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Bat Activity Increases with Barometric Pressure and Temperature During Autumn in Central Georgia
Michael J. Bender and Gregory D. Hartman

Southeastern Naturalist, Volume 14, Issue 2 (2015): 231–242

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Southeastern Naturalist 231 M.J. Bender and G.D. Hartman 22001155 SOUTHEASTERN NATURALIST 1V4o(2l.) :1243,1 N–2o4. 22 Bat Activity Increases with Barometric Pressure and Temperature During Autumn in Central Georgia Michael J. Bender1,* and Gregory D. Hartman1 Abstract - Activity patterns of bats are known to vary among nights, seasons, years, and geographic regions, but the underlying reasons for those patterns are poorly understood. Our objectives were to assess the temporal variability of bat activity during autumn in central Georgia, and to evaluate the influence of barometric pressure and nighttime temperature on nightly activity using Akaike’s information criterion and regression models. We recorded 43,168 bat calls and 5839 sequences using an ANABAT SD2 detector during 65 sample nights (4 September to 11 November 2011) at a residence in Barnesville, GA. The number of sequences recorded nightly ranged from 3 to 551. Nightly bat activity was positively related to average nightly temperature and average nightly barometric pressure. In contrast to our expectations, bat activity was not related to changes in barometric pressure prior to or during sample nights. The positive relationship between bat activity, temperature, and barometric pressure may be related to the energetic costs and benefits associated with flight and prey availability during autumn in central Georgia. Introduction Activity patterns play a critical role in an individual’s resource acquisition and energy consumption (Conover and Caudell 2009), competition and exposure to predators (Speakman et al. 2000), and exposure to environmental conditions (Cain et al. 2006). Effective and science-based wildlife management requires knowledge of activity patterns and the factors that drive those patterns. The primary drivers or cues for seasonal and annual activity patterns, such as photoperiod (Dixit and Singh 2011), temperature (Rambaldini and Brigham 2008, Vaughan et al. 1997), and resource availability (Vasey 2005), have been reasonably well researched and documented. The drivers of smaller-scale temporal activity patterns have been studied less, but a variety of weather-related factors seem to influence these patterns in many animals, including bats (Hayes 1997). For example, cool temperatures (Anthony et al. 1981) and precipitation generally suppress bat activity (Fenton et al. 1977, Kunz 1973), possibly because these conditions may increase thermoregulatory costs or decrease the amount of prey available to bats (Burles et al. 2009). Precisely which weather factors influence the activity of bats and why is poorly understood, and it is not known if the influence of these factors varies by habitat, region, or season. Changes in weather conditions often are associated with changes in barometric pressure. The hypothesis that barometric pressure influences the activity of animals, possibly because of associated weather changes, is entrenched in popular 1Department of Biology, Gordon State College, Barnesville, GA 30204. *Corresponding author - Manuscript Editor: Andrew Edelman Southeastern Naturalist M.J. Bender and G.D. Hartman 2015 Vol. 14, No. 2 232 media directed at sportsmen (e.g., Nelson 2011, Takasaki and Richardson 2009). Albeit scarce, there is empirical evidence to support this hypothesis. For example, Hein et al. (2011) reported that activity of a night-migratory songbird decreased as barometric pressure increased. However, a thorough understanding of the influence of atmospheric pressure on bats is precluded by a paucity of data and contrasting results from previous studies. For example, a positive relationship between barometric pressure and activity of bats was reported by Berkova and Zukal (2010), Milne et al. (2005), and Wolcott and Vulinec (2012), but a negative relationship between barometric pressure and activity of bats was reported by Paige (1995) and Turbil (2008). Contrasting patterns reported in these published studies may have been the result of differences in sampling methodology, season, resident-bat species, or sampled habitat types. The primary objective of our study was to evaluate the influence of temperature and barometric pressure on the autumn-activity pattern of bats in central Georgia. Responses to environmental and habitat conditions may vary among bat species, however, investigations of the activity of bats as a group often have revealed important aspects of bat ecology (Barros et al. 2014, Hagen and Sabo 2011, Threlfall et al. 2012, Wolcott and Vulinec 2012). We sampled during autumn because we considered it likely that bats make foraging choices during this season primarily based on weather conditions. At other times of the year, the energy demands of gestation, lactation, or emergence from hibernation might override the influence of weather on foraging. Because changes in barometric pressure are linked with weather changes, we predicted that change in barometric pressure would be a better predictor of bat activity than average nightly pressure. Based on what is known about bat ecology in temperate regions, we predicted that bats would be active throughout our sample period and that activity levels would vary with average nightly temperature. Because activity patterns likely are regulated by many factors, both exogenous and endogenous (Anthony et al. 1981, Hayes 1997), we expected that bat activity would be modeled best with some additive combination of temperature and barometric pressure. Another objective of our study was to document the temporal variability in bat activity recorded at a sample point. Few data are available concerning the variability of bat activity in the southeastern US, particularly during autumn, and without such data researchers and land managers must rely on information that may not be applicable to the southeast or during autumn. Field-site Description We conducted our study in a residential area in Barnesville, the county seat of Lamar County, in central Georgia. Geographic coordinates of our sampling locality were 33°03'02.19"N, 84°09'34.30"W. Barnesville is a small town with a human population of approximately 6700. Lamar County is ~76% forested and is characterized by warm and humid summers and relatively mild winters. To date, 9 bat species have been captured in Lamar County including: Tadarida brasiliensis (I. Geoffroy) (Mexican Free-Tailed Bat), Eptesiscus fuscus (Beauvois) (Big Brown Bat), Lasionycteris noctivagans (Le Conte) (Silver-haired Bat), Lasiurus borealis Southeastern Naturalist 233 M.J. Bender and G.D. Hartman 2015 Vol. 14, No. 2 (Müller) (Red Bat), Lasiurus seminolus (Rhoads) (Seminole Bat), Myotis austroriparius (Rhoads) (Southeastern Bat), Nycticeius humeralis (Rafinesque) (Evening Bat), and Perimyotis subflavus (F. Cuvier) (Tri-colored Bat) (M.J. Bender and G.D. Hartman, unpubl. data, Menzel et al. 2000). Methods Data collection We used an Anabat SD2 bat detector (Titley Scientific, Brisbane, Australia) to record ultrasonic calls of bats at a residential location from 4 September to 11 November 2011. To protect the detector from direct exposure to the weather, it was housed in a plastic box fitted with a 45°-polyvinylchloride (PVC)-pipe elbow (see Britzke et al. 2010 for details). We placed the box ~1 m above the ground on a table, with the opening of the PVC elbow oriented in the direction of an open area suitable for flying and foraging bats. We programmed the detector to record from ~30 min prior to sunset until 30 min after sunrise. We used a sensitivity setting of ~7 to minimize the amount of non-bat ultrasonic sounds recorded by the detector. We recorded temperature, humidity, and barometric pressure every 15 min using a data logger (Model 73U, T and D Corporation, Nagano, Japan) at the same residential location where we recorded bat calls. Data analysis We used Analook software (version 4.8; to automatically eliminate (i.e., filter) all data files comprised of ultrasonic sounds that were not search-phase bat calls. Call-parameter values used to filter data files were identical to those reported by Britzke and Murray (2000) as appropriate for bats known to occur in the southeastern US. We eliminated data files containing fewer than 3 search-phase bat calls from our analyses to minimize the likelihood that non-bat ultrasonic sounds would be counted as calls. After filtering the acoustic data, we used Analook to determine the number of calls and call sequences recorded each evening. We defined a single vocalization by a bat as a call and the series of calls emitted by a bat during a single pass over the detector as a call sequence. We calculated the mean temperature (AVG_TEMP), humidity (AVG_HUMID), and barometric pressure (AVG_PRESS) from readings taken between 7:15 pm and 8:45 am for every night that we operated the Anabat detector, which was approximately the same time period as the nightly acoustic-sampling period. We calculated changes in barometric pressure as the difference in average pressure values of subsequent nights (NIGHTΔPRESS), for the period prior to roost emergence (i.e., pressure reading at 7:15 pm minus pressure reading at 12:15 pm; NOONΔPRESS), and for the change in pressure during the foraging period (7:15 pm to 8:45 am; FORAGINGΔPRESS). We used an information-theoretic approach (Burnham and Anderson 2002) to evaluate the relative plausibility of 13 candidate models that, a priori, we considered biologically plausible. These candidate models related nightly counts of call sequences to temperature, humidity, sample date (Julian day), and barometricSoutheastern Naturalist M.J. Bender and G.D. Hartman 2015 Vol. 14, No. 2 234 pressure measurements (Table 1). We included an intercept-only model in the candidate set as a baseline for comparison (Burnham et al. 2011). We used SAS software (SAS Institute Inc., Cary, NC) for all steps of the modeling process. We calculated the correlation between numbers of calls and numbers of sequences because we considered each as a plausible measure of bat activity and a potential response variable. We calculated pairwise correlations (Pearson’s r) between all predictor variables to avoid multicollinearity; pairwise predictors with Pearson’s r > 0.5 were not included in the same model. Based on our preliminary modeling efforts, nightly counts of bat activity were overdispersed (i.e., Pearson chi-square ÷ degrees of freedom > 1; Hilbe 2007) so we used a negative binomial distribution to model data (White and Bennetts 1996). We used Akaike’s information criterion (Akaike 1973) adjusted for small sample sizes (AICc) to rank our candidate set of models. AIC weights range from 0 to 1; given the data and candidate set of models, the model with the highest weight is considered the most plausible model (Burnham and Anderson 2002). The ratio of 2 AIC weights can be used to assess the relative strength of models within the candidate set. A model or models with AIC weight less than 10% of the best model indicate that the model or models in question should not be considered plausible based on the data. A null model within 10% of the best model generally indicates that the explanatory variables investigated (i.e., the alternative models in the candidate set) did not have a strong relationship to the response variable (Ober and Hayes 2008). Results During 65 nights of sampling, we recorded 5839 call sequences totaling 43,168 bat calls. The average number of call sequences recorded nightly was 90 (range = 3–551), but often varied several-fold in subsequent nights (Fig. 1). The range in average nightly temperature was 5.4 °C to 23.6 °C (mean = 16.0 °C), humidity was 53 to 99% (mean = 75%), and pressure was 969 to 996 hPa (mean = 986 hPa). Nightly temperature, barometric pressure, and humidity were not strongly correlated (Pearson’s r ≤ 0.48). The number of calls and call sequences recorded during each night were strongly correlated (Pearson’s r = 0.96), and we conducted all further analyses using number of call sequences. The highest-ranked and only plausible model among our candidate set of models contained average nightly temperature (AVG_TEMP) and average barometric pressure (AVG_PRESS) as predictor variables (Table 1). The AICc weight for this model was 0.9958 and, given our data, was >270 times more likely to be the best model than the second-ranked model (AVG_TEMP + NOONΔPRESS + AVG_HUMID + FORAGINGΔPRESS; AICc weight = 0.0036). The most plausible model (i.e., highest ranked) was >29,000 times more likely than the average temperature model or the average pressure model indicating an additive effect of these 2 variables. Although average temperature and average barometric pressure influenced bat activity, temperature had a greater per-unit effect on bat activity than did barometric pressure (Table 2, Fig. 2). For every 1-unit increase in temperature, the expected log-count of nightly sequences increased by 0.2, and for every 1-unit Southeastern Naturalist 235 M.J. Bender and G.D. Hartman 2015 Vol. 14, No. 2 Table 1. List of negative-binomial candidate models, Akaike’s information-criterion values adjusted for small sample size (AICc), delta AICc values (ΔAICc), and relative AICc weights (w) for the candidate models (i) used to evaluate the influence of atmospheric conditions on overall acoustic bat-activity during the summer to autumn seasonal change (4 September to 11 November 2011) in Barnesville, GA. Parameters incorporated into models include sampling date (DATE), average nightly temperature (TEMP), barometric pressure (AVG_PRESS), humidity (HUMID), and measures of barometric-pressure change (change from previous nightly average, NIGHTΔPRESS; change from 12:15 pm to 7:15 pm, NOONΔPRESS; change from 7:15pm to 8:45am, FORAGINGΔPRESS). Models are ranked from most to least plausible based on AICc. Model AICc ΔAICc wi TEMP + AVG_PRESS 669.51 0.00 0.9958 TEMP + HUMID + NOONΔPRESS + FORAGINGΔPRESS 680.73 11.22 0.0036 TEMP + NOONΔPRESS 684.90 15.39 0.0005 TEMP 690.08 20.58 0.0000 TEMP + FORAGINGΔPRESS 691.48 21.97 0.0000 TEMP + NIGHTΔPRESS 691.69 22.19 0.0000 DATE 700.69 31.18 0.0000 NOONΔPRESS 716.51 47.01 0.0000 INTERCEPT ONLY 718.92 49.41 0.0000 AVG_PRESS 719.06 49.56 0.0000 FORAGINGΔPRESS 719.82 50.31 0.0000 NIGHTΔPRESS 720.33 50.83 0.0000 HUMID 720.70 51.19 0.0000 Figure 1. Numbers of bat ultrasonic-call sequences (closed circles) and the mean values of nightly barometric pressures (open triangles) recorded from 4 September to 11 November 2011 in Barnesville, GA. Southeastern Naturalist M.J. Bender and G.D. Hartman 2015 Vol. 14, No. 2 236 change in barometric pressure, log-count increased by 0.1. The null model was not supported by the data. Table 2. List of parameters, parameter estimates, standard errors, and upper and lower 95% confidence intervals for all parameters included in the single plausible negative-binomial model from our candidate-model set investigating the influence of environmental conditions on overall acoustic batactivity in Barnesville, GA, between 4 September and 11 November 2011. Parameter Estimate Std. Error Lower 95% CI Upper 95% CI Intercept -81.317 15.369 Average temperature 0.195 0.022 0.152 0.238 Average pressure 0.084 0.015 0.053 0.114 Figure 2. Predicted number of nightly bat-call sequences across the range of observed average nightly temperatures and barometric pressures recorded from 4 September 2011 to 11 November 2011 in Barnesville, GA. Predicted values were calculated using parameter estimates from the single plausible negative binomialregression model. When constructing graph A, barometric pressure was held constant at the mean observed value of 986 hPa. When constructing graph B, temperature was held constant at the mean observed value of 16.0 °C. Southeastern Naturalist 237 M.J. Bender and G.D. Hartman 2015 Vol. 14, No. 2 Discussion Bat activity was modeled best using an additive combination of nightly average temperature and nightly average barometric pressure. We predicted that some measure of change in barometric pressure would affect activity of bats more than average barometric pressure because changes in barometric pressure reportedly influence activity patterns of insects (Edwards 1961), and extreme weather events and rapid barometric-pressure changes have been reported to influence animalactivity patterns (Red Fox [Ables 1969], sharks [Heupel et al. 2003]). Given our data, no measure of change in barometric pressure that we calculated was as plausibly related to bat activity as average nightly barometric pressure. We do not know if changes in barometric pressure greater or more rapid than those recorded during our study would have been more influential on bat activity . There have been several studies wherein a positive relationship between barometric pressure and bat activity was reported (Berkova and Zukal 2010, Milne et al. 2005, Wolcott and Vulinec 2012, this study), but Paige (1995) and Turbill (2008) reported a negative relationship between barometric pressure and bat activity. These 2 patterns appear to contradict one another, but different methodologies and statistical analyses were used in all of these studies making it difficult to determine if the patterns reported represented biological differences or were artifacts of methodological differences. For example, the studies used different measures of barometric pressure in their analyses (e.g., hourly average, nightly average, change in average from previous night) and different indices of bat activity, and measurements of barometric pressure sometimes were recorded at a substantial distance from the site where bat activity was recorded (36 km in one case; Milne et al. 2005). It is likely that the effects of barometric pressure on animal activity vary among seasons, regions, or species (Ables 1969, Baerwald and Barclay 2011, Edwards 1961, Jung et al. 2014). Based on our data and those reported in other studies, bats respond to barometric pressure, but for the time being, our ability to make general statements or predictions regarding the influence of barometric pressure on bat activity remains limited. The ability to make strong inferences and predictions may improve with studies designed to examine species-specific responses to barometric pressure in a variety of geographic regions and during all seasons. How bats are able to sense barometric pressure, and precisely why pressure and bat activity were positively related in our study, are uncertain. Paige (1995) postulated that some bat species can use barometric pressure as an indicator of insect activity, and therefore, the suitability of conditions for foraging. Typically, high levels of foraging activity by bats correspond with periods of high insect-abundance (Hayes 1997, O’Donnell 2000). Based on Paige’s (1995) study, in which fewer insects were captured when barometric pressure increased, we would predict an inverse relationship between bat activity and barometric pressure. This prediction contrasts with the positive relationship we documented between activity and barometric pressure. Although we hypothesize that a link between barometric pressure and insect activity exists and is one reason that bat activity varies with barometric pressure, there is a need for more data on the Southeastern Naturalist M.J. Bender and G.D. Hartman 2015 Vol. 14, No. 2 238 relationship between barometric pressure and the activity of nocturnal insects during autumn in Georgia. The energetic cost of flight may contribute to the positive relationship we documented between activity and barometric pressure. Flying involves movement though a fluid medium, and the energetic cost of flying is high when compared to other modes of locomotion used by many mammals (Schmidt-Nielsen 1990). There are estimates of the energetic cost of flying for bats (Speakman and Thomas 2003, and references therein), but the influence of atmospheric density on the energetic cost of flight has not been studied. At a given altitude, density of the atmosphere varies with barometric pressure, temperature, and humidity: positively with barometric pressure and inversely with temperature and humidity (FAA 2008). In theory, at a given temperature and humidity, the energetic cost of flight for a bat should decrease as barometric pressure increases because as the density of air increases, thrust and lift forces also increase (FAA 2008); however, this energetic benefit for bats has not been demonstrated empirically. We observed a positive relationship between bat activity and nightly temperature, a finding that also has been reported by some other investigators (O’Donnell 2000, Vaughan et al. 1997). However, a positive influence of temperature on bat activity is not universal, and seems to be related to sampling conditions. In northwestern California, there was no correlation between activity and temperature during summer (June through August), when the average minimum nightly temperature was approximately 11 °C (Seidman and Zabel 2001), whereas in New Zealand, O’Donnell (2000) found a positive relationship between nightly activity and temperature. In the New Zealand study, sampling was conducted during all 4 seasons and in a relatively cold, temperate environment where overnight minimum temperatures ranged from -1 °C to 13 °C (O’Donnell 2000). Our sampling period extended from September (late summer) to November (mid-autumn) and was characterized by nighttime temperatures lower than typically would be experienced by bats in central Georgia during summer. During the warm and humid conditions characteristic of summer in central Georgia, bats may face the risk of becoming hyperthermic if they are unable to dissipate the heat that is created as a byproduct of flight (Reichard et al. 2010, Voigt and Lewanzik 2011). Therefore, a positive relationship between bat activity and temperature is unlikely during the summer in central Georgia. However, during cool autumn nights hyperthermia likely is less of a concern, and we postulate that the positive relationship between bat activity and temperature in cool conditions likely was related to the availability of invertebrate prey. Although some insects are known to fly at temperatures as low as 0 °C (Heinrich 1987), numbers of volant insects during autumn in central Georgia typically decrease with decreasing temperature (M.J. Bender, unpubl. data). Below some critical temperature, the energetic costs of flying will exceed energy intake because insufficient numbers of prey are available. Some bats do remain active throughout winter in central Georgia (M.J. Bender and G. Hartman, unpubl. data), but bat activity during winter likely is driven by needs other than foraging, such as roost switching or drinking (Hayes 1997). Southeastern Naturalist 239 M.J. Bender and G.D. Hartman 2015 Vol. 14, No. 2 Bat activity at our sampling location often varied from night to night, a pattern similar to that reported by Hayes (1997) for bats in Oregon, and in other investigations of bats in Georgia (M.J. Bender, unpubl. data). Because bat activity may vary substantially from night to night, researchers should be cautious when making inferences about bat activity that are based on acoustic data recorded during a single or even a few nights. This same caution should apply when making inferences from data collected during different weather conditions. Failure to sample during a sufficient number of nights can lead to erroneous conclusions regarding site or habitat suitability for bats. Foraging behaviors of bats may differ seasonally (Clare et al. 2011), among species (Bergeson et al. 2013), among age classes (Hamilton and Barclay 1998), between sexes (Levin et al. 2013), and with reproductive status (Barclay 1991, Johnson and Lacki 2013). Because we did not identify bats by species, sex, or age class, we do not know how these variables may have influenced our results. At present, species-level identification of bat calls is problematic because low-quality calls must be discarded, and accuracy rates vary among species, recording conditions, and identification methods. These issues can confound studies that rely on count data collected at a single or few locations particularly when investigating weatherrelated factors known to influence the attenuation of sound (Stilz and Schnitzler 2012). In the future, advances in call-detector technology and call analysis may allow for accurate identification of all calls, and allow researchers to conduct species-specific investigations using count data collected from areas with a diverse bat community and under myriad recording conditions. Additionally, our acoustic methods and the paucity of data related to bat migration in Georgia preclude an estimate of migration activity during our study. If seasonal migration was occurring, the composition of the bat community and local population size may have varied during our sample period and influenced activity patterns. During the transition from summer to autumn in central Georgia, bat activity was positively related to nightly temperature and barometric pressure. Within the framework of what thus far has been reported, it appears that the effects of temperature and barometric pressure on bat activity may vary by season, geography, and species. The relationship between bat activity, temperature, and barometric pressure likely is related to the energetic costs and benefits associated with flight and the availability of prey. The amount of variation in bat activity we observed among nights during our study suggests that there is some minimum amount of sampling effort required if acoustic-survey data are to be used to make inferences about the suitability or relative “importance” of a particular habitat for bats. 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