A novel method for identifying behavioural changes in animal movement data

Abstract

A goal of animal movement analysis is to reveal behavioural mechanisms by which organisms utilize complex and variable environments. Statistical analysis of movement data is complicated by the fact that the data are multidimensional, autocorrelated and often marked by error and irregular measurement intervals or gappiness. Furthermore, movement data reflect behaviours that are themselves heterogeneous. Here, we model movement data as a subsampling of a continuous stochastic processes, and introduce the behavioural change point analysis (BCPA), a likelihood-based method that allows for the identification of significant structural changes. The BCPA is robust to gappiness and measurement error, computationally efficient, easy to implement and reveals structure that is otherwise difficult to discern. We apply the analysis to a GPS movement track of a northern fur seal (Callorhinus ursinus), revealing an unexpectedly complex diurnal behavioural profile, and demonstrate its robustness to the greater errors associated with the ARGOS tracking system. By informing empirical interpretation of movement data, we suggest that the BCPA can eventually motivate the development of mechanistic behavioural models.

Comparative analysis of methods for inferring successful foraging areas from Argos and GPS tracking data

“…Recently, behavioural change point analysis has allowed the detection of behavioural changes in movement tracks with irregular measurement intervals (Gurarie et al 2009). Mechanistic models, such as process-based models, allow a functional relationship between the system's probability of being in a state at time t and the previous system states.…”

Addressing Challenges in the Application of Animal Movement Ecology to Aquatic Conservation and Management

“…Given the novelty of these tools to managers, it is necessary to understand how the use of information generated by new technologies compares to the use of information provided by more conventional tools or dogma/experience. It is also important to communicate the types of new knowledge that can be generated by electronic tagging that have here-to-fore been unattainable [e.g., determining fate of individual animals (Yergey et al, 2012); identifying behavioral changes using animal movement data (Gurarie et al, 2009); evaluating consistency of behaviors and thus behavioral syndromes in wild animals (Harrison et al, 2015)]. Preparing managers for the capabilities of the technology so that they are ready to receive new, potentially transformative, information may be a useful strategy for helping to ensure that data from electronic tagging studies are more likely to be used by managers.…”

Bayesian Inference for Multistate ‘Step and Turn’ Animal Movement in Continuous Time

“…Within a behaviour, movement is defined by the straight line 'step length' between two consecutive locations and the 'turning angle' between three consecutive locations, following parametric distributions such as the Weibull and the wrapped Cauchy, respectively (Morales et al 2004;McClintock et al 2014). Popular variants on this include state space models to incorporate observation error (Patterson et al 2010;Jonsen et al 2013), hidden Markov models for efficiency (Langrock et al 2012) and change point analysis rather than Markov chains to identify behavioural switches (Gurarie et al 2009;Nams 2014). Parton et al (2017) introduce a continuous-time movement model based on similar quantities to those of the popular discrete-time 'step and turn' models.…”