The main study aims of path segmentation and types of methods to address them. a Pattern description: Topology-based analyses rely directly on signals calculated from the movement trajectory (e.g. step length and bearing). They combine movement steps into groups based on similarity in the considered path-signals, for example by applying clustering algorithms. b Change-point detection: Time-series analyses assess a path-signal (y-axis) along its time-axis. For example, a moving window (rectangle) can be used to search for points along the time-series where local parameters (e.g. the mean) of the path-signal are significantly different from the global averages of these parameters. Significant change-points are assumed to indicate switches in underlying movement modes or behavioral states, and are used to separate the trajectory into segments (dashed lines). c Process identification: The majority of the presented state-space models link two stochastic models describing the state process and its observation. For example, the state process could consist of two discrete behavioral states (red and blue). The process model describes how the hidden state (x) emerges based on a Markov process. Therefore, it accounts for the conditional probability of a future state depending on the one of the current relocation. The observation model links the actual observed data (y) at given points in time to the hidden state. As a result, the most probable state of each observation, the switching probabilities between the states, as well as the distributions of the measured path-signals within each state are provided.