Deriving Animal Movement Behaviors Using Movement Parameters Extracted from Location Data

Abstract

We present a methodology for distinguishing between three types of animal movement behavior (foraging, resting, and walking) based on high-frequency tracking data. For each animal we quantify an individual movement path. A movement path is a temporal sequence consisting of the steps through space taken by an animal. By selecting a set of appropriate movement parameters, we develop a method to assess movement behavioral states, reflected by changes in the movement parameters. The two fundamental tasks of our study are segmentation and clustering. By segmentation, we mean the partitioning of the trajectory into segments, which are homogeneous in terms of their movement parameters. By clustering, we mean grouping similar segments together according to their estimated movement parameters. The proposed method is evaluated using field observations (done by humans) of movement behavior. We found that on average, our method agreed with the observational data (ground truth) at a level of 80.75% ± 5.9% (SE).

A dissimilarity measure estimation for analyzing trajectory data

“…In the case of discrete directions, this term is reduced to categorical distribution and the variable can take one of the quantized directions given the probability distribution for different directions. In our case where we used four directions, the event probabilities in cell is a four-dimensional probability simplex defined by 0, 0, 0, 0 where 1. randomly generated using equation (1) once the object enters the grid cell. This vector will be kept the same while the k th object resides in that cell.…”

“…Despite its simplicity, trajectory is a powerful motion descriptor that can explain the object's moving pattern and activity. Comparing two trajectories and quantifying their (dis)-similarity is a fundamental research problem with a wide range of applications including animals movement analysis [1], trajectory clustering [2], scene modelling [3] and trajectory retrieval [4].…”

Parasite-induced shifts in host movement may explain the transient coexistence of high- and low-pathogenic disease strains

“…Animal movement is frequently modelled as a Markov process with probabilistic transitions between discrete movement states, which are defined based on distributions of step lengths and turning angles recovered from animal movement data (Morales et al, 2004;Patterson et al, 2008;Gurarie et al, 2009;Moorter et al, 2010;McKellar et al, 2014;Edelhoff et al, 2016;Teimouri et al, 2018). These discrete state movement models inspire our model formulation, as our epidemiological model considers two infective classes: moving and resting (or lethargic), which have distinct epidemiological characteristics due to distinct movement behaviours.…”

“…The aim of this paper is to explicitly represent parasite-induced effects on host movement as a process underlying the transmission-virulence trade-off. Notably, we consider that infected hosts can shift between two discrete movement states: moving and resting and we justify this formulation based on studies from the animal movement literature (Edelhoff et al, 2016;Teimouri et al, 2018). We investigate the evolution of the rate of parasite replication within a host when the infection is potentially lethal and when the infection is non-lethal.…”

Movement patterns of lesser flamingos Phoeniconaias minor: nomadism or partial migration?

“…These patterns are most effectively detected by means of global positioning system (GPS) or satellite telemetry, advances in which have revolutionised our ability to track the trajectories of animal movement paths (Kays et al 2015). In this context trajectory is as a sequence of time-stamped locations from which we can derive inferences about animal movement behaviour (Teimouri et al 2018). The choice of movement trajectory is driven by certain environmental cues and the behavioural responses to these cues ultimately impact the fitness of individual animals (Cattarino et al 2016).…”