Feature-based and shape-match classifications of animal population time series

Abstract

Estimation of long-term population trends can help population ecologists and conservation biologists assess the status and conditions of wildlife populations to inform the decision making of wildlife management and conservation. Machine learning methods such as time series clustering classify population time series into different clusters that have different trends and dynamic patterns as an alternative to statistical models. We used dynamic time warping (DTW) clustering and feature-based clustering to classify 15 rabbit population times in Mississippi, the United States from 1985 to 2005. Our feature-based method used the parameters of the order-2 Gompertz population models (or autoregressive time series models) as the features of population dynamics. We also estimated the long-term trends of the rabbit populations with the Theil-Sen analysis (TSA). The DTW clustering identified two clusters as optimal classification having the highest Silhouette score. Dynamic time warping clustering had 80% consistency in time series memberships with the TSA, whereas feature-based clustering had 87% consistency. Shape-match DTW clustering not only matches the shapes of trajectories, but also trends between time series. The features based on the Gompertz models capture trend, linear, and nonlinear dynamics. Our shape-match and feature-based time series clustering can cluster time series of different lengths and missing data. The Theil-Sen analysis generates an explicit estimate of linear trends and accounts for temporal autocorrelation. Since the true trends or patterns are often unknown, we recommend using two or all three methods of this study to assess the trends of wildlife populations.