A pinboard by
Alison Parton

PhD student, University of Sheffield


Using statistical methods to learn about the movements of animals from location data obtained from GPS trackers. Using these observations of the animal's location throughout time (along with information on the environment at these locations) we can learn about the different movement behaviours that an animal exhibits (such as foraging and migration) and how the animal is interacting with their environment (e.g. are they attracted to a particular type of vegetation, how large is their home range). Research outcomes therefore allow us to extend our knowledge of the secret lives that animals lead along with the potential to direct conservation efforts and policy.


Multi-scale modeling of animal movement and general behavior data using hidden Markov models with hierarchical structures

Abstract: Hidden Markov models (HMMs) are commonly used to model animal movement data and infer aspects of animal behavior. An HMM assumes that each data point from a time series of observations stems from one of $N$ possible states. The states are loosely connected to behavioral modes that manifest themselves at the temporal resolution at which observations are made. However, due to advances in tag technology, data can be collected at increasingly fine temporal resolutions. Yet, inferences at time scales cruder than those at which data are collected, and which correspond to larger-scale behavioral processes, are not yet answered via HMMs. We include additional hierarchical structures to the basic HMM framework in order to incorporate multiple Markov chains at various time scales. The hierarchically structured HMMs allow for behavioral inferences at multiple time scales and can also serve as a means to avoid coarsening data. Our proposed framework is one of the first that models animal behavior simultaneously at multiple time scales, opening new possibilities in the area of animal movement modeling. We illustrate the application of hierarchically structured HMMs in two real-data examples: (i) vertical movements of harbor porpoises observed in the field, and (ii) garter snake movement data collected as part of an experimental design.

Pub.: 12 Feb '17, Pinned: 13 Sep '17

Statistical modelling of animal movement: a myopic review and a discussion of good practice

Abstract: With the influx of complex and detailed tracking data gathered from electronic tracking devices, the analysis of animal movement data has recently emerged as a cottage industry amongst biostatisticians. New approaches of ever greater complexity are constantly being added to the literature. In this paper, we review what we believe to be some of the most popular and most useful classes of statistical models used to analyze animal movement data. Specifically we consider discrete-time hidden Markov models, more general state-space models and diffusion processes. We argue that these models should be core components in the toolbox for quantitative researchers working on stochastic modelling of animal movement. The paper concludes by offering some general observations on the direction of statistical analysis of animal movement. There is a trend in movement ecology toward what are arguably overly-complex modelling approaches which are inaccessible to ecologists, unwieldy with large data sets or not based in mainstream statistical practice. Additionally, some analysis methods developed within the ecological community ignore fundamental properties of movement data, potentially leading to misleading conclusions about animal movement. Corresponding approaches, e.g. based on Levy walk-type models, continue to be popular despite having been largely discredited. We contend that there is a need for an appropriate balance between the extremes of either being overly complex or being overly simplistic, whereby the discipline relies on models of intermediate complexity which are usable by general ecologists, but which are grounded in well-developed statistical practice and are efficient to fit to large data sets.

Pub.: 24 Mar '16, Pinned: 13 Sep '17