Estimating the abundance of a group-living species using multi-latent spatial models

Torney, C. J. , Laxton, M., Lloyd‐Jones, D. J., Kohi, E. M., Frederick, H. L., Moyer, D. C., Mrisha, C., Mwita, M. and Hopcraft, J. G. C. (2023) Estimating the abundance of a group-living species using multi-latent spatial models. Methods in Ecology and Evolution, 14(1), pp. 77-86. (doi: 10.1111/2041-210x.13941)

[img] Text
276221.pdf - Published Version
Available under License Creative Commons Attribution.

3MB

Abstract

Statistical models use observations of animals to make inferences about the abundance and distribution of species. However, the spatial distribution of animals is a complex function of many factors, including landscape and environmental features, and intra- and interspecific interactions. Modelling approaches often have to make significant simplifying assumptions about these factors, which can result in poor model performance and inaccurate predictions. Here, we explore the implications of complex spatial structure for modelling the abundance of the Serengeti wildebeest, a gregarious migratory species. The social behaviour of wildebeest leads to a highly aggregated distribution, and we examine the consequences of omitting this spatial complexity when modelling species abundance. To account for this distribution, we introduce a multi-latent framework that uses two random fields to capture the clustered distribution of wildebeest. Our results show that simplifying assumptions that are often made in spatial models can dramatically impair performance. However, by allowing for mixtures of spatial models accurate predictions can be made. Furthermore, there can be a non-monotonic relationship between model complexity and model performance; complex, flexible models that rely on unfounded assumptions can potentially make highly inaccurate predictions, whereas simpler more traditional approaches involve fewer assumptions and are less sensitive to these issues. We demonstrate how to develop flexible spatial models that can accommodate the complex processes driving animal distributions. Our findings highlight the importance of robust model checking protocols, and we illustrate how realistic assumptions can be incorporated into models using random fields.

Item Type:Articles
Additional Information:J.G.C.H. acknowledges support from the British Ecological Society large grant scheme, the Friedkin Foundation and the European Union Horizon 2020 grant no 641918. C.J.T. acknowledges support from a James S. McDonnell Foundation Studying Complex Systems Scholar Award. M.L. is supported by an EPSRC scholarship.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Laxton, Ms Megan and Hopcraft, Dr Grant and Torney, Dr Colin
Authors: Torney, C. J., Laxton, M., Lloyd‐Jones, D. J., Kohi, E. M., Frederick, H. L., Moyer, D. C., Mrisha, C., Mwita, M., and Hopcraft, J. G. C.
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 > Mathematics
Journal Name:Methods in Ecology and Evolution
Publisher:Wiley
ISSN:2041-210X
ISSN (Online):2041-210X
Published Online:03 August 2022
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Methods in Ecology and Evolution 14(1): 77-86
Publisher Policy:Reproduced under a Creative Commons licence
Data DOI:10.5281/zenodo.6684421

University Staff: Request a correction | Enlighten Editors: Update this record

Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
171925AfricanBioServicesDaniel HaydonEuropean Commission (EC)641918Institute of Biodiversity, Animal Health and Comparative Medicine