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Article

Activity Patterns of Bharal (Pseudois nayaur) from a Subtropical Forest Area Based on Camera Trap Data

by
Zhuo Tang
1,2,†,
Wei Chen
3,†,
Shufeng Wang
2,
Zhouyuan Li
4,
Tianpei Guan
1,* and
Jian Yang
5,*
1
College of Grassland Resources, Southwest Minzu University, Chengdu 610225, China
2
Administration Bureau of Wolong National Nature Reserve, Wenchuan 623006, China
3
School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
4
China Grassland Research Center, School of Grassland Science, Beijing Forestry University, Beijing 100083, China
5
China Conservation and Research Center for the Giant Panda, Chengdu 611830, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Diversity 2025, 17(8), 525; https://doi.org/10.3390/d17080525
Submission received: 2 June 2025 / Revised: 16 July 2025 / Accepted: 18 July 2025 / Published: 28 July 2025
(This article belongs to the Section Biodiversity Conservation)
Browse Figures
Versions Notes

Abstract

Understanding the activity patterns of a species is essential for developing sound conservation and management plans. In this study, we used a camera-trapping technique to determine the activity patterns of bharal (Pseudois nayaur) in a marginal population in Wolong National Nature Reserve, Sichuan, China. Our results showed that these animals preferred to be active in the daytime from 08:00 to 20:00, with an activity peak between 10:00 and 14:00. In addition, we found that the species had a seasonal activity pattern with higher activity frequency in summer than in winter and that bharal were most active in a temperature range of 3–11 °C and at night with a waxing crescent moon, implying that the activity rhythm of the species is an adaptation to a subtropical high-altitude alpine area with vertical zonation in temperature. The pattern of movement and activity was also correlated with the moon phase.

1. Introduction

The activity patterns of animals are an adaptation to the environmental conditions in which they live [1,2], but they are also influenced by other factors, including food availability, predation risk, interspecies competition, moon phase, light intensity, altitude, slope, and the air temperature of their habitats [3,4,5]. The degree of influence of these ecological factors differs depending on species and environment [6,7], but an understanding of the activity patterns of a species is essential for the development of sound conservation and management plans [6,8,9].
Bharal or blue sheep (Pseudois nayaur) are medium-sized, widely distributed alpine ungulates that belong to the Bovidae family. The species is listed in Appendix III of the Convention of International Trade in Endangered Species of Wild Fauna and Flora (CITES), classified as a Category II protected species in China and considered as of least concern by the International Union of Conservation of Nature (IUCN) [10]. The species is distributed in Central Asian regions, including India, Nepal, Pakistan, Sikkim, and China [11,12]. Previous researchers have studied the population structure [13,14,15], habitat [16,17,18], flocking behavior [19,20,21,22], reproductive behavior [23], diet [24], and interspecies competition [25,26] of bharal within their core distribution range.
Marginal populations represent the leading edge of a species’ dispersal or retreat as environmental conditions change, such as with increasing human disturbance or climate shifts [27,28]. Because of their behavioral flexibility, activity patterns are often considered a valuable proxy for assessing a species’ responses to these changes [4,29]. However, despite their significance, marginal populations of bharal have received very limited attention, and information on their activity patterns in these regions remains scarce [30]; that is the case with the population in Sichuan province, southwest China.
To the best of our knowledge, studies on the Sichuan population of blue sheep are limited, probably because their habitat is so difficult to reach. They inhabit the high rocky slopes and alpine meadow (typically 2500–5000 m elevation) within the Qionglai and Minshan Mountains. They form mixed-sex herds of 10–30 individuals during summer, while males form smaller groups or become solitary in winter [22,31]. Their diet is dominated by graminoids and shrubs, with seasonal altitudinal migrations: ascending to alpine meadows (>4000 m) in summer for fresh grasses and descending to sun-exposed slopes in winter to forage on woody plants [31]. The breeding season lasts from November to January, with parturition (usually one young per female) taking place in June–July on steep cliffs to reduce predation risk [22].
Camera trapping is a reliable, non-invasive method for wildlife investigation [32] that has been extensively used to determine wildlife population density [33], distribution [34], habitat selection [35], and species diversity [36]. It is especially useful for observing the activity patterns [37,38] and diurnal rhythms [9] of animals that are difficult to observe because of their wariness toward humans [34] or because they inhabit environments that are difficult for observers to work in [39]. Hence, this technology plays a key role in wildlife research, conservation, and management [8,40,41,42,43].
In this study, we used camera trapping to record the occurrence and behavior of bharal inhabiting the subtropical forest areas of the Wolong National Natural Reserve. Our aims were to analyze bharals’ activity rhythm with reference to two temporal scales (daily and seasonal) and to quantify the interrelationship among behavior types, activity intensity, individual types, atmospheric temperature and moon phase. With this knowledge, we aim to establish a baseline for future conservation assessment of this marginal population and offer an opportunity to gain deeper insights into their evolutionary process by comparing them to other populations.

2. Material and Methods

2.1. Study Site

The Wolong National Nature Reserve is in Wenchuan county, Sichuan province, southwestern China (102°52′–103°24′ E, 30°45′–31°25′ N, 2000 km2), situated between the Sichuan basin and the Qinghai-Tibet plateau (Figure 1). It is characterized by high mountains and deep valleys, with elevations ranging from 1150 to 6250 m. The climate data from the local station show that the annual average temperature is 8.5 ± 0.5 °C, annual average relative humidity is 80.3%, and annual precipitation as rain is 890 ± 100 mm. The reserve encompasses several climatic zones and has high habitat diversity, with more than six thousand animal, insect, and plant species [44]. The climate of the reserve can be divided into four seasons: spring (March to May), summer (June to August), autumn (September to November), and winter (December to February).
To study the activity patterns of the species, from November 2013 to March 2016, 27 infrared cameras (Ltl-6210MC) were set up 100–500 m apart inside the reserve at an altitude of 3536–4481 m (4174 ± 47.9 m), which encompassed all the different habitat types of the species (20 alpine screes, 5 alpine shrub lands, and 2 alpine meadows; Li et al., 2021 [45]). During the investigation period, we did not use bait to obtain real field activity data. All cameras were installed approximately 40–50 cm above the ground and set to operate for 24 h and take a 10–20 s video after three photos were shot [45].

2.2. Data Collection

Digital photographs were extracted from the SD camera cards after collecting the cameras. Effective photos of the species were extracted using the method of Yasuda [46], and photos of the same species taken over a 30 min interval were considered as one effective capture. Next, we checked the age classes of the species using the method of Liu et al. [45] and checked the behavioral type of the species based on the division standards used by Li et al. [30]. In addition, according to the study of Tang et al. [47], we defined three indices. (1) The time-period activity intensity index, TAII, was defined as
TAII = (Ai/A) × 100,
where A is the total number of effective captures and Ai is the time-period of the captures. (2) The temperature activity intensity index, TEAII, was calculated by
TEAII = (Ti/A) × 100,
where A is the total number of effective captures and Ti is the number of effective captures at i °C. (3) The relative abundance index of animals, RAI, was given by
RAI = (A/N) × 1000,
where A is an effective capture and N is a working day (24 h) of the camera, which can reflect the activity intensity of animals, as the more often animals appear, the more effective captures are recorded [47].

2.3. Statistical Analysis

Since the data in this study were non-normally distributed, the Chi-squared test (χ2) was used to compare differences in diurnal and seasonal activity patterns and the differences in activity patterns at different temperatures and moon phases. All statistical tests were performed with R software (Version 4.2.2). Statistical packages designed to evaluate activity and overlap were applied to identify rhythms and plot activity. We described the pattern of daily activity rhythms of each species by kernel-density estimation [48] and then used the Wald test to further analyze the difference in activity rhythms between seasons.

3. Results

3.1. Effective Captures

Approximately 120,000 photos and video clips were collected from 7056 camera-days. Among these photos, 823 effective captures of the species were obtained by the infrared cameras and seven types of behavior (standing, moving, licking salt, grazing, resting, fighting, and grooming, Figure 2) were displayed in the effective captures.

3.2. Diurnal Activity Patterns

Our results showed a typical diurnal activity pattern (χ2 = 70.116, df = 11, p < 0.001). The animal’s activity increased after 08:00 in the morning and decreased after 20:00 at night, with the greatest activity between 10:00 and 14:00 in the daytime and the lowest between 20:00 and 06:00 during the night (Figure 3).

3.3. Seasonal Activity Patterns

Our results also showed an obvious seasonal difference in activity (χ2 = 292.51, df = 3, p < 0.001) with the highest activity frequency in summer and lowest activity frequency in winter (Figure 4). Also, we found that different types of behavior (F = 12.931, df = 6, p < 0.001) and age classes (F = 5.164, df = 3, p < 0.001) also displayed seasonal patterns (Table 1; Figure 5).

3.4. Atmospheric Temperature and Activity Patterns

Annually, when atmospheric temperatures fluctuated between −12 °C and 37 °C (12.14 ± 14.51 °C), we observed bharal activity. Different activity frequencies were observed at different temperatures (χ2 = 130.13, df = 46, p < 0.001), with the most activity occurring between 3 °C and 12 °C (Figure 6), suggesting that this is the favored physiological temperature range for activity.

3.5. Moon Phase and Nocturnal Activity Pattern

Our results also showed that bharal had different activity frequencies during different moon phases (χ2 = 17.897, df = 5, p = 0.003), with the highest activity (RAI = 11.07) on nights with a waxing crescent moon and lowest activity frequency (RAI = 3.88) with new moon, waning gibbous, and waning crescent moon (Figure 7), indicating a favored moon phase for activity at night.

4. Discussion

An animal’s activity patterns reflect their adaptation to the environment where they live [1]. In mammals, the diurnal ‘clock’ located in the suprachiasmatic nuclei regulates metabolism and coordinates various physiological functions, ensuring that their activity is synchronized with their environment [49,50]. Our results showed that the bharal preferred to appear in the daytime from 08:00 to 18:00 h with an activity peak between 10:00 and 12:00 h. We found that the animals also had a seasonal activity pattern with the highest activity intensity in summer and lowest intensity in winter. Other important findings were that the bharal had a temperature preference of 3–11 °C and displayed the highest activity on nights with a young moon.
Previous studies [51,52] reported that bharal have two obvious daytime activity peaks, one at 06:00 to 12:00 h and another at 14:00 to 17:00 h. However, our result differs from this in that the bharal in Wolong Reserve only showed one obvious diurnal activity peak from 10:00 to 12:00 h. This difference could be explained by several factors. (1) Climate-induced population differences: Although previous researchers [51,52] observed two activity peaks for animals from different populations in the daytime, the activity peaks observed were not the same. It is likely that the bharal from different zones may have different activity patterns due to local climatic differences [52]. (2) Food-induced population differences: food quality and quantity in the local environment can also result in more than one activity peak [53]. (3) Predator-induced population differences: The bharal activity patterns could also have been influenced by differences in local predator risk [54]. For example, the snow leopard, Panthera uncia, is the main predator of the bharal in the Wolong reserve, and it could influence the bharals’ behavior [47]. (4) Previous studies relied on direct observation, which can influence the behavior of focal animals and restrict data collection to periods with sufficient light. In contrast, the infrared-triggered cameras used in our study offer several advantages, including minimizing human disturbance and enabling continuous monitoring regardless of lighting conditions. Our cameras recorded animals moving in front of the lens but could not show what they were doing away from the cameras. These activity pattern differences should be explored in future research.
Seasonal activity rhythms are commonly observed for species that inhabit an environment with seasonal resource fluctuation [55]. The bharal live in an alpine region and are expected to show significant activity responses. In Wolong, we also found that the bharal showed seasonal differences in activity patterns with high activity frequency in summer and low frequency in winter. This observation could be the result of several influences. Female bharal need extra energy after the stress of pregnancy and giving birth, as well as to produce enough milk for the calves. The males also have to replace fat reserves after devoting much time and energy to increasing their chances of mating at the cost of reduced foraging. However, due to the low energy gain or loss during winter and early spring, they must allocate more time to replenishing fat reserves during the summer to survive the coming seasons. Blue sheep showed significantly different activity patterns during fall, probably representing a behavioral modification after summer and ahead of rutting season. During the summer with its high temperature, the bharal need more water and have to drink more often, so they must move with higher frequency. Food is scarce in winter and the blue sheep reduce their activity level to conserve energy in the harsh environment. Also, snow leopard predation increases during winter, which causes the bharal to be more wary and move around less [47].
Bharal exhibited peak activity within a 3–11 °C thermal range [49,56], reflecting physiological requirements for thermoregulation. Despite cold adaptations, excessive energy expenditures from snow traversal in winter or summer water sourcing risk negative net energy gains. Activity reduction under non-optimal temperatures thus constitutes a fitness-enhancing strategy. This documented thermal preference provides critical baseline data for predicting climate change responses, including distribution shifts under global warming scenarios.
Moonlight amplifies nocturnal visibility for predators and prey [57], creating illumination gradients from new to full moons that modulate detection capacity for resources and threats [58]. Snow leopards and bharal exhibit differential detection abilities under low light, with specific illumination types favoring each species [59]. Crucially, while optimal light enhances resource location for both, neither benefit from being detected. Prey optimize fitness when illumination permits identification of food/predators while minimizing their own detectability [60]. Our findings indicate that bharal strike an evolutionary equilibrium between predation avoidance and foraging efficiency.
Bharal face fitness costs when safety-foraging thresholds are exceeded, exhibiting a preference for nocturnal activity during waxing crescents (new to quarter moon). Peak pre-midnight movement aligned with moonrise timing, despite comparable illuminance during waning phases. This exclusive waxing-phase selection reflects a broader lunar-activity phenomenon [47]. Though energy acquisition efficiency likely regulates this rhythm, mechanistic clarification of light-activity relationships requires integrated camera-trapping with real-time lux monitoring.

5. Conclusions

In this study, we used infrared-triggered camera-trapping technology to investigate the activity patterns of bharal in a subtropical alpine forest area. Our results showed that the animals have diurnal and seasonal activity patterns and temperature and moon-phase preferences for their activities. Our results provide valuable information for their management and conservation. We acknowledge that these findings may vary in the future with the publication of more comprehensive studies, and consequently, the interpretations presented here should be regarded as partial at this stage of research. The infrared-triggered camera-trapping technology, utilized in various environments, is very useful in wildlife conservation, and we expect to use the technology for long-term wildlife monitoring and research in various natural reserves and parks in future.

Author Contributions

Z.T.: investigation, data curation, visualization, funding acquisition, writing—original draft; W.C.: investigation, software, visualization, writing—review and editing; S.W.: project administration, resources, writing—review and editing; Z.L.: formal analysis, validation, writing—review and editing; J.Y.: conceptualization, methodology, supervision, writing—review and editing; T.G.: conceptualization, methodology, supervision, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Natural Science Foundation of China (32370548); the Continuous Cooperation on Wolong National Nature Reserve between Sichuan and Hong Kong (SC07); and the 2025 Provincial Fiscal Forestry and Grassland Ecological Conservation and Restoration Subsidy Funds for National Park (51000025T000013131056).

Institutional Review Board Statement

Research was conducted in compliance with applicable animal care guidelines and appropriate permits. Infrared cameras were employed for passive data collection, posing minimal disturbance or stress to bharal and other wildlife. No animals were captured, handled, or otherwise interfered with during the course of research.

Data Availability Statement

The data are not publicly available due to conservation concerns for the species.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Study area and camera sites in Wolong National Nature Reserve, Sichuan Province, China.
Figure 1. Study area and camera sites in Wolong National Nature Reserve, Sichuan Province, China.
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Figure 2. Examples of bharal (Pseudois nayaur) photographed by infrared cameras in Wolong National Nature Reserve, Sichuan, China.
Figure 2. Examples of bharal (Pseudois nayaur) photographed by infrared cameras in Wolong National Nature Reserve, Sichuan, China.
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Figure 3. Daily activity rhythms of bharal in Wolong National Nature Reserve.
Figure 3. Daily activity rhythms of bharal in Wolong National Nature Reserve.
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Figure 4. Seasonal dynamics of activity intensity of bharal in Wolong National Nature Reserve.
Figure 4. Seasonal dynamics of activity intensity of bharal in Wolong National Nature Reserve.
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Figure 5. Seasonal patterns of major bharal behavior in Wolong National Nature Reserve.
Figure 5. Seasonal patterns of major bharal behavior in Wolong National Nature Reserve.
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Figure 6. Relationship between air temperature and bharal activity.
Figure 6. Relationship between air temperature and bharal activity.
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Figure 7. Relationship between the moon phase and bharal activity.
Figure 7. Relationship between the moon phase and bharal activity.
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Table 1. Results of one-way ANOVA analysis of bharals’ activity.
Table 1. Results of one-way ANOVA analysis of bharals’ activity.
dfFp
Age class35.1640.002
Season312.914<0.001
Type of behavior612.931<0.001
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Tang, Z.; Chen, W.; Wang, S.; Li, Z.; Guan, T.; Yang, J. Activity Patterns of Bharal (Pseudois nayaur) from a Subtropical Forest Area Based on Camera Trap Data. Diversity 2025, 17, 525. https://doi.org/10.3390/d17080525

AMA Style

Tang Z, Chen W, Wang S, Li Z, Guan T, Yang J. Activity Patterns of Bharal (Pseudois nayaur) from a Subtropical Forest Area Based on Camera Trap Data. Diversity. 2025; 17(8):525. https://doi.org/10.3390/d17080525

Chicago/Turabian Style

Tang, Zhuo, Wei Chen, Shufeng Wang, Zhouyuan Li, Tianpei Guan, and Jian Yang. 2025. "Activity Patterns of Bharal (Pseudois nayaur) from a Subtropical Forest Area Based on Camera Trap Data" Diversity 17, no. 8: 525. https://doi.org/10.3390/d17080525

APA Style

Tang, Z., Chen, W., Wang, S., Li, Z., Guan, T., & Yang, J. (2025). Activity Patterns of Bharal (Pseudois nayaur) from a Subtropical Forest Area Based on Camera Trap Data. Diversity, 17(8), 525. https://doi.org/10.3390/d17080525

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