Identifying Movement States From Location Data Using Cluster Analysis

Abstract

ABSTRACT Animal movement studies regularly use movement states (e.g., slow and fast) derived from remotely sensed locations to make inferences about strategies of resource use. However, the number of movement state categories used is often arbitrary and rarely inferred from the data. Identifying groups with similar movement characteristics is a statistical problem. We present a framework based on k‐means clustering and gap statistic for evaluating the number of movement states without making a priori assumptions about the number of clusters. This allowed us to distinguish 4 movement states using turning angle and step length derived from Global Positioning System locations and head movements derived from tip switches in a neck collar of free‐ranging elk (Cervus elaphus) in west central Alberta, Canada. Based on movement characteristics and on the linkage between each state and landscape features, we were able to identify inter‐patch movements, intra‐patch foraging, rest, and inter‐patch foraging movements. Linking behavior to environment (e.g., state‐dependent habitat use) can inform decisions on landscape management for wildlife.