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Article

Bat Hibernation: In Groups or Individually?

by
Grzegorz Kłys
1,*,
Zbigniew Ziembik
2 and
Joanna Makuchowska-Fryc
2
1
Institute of Biology, University of Opole, Oleska Street 22, 45-052 Opole, Poland
2
Institute of Environmental Engineering and Biotechnology, University of Opole, Kardynała B. Kominka Street 6. 6a, 45-032 Opole, Poland
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(5), 2125; https://doi.org/10.3390/app14052125
Submission received: 29 January 2024 / Revised: 22 February 2024 / Accepted: 27 February 2024 / Published: 4 March 2024
Browse Figures
Versions Notes

Abstract

:
This study focuses on the hibernation behavior of the western barbastelle bat (Barbastella barbastellus) in underground systems in Poland from 2006 to 2011, specifically during the peak hibernation months of December to February. The impact of climate parameters, namely temperature (T), humidity (Rh), and air flow velocity (v), on the clustering behavior of bats during hibernation was investigated. The climate parameters varied within specific ranges: T fluctuated between 6.0 and 12.4 °C, Rh ranged from 56.4 to 91.8%, and v varied from 0.01 to 1.17 m/s. The quantile linear regression method for statistical analysis of the results was employed. This study found that certain combinations of climate parameters influenced the grouping behavior of bats during hibernation. The model structural parameters revealed the following relationships: 1. An increase in the product of T and v led to an increase in the bats’ group size. 2. For pairs of variables such as T and Rh, and Rh and v, an increase in their product resulted in a decrease in the bats’ group size. 3. When considering the product of T, Rh, and v, a decrease in the bats’ group size was also observed.

Graphical Abstract

1. Introduction

Grouping of bats during hibernation is an important physiological and ecological behavior [1,2]. Although group formation is mainly aimed at minimizing heat loss to the environment and reducing water loss [3,4], it can also serve as protection from predators or maintaining emotional bonds, family ties, flock hierarchy, etc. [5,6].
The grouping of individuals during wintering is variable both within the species and between species [7]. Some bat species winter separately, some form small clusters, while others gather in dense groups [8,9,10], numbering up to several thousands of individuals [11]. Dissimilar aggregation tendencies are also observed within the species in the same winter accommodation. During winter, bats may move and change their roosting place, which sometimes leads to change in the size and composition of grouping in the hibernaculum [9,12].
Hibernation is precisely regulated and controlled [13,14]. The purpose of hibernation is to minimize energy consumption during the unfavorable period of food shortage. Energy consumption is closely related to the refugioclimate of the bat habitat [15]. The metabolic changes in the state of energy equilibrium during hibernation depend on constant factors (species, size, weight, sex, and age of the individual) and variables (values of physical parameters of the environment).
The duration of this condition varies depending on the species [16,17,18,19,20,21,22]. The hibernation state is punctuated by arousals, which can be due to many causes, including an unfavorable hibernaculum microclimate [7] or disease (white-nose syndrome) [23]. An increased number of arousals is a cause of excessive loss of fat reserves, which may make it impossible for bats to survive the winter [24]. Bachorec et al. [25] showed that there is a large individual variability in both the duration of arousals and the maximum body temperature of the bats during arousals.
The study of bat hibernation is often limited to descriptive field observations (e.g., when and where mammals have wintered, whether they wintered separately or in clusters, and what size were the clusters) [26] or physiological research in the laboratory (e.g., study of the metabolism and endocrine system functions of animals), without the environmental context [3]. Initial studies assess the changes in the number of individuals depending on the microclimate of the caves. Most of them concern the bat’s thermopreferendum [27,28,29]. Currently, the description of hibernation is based on the measurements of temperature as the only parameter of the roost site microclimates [29,30], and occasionally temperature and humidity [1,31,32,33,34], or temperature and air flow velocity [24]. Hibernation is also described in terms of physiology [35,36,37]. Authors rarely take into account the temperature, humidity, and air velocity in the thermoneutral zone of selected species at the same time [10]. While many experiments have been conducted on energy expenditure during hibernation, there have been few that were designed to assess the interaction of the various factors that determining the hibernation process [4]. Physiologists often define the conditions that minimize energy expenditure as “optimal” [38]; research and analysis on the hibernation process should not be based only on the determination of these conditions, but should include predicting the behavior of bats (will they hibernate individually, will they form clusters, will they move within the hibernaculum, etc.) [29,39].
The authors selected the western barbastelle bat (Barbastella barbastellus, Schreber. 1774) for their study due to its unique hibernation behavior, which involves both individual and clustered hibernation in underground systems. This species is known to hibernate in highly variable environmental conditions, sometimes forming clusters of several hundred individuals [10]. The choice of this species is justified by its specific requirements, including lower humidity levels and a wide range of ambient temperatures compared to other bat species such as Myotis myotis, Myotis daubentonii, and Rhinolophus hipposideros.
This study aimed to investigate the influence of air temperature (T), relative humidity (Rh), and air flow velocity (v) on the clustering behavior of the western barbastelle during its winter hibernation. The researchers emphasized that the species’ ability to hibernate in diverse conditions makes it suitable for assessing the relationship between these environmental variables [40,41].
The research is deemed important for understanding the hibernation conditions and strategies of the western barbastelle bat. By analyzing changes in roost site climate, specifically in terms of temperature, humidity, and air flow velocity, the study aimed to provide insights into the formation of bat aggregations [42]. The changes in roosting place parameters were examined in the context of their influence on the aggregation of this species.
To achieve their goals, the authors constructed a model describing the influence of physical air parameters on bat grouping during hibernation. The analysis of this model allowed them to outline the relationship between grouping behavior and the determined air parameters, providing an explanation for the observed behavior of the bats. The findings of this research contribute to the understanding of the ecological and physiological factors influencing bat aggregation during hibernation, ultimately supporting conservation measures for the western barbastelle bat.

2. Methods

The research was carried out in the underground systems of Poland: Forty Nyskie (17°18′5″ E; 50°29′4″ N), the fortified part of Międzyrzecze (15°29′2″ E; 52°23′4″ N), and Mopkowy Tunel (15°12′4″ E; 51°48′4″ N). The data on the roost site climate was collected between the years 2002 and 2007 on the basis of the permits of the Provincial Nature Conservators and the Ministry of the Environment (DOPog-4201-04A-2/03/al.; DOPog-4201-04A-6/04/al.; DLOPiK-op/Ozgi-4200/IV.D-16/6568/06/aj). The most important criteria for selecting research sites were the large number of wintering specimens of the selected species [43,44].
Data were collected during the peak of hibernation (December–February). Due to the fact that the hibernating animals were not awakened, neither gender nor age was determined. In this research, the authors limited the impact of the researcher’s presence by minimizing the measurement time and taking measurements from the leeward side. To measure refugioclimate parameters (temperature (T), humidity (Rh), and air flow velocity (v)) SENSOTRON measuring instruments, modified and calibrated for the purposes of this study, were used with the following accuracy ratings [10,15]:
-
Gas parameter meter—for humidity (Rh), range of 0–100%, resolution of indications 0.1% with an uncertainty of indications ±1.5%; for temperature (Ta), range of −50–200 °C, resolution of indications 0.1 °C with an uncertainty of ±0.1 °C; and for atmospheric pressure (p), range of 500–1500 hPa, resolution of indications 1 hPa with an uncertainty of ±2 hPa;
-
Thermo-anemometer (portable digital air speed (v) and temperature meter)—the measurement range was 0.01–20 m/s, with an uncertainty of indications ±0.01 m/s.
The measurements were taken from the side of the incoming air using an extendable arm to prevent the observer from interfering with the readings [15].
This work distinguishes bat hibernation: individual and group (Figure 1 and Figure 2). The influence of the air temperature (T), relative humidity (Rh), and flow speed (v) on bats’ group formation was studied. Using explanatory variables T, Rh, and v, the bats’ count n in the group was modeled. In calculation, total number of 180 observations was used. The authors regard n as the number of observations, this means 180 different hibernation sites (refugioclimate) where T, Rh, and v were measured.

Statistical Methods

The aim of the statistical methods application was the construction of a model describing the influence of the physical air parameters on bats’ grouping.
At the starting point of the analysis, the relationships between explanatory variables T, Rh, and v were studied. For this purpose, Principal Component Analysis (PCA) was used [45,46]. Because the explanatory variables come from different units, before PCA, they were centered with the mean column and scaled with the standard deviation.
Due to relatively low number of bats’ clusters, the quantile regression (QR) model was used [47,48] for the cluster size modeling. In quantile regression, the conditional quantile τ of the response variable, for example median, was estimated.
QR model is an extension of the ordinary linear regression that is used when the conditions of linear regression are not met.
In the quantile regression model, all determined explanatory variables and interactions between them were included. According to Chambers [49], the model is described by the mathematical formula below:
n ~ T*Rh*v
where “~“ separates response variable on the left from the explanatory variables on the right. The “*” operand adds interactions between variables to the model. For numerical variables it includes a point-wise product of the variables. Initially, in model construction, T, Rh, v, and time of observation were included. Taking into account length of the hibernation period and frequency of observations, for time characterization the week number in a year was chosen. Starting from 49th (December) to 7th (February), the week numbers became values of the time categorical variable. In the model including time, for τ = {0.85, 0.9, 0.925, 0.95}, the p-values for the structural parameter βt,τ, related to time were bigger than 0.05. The hypothesis H0: βt,τ = 0 cannot be rejected implying no influence of time variable on bats’ grouping.
In Equation (2) the expanded, but equivalent to Equation (1), form is presented:
n ~ T + Rh + v + T:Rh + T:v + Rh:v + T:Rh:v
where “+” separates predictor terms in an additive model and “:” separates interacting terms.
The statistical computations were performed with use of R language [50]. For QR computation, the R package quantile regression was used [51].

3. Results

Table 1 shows the summary of statistical parameters that describe the observations.
During observations, the range of the temperature variations was 6.4 °C. But, at most, 50% of the temperatures changed in the interquartile range IQR (IQR = q75 − q25) of the width 1.62 °C. This value indicates stable temperature conditions during at least a half of the bats’ observation period. Changes in relative humidity exceeded 34%. But, similarly to for T, the predominating changes measured by IQR did not exceed 7%. The wind speed change fluctuated from nearly 0 up to almost 1.2 m/s. The corresponding IQR was 0.10. Group formation was not frequent. Starting from the 91% quantile of n, values bigger than 1 were observed.
The structure and relationships between explanatory variables were assessed with use of the PCA method. The share of the first principal component (PC) in variability is 58% of the total variance and the share of PC2 is 25%. The first two PCs contain 83% of the total variance.
In Figure 3, a biplot of PC1 and PC2 is presented. There were no significant relationships between the parameters during the study. The points describing physical parameters form a relatively homogeneous structure; however, several outliers can be observed. The lack of a tendency to grouping of observations and common relationships among them makes these variables suitable for predictors in a regression model.
Equation (3) shows the expression describing the relationship between three explanatory variables and the response:
n = β0+ β1T + β2Rh + β3v + β4TRh + β5Tv + β6Rhv + β7TRhv
In the expression βi (I = {1…7}) are the structural parameters of the model.
For τ = {0.925, 0.950, 0.975}, the values of the structural parameters and corresponding standard errors SE, and p-values for the null hypothesis H0: βi = 0 were calculated. The standard errors were computed with the use of the bootstrap method.
Table 2 shows the results of the computations.
The rejection of the null hypothesis H0 for a structural parameter implies that the associated variables are relevant and contribute meaningfully to the model. These variables or their functions are needed to effectively model and explain the response variable in the study. For p-values higher than a certain limit, for example 0.05 or 0.01, H0 cannot be rejected. Since a 0 value of the structural parameter can be expected, the contribution of the corresponding component to the response variable is also 0.
The critical p-value = 0.05 was considered for rejection of the null hypothesis. The H0 hypothesis cannot be rejected for all structural parameters when τ = 0.95. No statistically significant relationships between air parameters and bats’ grouping were observed. Both for τ = 0.95 and 0.975, the interaction terms were significantly different from 0, though for β7 the critical value was slightly exceeded. The null hypothesis can also be rejected for the intercept β0.

4. Discussion

The visible influence of T, Rh, and v on the grouping of the bats was not observed. The computation results indicate the influence of interactions between these parameters on the response. The interaction between the two numerical variables in the regression model produces a new variable—the product of the selected variables. Because interactions are products, their influence is important when both factors increase or decrease together. If the value of one factor increases and the value of the other decreases, their product changes only slightly and no influence on the predictor is observed.
The main results from the quantile regression model application for the bats’ cluster size description can be summarized as follows:
  • A negative value of β4 indicates a decrease in the bats’ group size with an increasing product of T and Rh;
  • A positive value of β5 indicates an increase in the bats’ group size with an increasing product of T and v;
  • A negative value of β6 indicates a decrease in the bats’ group size with an increasing product of v and Rh;
  • A negative value of β7 indicates a decrease in the bats’ group size with an increasing product of T, v, and Rh.
The purpose of hibernation is to minimize energy consumption during the unfavorable period of food shortage. Energy consumption is closely related to the refugioclimate of the bat habitat [15].
Basic parameters characterizing the microclimate and refugioclimate are temperature (T), humidity (Rh), and air velocity (v) [52,53]. Energy consumption is closely related to the refugioclimate and changes in these parameters impact the thermal comfort of hibernating bats.
An increase in the product of temperature (T) and air velocity (v) leads to clustering of Barbastella barbastellus. The temperature of bats usually oscillates around the ambient temperature [1,10,54,55], regardless of whether the animals hibernate individually or in aggregation.
Air flow velocity affects the intensity of the heat exchange process in convection mechanisms. The speed of the air flow has a decisive influence on the intensity of the heat exchange process in the convection mechanism. It determines the type of flow (laminar, transitional, or turbulent), which, in turn, determines the thickness of the laminar (Prandtl) layer that forms around the bat’s body. The (Prandtl) film has the main resistance to heat penetration because it conducts heat less efficiently compared to heat transfer in a turbulent area. The greater the velocity values, the smaller the Prandtl film thickness and the more intense the heat transfer.
An increase in the air flow velocity (v) also increases the reception of moisture from the bat’s organism to the environment or increases evaporation due to the transport of warmer or drier air, displacing the moist, cool air surrounding the bat [56]. At the same time, the higher the degree of air saturation with water vapor, the more difficult it becomes to collect heat by evaporation [13,57].
With an increase in T and Rh, the metabolic rate increases and heat transfer occurs mainly by convection. The increase in T also raises the evaporation of water from the bat’s organism, increasing energy loss. The increase in air Rh, on the other hand, reduces water loss [56], allowing bats to hibernate individually.
Energy consumption depends on the microclimate, specifically the roost site climate in which bats hibernate. Bats may form clusters or leave the hibernaculum in conditions causing excessive energy loss [6,58]. The climatic conditions of the underground are influenced by factors such as the height of the opening above sea level, the exposure of the opening, the existence of vegetation in front of the opening, the morphology of the cave (length and shape of the passages and halls), the existence of heat-transporting water streams, and heat energy from within the earth [24,59,60,61]. Both “warm” and “cold” underground systems can be found in each of the mentioned climate zones. Weather also influences the behavior of bats hibernating in underground systems [61]. The authors show that air and soil temperature, atmospheric pressure, the atmospheric pressure trend, precipitation rate, wind speed, and cloud coverage affect the spring activity of bats in wintering sites. However, there is a lack of reference to the reasons for and criteria for the selection of hibernation sites by bats.
Factors influencing the selection of hibernation sites by bats include temperature, humidity, and other air parameters. Different bat species may exhibit varying hibernation strategies influenced by air parameters. Some species may prefer thermally stable habitats, while others choose conditions that vary in temperature. The work of Wermundsen and Siivonen [56] verified the belief that some bat species prefer thermally stable habitats, while others choose conditions that vary in terms of temperature. The authors of this article observed that bats (Eptesicus nilssonii, Myotis brandtii/mystacinus, Myotis daubentonii, and Plecotus auritus) in the initial period of hibernation chose places with the highest air temperature and humidity, and, in the last stage, places with the lowest values of these parameters, although the range and extremes of temperatures were similar throughout all hibernation periods. Encarnação et al. [62], in studies on the influence of the habitat on the hibernation strategy of Myotis daubentonii bats, observed the occupation of more favorable habitats by reproductive females and less favorable by males. Males from different habitats showed different hibernation strategies that depended, among other things, on air parameters. In less favorable conditions of the habitat, they observed the grouping of males into clusters, which they explained as an energy-saving strategy. They used temperature and air humidity measurements to determine the conditions of the roost site climate. Koch et al. [61] claim that social thermoregulation can compensate for the unfavorable microclimatic conditions prevailing in roosts in late spring. According to our observations, bats, especially Barbastella barbastellus, hibernate both in clusters and individually throughout the hibernation period. McGuir et al. [4] argue that bat species that are less susceptible to dehydration may be more flexible in their microhabitat requirements and thus choose cooler, drier areas in underground hiding places.
The formation of clusters causes the reduction of ratio in the surface area to body weight and, consequently, a smaller evaporation and heat exchange (giving up). This ratio is of great importance especially in small animals. It is commonly believed that clusters can facilitate energy saving, e.g., by reducing water losses in the body [34,57,63,64,65,66,67,68,69] or compensate for energy losses [1]. Often the effect of hydration is more important than nutritional status. Even in conditions of high air humidity (90–98%), the loss of moisture can be significant, depending on the intensity of metabolic processes, which in turn depend on the rate of heat loss to the environment.
Boratyński et al. [3] have shown that moisture loss (EWL) by evaporation in grouping individuals was lower than the EWL of individually hibernating bats at both low and high relative air humidity. The experiments were conducted in the laboratory; Myotis nattereri bats were studied. Hibernation strategies (individual, group) and preset of environmental conditions were planned. The reduction in EWL in grouped bats was due to a reduction in the contact area between the skin and ambient air, rather than respiration. The effect of grouping was a reduced number of arousals due to the need to restore water balance. However, these authors did not study under what physical conditions of the air the clustering of bats occurs. Similar conclusions were reached by Thomas and Cloutier [57] who showed that more than 99% of EWL comes from the body surface and that any reduction in exposed surface area results in a proportional reduction in EWL.
Our own observations show that, unlike other species, Barbastella barbastellus bats hibernate at a relative air humidity that rarely exceeds 90%. Approaching the upper limit of the humidity range causes the aggregation of individuals into clusters, which is confirmed by the studies of other authors [2,13,57], while there is no reference to the correlation of the values of other air parameters.
Analyzing the microclimate during bat hibernation in relation to one of these parameters (e.g., T, Rh, or v) does not answer the question of whether a given environment is suitable for both a single individual and a group of individuals. When taking into account the variables T, Rh, and v, the hibernation of individual animals was observed in the conditions of a simultaneous increase in T, Rh, and v values or a simultaneous decrease in the values of these parameters. When the values of all parameters are decreasing simultaneously, the metabolic rate decreases and the evaporation intensity decreases, so low air velocities are sufficient for heat dissipation.
In summary, this text provides a detailed exploration of the physiological and ecological factors influencing the hibernation behavior of Barbastella barbastellus and other bat species. It highlights the complex interplay of temperature, humidity, and air velocity in shaping the hibernation strategies of these fascinating mammals.

5. Conclusions

It was found that the clustering of bats is closely associated with the analyzed parameters. The statistical analysis using the quantile linear regression method showed that the interactions (products) between the parameters T, v, and Rh are statistically significant in opposition to the single parameter values.
It was found that the clustering of bats is closely associated with the observed parameters of the air. The statistical analysis using the quantile linear regression method showed an influence of T, v, and Rh on bats’ grouping. The observed relationship between the bats’ number in cluster and the air parameters is not additive, simply summing the independent effects of T, v, and Rh. For the air parameter products, T*Rh, T*v, and Rh*v, the p-values of the corresponding structural parameter βi in the model were lower than 0.05. A significant impact of the air parameter products on the bats’ grouping was concluded.

Author Contributions

G.K., J.M.-F. and Z.Z. developed the research questions, experimental design, and methods. G.K. conducted the fieldwork and lab work, and led data analysis with input from J.M.-F. and Z.Z. They wrote and edited the manuscript. Z.Z. carried out a statistical analysis. All authors have read and agreed to the published version of the manuscript.

Funding

This research was financed by the University of Opole within the institutes: Biology, Environmental Engineering and Biotechnology.

Institutional Review Board Statement

The permits were issued by the Provincial Conservators of Nature and the Minister of the Environment (DO-Pog-4201-04A-2/03/al, 12 February 2006; DOPog-4201-04A-6/04/al, 5 June 2004; DLOPiK-op/Ozgi 4200/IV.D-16/6568/06/aj, 16 March 2006). These studies did not interfere with the bats’ bodies.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data and code are provided as private for peer review via the following link: https://repo.uni.opole.pl/info/researchdata/UO30dbd9c5ed004e7eae780273fdb8c917/Szczeg%25C3%25B3%25C5%2582y%2Brekordu%2B%25E2%2580%2593%2BDane%2Bbadawcze%2B%25E2%2580%2593%2BUniwersytet%2BOpolski?r=researchdata&ps=20&tab=&lang=pl&pn=1&cid=1706867. Upon acceptance, data will be provided via the Knowledge Base of the University of Opole via the link provided above.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Boyles, J.G.; Storm, J.J.; Brack, V., Jr. Thermal benefits of clustering during hibernation: A field test of competing hypotheses on Myotis sodalis. Funct. Ecol. 2008, 22, 632–636. [Google Scholar] [CrossRef]
  2. Ransome, R.D. The Natural History of Hibernating Bats; Christopher Helm: London, UK, 1990. [Google Scholar]
  3. Boratyński, J.S.; Willis, C.K.R.; Jefimow, M.; Wojciechowski, M.S. Huddling reduces evaporative water loss in torpid Natterer’s bats, Myotis nattereri. Comp. Biochem. Physiol. Part A 2015, 179, 125–132. [Google Scholar] [CrossRef]
  4. McGuire, L.P.; Johnson, E.M.; Frick, W.F.; Boyles, J.G. Temperature alone is insufficient to understand hibernation energetics. J. Exp. Biol. 2021, 224, jeb239772. [Google Scholar] [CrossRef]
  5. Kunz, T.H.; Linda, F.L. Ecology of cavity and foliage roosting bats. In Bat Ecology; Kunz, T.H., Fenton, M.B., Eds.; The University of Chicago Press: Chicago, IL, USA, 2003; pp. 3–89. [Google Scholar]
  6. Geiser, F. Ecological Physiology of Daily Torpor and Hibernation; Springer: Cham, Switzerland, 2021. [Google Scholar]
  7. Haase, C.G.; Fuller, N.W.; Dzal, Y.A.; Hranac, C.R.; Hayman, D.T.S.; Lausen, C.L.; Silas, K.A.; Olson, S.H.; Plowright, R.K. Body mass and hibernation microclimate may predict bat susceptibility to white-nose syndrome. Ecol. Evol. 2021, 11, 506–515. [Google Scholar] [CrossRef]
  8. Smirnov, D.G.; Kurmaeva, N.M.; Vekhnik, V.P. Population dynamics and spatial distribution of wintering bats (Chiroptera, Vespertilionidae) in one of the galleries of Samarskaya Luka. Plecotus 1999, 2, 67–78. (In Russian) [Google Scholar]
  9. Tomilenko, A.A. Wintering of bats (Vespertilionidae) in the Novosibirsk region. Plecotus 2002, 99–106. (In Russian) [Google Scholar]
  10. Kłys, G. Multifactor Analysis of Refugioclimate in Places of Hibernation of Chosen Bat Species; T. 8 Studia Chiropterologica; Chiropterological Information Center, Institute of Animal Systematics and Evolution, Polish Academy of Sciences: Krakow, Poland, 2013. [Google Scholar]
  11. Betke, M.; Hirsch, D.E.; Makris, N.C.; McCracken, G.F.; Procopio, M.; Hristov, N.I.; Teng, S.; Bacchi, A.; Reichard, J.; Horns, J.W.; et al. Termal imaging reveals significantly smaller Brazilian free-tailed bat colonie than previously estimated. J. Mammal. 2008, 89, 18–24. [Google Scholar] [CrossRef]
  12. Orlova, N.G.; Dmitriev, V.E.; Rybakov, S.A. Conditions and places of wintering of bats on the eastern slope of the Kuznetsk Alatau. In Ecology of Terrestrial Vertebrates of Siberia; TGU: Tomsk, Russia, 1983; pp. 53–59. (In Russian) [Google Scholar]
  13. Thomas, D.W. The physiological ecology of hibernation in vespertilionid Bats. Symp. Zool. Soc. Lond. 1995, 67, 233–244. [Google Scholar]
  14. Janicki, B.; Cygan–Szczegielniak, D. Hibernacja zwierząt. Med. Weter. 2006, 4, 366–369. [Google Scholar]
  15. Kłys, G.; Makuchowska-Fryc, J. Wintering Conditions and Heat Loss during Hibernation in the Brown Long-Eared Bat. Appl. Sci. 2024, 14, 716. [Google Scholar] [CrossRef]
  16. Nelson, R.A. Protein and FAT metabolizm, in hibernating bears. Fed. Proc. 1980, 39, 2955–2958. [Google Scholar]
  17. Storey, K.B.; Storey, J.M. Facultative metabolic rate depression: Molecular regulation and biochemical adaptation in anaerobiosis, hibernation and aestivation. Q. Rev. Biol. 1990, 65, 145–174. [Google Scholar] [CrossRef]
  18. Hoffman, R.A. Speculations on the regulation of hibernation. Ann. Acad. Sci. Fenn. Set A 4 1964, 71, 199–216. [Google Scholar]
  19. Lyman, C.P.; Willis, J.S.; Malan, A.; Wang, L.H.C. Hibernation and Torpor in Mammals and Birds; Academic Press: New York, NY, USA, 1982. [Google Scholar]
  20. Wang, L.C.H. Mammalian hibernation. In The Effects of Low Temperature on Biological Systems; Grout, B.W.W., Morris, G.J., Eds.; Edward Arnold: London, UK, 1987; pp. 349–386. [Google Scholar]
  21. French, A.R. The patterns of mammalian hibernation. Am. Sci. 1988, 76, 569–575. [Google Scholar]
  22. Geiser, F.; Ruf, T. Hibernation versus daily torpor in mammals and birds: Physiological variables and classification of torpor patterns. Physiol. Zool. 1995, 68, 935–966. [Google Scholar] [CrossRef]
  23. Fritze, M.; Pham, T.L.H.; Zaspel, I. Effekt des Bodenbakteriums Pseudomonas veronii-like PAZ1 auf das Wachstum des White-Nose Erregers Geomyces destructans in Antagonisten-Tests. Nyctalus 2012, 17, 104–107. [Google Scholar]
  24. Hranac, C.R.; Haase, C.G.; Fuller, N.W.; McClure, M.L.; Marshall, J.C.; Lausen, C.L.; McGuire, L.P.; Olson, S.H.; Hayman, D.T. What is winter? Modeling spatial variation in bat host traits and hibernation and their implications for overwintering energetics. Ecol. Evol. 2021, 11, 11604–11614. [Google Scholar] [CrossRef] [PubMed]
  25. Bachorec, E.; Bartonicka, T.; Heger, T.; Pikula, J.; Zukal, J. Cold arousal—A mechanism used by hibernating bats to reduce the energetic costs of disturbance. J. Therm. Biol. 2021, 101, 103–107. [Google Scholar] [CrossRef] [PubMed]
  26. Martínková, N.; Baird, S.J.E.; Kána, V.; Zima, J. Bat population recoveries give insight into clustering strategies during hibernation. Front. Zool. 2020, 17, 26. [Google Scholar] [CrossRef]
  27. Gaisler, J. Remarks on the thermopreferendum of Palearctic bats in their Natural Habitats. Bijdr. Dierkd. 1970, 40, 33–36. [Google Scholar] [CrossRef]
  28. Bauerova, Z.; Zima, J. Seasonal changes in visits to a cave by bats. Folia Zool. 1988, 37, 97–111. [Google Scholar]
  29. Boyles, J.G.; Johnson, J.S.; Thomas, A.B.; Lilley, M. Optimal hibernation thery. Mammal Rev. 2019, 50, 91–100. [Google Scholar] [CrossRef]
  30. Harmata, W. The thermopreferendum of some species of bats (Chiroptera). Acta Theriol. 1969, 14, 49–62. [Google Scholar] [CrossRef]
  31. Kunz, T.H.; Anthony, E.L.P. Age estimation and post-natal growth in the bat Myotis lucifugus. J. Mammal. 1982, 63, 23–32. [Google Scholar] [CrossRef]
  32. Nagel, A.; Nagel, R. How do bats choose optimal temperatures for hibernation? Comp. Biochem. Physiol. Part A Physiol. 1991, 99, 323–326. [Google Scholar] [CrossRef]
  33. Visnovska, Z. Spatial distribution of hibernating bats (Chiroptera) in relation to climatic conditions in the Demanovska ice cave (Slovakia). In Proceedings of the 2nd International Workshop on Ice Caves, Demanovska Dolina, Slovakia, 8–12 May 2006; pp. 87–97. [Google Scholar]
  34. Boratyński, J.; Rusiński, M.; Kokurewicz, T.; Bereszyński, A.; Wojciechowski, M. Clustering behavior in wintering great er Mouse-eared bats Myotis myotis—The effect of microenvironmental conditions. Acta Chiropterologica 2012, 14, 417–424. [Google Scholar] [CrossRef]
  35. Stawski, C.; Willis, C.K.R.; Geiser, F. The importance of temporal heterothermy in bats. J. Zool. 2014, 292, 86–100. [Google Scholar] [CrossRef]
  36. Willis, C.K.R. Trade-offs Influencing the Physiological Ecology of Hibernation in Temperate-Zone Bats. Integr. Comp. Biol. 2017, 57, 1214–1224. [Google Scholar] [CrossRef]
  37. McGuire, L.P.; Fuller, N.W.; Dzal, Y.A.; Haase, C.G.; Silas, K.A.; Willis, C.K.R.; Olson, S.H.; Lausen, C.L. Similar hibernation physiology in bats across broad geographic ranges. J. Comp. Physiol. B 2022, 192, 171–181. [Google Scholar] [CrossRef]
  38. Day, K.M.; Tomasi, T.E. Winter energetics of female Indiana bats Myotis sodalis. Physiol. Biochem. Zool. 2014, 87, 56–64. [Google Scholar] [CrossRef]
  39. Humphries, M.M.; Thomas, D.W.; Kramer, D.L. The role of energy availability in mammalian hibernation: A cost-benefit approach. Physiol. Biochem. Zool. 2003, 76, 165–179. [Google Scholar] [CrossRef]
  40. Bogdanowicz, W.; Urbańczyk, Z. Some ecological aspects of bats hibernating in city of Poznań. Acta Theriol. 1983, 28, 371–385. [Google Scholar] [CrossRef]
  41. Lesiński, G. Wpływ Antropogenicznych Przekształceń Krajobrazu na Strukturę i Funkcjonowanie Zespołów Nietoperzy w Polsce; Wydawnictwo SGGW: Warsaw, Poland, 2006. [Google Scholar]
  42. Kłys, G.; Wołoszyn, B. The influence of weather and interior microclimate on the hibernation of common long-eard bats (Plecotus auritus). Nat. J. 2005, 38, 57–68. [Google Scholar]
  43. Kłys, G. Wybrane aspekty hibernacji nietoperzy. In Wpływ Środowiskowych Warunków na Wybór Hibernaculum Przez Nietoperze; Wołoszyn, B.W., Yagt-Yazykova, E., Kuśnierz, A., Eds.; ZPW Plik: Bytom, Poland, 2008. [Google Scholar]
  44. Kokurewicz, T. Ochrona nietoperzy w obszarze Natura 2000 “Nietoperek” z perspektywy 20 lat do’swiadcze´n. In Materiały Ogólnopolskiej Konferencji Chiropterologicznej; Warchałowski, M., Ed.; Grunwald24: Krynica Zdrój, Poland, 2013; pp. 36–37. (In Polish) [Google Scholar]
  45. Jackson, J.E. A User’s Guide to Principal Components; John Wiley & Sons: New York, NY, USA, 1991. [Google Scholar] [CrossRef]
  46. Jolliffe, I.T. Principal Component Analysis, 2nd ed.; Springer: New York, NY, USA, 2002. [Google Scholar]
  47. Koenker, R.; Chernozhukov, V.; He, X.; Peng, L. Handbook of Quantile Regression; Chapman & Hall/CRC Handbooks of Modern Statistical Methods; CRC Press, Taylor & Francis Group: Boca Raton, FL, USA, 2018. [Google Scholar]
  48. Davino, Q.C.; Furno, M.; Vistocco, D. Quantile Regression; Wiley Series in Probability and Statistics; Wiley: Hoboken, NJ, USA, 2014. [Google Scholar]
  49. Chambers, J.M.; Hastie, T.J. Statistical Models in S; Chapman & Hall Computer Science Series; Chapman & Hall: New York, NY, USA, 1993. [Google Scholar]
  50. R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2020; Available online: http://www.R-project.org (accessed on 2 November 2023).
  51. Koenker, R. Quantreg: Quantile Regression. R Package Version 5.67 (Version 5.67). R. 2020. Available online: https://CRAN.R-project.org/package=quantreg (accessed on 2 November 2023).
  52. Pawiński, J.; Roszkowski, J.; Strzemiński, J. Przewietrzanie Kopalń; Śląskie Wydawnictwo Techniczne: Katowice, Poland, 1995. [Google Scholar]
  53. Paszyński, J.; Miara, K.; Skoczek, J. Wymiana Energii Między Atmosferą a Podłożem Jako Podstawa Kartowania Topoklimatycznego; Dokumentacja Geograficzna 14; IG i PZ PAN: Warsaw, Poland, 1999. [Google Scholar]
  54. Geiser, F. Metabolic rate and body temperature reduction during hibernation and daily torpor. Annu. Rev. Physiol. 2004, 66, 239–274. [Google Scholar] [CrossRef]
  55. Dunbar, M.B.; Tomasi, T.E. Arousal patterns, metabolic rate, and an energy budget for ekstern red bats (Lasiurus borealis) in Winter. J. Mammal. 2006, 87, 1096–1102. [Google Scholar] [CrossRef]
  56. Wermundsen, T.; Siivonen, Y. Seasonal variation in use of winter roosts by five bat species in south-east Finland. Cent. Eur. J. Biol. 2010, 5, 262–273. [Google Scholar] [CrossRef]
  57. Thomas, D.W.; Cloutier, D. Evaporative water by hibernating little brown bats, Myotis lucifugus. Physiol. Zool. 1992, 65, 443–456. [Google Scholar] [CrossRef]
  58. Gilbert, C.; McCafferty, D.; LeMaho, Y.; Martrette, J.M.; Giroud, S.; Blanc, S.; Ancel, A. One for all and all for one: The energetics benefits of huddling in endotherms. Biol. Rev. Camb. Philos. Soc. 2010, 85, 545–569. [Google Scholar] [CrossRef]
  59. Gottfried, I.; Gottfried, T.; Lesiński, G.; Hebda, G.; Ignaczak, M.; Wojtaszyn, G.; Jurczyszyn, M.; Fuszara, M.; Fuszara, E.; Grzywiński, W.; et al. Long-term changes in winter abundance of the barbastelle Barbastella barbastellus in Poland and the climate change—Are current monitoring schemes still reliable for cryophilic bat species? PLoS ONE 2020, 15, e0227912. [Google Scholar] [CrossRef]
  60. McClure, M.L.; Crowley, D.; Haase, C.G.; McGuire, L.P.; Fuller, N.W.; Hayman, D.T.S.; Lausen, C.L.; Plowright, R.K.; Dickson, B.G.; Olson, S.H. Linking surface and subterranean climate: Implications for the study of hibernating bats and other cave dwellers. Ecosphere 2020, 11, e03274. [Google Scholar] [CrossRef]
  61. Koch, M.; Manecke, J.; Burgard, J.P.; Münnich, R.; Kugelschafter, K.; Kiefer, A.; Veith, M. How weather triggers the emergence of bats from their subterranean hibernacula. Sci. Rep. 2023, 13, 6344. [Google Scholar] [CrossRef] [PubMed]
  62. Encarnação, J.A.; Reiners, T.E. Erratum to: Mating at summer sites: Indications fromparentage analysis and roosting behaviour of Daubenton’s bats (Myotis daubentonii). Conserv. Genet. 2012, 13, 1433. [Google Scholar] [CrossRef]
  63. Studier, E.H. Evaporative water loss in bats. Comp. Biochem. Physiol. 1970, 35, 935–943. [Google Scholar] [CrossRef]
  64. Procter, J.W.; Studier, E.H. Effects of ambient temperature and water vapor pressure on evaporative water loss in Myotis lucifugus. J. Mammal. 1970, 51, 799–804. [Google Scholar] [CrossRef]
  65. Stapp, P.; Pekins, P.J.; Mautz, W.W. Winter energy expenditure and the distribution of southern flying squirrels. Can. J. Zool. 1991, 69, 2548–2555. [Google Scholar] [CrossRef]
  66. Canals, M. Thermal energetic of small animals. Biol. Res. 1998, 31, 367–374. [Google Scholar]
  67. Brown, P.E. California leaf-nosed bat Macrotus californicus. In The Smithsonian Book of North American Mammals; Wilson, D.E., Ruff, S., Eds.; Smithsonian Institution Press: Washington, DC, USA, 1999; pp. 74–75. [Google Scholar]
  68. Jefimow, M.; Głąbska, M.; Wojciechowski, M.S. Social thermoregulation and torpor in Siberian hamster. J. Exp. Biol. 2011, 214, 1100–1108. [Google Scholar] [CrossRef]
  69. Wojciechowski, M.; Jefimow, M.; Pinshow, B. Heterothermy, and the Energetic Consequences of Huddling in Small Migrating Passerine Birds. Integr. Comp. Biol. 2011, 51, 409–418. [Google Scholar] [CrossRef]
Applsci 14 02125 g001
Figure 1. Wintering of a single individual of Barbastella barbastellus.
Figure 1. Wintering of a single individual of Barbastella barbastellus.
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Figure 2. Group wintering of Barbastella barbastellus.
Figure 2. Group wintering of Barbastella barbastellus.
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Figure 3. The biplot containing projected scores in PC1 and PC2 coordinates.
Figure 3. The biplot containing projected scores in PC1 and PC2 coordinates.
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Table 1. The statistical parameters of the data collected during observation. The min and max are the minimum and maximum values in the data, q25 and q75 are, respectively, the lower and upper quartile, median is the median (q50) value, mean is the arithmetic average, and SD stands for the standard deviation.
Table 1. The statistical parameters of the data collected during observation. The min and max are the minimum and maximum values in the data, q25 and q75 are, respectively, the lower and upper quartile, median is the median (q50) value, mean is the arithmetic average, and SD stands for the standard deviation.
ParameterT [°C]Rh [%]v [m/s]n [-]
min6.0056.40.011
q258.2874.50.051
median9.0578.40.121
q759.9081.30.151
max12.4091.81.17220
mean8.8777.70.194.5
SD1.296.80.2518.9
T—air temperature; Rh—relative humidity of the air; v—air flow speed; n—number of bats in a given location.
Table 2. The structural parameter βi, its standard error SE, and p-values for null hypothesis H0: βi = 0.
Table 2. The structural parameter βi, its standard error SE, and p-values for null hypothesis H0: βi = 0.
Structural ParameterValueSEp-Value
τ = 0.925
β010130.431
β1−3.37.80.676
β21.48.80.871
β32170.916
β4−3.55.60.532
β54110.745
β6−3120.810
β7−1.86.80.794
τ = 0.950
β046160.006
β19.29.60.335
β2−14100.185
β335190.061
β4−16.77.10.019
β531140.029
β6−29130.030
β7−16.98.20.040
τ = 0.975
β056160.001
β13110.799
β2−9130.489
β328190.145
β4−20.78.30.014
β537160.021
β6−29130.030
β7−18.09.60.062
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Kłys, G.; Ziembik, Z.; Makuchowska-Fryc, J. Bat Hibernation: In Groups or Individually? Appl. Sci. 2024, 14, 2125. https://doi.org/10.3390/app14052125

AMA Style

Kłys G, Ziembik Z, Makuchowska-Fryc J. Bat Hibernation: In Groups or Individually? Applied Sciences. 2024; 14(5):2125. https://doi.org/10.3390/app14052125

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Kłys, Grzegorz, Zbigniew Ziembik, and Joanna Makuchowska-Fryc. 2024. "Bat Hibernation: In Groups or Individually?" Applied Sciences 14, no. 5: 2125. https://doi.org/10.3390/app14052125

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