Modeling activity patterns of wildlife using time‐series analysis

Abstract

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.

Individual Daily and Seasonal Activity Patterns in Fox Squirrels (Sciurus niger) Quantified by Temperature-Sensitive Data Loggers

“…The appropriateness of an animal's activity level and pattern over the cycle of day and night and over the time course of the seasons is essential for its survival (DeCoursey et al, 2000;Larivée et al, 2010;Zhang et al, 2017) and reproductive success (Schmidt et al, 2008;Speakman, 2008;Zhao et al, 2013). The selective pressures to forage efficiently while avoiding injury and death lead to trade-offs in an animal's strategies between starvation and predation (Dall and Boyd, 2002;Higginson et al, 2012;Quinn et al, 2012;McNamara et al, 2016) that will affect both the daily proportions of time to be active and rest and the timing and regularity of rest and activity.…”

Mixed‐method study on medicinal herb collection in relation to wildlife conservation: the case of giant pandas in China

“…The temporal pattern of herb collection potentially overlaps with that of giant panda migration (Table 1; Hull et al 2015;Liu et al 2015). Arguably, the most important time for pandas is the mating season, which occurs from March to May (Schaller et al 1985;Zhang et al 2015Zhang et al , 2017. This time frame intersects with the collection period for Chonglou, the herb that was named by villagers as having the greatest increase in collection in recent years.…”

What Can We Quantify About Carer Behavior?

“…A more detailed picture can be obtained by explicitly fitting a model of interval length, with linear and higher-order time terms as predictors. For more complex trends, especially those with periodicity, one could investigate temporal patterns of care using methods developed for time series analysis that have been previously applied to other aspects of behavior, such as cross-correlations (Hall et al, 2014) or wavelet analysis (Zhang et al, 2017). An alternative approach is to group care events by hour or by day and then fit Poisson-based mixed models to the counts of care behavior, with environmental metrics as covariates (Nomano et al, submitted); the best approach will depend on the study system and length of time analyzed.…”