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. 2022 May 14;69(2):200–207. doi: 10.1093/cz/zoac035

Using a behavior random permutation model to identify displacement grooming in ungulates

Yunchao Luo 1, Lin Wang 2, Le Yang 3, Xiao-Fen Li 4, Patrick Anselme 5, Xinxin Wang 6, Xingjun Tian 7, Zhongqiu Li 8,✉
Editor: Zhi-Yun Jia
PMCID: PMC10120960  PMID: 37091989

Abstract

Behavior affects an individual’s life in all aspects, e.g., enhancing fitness, leveraging predation risk, and reducing competition with conspecifics. However, the sequential distribution of behaviors received less attention and is unclear what the function of displacement behavior is. Displacement activities can be found in vertebrate species but there is no formal method to determine whether a behavior is expressed as a displaced or normal activity. Analyzing the sequential distributions of behaviors in a natural setting may allow researchers to identify unexpected distributions as a possible signature of displacement activities. In this study, we used a behavior random permutation model to detect the presence of a displacement activity in the Tibetan antelope Pantholops hodgsonii and the Tibetan gazelle Procapra picticaudata. The results showed that grooming in both ungulates tended to be accompanied with vigilance, and the frequency of grooming after vigilance was significantly higher than before vigilance. A significant positive correlation between the scan rate and grooming rate in the 2 ungulates was obtained. We suggest that grooming could sometimes be expressed as a displacement activity in ungulates. In addition to providing a general method for further research on displacement activities in a variety of animal species, this study sheds light on the importance of a spectral analysis of sequential distribution of animal behaviors. Behavior random permutation models can be used to explore the relevance between any 2 behaviors in a specific sequence, especially to identify a myriad of unexpected behaviors relative to their normal context of occurrence.

Keywords: behavior sequence, displacement activity, self-grooming, vigilance, random distribution, behavioral analysis method

Appropriate behavior helps animals cope with various environmental stresses, including food shortage, predation risk, and intraspecific competition. The animal behavioral features developed under these pressures enable them to better adapt to the environment. If the behaviors of an animal were aimless or disconnected from each other, these behaviors should express randomly and last for irregular periods of time. Therefore, the distribution of behaviors is the manifestation of an animal’s motivation to do a specific activity at a given time. In some cases, however, a behavior may occur in a situation that does not seem to require its expression. For example, when someone experiences a difficult or embarrassing situation, he/she may involuntarily scratch his/her head, touch his/her nose, or have a drink (Mohiyeddini and Semple 2013). Psychologists and ethologists call these behavioral patterns “displacement activities” (McFarland 1966; Troisi 2002; Anselme 2008). A displacement activity is an instinctive, stereotyped response to stimuli that is inappropriate or irrelevant to the situation (Tinbergen 1952; Buckley and Semple 2012). The initial hypothesis was that a displacement activity is not “fed” from its usual internal source but results from “displaced” energy originating from a different source, blocked in its normal expression (Tinbergen 1951; Kortmulder 1998; Troisi 2002). However, this concept of behavioral energy is vague (Hinde 1960) and hardly compatible with the modern incentive theory of motivation, which got rid of the concept of energy for that of incentive: Behavior does not result from an internal drive but is triggered by environmental cues (Berridge and Robinson 1998). More compelling interpretations have been provided for the so-called displacement and its characteristics, such as attentional disinhibition and attentional interference (McFarland 1966; Anselme 2008).

Displacement activities occur when an animal experiences a high motivation for 2 or more conflicting behaviors. Some birds, for example, may come to peck at the ground when uncertain whether to attack or to flee a rival that encroaches on their territory (Tinbergen 1952). Similarly, a male three-spined stickleback Gasterosteus aculeatus that hesitates between courtship and attack may display fanning behavior, aimed at ventilating eggs (Wilz 1970a, 1970b). Displacement activities may also arise when the expression of a single, highly motivated behavior has to be interrupted for some reason. For example, a wild duck may suddenly display grooming while courting (Lorenz 1965). Although the main function of grooming is to alleviate itchiness, such as mosquito bites, fungal dermatosis, and parasites (Mooring and Samuel 1999; Blanchard et al. 2017; Yang et al. 2018), many studies have found that displacement grooming is related to stress. For example, anxious mice Mus musculus with Shank3 gene deletions (mouse models of autism) display more frequent self-grooming compared to wild-type controls (Peca et al. 2011); also, displacement grooming among non-human primates tend to occur in situations of psychosocial stress, and it can be used as an indicator of emotional states arising out of a variety of primates social interactions (Maestripieri et al. 1992; Whitehouse et al. 2017). Therefore, the need to recover from stress might be the internal cause of displacement activities, and the stressors their external cause. Stress is likely to play a crucial role in the expression of displacement activities, given that they always occur in a stressful context associated with uncertainty (Troisi 2002; Mohiyeddini and Semple 2013).

However, there is a lack of effective methods to distinguish displacement behavior from the normal expression of the same behavior. Because of the absence of verbal reports in animals, identifying displacement activities may be more difficult than in humans. Perhaps for this reason, the study of displacement behavior has only been conducted for a limited number of species. Thus, we need a sensible method to explore the pattern of behavioral sequences. At present, some studies have paid attention to the sequential distribution of animal behaviors, such as the classic Pulliam model in vigilance (Pulliam 1973; Luo et al. 2020b) and the fractal analysis for disentangling sequential behaviors (Alados et al. 1996; MacIntosh et al. 2013). However, identifying a displacement activity requires to find evidence for the inappropriateness or irrelevance of a behavior to a situation. In this study, we developed a standard method, called behavior random permutation model, to test the presence of a bias in the frequency and distribution of an activity relative to another within a behavioral sequence. Such a bias would reveal the presence of a non-random process explaining why the frequency and distribution of a behavior deviates from its expected value (EV).

We explored the ability of the behavior random permutation model to possibly identify displacement grooming in Tibetan antelopes Pantholops hodgsonii and Tibetan gazelles Procapra picticaudata through an analysis of their behavior distributions. Predation risk is the most important straining factor faced by ungulates on plateaus, explaining why they have to raise their head to frequently take a look around them (Beauchamp 2015). Vigilance posture reflects the level of predation risk (Hunter and Skinner 1998; Laundre et al. 2001). We have previously studied vigilance in the Tibetan antelope and the Tibetan gazelle: The frequency and the duration of the vigilance bouts in those plateau ungulates were affected by body size, sex-age, and group size (Luo et al. 2020a). Surprisingly, we found that self-grooming (hereafter referred to as grooming, Figure 1A and B) was often accompanied with vigilance. So far, there has been no behavioral or physiological investigation to determine whether ungulates can exhibit displacement activities. In particular, it is difficult to find direct measurements indicating that grooming behavior in ungulates might sometimes be a way to recover from a stressful experience. If part of their grooming activity is a displacement activity, we expect that grooming will be distributed nearer the vigilance bouts, and that the vigilance-grooming sequence (VG) will be more frequent than the grooming-vigilance sequence (GV). These phenomena should be amplified in the Tibetan gazelle, which is a smaller animal and hence an easier target for predators than the Tibetan antelope.

Figure 1.

Figure 1.

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Behavior random permutation model. (A) Grooming in a male Tibetan antelope. (B) Grooming in a male Tibetan gazelle. (C) Example of simulation results with the behavior random permutation model. 100,000 random permutations for the behavior data example (including 12 grooming behaviors, 37 vigilance behaviors and 39 other behaviors, for an observed value (OV) of 12) were carried out using MATLAB. The abscissa axis represents the total number of vigilance-grooming sequences (VG) and grooming-vigilance sequences (GV), and the ordinate axis shows the number of random permutation experiments. The algorithm found the expected value (EV, 7.99) and the number with the highest probability of occurrence (NHP, 8) of grooming accompanied with vigilance. After 100,000 random experiments, approximately 2.5 × 104 random permutations gave an estimate of 8 VG or GV sequences for this behavioral sample.

Materials and Methods

Study area

This study was conducted in the Selincuo National Nature Reserve (30°02ʹ39″~32°19ʹ33″N, 87°45ʹ30″~89°47ʹ49″E), Shenzha County of Tibet, which is located in the central part of the Qiangtang Plateau. Elevations range from 4,530 to 6,448 m, with an average of more than 4,700 m. Local climate is characterized by extreme cold and long winters, strong winds, and high levels of solar radiation. Mean annual temperature was 0.4 °C. Annual precipitation is about 330 mm, and most rainfall occurs between June and September. Alpine meadow is the main vegetation type, and no shrubs appear in this area (Luo et al. 2018).

Studied species

The Tibetan antelope, a flagship species of the Qinghai–Tibet Plateau, belongs to Bovidae, Pantholops. It has been classified as a Category I (Endangered in China) National Protected Wild Animal Species in China since 1989 and as Near Threatened by IUCN (International Union for Conservation of Nature) since 2016. The Tibetan gazelle belongs to Bovidae, Procapra, which is distributed on nearly all the Qinghai–Tibetan Plateau. It has been classified as a Category II Protected Wild Animal Species in China, and Near Threatened in the IUCN Red list of Threatened Species (Schaller 1998; Zhang and Jiang 2006). However, the scientific literature on the biology and the behavior of the Tibetan antelope and the Tibetan gazelle remains poorly documented because of the high altitude and the harsh environmental conditions (Luo et al. 2020b; Zhang et al. 2021).

In addition to the harsh living environment, these 2 ungulates also face a great risk of predation. Many natural enemies, including wolf Canis lupus, snow leopard Panthera uncia, lynx Felis lynx, and brown bear Ursus arctos, can hunt the Tibetan antelope and the Tibetan gazelle (Schaller 1998). Besides those natural enemies, these ungulates have to be vigilant against human activity disturbance (e.g., grazing) and free-roaming dogs C. lupus familiaris (Yang et al. 2019). The Tibetan antelope shows strong sexual dimorphism. Male antelope are much heavier than females (adult males with an average body weight of 39 kg; females with an average body weight of 26 kg) (Leslie et al. 2008). The Tibetan gazelle is much smaller than the Tibetan antelope, and its body weight is similar in both sexes (with an average of 14 kg) (Leslie and David 2010). In both species, only males have horns, which start to grow in one-year old individuals. The presence of horns allowed us to distinguish between male adults and subadults, but this was not possible with females. Both species formed mixed-sex groups in the rutting season (December to January). In our study area, the group size of the Tibetan gazelle was 1–9 individuals, while that of the Tibetan antelope sometimes exceeded 400 individuals.

Behavioral observation

Daytime behavioral observations were carried out from sunrise to sunset (Chinese standard time 9:00–20:00) during 2 summer periods (July and August in 2016, June and July in 2017), the lambing season of the Tibetan antelope and the Tibetan gazelle. We took video samples using camcorders and binoculars in this study, and the individuals were observed by a focal sampling method (Altmann 1974). We already recorded detailed information about focal groups and individuals, including species, sex-age, and group size.

We randomly selected the encountered groups while we drove along the road. We randomly selected one focal subject in the selected group, and sometimes a few more (2–5 individuals) from different parts of the group when the group was large. Each individual was treated as an independent sample. To avoid duplicating a sample, the same portions of the road were not followed for 3 consecutive days. Although the animals were not marked, it was unlikely that the same individuals were sampled more than once.

Behavioral events were videotaped or dictated to a mobile phone recorder. Observations lasted for 30 min unless we lost sight of the focal individual. The actual observation duration was 13.83 ± 0.26 (mean ± SE) min. We viewed and analyzed 4,080 min video and voice data of Tibetan antelopes and 3,540 min of Tibetan gazelles in total. The dataset consisted of 313 focal observations collected from 144 groups of Tibetan antelopes and 238 focal observations collected from 97 groups of Tibetan gazelles. All these samples were reviewed to the nearest 1 s. The behaviors of ungulates can be classified into several categories including moving, feeding, vigilance, grooming, resting, defecating, fighting, etc. In this study, we categorized these behaviors as vigilance, grooming, and others. The detailed information for the other behaviors did not affect our analysis of the relationship between vigilance and grooming, so they were categorized as “others” for clarity and concision. Successive other-behaviors were scored as multiple other-behaviors in succession—i.e., each different behavior was scored once. After removing the samples that did not contain vigilance or grooming, the dataset consisted of 126 Tibetan antelope samples and 201 Tibetan gazelle samples (Supplementary material).

Statistical analysis

The behavior random permutation model we developed can test whether the position of certain behavior in the behavior sequence is random. Count the number of various behaviors in the sequence, and let the algorithm randomly arrange these behaviors. Then compare the difference between random sequences and original sequence, to explore whether there is a close relationship between any 2 kinds of behaviors (Supplemental material for the program code).

Take the grooming and vigilance of ungulates as an example: We counted the total number of behaviors for each ungulate sample, and the frequency of 3 kinds of behaviors (vigilance, grooming, and others). Then, we counted how many grooming events were accompanied with vigilance (observed value, hereinafter referred to as “OV”). The random permutation model (Figure 1C) was used to generate large-scale random permutations. The algorithm can calculate how many grooming events accompanied with vigilance from the same behavior sequence sample could theoretically be expected (EV; Supplementary material). Finally, the algorithm provided the number with the highest probability of occurrence that grooming events accompanied with vigilance in all case of random permutations (of note, this number is not a probability, and is hereinafter referred to as ‘NHP’, i.e., the number with the highest probability of occurrence). Using MATLAB (R2020a), we ran 100,000 random permutations of the observed behavioral data in each sample—i.e., each category (vigilance, grooming, others) was treated as an independent unit and was randomly permuted and combined with the other categories. This method allowed the algorithm to uncover the EV and NHP of grooming accompanied with vigilance.

We used Wilcoxon signed-rank tests to compare the differences in mean values of EV-OV (NHP-OV) within each species. Wilcoxon tests were also used to determine the effects of the frequency of vigilance-grooming and grooming-vigilance pairings. Then, we calculated the correlation between scan rate (number of vigilance scans per min) and grooming rate (number of grooming events per min) for the 2 ungulates using a Spearman correlation analysis. Finally, the differences in the proportion of grooming with vigilance between the 2 species were compared using a mixed linear model. Data of proportion were log10(X + 1) transformed to approach normal distribution. Set species and sex-age (male adult, male subadult, female and lamb) were fixed factors, group size was covariate, and group ID was a random factor since some samples were collected from the same group. We used IBM SPSS Statistics 22 and R 4.1.0 for the statistical analysis and the 2-tailed probability P < 0.05 was considered significant.

Results

We found that the observed value (OV) was significantly higher than the expected value (EV) and the number with the highest probability of occurrence (NHP) computed by the random permutation model in the 2 ungulate species studied (Table 1). The OV of the Tibetan antelope was 53.54% higher than the EV, and 94.11% higher than the NHP (Figure 2A and C). The OV of the Tibetan gazelle was 40.38% higher than the EV, and 39.38% higher than the NHP (Figure 2B and D).

Table 1.

Wilcoxon test (EV vs. OV and NHP vs. OV) in both ungulate species

Species Wilcoxon test Negative ranks Positive ranks N Z P
Tibetan antelope EV-OV 5889 2112 126 −4.598 < 0.001
NHP-OV 2021.5 256.5 126 −5.955 < 0.001
Tibetan gazelle EV-OV 17070.5 3029.5 201 −8.566 < 0.001
NHP-OV 9595.5 1135.5 201 −8.393 < 0.001
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The OV (observed value) was significantly higher than the EV (expected value) and NHP (the number with the highest probability of occurrence) computed by the random permutation model in both the Tibetan antelope and the Tibetan gazelle.

Figure 2.

Figure 2.

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Comparison between the reference model and the observed value. (A) EV (expected value)-OV (observed value) in the Tibetan antelope. The circles depict the data points, and the solid line is the normal curve. (B) EV-OV in the Tibetan gazelle. (C) NHP (the number with the highest probability of occurrence) and OV distributions in the Tibetan antelope. The curve of OV is shifted to the right relative to that of NHP, and the peak value of NHP is higher than that of OV. (D) NHP and OV distributions in the Tibetan gazelle show a pattern similar to that of the Tibetan antelope. To optimize readability, we used different scales for the results in each species.

We further analyzed grooming accompanied with vigilance, and found that the vigilance-grooming sequence (VG) was significantly more frequent than the grooming-vigilance sequence (GV) (Figure 3A and B and Table 2). Thus, grooming often appeared after vigilance. Figure 4 shows the distributions of scan rate and grooming rate in the 2 ungulates; scan rate (number of vigilance scans per min) and grooming rate (number of grooming events per min) were significantly positively correlated in both ungulates (Tibetan antelope: r = 0.274, df = 125, P = 0.002; Tibetan gazelle: r = 0.314, df = 200, P < 0.001). Finally, we compared the proportion of grooming events accompanied with vigilance between the 2 species and found that this proportion was significantly higher in the small Tibetan gazelle than in the large Tibetan antelope (t = 4.133, P < 0.001; Figure 3C). Sex-age and group size had no effect on this proportion (Table S1).

Figure 3.

Figure 3.

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Proportion of grooming events accompanied with vigilance. (A) Proportion of the vigilance-grooming sequence (VG) and the grooming-vigilance sequence (GV) in the Tibetan antelope. (B) Proportion of VG and GV in the Tibetan gazelle. (C) The proportion of grooming events accompanied with vigilance (the overall result of both GV and VG) in the Tibetan antelope is 45.81 ± 3.83% (mean ± SE). This proportion in the Tibetan gazelle is 63.54 ± 2.31% (mean ± SE). The error bar represents the 95% confidence interval.

Table 2.

Wilcoxon test (VG vs. GV) in both ungulate species

Species VG mean GV mean Negative Ranks Positive Ranks N Z P
Tibetan antelope 0.3691 0.1707 1751 460 126 −4.172 < 0.001
Tibetan gazelle 0.5157 0.3818 7976 2320 201 −5.702 < 0.001
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The vigilance-grooming sequence (VG) was significantly more frequent than the grooming-vigilance sequence (GV) in both the Tibetan antelope and the Tibetan gazelle.

Figure 4.

Figure 4.

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Distribution of scan rates and grooming rates. (A) Distribution of scan rate (number of vigilance scans per min) and grooming rate (number of grooming events per min) in the Tibetan antelope. (B) Distribution of scan rate and grooming rate in the Tibetan gazelle. The red dashed line is the regression line, the black diamonds are the data points. The dark red shadow shows the 95% confidence interval.

Discussion

In this study, the behavior random permutation model turned out to be a useful tool for measuring deviations from randomness in the expression of animal behaviors. We found that grooming tends to be accompanied with vigilance in the Tibetan antelope and the Tibetan gazelle, rather than a random pattern (Figure 2; Table 1). Pruritus caused by parasites is persistent in time, so grooming for pruritus should be random rather than occurring in association with vigilance. Thus, if grooming was only a solution to parasitism, such an asymmetry should not be expected. We cannot rule out the possibility that grooming accompanied with vigilance expresses as a body care strategy as well, at least in part, but more appears necessary to fully explain our results.

The results showed that scan rate and grooming rate were significantly positively correlated in both ungulates (Figure 4). This seems to conflict with the time allocation hypothesis, which means that the expression of a behavior (e.g., foraging and grooming) takes time and may naturally reduce the time available for another behavior (e.g., vigilance). The vigilance-foraging conflict has been extensively investigated (Beauchamp 2015), as mammals and birds typically raise their head when they are alert and bow it when they forage. Therefore, prey faces potential fitness costs associated with antipredator-feeding trade-offs (Blanchard et al. 2017). Given that grooming is also risky for survival (preventing the animal from looking around), a vigilance-grooming conflict is likely to exist. In this case, however, it is still unclear why the time allocation hypothesis would predict that grooming occurring after vigilance (VG) is more frequent than the reverse (GV) pattern (Figure 3A and B and Table 2). Of course, grooming after vigilance might be safer than the reverse strategy. But in the behavioral repertoire of ungulates, the riskiest behaviors should be foraging (because of complete head bowing) and resting (because in recumbence) (Lima et al. 2005). If a behavior performed after vigilance had to occur under predation risk, this should be feeding or resting, instead of grooming. However, our data showed that grooming occupied more of this position (see Supplementary material). Thus, we cautiously suggest another interpretation: Grooming after vigilance might be a displacement activity aimed at reducing stress. This would explain the observed association and asymmetry between both behaviors, revealed by the behavior random permutation model: Both behaviors seem to share a functional relationship. Grooming after vigilance is a good outlet to release nervous tension under predation risk, while grooming before vigilance is just body care. Animals experience stress and anxiety when faced with predation risk (Mashoodh et al. 2009; Tkaczynski et al. 2014). They therefore need a strategy to alleviate the effects of these negative emotions (Higham et al. 2011; Buckley and Semple 2012). The living conditions of animals are extremely harsh on the Qinghai–Tibet Plateau, where oxygen concentration is very low due to the rarefied air (Li et al. 2018). Grooming expressed as a displacement activity might be suitable for this hostile environment; ungulates are more likely to have control on stress-related feelings while behaving in a relaxed manner. This view, which is not about behavioral economics but about the patterns of behavior and the timing of their expression, is more appropriate to explain our findings—while being not in opposition to the time allocation hypothesis.

In addition, we found a difference in the proportion of grooming accompanied with vigilance between the 2 species (Figure 3C). The body size principle of grooming predicts that smaller-sized animals, because of their greater surface-to-mass ratio, should groom their body more often than larger-sized animals (Mooring et al. 2000). We should also consider the fact that small ungulates show higher vigilance rates than larger ones (Beauchamp 2015; Luo et al. 2020a), so the small Tibetan gazelle should show more displacement grooming than the large Tibetan antelope. This prediction is consistent with our observations and may mean that a lower risk of predation in large ungulates induces lower stress levels. This offers an alternative explanation for the size principle of grooming when part of it occurs as a displacement activity.

How to identify a behavior as a displacement activity is the key problem we tried to solve using the behavior random permutation model (Figure 1C). A displacement activity is a behavior that appears out of context, after the ongoing activity was interrupted. The behavior random permutation model can indeed analyse the behavioral sequences to identify the unexpected distributions cause by potential displacement activity. After arguing for the possibility of displacement grooming in ungulates, we would like to point out the potential that the behavior random permutation model represents for the study of animal behavior sequence in general. The distribution and frequency of behaviors is affected by various internal and environmental factors. For example, albatrosses use more complex search strategies to respond to sparse resources and scan information (Humphries et al. 2012), black necked cranes tend to scan the environment randomly to cope with higher predation risk (Li et al. 2018), and the Japanese macaques’ sequential complexity of behavior depends on different physiological states (MacIntosh et al. 2011). Thus, we can understand how animals interact with their environment—i.e., how they explore and use it—by studying the randomness of their behavioral sequences (Meyer et al. 2020). In short, we suggest that the behavior random permutation model, like fractal analysis and run test for Pulliam model, can be used as a tool for disentangling sequential behaviors. In particular, the relevance and compactness between 2 different behaviors in a sequence can be effectively identified by the behavior random permutation model. This non-invasive behavior analysis method would improve our understanding of animal behavior and allow to detect some differences in the complexity of behavioral patterns under specific conditions (MacIntosh et al. 2011).

This study provides a standard method for further research on displacement activities. In particular, the modern concept of displacement activity in animals is strongly tied into animal welfare (Norscia et al. 2021). Displacement activity is an effective index in zoo management, considering it is related to the expression of negative emotions in animals (Roth and Cords 2020; Robinson et al. 2021). In this context, the behavior random permutation model might contribute to determine whether welfare is compromised among captive individuals. Given that this kind of model is appropriate for studying the relationship between any kind of behaviors, it could for example be used to detect differences in the behavior of individuals reared in isolation (stressful conditions) as opposed to individuals reared in an enriched environment (more akin to natural conditions). Variations in the sequence of fixed actions could also be identified in animal communication (Peters et al. 2002), and the behavioral pathologies that often develop in captive animals or in animals suffering from pathogens should also involve changes in the order and frequency of behaviors. Future research should integrate ethological observation and neurobiological measures to explore the biobehavioral mechanisms that distinguish a behavior expressed in its normal context and the same behavior expressed at places and times where its occurrence is not expected.

Supplementary Material

zoac035_suppl_Supplementary_Materials
Click here for additional data file. (37.8KB, docx)
zoac035_suppl_Supplementary_Data
Click here for additional data file. (68.6KB, xlsx)

Acknowledgments

Authors thank the Tibet Major Science and Technology Project (XZ201901-GA-06), National Natural Science Foundation of China (grant nos. 31360141 and 31772470), and West Light Foundation of Chinese Academy of Sciences (2015) in supporting the study financially. We also thank Yiqian Wu, Yuhang Li, Ming Tan, Jinxuan Shen, Gesang, and Jiabu for their field help, especially Yiqian Wu for providing photos.

Contributor Information

Yunchao Luo, Lab of Animal Behavior and Conservation, School of Life Sciences, Nanjing University, Nanjing 210023, China.

Lin Wang, Lab of Animal Behavior and Conservation, School of Life Sciences, Nanjing University, Nanjing 210023, China.

Le Yang, Tibet Plateau Institute of Biology, Lhasa 850000, China.

Xiao-Fen Li, Department of Electrical Engineering, Shanghai Jiao Tong University, 800 DongChuan Road, Shanghai 200240, China.

Patrick Anselme, Department of Biopsychology, Ruhr-Universität Bochum 44801, Bochum, Germany.

Xinxin Wang, Lab of Animal Behavior and Conservation, School of Life Sciences, Nanjing University, Nanjing 210023, China.

Xingjun Tian, Lab of Animal Behavior and Conservation, School of Life Sciences, Nanjing University, Nanjing 210023, China.

Zhongqiu Li, Lab of Animal Behavior and Conservation, School of Life Sciences, Nanjing University, Nanjing 210023, China.

Author Contributions

Y.L. conceived of the study. Y.L. and Z.L. designed the study. Y.L., L.W., and L.Y. collected the data. Data analysis: X.-F.L. developed the random permutation model, X.W. and Y.L. for other analyses. Y.L. wrote the manuscript. P.A., L.W., X.T., and Z.L. edited the manuscript. All authors approved the final version.

Ethical Approval

This is an observational experiment and all observations were made at a distance of more than 200 m. All the experimental procedures in this study were approved by the Chinese Wildlife Management Authority.

Conflict of Interest

The authors declare that they have no conflicting interests.

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