Modelling group dynamic animal movement

Abstract

Summary1. Group dynamics are a fundamental aspect of many species' movements. The need to adequately model individuals' interactions with other group members has been recognized, particularly in order to differentiate the role of social forces in individual movement from environmental factors. However, to date, practical statistical methods, which can include group dynamics in animal movement models, have been lacking. 2. We consider a flexible modelling framework that distinguishes a group-level model, describing the movement of the group's centre, and an individual-level model, such that each individual makes its movement decisions relative to the group centroid. The basic idea is framed within the flexible class of hidden Markov models, extending previous work on modelling animal movement by means of multistate random walks. 3. While in simulation experiments parameter estimators exhibit some bias in non-ideal scenarios, we show that generally the estimation of models of this type is both feasible and ecologically informative. 4. We illustrate the approach using real movement data from 11 reindeer (Rangifer tarandus). Results indicate a directional bias towards a group centroid for reindeer in an encamped state. Though the attraction to the group centroid is relatively weak, our model successfully captures group-influenced movement dynamics. Specifically, as compared to a regular mixture of correlated random walks, the group dynamic model more accurately predicts the non-diffusive behaviour of a cohesive mobile group. 5. As technology continues to develop, it will become easier and less expensive to tag multiple individuals within a group in order to follow their movements. Our work provides a first inferential framework for understanding the relative influences of individual versus group-level movement decisions. This framework can be extended to include covariates corresponding to environmental influences or body condition. As such, this framework allows for a broader understanding of the many internal and external factors that can influence an individual's movement.

Estimating interactions between individuals from concurrent animal movements

“…Additionally, our approach is suitable to analyzing possible fine‐scale attraction and avoidance mechanisms between heterospecific individuals in the context of competition or predation (Vanak et al, ). As such, our approach complements existing methods that focus more on group‐influenced movement dynamics of herding or swarming organisms or when movements are correlated due to large‐scale joint drivers of movement, for example as in the case of migratory behaviour (Calabrese et al, ; Langrock et al, ; Niu et al, ; Russell, Hanks, & Haran, ). Although these approaches also account for individual variability in movement to some extent, they focus on movement processes in which individuals within a group express the same qualitative behaviour.…”

Guest Editor’s Introduction to the Special Issue on “Animal Movement Modeling”

“…Thus, it is a golden era for statisticians to make useful contributions to the field by finding ways to obtain new types of inference based on telemetry data and efficient ways to analyze massive tracking data sets. The future of animal movement modeling will continue to see a need for advancements in models for multiple individuals (Langrock et al 2014;Hooten et al 2016) while incorporating measurement error, environmental covariates, and real-time tracking data. Also, the analyses of auxiliary telemetry data such as accelerometer data that can provide deeper insights into animal behavior and health are still nascent (Leos-Barajas et al 2017), and reconciling these data with more traditional spatially referenced tracking data will be key for tying animal movement models to management and conservation decision making.…”

Gaussian quadrature approximations in mixed hidden Markov models for longitudinal data: A simulation study

“…Further alternatives are available: the direct maximization of the likelihood function (Altman, 2007;Langrock et al, 2014), the MCEM algorithm described, in a very simple development, by Altman (2007) and in a quite more elaborated form by Chaubert-Pereira et al (2010). However, in our opinion, the EM algorithm represents a conventional choice, it is computationally simple and widely adopted by non-statisticians also.…”