Statistical approaches towards analysing ungulate movement patterns in the Kruger National Park
Date
2014-06-12
Authors
Goodall, Victoria Lucy
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Abstract
In this thesis I investigate the application of various statistical approaches towards
analysing time series data collected using GPS collars placed on three ungulate
species in the same region of the Kruger National Park, South Africa. Animal
movement tracking is a rapidly advancing area of ecological research and large
datasets are being collected with GPS locations of the animal, with shorter periods
between successive locations. A statistical challenge is to segment the movement
paths into groups which correspond to different behavioural activities. The aim of
my study was to investigate and compare alternative statistical approaches for
analysing GPS data and to establish the best statistical framework for interpreting
these large herbivore movements. The focus was on which methods are the most
appropriate for these animals and the comparison of the movement patterns across
species and season. Independent Mixture, Hidden Markov and Bayesian State-Space Models were used to analyse the hourly and daily movements of sable antelope, buffalo and zebra. Mixture Models provide a basic clustering technique to segment the
movement paths and identify different underlying groups within the data assumed
to correspond to different behavioural states. Posterior probabilities of group
membership are used to allocate movements between successive locations to
different states. This method ignores the dependence between successive
movements. Hidden Markov models (HMMs) use a time series technique and
include a dependency between successive observations via a Markov process.
Extensions to the HMMs were applied to allow for the inclusion of seasonal
covariates and irregular time gaps between successive observations caused by
missing locations. A Bayesian state-space model fits a random walk using MCMC
methods. The results were very similar to the HMMs but were more challenging
to fit and required much more processing time. In the absence of informative prior information, the Bayesian method does not provide any improvement on the HMMs. The HMMs perform slightly better in terms of state allocation accuracy than the Mixture Models. However Mixture Models perform acceptably if only a straightforward clustering of the observations is required. However, if a more robust method is required, the HMMs are relatively easy to fit and extend, allow for investigation of the state switching
probabilities and are recommended as the best method for analysing this type of data.