Abstract:
Animal movement is a fundamental part of ecology, and aids in understanding and modelling of social responsibility phenomena including population and community structure dynamics. Movement of animals is often characterised by direction (measured on the circle) and distance (measured on the real line); but traditional employed models often do not account for potential asymmetric directional movement, or departures from the usual gamma or Weibull assumptions for distance. This study focuses on the modelling of circular data in this animal movement environment on previously unconsidered circular distributions such as the sine-skewed von Mises distribution which may allow and account for departures from symmetry. In addition, alternative models to the aforementioned gamma or Weibull assumptions for distance are considered, namely the power Lindley (as a mixture of gamma and Weibull) as well as a Gumbel candidate. Computational aspects and investigations of this joint modelling is highlighted, particularly via the illustration of an extensive bootstrap study. A general hidden state Markov model is used to incorporate both these essential components when estimating via the use of the EM algorithm, and goodness of fit measures verifies the validity and viable future consideration of the newly proposed theoretical models within this practical and computational animal movement environment. The ethics number for this study is NAS124/2019.
