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Beyond resource selection: emergent spatio–temporal distributions from animal movements and stigmergent interactions

Jonathan R. Potts Orcid Logo, Valeria Giunta Orcid Logo, Mark A. Lewis

Oikos, Volume: 2022, Issue: 6

Swansea University Author: Valeria Giunta Orcid Logo

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DOI (Published version): 10.1111/oik.09188

Abstract

A principal concern of ecological research is to unveil the causes behind observed spatio–temporal distributions of species. A key tactic is to correlate observed locations with environmental features, in the form of resource selection functions or other correlative species distribution models. In r...

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Published in: Oikos
ISSN: 0030-1299 1600-0706
Published: Wiley 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa64698
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spelling v2 64698 2023-10-10 Beyond resource selection: emergent spatio–temporal distributions from animal movements and stigmergent interactions 50456cce4b2c7be66f8302d418963b0c 0000-0003-1156-7136 Valeria Giunta Valeria Giunta true false 2023-10-10 SMA A principal concern of ecological research is to unveil the causes behind observed spatio–temporal distributions of species. A key tactic is to correlate observed locations with environmental features, in the form of resource selection functions or other correlative species distribution models. In reality, however, the distribution of any population both affects and is affected by those surrounding it, creating a complex network of feedbacks causing emergent spatio–temporal features that may not correlate with any particular aspect of the underlying environment. Here, we study the way in which the movements of populations in response to one another can affect the spatio–temporal distributions of ecosystems. We construct a stochastic individual-based modelling (IBM) framework, based on stigmergent interactions (i.e. organisms leave marks which cause others to alter their movements) between and within populations. We show how to gain insight into this IBM via mathematical analysis of a partial differential equation (PDE) system given by a continuum limit. We show how the combination of stochastic simulations of the IBM and mathematical analysis of PDEs can be used to categorise emergent patterns into homogeneous versus heterogeneous, stationary versus perpetually-fluctuating and aggregation versus segregation. In doing so, we develop techniques for understanding spatial bifurcations in stochastic IBMs, grounded in mathematical analysis. Finally, we demonstrate through a simple example how the interplay between environmental features and between-population stigmergent interactions can give rise to predicted spatial distributions that are quite different to those predicted purely by accounting for environmental covariates. Journal Article Oikos 2022 6 Wiley 0030-1299 1600-0706 Animal movement, animal space use, individual based models, partial differential equations, resource selection, species distribution models, stigmergy 1 6 2022 2022-06-01 10.1111/oik.09188 http://dx.doi.org/10.1111/oik.09188 COLLEGE NANME Mathematics COLLEGE CODE SMA Swansea University JRP and VG acknowledge support of Engineering and Physical Sciences Research Council (EPSRC) grant EP/V002988/1. MAL acknowledges support from the NSERC Discovery and Canada Research Chair programs. 2023-11-28T14:38:53.6873930 2023-10-10T12:16:31.7466227 Faculty of Science and Engineering School of Mathematics and Computer Science - Mathematics Jonathan R. Potts 0000-0002-8564-2904 1 Valeria Giunta 0000-0003-1156-7136 2 Mark A. Lewis 3 64698__29134__9d2f8386a8184f2181fa6e77e71b18c6.pdf 64698.VOR.pdf 2023-11-28T14:37:32.8708999 Output 803183 application/pdf Version of Record true © 2022 The Authors. Oikos published by John Wiley & Sons Ltd on behalf of Nordic Society Oikos. Distributed under the terms of a Creative Commons Attribution 3.0 Unported License (CC BY 3.0). true eng https://creativecommons.org/licenses/by/3.0/
title Beyond resource selection: emergent spatio–temporal distributions from animal movements and stigmergent interactions
spellingShingle Beyond resource selection: emergent spatio–temporal distributions from animal movements and stigmergent interactions
Valeria Giunta
title_short Beyond resource selection: emergent spatio–temporal distributions from animal movements and stigmergent interactions
title_full Beyond resource selection: emergent spatio–temporal distributions from animal movements and stigmergent interactions
title_fullStr Beyond resource selection: emergent spatio–temporal distributions from animal movements and stigmergent interactions
title_full_unstemmed Beyond resource selection: emergent spatio–temporal distributions from animal movements and stigmergent interactions
title_sort Beyond resource selection: emergent spatio–temporal distributions from animal movements and stigmergent interactions
author_id_str_mv 50456cce4b2c7be66f8302d418963b0c
author_id_fullname_str_mv 50456cce4b2c7be66f8302d418963b0c_***_Valeria Giunta
author Valeria Giunta
author2 Jonathan R. Potts
Valeria Giunta
Mark A. Lewis
format Journal article
container_title Oikos
container_volume 2022
container_issue 6
publishDate 2022
institution Swansea University
issn 0030-1299
1600-0706
doi_str_mv 10.1111/oik.09188
publisher Wiley
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Mathematics{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Mathematics
url http://dx.doi.org/10.1111/oik.09188
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description A principal concern of ecological research is to unveil the causes behind observed spatio–temporal distributions of species. A key tactic is to correlate observed locations with environmental features, in the form of resource selection functions or other correlative species distribution models. In reality, however, the distribution of any population both affects and is affected by those surrounding it, creating a complex network of feedbacks causing emergent spatio–temporal features that may not correlate with any particular aspect of the underlying environment. Here, we study the way in which the movements of populations in response to one another can affect the spatio–temporal distributions of ecosystems. We construct a stochastic individual-based modelling (IBM) framework, based on stigmergent interactions (i.e. organisms leave marks which cause others to alter their movements) between and within populations. We show how to gain insight into this IBM via mathematical analysis of a partial differential equation (PDE) system given by a continuum limit. We show how the combination of stochastic simulations of the IBM and mathematical analysis of PDEs can be used to categorise emergent patterns into homogeneous versus heterogeneous, stationary versus perpetually-fluctuating and aggregation versus segregation. In doing so, we develop techniques for understanding spatial bifurcations in stochastic IBMs, grounded in mathematical analysis. Finally, we demonstrate through a simple example how the interplay between environmental features and between-population stigmergent interactions can give rise to predicted spatial distributions that are quite different to those predicted purely by accounting for environmental covariates.
published_date 2022-06-01T14:38:54Z
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