Development and simulation testing for a new approach to density dependence in species distribution models

Abstract Density dependence is included in many population–dynamics models, but few options exist within species distribution models (SDMs). One option for density-dependence in SDMs proceeds by including an independent time-series of population abundance as covariate using a spatially varying coeff...

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Published in:ICES Journal of Marine Science
Main Author: Thorson, James T
Other Authors: Bartolino, Valerio
Format: Article in Journal/Newspaper
Language:English
Published: Oxford University Press (OUP) 2021
Subjects:
Online Access:http://dx.doi.org/10.1093/icesjms/fsab247
https://academic.oup.com/icesjms/article-pdf/79/1/117/42754313/fsab247.pdf
id croxfordunivpr:10.1093/icesjms/fsab247
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spelling croxfordunivpr:10.1093/icesjms/fsab247 2024-04-07T07:51:30+00:00 Development and simulation testing for a new approach to density dependence in species distribution models Thorson, James T Bartolino, Valerio 2021 http://dx.doi.org/10.1093/icesjms/fsab247 https://academic.oup.com/icesjms/article-pdf/79/1/117/42754313/fsab247.pdf en eng Oxford University Press (OUP) ICES Journal of Marine Science volume 79, issue 1, page 117-128 ISSN 1054-3139 1095-9289 Ecology Aquatic Science Ecology, Evolution, Behavior and Systematics Oceanography journal-article 2021 croxfordunivpr https://doi.org/10.1093/icesjms/fsab247 2024-03-08T03:10:30Z Abstract Density dependence is included in many population–dynamics models, but few options exist within species distribution models (SDMs). One option for density-dependence in SDMs proceeds by including an independent time-series of population abundance as covariate using a spatially varying coefficient (SVC). We extend this via three alternative approaches that replace the independent time-series with information available within the SDM. We recommend the “intermediate complexity” approach that estimates a SVC responding to median abundance in each time; this SVC indicates whether a given location has a smaller- or greater-than-average sensitivity to changes in median abundance. We next develop a reaction–advection–diffusive simulation model, wherein individuals avoid habitats that exceed a threshold in local density. This movement model results in an estimated SVC that is negatively correlated with the average spatial distribution. Finally, we show that a SVC can be identified using bottom trawl data for four species in the eastern Bering Sea from 1982 to 2019. We conclude that the common “basin-model” for animal movement results in an ecological teleconnection, wherein population depletion or recovery at one locations will affect resulting dynamics at geographically distant habitats, and that this form of density dependence can be detected using SDMs. Article in Journal/Newspaper Bering Sea Oxford University Press Bering Sea ICES Journal of Marine Science
institution Open Polar
collection Oxford University Press
op_collection_id croxfordunivpr
language English
topic Ecology
Aquatic Science
Ecology, Evolution, Behavior and Systematics
Oceanography
spellingShingle Ecology
Aquatic Science
Ecology, Evolution, Behavior and Systematics
Oceanography
Thorson, James T
Development and simulation testing for a new approach to density dependence in species distribution models
topic_facet Ecology
Aquatic Science
Ecology, Evolution, Behavior and Systematics
Oceanography
description Abstract Density dependence is included in many population–dynamics models, but few options exist within species distribution models (SDMs). One option for density-dependence in SDMs proceeds by including an independent time-series of population abundance as covariate using a spatially varying coefficient (SVC). We extend this via three alternative approaches that replace the independent time-series with information available within the SDM. We recommend the “intermediate complexity” approach that estimates a SVC responding to median abundance in each time; this SVC indicates whether a given location has a smaller- or greater-than-average sensitivity to changes in median abundance. We next develop a reaction–advection–diffusive simulation model, wherein individuals avoid habitats that exceed a threshold in local density. This movement model results in an estimated SVC that is negatively correlated with the average spatial distribution. Finally, we show that a SVC can be identified using bottom trawl data for four species in the eastern Bering Sea from 1982 to 2019. We conclude that the common “basin-model” for animal movement results in an ecological teleconnection, wherein population depletion or recovery at one locations will affect resulting dynamics at geographically distant habitats, and that this form of density dependence can be detected using SDMs.
author2 Bartolino, Valerio
format Article in Journal/Newspaper
author Thorson, James T
author_facet Thorson, James T
author_sort Thorson, James T
title Development and simulation testing for a new approach to density dependence in species distribution models
title_short Development and simulation testing for a new approach to density dependence in species distribution models
title_full Development and simulation testing for a new approach to density dependence in species distribution models
title_fullStr Development and simulation testing for a new approach to density dependence in species distribution models
title_full_unstemmed Development and simulation testing for a new approach to density dependence in species distribution models
title_sort development and simulation testing for a new approach to density dependence in species distribution models
publisher Oxford University Press (OUP)
publishDate 2021
url http://dx.doi.org/10.1093/icesjms/fsab247
https://academic.oup.com/icesjms/article-pdf/79/1/117/42754313/fsab247.pdf
geographic Bering Sea
geographic_facet Bering Sea
genre Bering Sea
genre_facet Bering Sea
op_source ICES Journal of Marine Science
volume 79, issue 1, page 117-128
ISSN 1054-3139 1095-9289
op_doi https://doi.org/10.1093/icesjms/fsab247
container_title ICES Journal of Marine Science
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