Inferring spatially varying animal movement characteristics using a hierarchical continuous-time velocity model

Paun, I., Husmeier, D. , Hopcraft, J. G. C. , Masolele, M. M. and Torney, C. J. (2022) Inferring spatially varying animal movement characteristics using a hierarchical continuous-time velocity model. Ecology Letters, 25(12), pp. 2726-2738. (doi: 10.1111/ele.14117) (PMID:36256526)

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Abstract

Understanding the spatial dynamics of animal movement is an essential component of maintaining ecological connectivity, conserving key habitats, and mitigating the impacts of anthropogenic disturbance. Altered movement and migratory patterns are often an early warning sign of the effects of environmental disturbance, and a precursor to population declines. Here, we present a hierarchical Bayesian framework based on Gaussian processes for analysing the spatial characteristics of animal movement. At the heart of our approach is a novel covariance kernel that links the spatially varying parameters of a continuous-time velocity model with GPS locations from multiple individuals. We demonstrate the effectiveness of our framework by first applying it to a synthetic data set and then by analysing telemetry data from the Serengeti wildebeest migration. Through application of our approach, we are able to identify the key pathways of the wildebeest migration as well as revealing the impacts of environmental features on movement behaviour.

Item Type:Articles
Additional Information:IP was supported by a scholarship from the College of Science and Engineering at the University of Glasgow. CJT is supported by the Leverhulme Foundation, grant reference RPG-2018-398, and a James S. McDonnell Foundation Complex Systems Scholar Award. DH is supported by the Engineering and Physical Sciences Research Council (EPSRC), grant reference number EP/T017899/1. MM is supported by a scholarship from the Engineering and Physical Sciences Research Council (EPSRC). JGCH is supported by funding from the European Union's Horizon 2020 research and innovation program under grant agreement 641918 (AfricanBioServices). We are grateful to Ran Nathan and three anonymous reviewers whose helpful comments and suggestions improved the manuscript.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Masolele, Mr Majaliwa and Hopcraft, Dr Grant and Husmeier, Professor Dirk and Torney, Dr Colin and Paun, Ionut
Authors: Paun, I., Husmeier, D., Hopcraft, J. G. C., Masolele, M. M., and Torney, C. J.
College/School:College of Medical Veterinary and Life Sciences > School of Biodiversity, One Health & Veterinary Medicine
College of Science and Engineering > School of Mathematics and Statistics
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Ecology Letters
Publisher:Wiley
ISSN:1461-023X
ISSN (Online):1461-0248
Published Online:18 October 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Ecology Letters 25(12): 2726-2738
Publisher Policy:Reproduced under a Creative Commons License
Data DOI:10.5281/zenodo.7079680

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
303337Multiscale methods for inferring the structure and dynamics of collective animal behaviourColin TorneyLeverhulme Trust (LEVERHUL)RPG-2018-398M&S - Mathematics
308255The SofTMech Statistical Emulation and Translation HubDirk HusmeierEngineering and Physical Sciences Research Council (EPSRC)EP/T017899/1M&S - Statistics
171925AfricanBioServicesDaniel HaydonEuropean Commission (EC)641918Institute of Biodiversity, Animal Health and Comparative Medicine