Extrapolation in species distribution modelling. application to Southern Ocean marine species.
11 pages International audience Species distribution modelling (SDM) has been increasingly applied to Southern Ocean case studies over the past decades, to map the distribution of species and highlight environmental settings driving species distribution. Predictive models have been commonly used for...
Published in: | Progress in Oceanography |
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Online Access: | https://hal.archives-ouvertes.fr/hal-02985372 https://hal.archives-ouvertes.fr/hal-02985372/document https://hal.archives-ouvertes.fr/hal-02985372/file/S0079661120301774.pdf https://doi.org/10.1016/j.pocean.2020.102438 |
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ftunivnantes:oai:HAL:hal-02985372v1 2023-05-15T13:58:24+02:00 Extrapolation in species distribution modelling. application to Southern Ocean marine species. Guillaumot, Charlène Moreau, Camille Danis, Bruno Saucède, Thomas Laboratoire de Biologie Marine (LBM) Université libre de Bruxelles (ULB) Biogéosciences UMR 6282 (BGS) Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS) Work supported by a “Fonds pour la formation `a la Recherche dans l’Industrie et l’Agriculture” (FRIA) and “Bourse fondation de la mer”. 2020-10 https://hal.archives-ouvertes.fr/hal-02985372 https://hal.archives-ouvertes.fr/hal-02985372/document https://hal.archives-ouvertes.fr/hal-02985372/file/S0079661120301774.pdf https://doi.org/10.1016/j.pocean.2020.102438 en eng HAL CCSD Elsevier info:eu-repo/semantics/altIdentifier/doi/10.1016/j.pocean.2020.102438 hal-02985372 https://hal.archives-ouvertes.fr/hal-02985372 https://hal.archives-ouvertes.fr/hal-02985372/document https://hal.archives-ouvertes.fr/hal-02985372/file/S0079661120301774.pdf doi:10.1016/j.pocean.2020.102438 PII: S0079-6611(20)30177-4 http://creativecommons.org/licenses/by-nc/ info:eu-repo/semantics/OpenAccess CC-BY-NC ISSN: 0079-6611 Progress in Oceanography https://hal.archives-ouvertes.fr/hal-02985372 Progress in Oceanography, Elsevier, 2020, 188, pp.102438. ⟨10.1016/j.pocean.2020.102438⟩ https://www.sciencedirect.com/science/article/abs/pii/S0079661120301774 Multivariate Environmental Similarity Surface (MESS) Marine species Antarctic Modelling relevance Conservation issues [SDE.BE]Environmental Sciences/Biodiversity and Ecology [SDV.BID]Life Sciences [q-bio]/Biodiversity info:eu-repo/semantics/article Journal articles 2020 ftunivnantes https://doi.org/10.1016/j.pocean.2020.102438 2022-10-18T23:01:02Z 11 pages International audience Species distribution modelling (SDM) has been increasingly applied to Southern Ocean case studies over the past decades, to map the distribution of species and highlight environmental settings driving species distribution. Predictive models have been commonly used for conservation purposes and supporting the delineation of marine protected areas, but model predictions are rarely associated with extrapolation uncertainty maps.In this study, we used the Multivariate Environmental Similarity Surface (MESS) index to quantify model uncertainty associated to extrapolation. Considering the reference dataset of environmental conditions for which species presence-only records are modelled, extrapolation corresponds to the part of the projection area for which one environmental value at least falls outside of the reference dataset.Six abundant and common sea star species of marine benthic communities of the Southern Ocean were used as case studies. Results show that up to 78% of the projection area is extrapolation, i.e. beyond conditions used for model calibration. Restricting the projection space by the known species ecological requirements (e.g. maximal depth, upper temperature tolerance) and increasing the size of presence datasets were proved efficient to reduce the proportion of extrapolation areas. We estimate that multiplying sampling effort by 2 or 3-fold should help reduce the proportion of extrapolation areas down to 10% in the six studied species.Considering the unexpectedly high levels of extrapolation uncertainty measured in SDM predictions, we strongly recommend that studies report information related to the level of extrapolation. Waiting for improved datasets, adapting modelling methods and providing such uncertainy information in distribution modelling studies are a necessity to accurately interpret model outputs and their reliability. Article in Journal/Newspaper Antarc* Antarctic Southern Ocean Université de Nantes: HAL-UNIV-NANTES Antarctic Southern Ocean Progress in Oceanography 188 102438 |
institution |
Open Polar |
collection |
Université de Nantes: HAL-UNIV-NANTES |
op_collection_id |
ftunivnantes |
language |
English |
topic |
Multivariate Environmental Similarity Surface (MESS) Marine species Antarctic Modelling relevance Conservation issues [SDE.BE]Environmental Sciences/Biodiversity and Ecology [SDV.BID]Life Sciences [q-bio]/Biodiversity |
spellingShingle |
Multivariate Environmental Similarity Surface (MESS) Marine species Antarctic Modelling relevance Conservation issues [SDE.BE]Environmental Sciences/Biodiversity and Ecology [SDV.BID]Life Sciences [q-bio]/Biodiversity Guillaumot, Charlène Moreau, Camille Danis, Bruno Saucède, Thomas Extrapolation in species distribution modelling. application to Southern Ocean marine species. |
topic_facet |
Multivariate Environmental Similarity Surface (MESS) Marine species Antarctic Modelling relevance Conservation issues [SDE.BE]Environmental Sciences/Biodiversity and Ecology [SDV.BID]Life Sciences [q-bio]/Biodiversity |
description |
11 pages International audience Species distribution modelling (SDM) has been increasingly applied to Southern Ocean case studies over the past decades, to map the distribution of species and highlight environmental settings driving species distribution. Predictive models have been commonly used for conservation purposes and supporting the delineation of marine protected areas, but model predictions are rarely associated with extrapolation uncertainty maps.In this study, we used the Multivariate Environmental Similarity Surface (MESS) index to quantify model uncertainty associated to extrapolation. Considering the reference dataset of environmental conditions for which species presence-only records are modelled, extrapolation corresponds to the part of the projection area for which one environmental value at least falls outside of the reference dataset.Six abundant and common sea star species of marine benthic communities of the Southern Ocean were used as case studies. Results show that up to 78% of the projection area is extrapolation, i.e. beyond conditions used for model calibration. Restricting the projection space by the known species ecological requirements (e.g. maximal depth, upper temperature tolerance) and increasing the size of presence datasets were proved efficient to reduce the proportion of extrapolation areas. We estimate that multiplying sampling effort by 2 or 3-fold should help reduce the proportion of extrapolation areas down to 10% in the six studied species.Considering the unexpectedly high levels of extrapolation uncertainty measured in SDM predictions, we strongly recommend that studies report information related to the level of extrapolation. Waiting for improved datasets, adapting modelling methods and providing such uncertainy information in distribution modelling studies are a necessity to accurately interpret model outputs and their reliability. |
author2 |
Laboratoire de Biologie Marine (LBM) Université libre de Bruxelles (ULB) Biogéosciences UMR 6282 (BGS) Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS) Work supported by a “Fonds pour la formation `a la Recherche dans l’Industrie et l’Agriculture” (FRIA) and “Bourse fondation de la mer”. |
format |
Article in Journal/Newspaper |
author |
Guillaumot, Charlène Moreau, Camille Danis, Bruno Saucède, Thomas |
author_facet |
Guillaumot, Charlène Moreau, Camille Danis, Bruno Saucède, Thomas |
author_sort |
Guillaumot, Charlène |
title |
Extrapolation in species distribution modelling. application to Southern Ocean marine species. |
title_short |
Extrapolation in species distribution modelling. application to Southern Ocean marine species. |
title_full |
Extrapolation in species distribution modelling. application to Southern Ocean marine species. |
title_fullStr |
Extrapolation in species distribution modelling. application to Southern Ocean marine species. |
title_full_unstemmed |
Extrapolation in species distribution modelling. application to Southern Ocean marine species. |
title_sort |
extrapolation in species distribution modelling. application to southern ocean marine species. |
publisher |
HAL CCSD |
publishDate |
2020 |
url |
https://hal.archives-ouvertes.fr/hal-02985372 https://hal.archives-ouvertes.fr/hal-02985372/document https://hal.archives-ouvertes.fr/hal-02985372/file/S0079661120301774.pdf https://doi.org/10.1016/j.pocean.2020.102438 |
geographic |
Antarctic Southern Ocean |
geographic_facet |
Antarctic Southern Ocean |
genre |
Antarc* Antarctic Southern Ocean |
genre_facet |
Antarc* Antarctic Southern Ocean |
op_source |
ISSN: 0079-6611 Progress in Oceanography https://hal.archives-ouvertes.fr/hal-02985372 Progress in Oceanography, Elsevier, 2020, 188, pp.102438. ⟨10.1016/j.pocean.2020.102438⟩ https://www.sciencedirect.com/science/article/abs/pii/S0079661120301774 |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.pocean.2020.102438 hal-02985372 https://hal.archives-ouvertes.fr/hal-02985372 https://hal.archives-ouvertes.fr/hal-02985372/document https://hal.archives-ouvertes.fr/hal-02985372/file/S0079661120301774.pdf doi:10.1016/j.pocean.2020.102438 PII: S0079-6611(20)30177-4 |
op_rights |
http://creativecommons.org/licenses/by-nc/ info:eu-repo/semantics/OpenAccess |
op_rightsnorm |
CC-BY-NC |
op_doi |
https://doi.org/10.1016/j.pocean.2020.102438 |
container_title |
Progress in Oceanography |
container_volume |
188 |
container_start_page |
102438 |
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