Modelling and Analysis of Individual Animal Movement
type: CreativeWork
description: Movement is ubiquitous to almost all life with most individuals undergoing some spatial change across their lifespans. Understanding how and why animals move through and interact with their environment is therefore key in understanding some of the most open and pressing questions in science; from the effects of climate and environmental change on local species, to preventing the spread of disease and infection. In this Thesis we show how theoretical approaches to modelling individual animal movement can lead to a better understanding of the processes behind movement. Byusing the framework of random walk (RW) theory we analyse observed movement data to predict and interpret movement behaviour of individuals. Chapters 2 and 3 introduce the field of Movement Ecology and concentrate on recent developments within the subject along and include derivations of key mathematical properties of RW theory which will be the analytical framework for analysing movement used throughout the Thesis. Chapter 4 uses a biased and correlated random walk (BCRW) as a model of individual animal movement to demonstrate efficiency in navigation. Chapter 5 explores the variation in movement of individual ground beetles (Poecilus cupreus) and demonstrates how this variation effects predictions of important population level movement dynamics, such as the expected displacement. Chapter 6 demonstrates that a highly peaked, heavy-tailed distribution found in the distribution of turning angles across an individual’s movement path can arise from the mixing of two distinct normal-type distributions, and provides an example of how this can indicate the presence of multiple behaviours in the movement path. Finally, Chapter 7 considers how animal ‘personality’ can effect individual movement behaviour by considering the movement of stickleback fish (Gasterosteus aculeatus)across three differing experimental environments.
abstract: Movement is ubiquitous to almost all life with most individuals undergoing some spatial change across their lifespans. Understanding how and why animals move through and interact with their environment is therefore key in understanding some of the most open and pressing questions in science; from the effects of climate and environmental change on local species, to preventing the spread of disease and infection. In this Thesis we show how theoretical approaches to modelling individual animal movement can lead to a better understanding of the processes behind movement. Byusing the framework of random walk (RW) theory we analyse observed movement data to predict and interpret movement behaviour of individuals. Chapters 2 and 3 introduce the field of Movement Ecology and concentrate on recent developments within the subject along and include derivations of key mathematical properties of RW theory which will be the analytical framework for analysing movement used throughout the Thesis. Chapter 4 uses a biased and correlated random walk (BCRW) as a model of individual animal movement to demonstrate efficiency in navigation. Chapter 5 explores the variation in movement of individual ground beetles (Poecilus cupreus) and demonstrates how this variation effects predictions of important population level movement dynamics, such as the expected displacement. Chapter 6 demonstrates that a highly peaked, heavy-tailed distribution found in the distribution of turning angles across an individual’s movement path can arise from the mixing of two distinct normal-type distributions, and provides an example of how this can indicate the presence of multiple behaviours in the movement path. Finally, Chapter 7 considers how animal ‘personality’ can effect individual movement behaviour by considering the movement of stickleback fish (Gasterosteus aculeatus)across three differing experimental environments.
author: Bailey, Joseph
language: en
keywords: QA Mathematics, QH301 Biology
date published: 2019-12
date modified: 2026-07-24T02:18:32Z
provider: University of Essex
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Essex