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J Acoust Soc Am. Oct 2010; 128(4): 2204–2211.
PMCID: PMC2981126

Variation in the resting frequency of Rhinolophus pusillus in Mainland China: Effect of climate and implications for conservation

Tinglei Jiang
Key Laboratory for Wetland Ecology and Vegetation Restoration of National Environmental Protection, Northeast Normal University, 5268 Renmin Street, Changchun 130024, China
Walter Metzner
Department of Integrative Biology and Physiology, UCLA, 621 Charles E. Young Drive South, Los Angeles, California 90095


This study describes variation patterns in the constant frequency of echolocation calls emitted at rest and when not flying (“resting frequency” RF) of the least horseshoe bat, Rhinolophus pusillus, on a broad geographical scale and in response to local climatic variables. Significant differences in RF were observed among populations throughout the species range in Mainland China, and this variation was positively and significantly related to climate conditions, especially environmental humidity, but the variability was only weakly associated with geographical distance. Sex dimorphism in the RF of R. pusillus may imply that female and male might keep their frequencies within a narrow range for sex recognition. Moreover, bats adjusted resting frequency to humidity, which may imply partitioning diet by prey size or the influence of rainfall noise. The results indicate that bats adjust echolocation call frequency to adapt to environmental conditions. Therefore, environmental selection shape the diversity of echolocation call structure of R. pusillus in geographically separated populations, and conservation efforts should focus on changes in local climate and effects of environmental noise.


Among the different variations observed, geographical variation in acoustic signals are a very valuable material for clarifying a wide variety of factors shaping the evolution and divergence of communication systems and for testing fundamental hypotheses about the evolution of behavior (Wilczynski and Ryan, 1999). The geographic differences could promote population divergence and ultimate speciation because they are related to variability in mating signals, communication and resource use (Kingston and Rossiter, 2004; Patten et al., 2004).

The primary functions of echolocation in bats are object detection and localization (Griffin, 1958; Jones and Holderied, 2007). However, mounting evidence suggests that echolocation is important for acoustic communication as well (Ma et al., 2006; Chiu et al., 2008; Jones, 2008; Kazial et al., 2008; Yovel et al., 2009). Such acoustic signals must relay different information and thus have a high degree of plasticity (Obrist, 1995). As a consequence, geographic variations in intraspecific communication signals between populations are expected to be common.

In geographically separated populations, variability in bat echolocation calls has been observed in response to a variety of conditions, including geographical barriers (O’Farrell et al., 2000; Davidson and Wilkinson, 2002), morphological differences between populations (Heller and von Helversen, 1989; Parsons, 1997; Francis and Habersetzer, 1998; Barclay et al., 1999; Guillén et al., 2000; Law et al., 2002; Aspetsberger et al., 2003; Yoshino et al., 2006; Armstrong and Coles, 2007), sex dimorphism in call structure (Jones et al., 1992; Jones et al., 1993; Francis and Habersetzer, 1998; Guillén et al., 2000), character displacement or release (Guillén et al., 2000; Russo et al., 2007), humidity (Guillén et al., 2000), vegetation types (Barclay et al., 1999; Denzinger et al., 2001), habitat acoustics (Gillam and McCracken, 2007), and genetic and cultural drift (Guillén et al., 2000; Jones and Holderied, 2007; Yoshino et al., 2008; Chen et al., 2009).

Although some factors have been shown to explain variations in the ultrasonic frequency of bat echolocation calls within species across geographically dispersed populations, both the cause and the meaning of such differences remain little understood, especially with in bats of the families Hipposideridae and Rhinolophidae. These bats produce calls dominated by an extremely stable constant frequency (CF) tone that is matched to their acoustic fovea (Schnitzler et al., 1976; Schuller and Pollak, 1979; Suga et al., 1987; Kingston et al., 2001). The differences in the CFs between populations in some horseshoe and roundleaf bats may be promoted by geographical barriers, such as in Rhinolophus ferrumequinum (Taniguchi, 1985; Heller and von Helversen, 1989; Huihua et al., 2003), Hipposideros cervinus (Francis and Habersetzer, 1998), and Hipposideros ruber (Guillén et al., 2000). In addition, recent studies indicated that mother–offspring transmission of CF (see Jones and Ransome, 1993) combined with a restricted dispersal of females between regions and small effective population size may maintain intercolony acoustic differences in Rhinolophus cornutus pumilus even within a relatively small geographical area (Yoshino et al., 2008) and R. monoceros (Chen et al., 2009).

So far, however, few studies found an effect of climate on geographical variation in the frequency of bat echolocation calls. Bats have been identified as an important bioindicator taxa for measurable responses to climate change (Jones et al., 2009). Climate change can be related to shifts in bat numbers or activity (Jones et al., 2009). Echolocation call structure also may have been influenced by climate conditions. Body temperature, which is to a certain degree affected by ambient temperature, is associated with call frequency in some species (Huffman and Henson, 1991). Additionally, Guillén et al. (2000) suggested that humidity can influence the call frequency of H. ruber through its effect on atmospheric attenuation of acoustic signals. In the present study, we therefore hypothesized that climatic factors would influence geographic variation in call frequency of horseshoe bats, especially at a broad geographical scale.

In this paper, we report intraspecific patterns of variation in the resting frequency (RF) of Rhinolophus pusillus across Mainland China and attempt to identify factors underlying this variation in RF. The results of our geographical analysis led us to test for the existence of: (1) a constraint of body size on RF, (2) sexual dimorphism of RF, (3) variation of RF associated with geographical distances, and (4) adaptation of RF to local climate conditions and implication for conservation.


Bat species and geographical distribution

The least horseshoe bat, Rhinolophus pusillus, is one of the smallest rhinolophids in the world, and is widely distributed in the Indomalayan region (Corbet and Hill, 1992; Csorba et al., 2003; Simmons, 2005). In Mainland China, the species is found in South and Southwest China, including the provinces of Xizang, Sichuan, Guizhou, Fujian, Guangdong, Guangxi, Yunnan, Hainan (Wang, 2003; Simmons, 2005). It has recently also been recorded in some provinces of Northern China, such as Beijing, Hubei, Jiangxi and Shandong (Zhang et al., 2007). We focused our collection efforts mainly on South China, but also included some samples from the North China.

Data collection and sound analysis

A total of 199 bats were collected from June 2006 to September 2008 at 11 locations, covering most of the distribution range in Mainland China (Fig. (Fig.1).1). At each site, we captured bats at caves using mist nets. To control for possible effects of age (Jones and Ransome, 1993; Masters et al., 1995), only the morphology and the calls of adult bats were measured and recorded. A bat was identified as adult when the phalangeal epiphyses were fused with the diaphyses (Racey, 1974). For each bat, we measured forearm length (FAL; to within±0.01 mm) using digital calipers and determined its sex. For each location, we also determined latitude, longitude and elevation using GPS (eTrex Vista).

Figure 1
Study area showing sampling localities of Rhinolophus pusillus analyzed in this study.

Bat echolocation calls were recorded with a real-time ultrasonic detector (UltraSoundGate 116, Avisoft Bioacoustics, Berlin, Germany) positioned approximately 30 cm in front of the hand–held bats, and sounds stored on a laptop computer. The sampling frequency was 441 kHz. The `resting frequency (RF)’ that the bats emitted under these recording conditions is the individually characteristic and stable CF portion of the echolocation pulses emitted by the motionless bat, which matches the bat’s ‘acoustic fovea’ (Schuller and Pollak, 1979).

From these recordings, we selected high–quality calls from call sequences of each individual to determine their resting frequency according to the following criteria: (1) initial calls within a call series were not considered for analysis, because such calls may show transient, lower frequency values before reaching the final RF level (Siemers et al., 2005); (2) when calls were emitted in groups (doublets, triplets, etc.), only the second call per group was chosen (Russo et al., 2001); (3) only calls with CF portions longer than 10 ms were analyzed. We obtained acoustic measurements from spectrograms using a 1024-point fast Fourier transform (94% overlap, spectral resolution: 195 Hz). The bats emitted typical rhiolophid echolocation calls, composed of three harmonics, with the first and third harmonic being weaker than the second (Fig. (Fig.2).2). From each individual, we selected 30–40 of the highest-quality calls and measured the frequency of the CF component in the dominant second harmonic using Avisoft SasLab Pro (Avisoft Bioacoustics, Berlin, Germany).

Figure 2
Sonogram (a) and power spectrum (b) of a typical echolocation call of Rhinolophus pusillus. Maximum energy is in the second harmonic around 105 kHz, which is used as the information carrier.

Climatic differences in habitats can be reflected in differences in mating songs in a song bird (Ruegg et al., 2006), and echolocation call frequency may be correlated with climate conditions (Guillén et al., 2000). Therefore, we also included climatic factors to describe environmental variation across our eleven locations. To test for correlations between observed call frequency and climate conditions at different locations, we obtained climate information for each collection site from the China Meteorological Data Sharing Service System (http://cdc.cma.gov.cn/shuju), including mean annual temperature (MAT), mean annual rainfall (MAR), mean annual atmospheric pressure (MAAP), and mean annual wind velocity (MAWV) (Table (Table1).1). In the present study, the geographic distance between the sampled localities and the weather stations ranged from 5 to 40 km.

Table 1
Latitude, longitude, and elevation of collection sites (listed from North to South) and corresponding climate data. MAT: mean annual temperature, MAR: mean annual rainfall, MAAP: mean annual atmospheric pressure, MAWV: mean annual wind velocity.

Statistical analysis

We conducted a univariate one–way ANOVA of the averaged call frequency values obtained at each location to determine if call frequencies differed between locations. In this analysis, data from females and males were initially tested separately, and then combined for each site. We performed a t test to evaluate gender differences in RF in each location. The same approach was used to determine FAL differences between the sexes at each location and differences between locations.

Because the distribution of both RF and FAL did not depart significantly from normality (Kolmogorov—Smirnov test, P>0.05), we conducted general linear models (GLM) for the analyses with type III sums of squares to develop the test in the SPSS statistical package. Full models were built initially, but they were subsequently simplified by removing non-significant terms.

Variation in the RF related to the sexual composition of populations and geographical structure found at the locations was studied with a linear model that included SEX and LOCATION as fixed effects. Since changes in RF may be promoted by variation in FAL, we used FAL as reference for assessing the importance of variation in RF. Therefore, a GLM with the same structure as the one presented above was selected for studying variation in FAL. In addition, an ANCOVA model was used to determine the effects of FAL to changes in RF. In this model, the means of RF and FAL per location (Table (Table2)2) were the dependent variable and covariate, respectively, and LOCATION and SEX were the main fixed effects.

Table 2
Resting frequency (RF) in echolocation calls and forearm length (FAL) of adult female (F) and male (M) Rhinolophus pusillus from 11 locations in China. Values are given as mean±SD. The results of the T—test for sexual difference are shown ...

To assess whether differences in RF were related to geographic distance, we first calculated a dissimilarity matrix of acoustic distances using RF differences in kHz between locations. We then calculated a geographical distance matrix from the latitude and longitude of each location, and compared the acoustic and geographical distance matrices using a nonparametric Mantel test of matrix association (Mantel, 1967; Schneider et al., 2000) in Arlequin (Schneider et al., 2000). The Mantel test was performed with 1000 permutations.

To test if climate conditions may have influenced echolocation call frequency, we first implemented a principal component analysis on the four climate variables (mean annual temperature, MAT; mean annual rainfall, MAR; mean annual atmospheric pressure, MAAP; mean annual wind velocity, MAWV; see above and Table Table1),1), and used the first two PC factor scores to obtain a matrix of Euclidean distances. This climate matrix was then compared to the previously calculated dissimilarity matrix of acoustic distances by a Mantel test as described above. Additionally, a stepwise multiple regression model was performed to test the relationship between RF and climate variables with variables entered and removed at the 0.05 and 0.10 significance level, respectively. In this analysis, RF of females, males, and all individuals (both sexes) was the dependent variable, respectively, and climatic factors were the independent variables.


From the 11 sampled locations, we analyzed 6077 calls from 199 individuals with an average of 30 calls per bat (range 22–40 calls/bat). For the entire sample, RF ranged from 104.10 to 113.90 kHz (SD=1.58) in females and from 101.50 to 111.30 kHz (SD=1.78) in males. FAL ranged from 31.71 to 41.51 mm (SD=1.77) in females and from 31.00 to 40.12 mm (SD=1.49) in males. For RF as well as FAL and for both sexes, the variation within locations was generally smaller than between locations (Table (Table22).

The RF values of the bats differed significantly between locations for both genders (ANOVA: F10=1796.72, P<0.001, in females; F10=1060.82, P<0.001, in males; F10=1152.46, P<0.001, for all samples) (Fig. (Fig.3).3). There were also significant differences in FAL between locations (ANOVA: F10=9.31, P<0.001, in females; F10=8.58, P<0.001, in males; F10=13.70, P<0.001, for all samples) (Table (Table2).2). For each site, the mean value of RF was consistently higher in females than in males except for Yixing in Jiangsu province and Jingxian in Anhui province, and this difference was statistically significant for each site (Fig. (Fig.3).3). Similarly, mean FAL was significantly larger in female than in male bats in four locations (Table (Table22).

Figure 3
Variation in resting frequency (RF) of echolocation calls among individuals of Rhinolophus pusillus between locations. Each triangle indicates the mean call frequency of an individual, and bars represent mean±SD. The sample sizes were showed in ...

LOCATION and SEX had a significant effect on RF (Table (Table3).3). There were significant differences between locations with colonies in Xingan (Guangxi province) and Hekou (Yunnan province) showing the highest and lowest RF values, respectively (Table (Table2;2; Fig. Fig.3).3). There was a distinct sexual dimorphism in RF: female used higher pitched calls in all locations except at Yixing in Jiangsu and Jingxian in Anhui, where the dimorphism was reversed (Table (Table2;2; Fig. Fig.3).3). Moreover, the prevalence of this dimorphism changed among populations (SEX×LOCATION term significant) (Table (Table33).

Table 3
Effect of SEX and LOCATION on the resting frequency (RF) and forearm length (FAL) of Rhinolophus pusillus in China, respectively. Tests were built with type III sum of squares. SEX and LOCATION were considered as fixed effect. The two models are significant ...

Similarly, LOCATION and SEX had a significant effect on FAL, although to a lesser degree than for RF (Table (Table3).3). Females had significantly longer forearms in four locations, but there were no significant difference in the other seven locations (Table (Table33).

Forearm length cannot explain variation in mean RF across locations (FAL: F1=1.56, P=0.246) (Table (Table4).4). Although the FAL×SEX term had a significant effect on RF, this was promoted only by SEX because FAL did not have a significant effect on RF (Table (Table44).

Table 4
Effect of SEX, LOCATION and FAL on the resting frequency (RF) of Rhinolophus pusillus in China. Tests were built with type III sum of squares. SEX and LOCATION were considered as fixed effect, and FAL was covariate. The model is significant and explains ...

In our study, there was a weak relationship between differences in RF and geographical distances between locations although they lacked sufficient statistical power to detect it [Mantel test: r=0.18, P=0.09; Fig. Fig.4a4a].

Figure 4
Relationship of RF difference with (a) geographical distance and (b) climate Euclidean distance. Points represent all possible pairwise comparisons of the 11 collection sites.

Interestingly, however, resting frequency differences were positively and significantly correlated with climate conditions [Mantel test: r=0.45, P=0.007; Fig. Fig.4b].4b]. From the four climate variables, multiple regression models showed that only MAR had a positive and significant effect on call frequency of females, males and all individuals (Fig. (Fig.5).5). Therefore, the other three climatic variables were removed in this analysis.

Figure 5
Relationships between mean annual rainfall (MAR) and resting frequency (RF).


In rhinolophid and hipposiderid bats, females normally call at a higher frequency than males (Neuweiler et al., 1987; Jones et al., 1992; Guillén et al., 2000; Yoshino et al., 2006; Yoshino et al., 2008), but there are exceptions to this rule (Jones, 1995; Pedley, 2004), as was also evident from our data for RFs in the least horseshoe bat, in which males broadcasted higher RFs than females at two locations, i.e., in Yixing (Jiangsu province) and Jingxian (Anhui province) where (Table (Table22 and Fig. Fig.3).3). Gender differences in RF may be based upon sex dimorphism in body size rather than directly gender. In our study, SEX had a significant effect on RF and FAL, respectively (Table (Table3),3), whereas FAL had no significant effect on RF despite the fact that the FAL×SEX term had a significant effect on RF (Table (Table4).4). Moreover, females of R. pusillus used higher pitched sounds than males (except for the two locations mentioned above; Table Table22 and Fig. Fig.3),3), but there was no significant difference between female FAL and male FAL. In addition, in only 4 of the 11 locations, there was a significant difference in FAL between the sexes (Table (Table2).2). As a result, sex dimorphism in RF of R. pusillus does not appear to be associated with FAL. In addition, many studies also showed that intraspecific shifts in the CF value were independent of differences in body size (Neuweiler et al., 1987; Jones et al., 1992; Jones et al., 1994; Francis and Habersetzer, 1998; Guillén et al., 2000). Therefore, it appears that body size does not constrain geographic variation of RF in these bats.

Sexual dimorphism in echolocation calls may have an important social function by signaling the sex of the caller, and therefore promote mate recognition and reproductive success (Guillén et al., 2000). Kazial and Masters (2004) showed that echolocation calls of bats can be used for sex-recognition, but direct empirical evidence showing that call frequency is a critical cue for sex recognition is virtually absent. Here, we found that the mean RF between females and males in each location was distinctly different (Table (Table22 and Fig. Fig.3),3), which may indicate that females and males at each site keep their frequencies within a narrow range for sex recognition.

The substantial variation in RF of R. pusillus that we found here corroborates reports in other rhinolophids (Guillén et al., 2000; Yoshino et al., 2006; Chen et al., 2009). We observed significant differences in RF between locations for females, males as well as all samples combined (Tables (Tables2,2, ,3).3). These differences were only weakly correlated with geographical distance between locations (Fig. (Fig.4).4). Similar to our results, several reports suggested that geographical distance cannot, however, always and completely explain the variation in call structure of bats (Guillén et al., 2000; Murray et al., 2001; Law et al., 2002; Aspetsberger et al., 2003; Armstrong and Coles, 2007; Gillam and McCracken, 2007).

Acoustic divergence in RF by more than 5 kHz between populations of CF rhinolophoid and hipposiderid bats separated by geographical barriers may be common (Heller and von Helversen, 1989; Francis and Habersetzer, 1998; Guillén et al., 2000). Some populations that are geographically separated by large distances also show large differences in RF (e. g. R. ferrumequinum between Europe and Japan, Heller and von Helversen, 1989; Hipposiders fulvus between India and Malaysia; Jones et al., 1994). In this study, although geographical distance was only weakly associated with RF difference between locations, we cannot completely rule out a potential impact of geographical distance for the following reasons: First, numerous high mountain ranges form a network of barriers within Mainland China, which may restrict gene flow. Second, least horseshoe bats have short, broad wings suitable for maneuverable flight but not for long-distance migration (Maeda, 1978). This may severely restrict seasonal migration in R. pusillus. Finally, the geographic distance between sampling localities were so large (in some cases over 1000 km) that vicariance may obscure any pattern of geographical distance mediated by dispersal. This so far suggests that genetic or cultural drift and natural selection could play an important role in promoting geographical variation in RF of R. pusillus.

Cultural drift has been suggested to yield variations in call frequency in the maternal transmission hypothesis. Cultural learning of call frequencies has been reported in the genus Rhinolophus, determining the fine-tuning of call frequencies in part by vertical transmission from mother to offspring (Jones and Ransome, 1993), a view subsequently supported by Chen et al. (2009) and Yoshino et al. (2008). Yoshino et al. (2008) suggested that call frequency difference of on average 5–8 kHz in R. cornutus across its area of distribution have developed through random cultural drift. Although in our study, the mean call frequency difference in R. pusillus was within a similar range and females exhibited a restricted dispersal between regions, differences in RF was not likely subject to cultural drift because of the three following reasons. First, variation in RF lacked any geographical pattern (Fig. (Fig.3),3), which is different from a cultural drift, in which variations should be geographically directed. Second, RF differences were only very weakly correlated with geographical distance. Finally, numerous high mountain ranges separating the different, distant locations paired with a limited flight capability of R. pusillus should make female dispersal between locations unlikely at a geographic scale as wide as seen in our study. However, only by determining a direct genetical basis for RF can this question be resolved.

As an alternative hypothesis, natural selection will adapt a population to a particular local environmental condition but immigrants from other populations will introduce genes adapted to other conditions (Slatkin, 1987). A number of studies demonstrated that differences in the structure of bat echolocation calls can occur in response to a variety of environmental conditions, including humidity (Jones et al., 1993; Guillén et al., 2000), vegetation types (Barclay et al., 1999; Denzinger et al., 2001) and habitat acoustics (Gillam and McCracken, 2007). Our own results also revealed that a particular climatic condition, i.e., MAR, was significantly and positively correlated with RF in R. pusillus. This implies that environmental selection plays an important role in shaping the geographic pattern of RF of R. pusillus, especially at a wide geographic scale.

Interestingly, we found that RF was positively correlated with the mean annual rainfall (MAR) for each sex and all individuals (Fig. (Fig.5).5). This is opposite to what would be expected since higher humidity should favor lower frequencies because of the lower attenuation for signals with longer wavelengths (Lawrence and Simmons, 1982; Hartley, 1989). This inverse correlation was reported by Guillén et al. (2000), who showed that MAR was negatively correlated with call frequency in H. ruber. The unexpected positive correlation between high humidity and high call frequency in our observations may have three possible causes. First, Remmert (1981) showed that moist environments contain a greater number and, on average, smaller insects than dry environments. This suggests that R. pusillus may forage on smaller insects by using a higher RF in wetter locations. However, the range of CF frequencies recorded in R. pusillus corresponds to wavelengths between 3.03 and 3.33 mm, assuming a sound speed of 340 m/s, a temperature of22 °C, and a relative humidity of 80%. Such small differences in wavelength, however, seem inappropriate to interpret the intraspecific variations as a basis for partitioning diet by prey size (Russo et al., 2001). Second, Chen et al. (2009) suggested for different populations of Rhinolophus monoceros in Taiwan, that any correlation between humidity (ranging between 76% and 86%) and call frequency may merely represent an artifact based on differences in the genetic population identity. In our study, however, the differences in MAR between sampling localities are so large (range between 0.86 m and 1.92 m; Table Table1)1) that the effect of population identity seems unfeasible. Finally, in general, a larger MAR corresponds to higher humidity (Guillén et al., 2000), but noise effects induced by the rainfall have so far always been neglected. It is therefore tempting to speculate that noise caused by rainfall may also interfere with the bats’ echolocation performance. There is mounting evidence implicating environmental noise in changes of echolocation call frequency in frequency modulated (FM) bats (Rydell et al., 1999; Gillam and McCracken, 2007; Schaub et al., 2008) and even constant frequency (CF) bats (Hage et al., 2009). However, obviously only further experimental examination will help in answering this paradoxical relation between call frequency and humidity.

Bats are sensitive to the effects of environmental stressors, such as direct exploitation, habitat loss and climate change (Jones et al., 2009). Global climate change, like extremes of drought, heat, cold and precipitation, are linked to changes in bat numbers or activity (Jones et al., 2009). Therefore, local climatic conditions may influence physiological reactions of bats and thus lead to changes in the echolocation behavior of bats, which ultimately impacts bat numbers and even their survival. Echolocation calls in bats, possessing both echolocation and communication functions (Ma et al., 2006; Jones, 2008), are the basis for survival and reproduction. Numerous evidence showed that foraging behavior and acoustic communication signals of bats were altered by ambient noise, such as noise caused by wind, rain, insect sounds, or anthropogenic noise (Arlettaz et al., 2001; Gillam and McCracken, 2007; Jones, 2008; Schaub et al., 2008). In the present study, RFs in R. pusillus apparently adapted to local climatic conditions, especially MAR, underscoring the importance for conservation efforts to also focus on local climate changes and environmental noise.


This project was supported by grants from the National Natural Science Foundation of China (Grant No. 30770361, 30870371), and the National Grand Fundamental Research 973 Program of China (No. 2009CB426305) to JF and NIH (DC005400) to WM.


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