Novel applications of machine learning for the study of animal movement

Campioni, Nazareno (2024) Novel applications of machine learning for the study of animal movement. PhD thesis, University of Glasgow.

Abstract

Animal movement data play a crucial role in our endeavour to decode the wildlife dynamics that unfold across the Earth’s varied terrains and waterways. Collecting animal movement data involves employing a variety of modern technologies and techniques. One common method is the use of Global Positioning System (GPS) devices that are attached to animals, providing accurate location data at regular intervals. These devices allow us to track animals’ movements over time and space, giving us access to intricate details about their ranging behaviours and daily routines.

These data provide insights into migration patterns, foraging strategies, habitat preferences, and responses to environmental changes and are therefore key to an improved understanding of the behaviours, habitats and ecological interactions of various species. There are many challenges associated with the analysis of such datasets and movement ecologists have been dilligently advancing state-of-the-art statistical methods for analysing animal movement data. This effort is crucial because it enables us to extract meaningful information from the complex, large-scale datasets generated by animal tracking studies. These advanced statistical techniques help uncover hidden patterns, such as the identification of significant stopover sites during migrations or the characterisation of nuanced movement behaviours. Moreover, they facilitate the integration of environmental variables, enhancing our ability to understand how animals respond to changing landscapes and climate conditions. By refining our analytical tools, movement ecologists can provide more accurate and comprehensive insights into wildlife behaviours, aiding conservation efforts and ecological research on a global scale.

Throughout this thesis, we will talk about models of animal movement and we will give our contribution to expand the array of statistical methods that can be employed for the analysis of telemetry datasets. We will begin by reviewing some of the most commonly employed methods found in the literature. Then, we will focus on a simple one-dimensional self-propelled particle model used to simulate the dynamics of a group of locusts placed in a ring-shaped arena for an experiment, and we will leverage Gaussian processes to infer microscale properties of the group from macroscale observed variables, without deriving a formal mathematical link between the two scales, which is in many cases intractable.

Our focus will then shift to the problem of identifying different behavioural patterns from relocation data. In the fourth chapter we will describe various methods that we have conceptualised all aimed at simulating semi-Markov chains. This will be needed in the subsequent chapter, where we will introduce a flexible model scalable to large datasets that finds its applications in solving the so-called switching problems. This will be achieved by modelling the locations via an integrated OU process and by reconstructing the latent behavioural patterns via a Monte Carlo Expectation-Maximisation algorithm, where the method introduced in the previous chapter will be employed.

In Chapter 6 we will employ this method on a flock of sheep, specifically on a group level metric describing the changes in coordination of the group motion. This will enable us to reconstruct the behavioural pattern of the flock. We end the thesis with a conclusion chapter.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: Q Science > QA Mathematics
• Colleges/Schools: College of Science and Engineering > School of Mathematics and Statistics
• Supervisor's Name: Torney, Professor Colin, Husmeier, Professor Dirk and Morales, Professor Juan
• Date of Award: 2024
• Depositing User: Theses Team
• Unique ID: glathesis:2024-84137
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 21 Mar 2024 14:43
• Last Modified: 22 Mar 2024 15:21
• Thesis DOI: 10.5525/gla.thesis.84137
• URI: https://theses.gla.ac.uk/id/eprint/84137

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