Modelling and inference for the movement of interacting animals

Jordan E Milner,Mu Niu,Paul G Blackwell,Timothée Poisot

doi:10.1111/2041-210x.13468

Abstract

Abstract Statistical modelling of animal movement data is a rapidly growing area of research. Typically though, these models have been developed for analysing the tracks of individual animals and we lose sight of the impact animals have on each other with regards to their movement behaviours. We aim to develop a model with a flexible social framework that allows us to capture that information. Our approach is based on the concept of social hierarchies, and this is embedded in a multivariate diffusion process which models the movement of a group of animals. The possibility of switching between behavioural states facilitates dynamic social behaviours and we augment the observed data with sampled state switching times in order to model the animals' behaviour naturally in continuous time. In addition, this enables us to carry out exact inference in a Bayesian setting with the benefits of being able to handle regular, irregular and missing data. All movement and behaviour parameters are estimated with Markov chain Monte Carlo methods. We examine the capability of our model with simulated data before fitting it to GPS locations of five wild olive baboons Papio anubis. The results enable us to identify which animals are influencing the movement of others and when, which provides both a dynamic and long‐term static insight into the group's social behaviours. Our model offers a flexible method in continuous time with which to model the network of social interactions within animal movement. Doing so avoids the limitations caused by a discrete‐time approach and it allows us to capture rich information with regards to a group's social structure, leading to constructive applications in conservation and management decisions. However, currently it is a computationally expensive task to fit the model to data, which in turns limits extending the model to more fruitful but complex cases such as heterogeneity in space or individual characteristics. Furthermore, our social hierarchy approach assumes all relevant animals are tracked and that any interactions have some ordering, both of which narrow the scope within which this approach is appropriate.

Highlights

The research area of statistically modelling animal movement has rapidly expanded in recent years
Our model offers a flexible method in continuous time with which to model the network of social interactions within animal movement
Dunn and Gipson (1977) introduced modelling animal movement as a diffusion process, a concept that has been built up on by Blackwell (1997, 2003), Harris and Blackwell (2013) and Blackwell, Niu, Lambert, and LaPoint (2016) to account for multiple movement behaviours. These methods have been developed for analysis of individual animals and so they fail to account for the impacts that social animals have on each other's movement behaviours

Summary

Introduction

The research area of statistically modelling animal movement has rapidly expanded in recent years This has in part been driven by the increase in availability of movement data (e.g. from GPS tags) coupled with the potential insight, from habitat preference to monitoring the impacts of a changing environment, that can be gained from analysing it (Kays, Crofoot, Jetz, & Wikelski, 2015). Dunn and Gipson (1977) introduced modelling animal movement as a diffusion process, a concept that has been built up on by Blackwell (1997, 2003), Harris and Blackwell (2013) and Blackwell, Niu, Lambert, and LaPoint (2016) to account for multiple movement behaviours These methods have been developed for analysis of individual animals and so they fail to account for the impacts that social animals have on each other's movement behaviours. Recent work has begun to explore that possibility (Langrock et al, 2014; Niu, Blackwell, & Skarin, 2016) through treating the group as a collective during the movement

Objectives

Methods

Findings

Conclusion