Modeling activity patterns of wildlife using time- series analysisDOWNLOAD FILE
May 30, 2017 - Author: Jindong Zhang, Vanessa Hull, Zhiyun Ouyang, Liang He, Thomas Connor, Hongbo Yang, Jinyan Huang, Siqiang Zhou, Zejun Zhang, Caiquan Zhou, Hemin Zhang and Jianguo Liu
Journal or Book Title: Ecology and Evolution
Keywords: animal behavior; external and internal influences; giant panda (Ailuropoda melanoleuca); GPS collar, time-series analysis
Page Number(s): 2575-2584
Year Published: 2017
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.
Type of Publication: Journal Article