Liu, J., V. Hull, J. Luo, W. Yang, W. Liu, A. Viña, C. Vogt, Z. Xu, H. Yang, J. Zhang, L. An, X. Chen, S. Li, Z. Ouyang, W. Xu and H. Zhang 2015. Multiple telecouplings and their complex interrelationships. Ecology and Society 20(3):44.http://dx.doi.org/10.5751/ES-07868-200344

One of the most fundamental questions in animal ecology concerns the activity patterns of animals and the environmental and intrinsic factors that influence such dynamics. Activity patterns of the elusive and endangered giant panda (Ailuropoda melanoleuca) are not well understood. Using GPS collars equipped with dual-axis accelerometers on captive and wild giant pandas for the first time, we investigated the impact of day, season, and weather on wild panda activity in Wolong Nature Reserve, Sichuan, China. Most pandas were not crepuscular as previously reported but had 3 apparent activity peaks, in the morning, afternoon, and around midnight. We found a peak in panda activity in June, then an apparent decrease in August and September, followed by an increase again from November to March of the following year. Activity patterns roughly corresponded to mean daily movement distances across seasons and movement behavior in a GPS-collared captive panda (studied to establish a baseline for interpreting collar-recorded activity of wild pandas). There was greater activity in times of higher solar radiation throughout every season, especially under cold conditions. This result suggests the potential for climate change to impact panda behavior in ways not previously reported. Our analysis also suggests that pandas may be constrained by tight energy budgets from their low-nutrient diet and may adjust their energy budgets by modifying their activity time and level across seasons. Our study has implications for understanding animal activity patterns across species, particularly relationships among forage, weather, and energy expenditure over time.

The study of wildlife activity patterns is an effective approach to understanding fundamental ecological and evolutionary processes. However, traditional statistical approaches used to conduct quantitative analysis have thus far had limited success in revealing underlying mechanisms driving activity patterns. Here, we combine wavelet analysis, a type of frequency‐based time‐series analysis, with high‐resolution activity data from accelerometers embedded in GPS collars to explore the effects of internal states (e.g., pregnancy) and external factors (e.g., seasonal dynamics of resources and weather) on activity patterns of the endangered giant panda (Ailuropoda melanoleuca). Giant pandas exhibited higher frequency cycles during the winter when resources (e.g., water and forage) were relatively poor, as well as during spring, which includes the giant panda's mating season. During the summer and autumn when resources were abundant, pandas exhibited a regular activity pattern with activity peaks every 24 hr. A pregnant individual showed distinct differences in her activity pattern from other giant pandas for several months following parturition. These results indicate that animals adjust activity cycles to adapt to seasonal variation of the resources and unique physiological periods. Wavelet coherency analysis also verified the synchronization of giant panda activity level with air temperature and solar radiation at the 24‐hr band. Our study also shows that wavelet analysis is an effective tool for analyzing high‐resolution activity pattern data and its relationship to internal and external states, an approach that has the potential to inform wildlife conservation and management across species.