Understanding the spatiotemporal pattern of grazing cattle movement

TL;DR

This study analyzes high-frequency cattle movement data, revealing hierarchical dispersal in moving states and scale-invariant waiting times, offering insights into grazing behavior and underlying biological mechanisms.

Contribution

It introduces a simple two-state mobility model and proposes a novel hypothesis linking movement patterns to energy status under maximum entropy.

Findings

Dispersal kernel fits a mixture exponential distribution.

Waiting times follow a truncated power-law distribution.

Movement patterns may be driven by energy status and environmental factors.

Abstract

In this study, we analyse a high-frequency movement dataset for a group of grazing cattle and investigate their spatiotemporal patterns using a simple two-state `stop-and-move' mobility model. We find that the dispersal kernel in the moving state is best described by a mixture exponential distribution, indicating the hierarchical nature of the movement. On the other hand, the waiting time appears to be scale-invariant below a certain cut-off and is best described by a truncated power-law distribution, suggesting heterogenous dynamics in the non-moving state. We explore possible explanations for the observed phenomena, covering factors that can play a role in the generation of mobility patterns, such as the context of grazing environment, the intrinsic decision-making mechanism or the energy status of different activities. In particular, we propose a new hypothesis that the underlying movement pattern can be attributed to the most probable observable energy status under the maximum entropy configuration. These results are not only valuable for modelling cattle movement but also provide new insights for understanding the underlying biological basis of grazing behaviour.