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Table 2 Characteristics of the methodological approaches for the three different categories of research questions. Different methods for answering the three type of broad research questions (study aims) are listed together with the analytical category they stem from, a short description of each method as well as the considered categories of input path-signals and important references

From: Path segmentation for beginners: an overview of current methods for detecting changes in animal movement patterns

Study aim Method Analytical category Description Input signal References
Movement pattern description Thresholding Topology-based Applies thresholding schemes (cut-off values) to separate relocations into different groups based on single or multiple path parameters (e.g., short- vs. Long-range movements) Primary and secondary signals [45, 80, 84, 127]
  Supervised Classification Topology-based Relocations (steps) of a trajectory are assigned to certain classes of movement behavior based on a classification scheme fitted with a training dataset Primary and secondary signals, additional information like activity data [129131]
  Clustering Topology-based Unsupervised classification for identifying distinctive groups within a multivariate set of path-signals Primary and secondary signals, additional information like activity data [21, 132]
  Bayesian Partitioning of Markov Models (BPMM) Topology- and time- series based Classification algorithm for determining the number and sequence of homogenous classes within a sequential path-signal (time series) Primary and secondary signals [35, 91, 92]
Change-point detection Line Simplification Topology- or time-series based Tests whether reducing the number of vertices in a trajecotry significantly impacts path topology to determine change points (can also be applied with graphs of sequential path-signals) Primitive signals (spatial position) [12, 133]
  Change Point Test Topology-based Detects significant changes in the observed movement direction (orientation) between the starting point and an attraction point of a trajectory Primitive signals (spatial position) [86, 134]
  Spatio-Temporal Criteria Segmentation Topology-based Special type of thresholding seeking optimal segmentation of a trajectory based on monotone criteria: relocations are included in a segment as long as they fullfill certain predefined requirements Primitive, primary and secondary signals [32, 87]
  Piecewise Regression Time-series analysis Splits time-series model into representative segments based on a signficant change-point (fits a polynomial model for each segment) Primary and secondary signals [86, 87]
  Penalized Contrast Method (PCM) Time-series analysis Non-parametric segmentation of a path-signal: the unknown number of segments is estimated by minimizing a penalized contrast function Mostly secondary signals [31, 40, 135]
  Behavioral Change Point Analysis (BCPA) Time-series analysis Likelihood-based method for detecting significant change points; applies moving window over continuous autocorrelated time series of a path-signal Mostly secondary signals [28, 35]
  Pruned Exact Linear Time (PELT) Algorithm Time-series analysis Search method for detecting optimal number and locations of change points minimizing different cost and penalty functions primary and secondary signals [42, 136, 137]
  Behavioral Movement Segmentation (BMS) Time-series analysis Combined search algorithm which optimizes segmentation based on parsimony and subsequent clustering for assigning segments to similar behaviors primary and secondary signals, additional information like activity data [43]
Process identification Hidden-Markov Models (HMM) State-space models Estimate the sequence and composition of a predifined number of discrete states (e.g., movement behaviors) as well as the switching-probabilities between these states Primary signals, additional information like activity data [33, 49, 5355]
State-Space Models with Location Filtering State-space models More complex models which can model hidden movement states and also correct for errors in the observation process (e.g., GPS errors) Primitive (spatial position) and primary signals, additional information like activity data [51, 52, 65, 88, 90, 138]
Hierarchical State-Space Models State-space models Hierarchical models accounting for variability of number and composition of movement states between individuals (further making inferences at population level) Primary signals [48, 52, 89]
Bayesian Partitioning of Markov Models (BPMM) Topology- and time- series based Can also be used as partitioning algorithm determining the number and sequence of homogenous models (“states”) within a sequential path-signal primary and secondary signals [35, 91, 92]