased foraging in the greater horseshoe bat (Rhinolophus ferrumequinum). Behavioral Ecology and Sociobiology 51:510–518. Rosvall, M., and C. T. Bergstrom. 2007. An information-theoretic framework for resolving community structure in complex networks. Proceedings of the National Academy of Sciences 104:7327–7331. Rosvall, M., and C. T. Bergstrom. 2008. Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences 105:1118–1123. Ruczyński, I. 2006. Influence of temperature on maternity roost selection by noctule bats (Nyctalus noctula) and Leisler’s bats (N. leisleri) in Białowieża Primeval Forest, Poland. Canadian Journal of Zoology 84:900–907. Rueegger, N. 2016. Bat boxes—a review of their use and application, past, present and future. Acta Chiropterologica 18:279–299. Rueegger, N., B. Law, and R. Goldingay. 2018. Interspecific differences and commonalities in maternity roosting by tree cavity-roosting bats over a maternity season in a timber production landscape. PLoS One 13:e0194429. Russo, D., and L. Ancillotto. 2015. Sensitivity of bats to urbanization: a review. Mammalian Biology 80:205–212. Russo, D., L. Cistrone, and G. Jones. 2005. Spatial and temporal patterns of roost use by tree‐dwelling barbastelle bats Barbastella barbastellus. Ecography 28:769–776. Russo, D., L. Cistrone, I. Budinski, G. Console, M. Della Corte, C. Milighetti, I. Di Salvo, V. Nardone, R. M. Brigham, and L. Ancillotto. 2017. Sociality influences thermoregulation and roost switching in a forest bat using ephemeral roosts. Ecology and Evolution 7:5310–5321. 93 Sachteleben, J. 1991. Zum ‘Invasions’—Verhalten der Zwergfledermaus (Pipistrellus pipistrellus). Nyctalus 4:51–66. Sah, P., J. Mann, and S. Bansal. 2018. Disease implications of animal social network structure: a synthesis across social systems. Journal of Animal Ecology 87:546–558. Salinas‐Ramos, V. B., L. Ancillotto, L. Bosso, V. Sánchez‐Cordero, and D. Russo. 2020. Interspecific competition in bats: state of knowledge and research challenges. Mammal Review 50:68–81. Sedgeley, J. A. 2001. Quality of cavity microclimate as a factor influencing selection of maternity roosts by a tree‐dwelling bat, Chalinolobus tuberculatus, in New Zealand. Journal of Applied Ecology 38:425–438. Sedgeley, J. A., and C. F. O’Donnell. 2004. Roost use by long-tailed bats in South Canterbury: examining predictions of roost-site selection in a highly fragmented landscape. New Zealand Journal of Ecology 28:1–18. Shizuka, D., and D. R. Farine. 2016. Measuring the robustness of network community structure using assortativity. Animal Behaviour 112:237–246. Shizuka, D., and D. R. Farine. 2016. Measuring the robustness of network community structure using assortativity. Animal Behaviour 112:237–246. Silk, J. B. 2007. The adaptive value of sociality in mammalian groups. Philosophical Transactions of the Royal Society B: Biological Sciences 362:539–559. Silk, J. B., S. C. Alberts, and J. Altmann. 2003. Social bonds of female baboons enhance infant survival. Science 302:1231–1234. Silk, M. J., A. L. Jackson, D. P. Croft, K. Colhoun, and S. Bearhop. 2015. The consequences of unidentifiable individuals for the analysis of an animal social network. Animal Behaviour 104:1–11. 94 Silvis, A., A. B. Kniowski, S. D. Gehrt, and W. M. Ford. 2014. Roosting and foraging social structure of the endangered Indiana bat (Myotis sodalis). PloS one 9:e96937. Silvis, A., N. Abaid, W. M. Ford, and E. R. Britzke. 2016. Responses of bat social groups to roost loss: more questions than answers. Pages 261–280 in Ortega J. (eds) Sociality in Bats. Springer International Publishing, Switzerland. Silvis, A., W. M. Ford, and E. R. Britzke. 2015. Effects of hierarchical roost removal on northern long-eared bat (Myotis septentrionalis) maternity colonies. PLoS One 10:e0116356. Slough, B. G., and T. S. Jung. 2020. Little brown bats utilize multiple maternity roosts within foraging areas: implications for identifying summer habitat. Journal of Fish and Wildlife Management 11:311–320. Speakman, J., R. Stone, and J. Kerslake. 1995. Temporal patterns in the emergence behaviour of pipistrelle bats, Pipistrellus pipistrellus, from maternity colonies are consistent with an anti-predator respose. Animal Behaviour 50:1147–1156. Speakman, J.R, and D.W. Thomas. 2003. Physiological Ecology and Energetics of Bats. Pages 430-492 In Kunz, T.H., and M.B. Fenton. 2003. Bat Ecology. University of Chicago Press, Chicago, IL. Stanton, M. A., and J. Mann. 2012. Early social networks predict survival in wild bottlenose dolphins. PloS one 7:e47508. Stawski, C., C. Willis, and F. Geiser. 2014. The importance of temporal heterothermy in bats. Journal of Zoology 292:86–100. Studier, E. H. 1981. Energetic advantages of slight drops in body temperature in little brown bats, Myotis lucifugus. Comparative Biochemistry and Physiology Part A: Physiology 70:537–540. 95 Studier, E. H., and M. J. O’Farrell. 1972. Biology of Myotis thysanodes and M. lucifugus (Chiroptera: Vespertilionidae)—I. Thermoregulation. Comparative Biochemistry and Physiology Part A: Physiology 41:567–595. Swift, S., and P. Racey. 1983. Resource partitioning in two species of vespertilionid bats (Chiroptera) occupying the same roost. Journal of Zoology 200:249–259. Thomas, D. W., M. B. Fenton, and R. M. Barclay. 1979. Social behavior of the little brown bat, Myotis lucifugus: I. Mating behavior. Behavioral Ecology and Sociobiology:129–136. Trune, D. R., and C. Slobodchikoff. 1978. Position of immatures in pallid bat clusters: a case of reciprocal altruism? Journal of Mammalogy 59:193–195. Turbill, C., and F. Geiser. 2006. Thermal physiology of pregnant and lactating female and male long-eared bats, Nyctophilus geoffroyi, and N. gouldi. Journal of Comparative Physiology B 176:165–172. Van Der Post, D. J., R. Verbrugge, and C. K. Hemelrijk. 2015. The evolution of different forms of sociality: behavioral mechanisms and eco-evolutionary feedback. PloS one 10:e0117027. van Harten, E., T. Reardon, L. F. Lumsden, N. Meyers, T. A. Prowse, J. Weyland, and R. Lawrence. 2019. High detectability with low impact: Optimizing large PIT tracking systems for cave‐dwelling bats. Ecology and Evolution 9:10916–10928. Vital, C., and E. P. Martins. 2009. Using graph theory metrics to infer information flow through animal social groups: a computer simulation analysis. Ethology 115:347–355. Vitousek, P. M., H. A. Mooney, J. Lubchenco, and J. M. Melillo. 1997. Human domination of Earth’s ecosystems. Science 277:494–499. Voelkl, B., C. Kasper, and C. Schwab. 2011. Network measures for dyadic interactions: stability and reliability. American Journal of Primatology 73:731–740. 96 Vonhof, M. J., and R. M. Barclay. 1996. Roost-site selection and roosting ecology of forest-dwelling bats in southern British Columbia. Canadian Journal of Zoology 74:1797–1805. Wang, L. C., and M. W. Wolowyk. 1988. Torpor in mammals and birds. Canadian Journal of Zoology 66:133–137. Webber, Q. M. R., and C. K. R. Willis. 2018. An experimental test of effects of ambient temperature and roost quality on aggregation by little brown bats (Myotis lucifugus). Journal of Thermal Biology 74:174–180. Webber, Q. M., R. M. Brigham, A. D. Park, E. H. Gillam, T. J. O’Shea, and C. K. Willis. 2016. Social network characteristics and predicted pathogen transmission in summer colonies of female big brown bats (Eptesicus fuscus). Behavioral Ecology and Sociobiology 70:701–712. Webster, M. D., and J. R. King. 1987. Temperature and humidity dynamics of cutaneous and respiratory evaporation in pigeons, Columba livia. Journal of Comparative Physiology B 157:253–260. Welch, J. N., and C. Leppanen. 2017. The threat of invasive species to bats: a review. Mammal Review 47:277–290. Weller, T. J., P. M. Cryan, and T. J. O’Shea. 2009. Broadening the focus of bat conservation and research in the USA for the 21st century. Endangered Species Research 8:129–145. Weller, T. J., T. J. Rodhouse, D. J. Neubaum, P. C. Ormsbee, R. D. Dixon, D. L. Popp, J. A. Williams, S. D. Osborn, B. W. Rogers, and L. O. Beard. 2018. A review of bat hibernacula across the western United States: Implications for white-nose syndrome surveillance and management. PloS one 13:e0205647. Whitaker Jr, J. O. 1998. Life history and roost switching in six summer colonies of eastern pipistrelles in buildings. Journal of Mammalogy 79:651–659. 97 Whitehead, H. 2008. Analyzing Animal Societies: Quantitative Methods for Vertebrate Social Analysis. University of Chicago Press, Chicago, IL. Whitehead, H., and S. Dufault. 1999. Techniques for analyzing vertebrate social structure using identified individuals. Advances in the Study of Behavior 28:33–74. Wickham, H. 2009. ggplot2: elegant graphics for data analysis (use R!). Springer, New York. Wilcox, A., and C. K. R. Willis. 2016. Energetic benefits of enhanced summer roosting habitat for little brown bats (Myotis lucifugus) recovering from white-nose syndrome. Conservation Physiology 4:1-12. Wilde, C. J., C. H. Knight, and P. A. Racey. 1999. Influence of torpor on milk protein composition and secretion in lactating bats. Journal of Experimental Zoology 284:35–41. Wilhelm, A. 2012. Use of artificial roosts by bats in central Arizona. Thesis, Northern Arizona University. Flagstaff, AZ. Wilkinson, G. S. 1985. The social organization of the common vampire bat. Behavioral Ecology and Sociobiology 17:123–134. Wilkinson, G. S. 1988. Reciprocal altruism in bats and other mammals. Ethology and Sociobiology 9:85–100. Willis, C. K., and R. M. Brigham. 2004. Roost switching, roost sharing and social cohesion: forest-dwelling big brown bats, Eptesicus fuscus, conform to the fission–fusion model. Animal Behaviour 68:495–505. Willis, C. K., and R. M. Brigham. 2007. Social thermoregulation exerts more influence than microclimate on forest roost preferences by a cavity-dwelling bat. Behavioral Ecology and Sociobiology 62:97–108. 98 Wimsatt, J., T. J. O’Shea, L. E. Ellison, R. D. Pearce, and V. R. Price. 2005. Anesthesia and blood sampling of wild big brown bats (Eptesicus fuscus) with an assessment of impacts on survival. Journal of Wildlife Diseases 41:87–95. Wood, S. N. 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 73:3–36. Zahn, A. 1999. Reproductive success, colony size and roost temperature in attic‐dwelling bat Myotis myotis. Journal of Zoology 247:275–280. Zahn, A., L. Rodrigues, A. Rainho, and J. M. Palmeirim. 2007. Critical times of the year for Myotis myotis, a temperate zone bat: roles of climate and food resources. Acta Chiropterologica 9:115–125. Zeale, M. R., E. Bennitt, S. E. Newson, C. Packman, W. J. Browne, S. Harris, G. Jones, and E. Stone. 2016. Mitigating the impact of bats in historic churches: the response of Natterer’s bats Myotis nattereri to artificial roosts and deterrence. PLoS ONE 11. Zeus, V. M., C. Reusch, and G. Kerth. 2018. Long-term roosting data reveal a unimodular social network in large fission-fusion society of the colony-living Natterer’s bat (Myotis nattereri). Behavioral Ecology and Sociobiology 72:1–13. Zeus, V. M., S. J. Puechmaille, and G. Kerth. 2017. Conspecific and heterospecific social groups affect each other’s resource use: a study on roost sharing among bat colonies. Animal Behaviour 123:329–338. 99 Appendices Appendix A: Example code and parameter estimates for mixed effect models used in Chapter 2. Equation A.1. Example of R code for “Switch” generalized additive mixed effects models, predicting the probability bats will switch (binary response) from a roost. Soothed variables represented with s(). M1 <- gam(switch~s(yesterday.MaxTemp) + s(yesterday.MaxRH) + s(today.SunriseAmibentTemp)+ ReproductiveCondition + s(Date) + (Individual, bs =”re”), data = SwitchData, family = binomial) Equation A.2 Example of R code for “Switch” generalized additive mixed effects models, predicting the probability bats will switch (binary response) from a roost with a interaction between humidity and temperature covariates using a tensor function M2 <- gam(switch~ti(yesterday.MaxTemp) + ti(yesterday.MaxRH) + ti(yesterday.MaxTemp, yesterday.MaxRH) +s(today.SunriseAmibentTemp)+ ReproductiveCondition + s(Date) + (Individual, bs =”re”), data = SwitchData, family = binomial) 100 Equation A.3. Example of R code for “Count” zero-inflated negative binomial mixed effect models, predicting the abundance (count, numeric response) of bats in a roost. Model1 <- glmmTMB(count~ (Tdiff) + (Date) + (1|Roost), data= CountData, ziformula=~1, family=nbinom2()) Model2 <- glmmTMB(count~ (Tdiff) + (1|Roost), data= CountData, ziformula=~1, family=nbinom2()) Model3 <- glmmTMB(count~ (Tr) + (date) + (1|Roost), data= CountData, ziformula=~1, family=nbinom2()) Model4 <- glmmTMB(count~ (Tr) + (1|Roost), data= CountData, ziformula=~1, family=nbinom2()) 101 Table A.1: Parameter estimates for non-parametric reproductive status covariate in generalized additive Switch Models by site in 2019. Reproductive female bats were labeled as “Repro Yes”, non-reproductive bats as “Repro No”, and indeterminate reproductive status was labeled as “Repro Unk”. All covariates were compared to non-reproductive bats as the control. Covariate Estimate Std. Error Z Value P value Alice Lake Unk Repro 0.01 0.31 0.03 0.97 Yes Repro -0.04 0.30 -0.15 0.88 Stave Lake Unk Repro 0.04 0.27 0.13 0.89 Yes Repro -0.16 0.14 -1.18 0.24 Table A.2: Approximate significance of smoother terms for continuous covariates in generalized additive Switch Models by site in 2019. Ambient temperature was measured as the daily temperature at sunrise, while roost temperature and humidity were the maximum values in the roost from the previous day. Significant smoother terms are denoted with **. Covariate EDF Ref. DF Chi square p Value Stave Lake Maximum Temperature (˚C) 5.30 6.41 17.95 <0.01** Maximum Humidity % 6.42 7.23 41.3 <0.01** Ambient Temperature (˚C) 3.39 16.71 16.71 <0.01** Date 6.09 84.64 84.64 <0.01** Alice Lake Maximum Temperature (˚C) 8.54 8.92 50.78 <0.01** Maximum Humidity % 8.65 8.88 23.35 <0.01** Ambient Temperature (˚C) 5.31 6.14 38.39 <0.01** Date 8.61 8.85 110.28 <0.01** 102 Table A 3: Parameter estimates for Count Models. Date represents the Julian Date in 2019. Tr represents the maximum daily roost temperature. Tdiff represented the maximum daily roost temperature minus the maximum daily ambient temperature. Significant covariates are bolded. Model Covariate Estimate Std. Error Z Value P Value Stave Lake Model 1 Tdiff (˚C) 0.25 0.13 1.9 0.6 Date 0.67 0.15 4.29 <0.01 Model 2 Tdiff (˚C) 0.33 0.14 2.27 0.02 Model 3 Tr (˚C) 0.03 0.04 0.85 0.4 Date -0.37 0.02 -20.9 <0.01 Model 4 Tr (˚C) -0.37 0.03 -11.58 <0.01 Alice Lake Model 1 Tdiff (˚C) 0.13 0.14 0.89 0.37 Date 0.69 0.11 5.88 <0.01 Model 2 Tdiff (˚C) -0.25 0.1 -2.36 0.02 Model 3 Tr (˚C) 0.31 0.11 3.00 <0.01 Date 0.69 0.11 6.18 <0.01 Model 4 Tr (˚C) 0.26 0.11 2.31 0.02
Thesis/Dissertation
eng
Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Hodges, Karen E.
Roost selection and social organization of myotis in maternity colonies
Text