Methods for improving species distribution models in data-poor areas: example of sub-Antarctic benthic species on the Kerguelen Plateau.
16 pages International audience Species distribution models (SDMs) are essential tools to aid conservation biologists in evaluating the combined effects of environmental change and human activities on natural habitats and for the development of relevant conservation plans. However, modeling species...
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Online Access: | https://hal.archives-ouvertes.fr/hal-01806930 https://doi.org/10.3354/meps12538 |
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ftunivnantes:oai:HAL:hal-01806930v1 2023-05-15T14:00:54+02:00 Methods for improving species distribution models in data-poor areas: example of sub-Antarctic benthic species on the Kerguelen Plateau. Guillaumot, Charlène Martin, Alexis Eléaume, Marc Saucède, Thomas Laboratoire de Biologie Marine Université libre de Bruxelles (ULB) Biologie des Organismes et Ecosystèmes Aquatiques (BOREA) Université de Caen Normandie (UNICAEN) Normandie Université (NU)-Normandie Université (NU)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA) Institut de Systématique, Evolution, Biodiversité (ISYEB ) Muséum national d'Histoire naturelle (MNHN)-Université Pierre et Marie Curie - Paris 6 (UPMC)-École pratique des hautes études (EPHE) Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS) Biogéosciences UMR 6282 (BGS) Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS) 2018 https://hal.archives-ouvertes.fr/hal-01806930 https://doi.org/10.3354/meps12538 en eng HAL CCSD Inter Research info:eu-repo/semantics/altIdentifier/doi/10.3354/meps12538 hal-01806930 https://hal.archives-ouvertes.fr/hal-01806930 doi:10.3354/meps12538 ISSN: 0171-8630 EISSN: 1616-1599 Marine Ecology Progress Series https://hal.archives-ouvertes.fr/hal-01806930 Marine Ecology Progress Series, Inter Research, 2018, 594, pp.149-164. ⟨10.3354/meps12538⟩ https://www.int-res.com/abstracts/meps/v594/p149-164 Species distribution modeling Model performance Historical datasets Kerguelen Plateau Presence-only data [SDE.BE]Environmental Sciences/Biodiversity and Ecology [SDV.EE.ECO]Life Sciences [q-bio]/Ecology environment/Ecosystems info:eu-repo/semantics/article Journal articles 2018 ftunivnantes https://doi.org/10.3354/meps12538 2022-10-05T00:42:39Z 16 pages International audience Species distribution models (SDMs) are essential tools to aid conservation biologists in evaluating the combined effects of environmental change and human activities on natural habitats and for the development of relevant conservation plans. However, modeling species distributions over vast and remote regions is often challenging due to poor and heterogeneous data sets, and this raises questions regarding the relevance of the modeling procedures. In recent years, there have been many methodological developments in SDM procedures using virtual species and broad data sets, but few solutions have been proposed to deal with poor or heterogeneous data. In the present work, we address this methodological challenge by studying the performance of different modeling procedures based on 4 real species, using presence-only data compiled from various oceanographic surveys on the Kerguelen Plateau (Southern Ocean). We followed a practical protocol to test for the reliability and performance of the models and to correct for limited and aggregated data, as well as accounting for spatial and temporal sampling biases. Our results show that producing reliable SDMs is feasible as long as the amount and quality of available data allow testing and correcting for these biases. However, we found that SDMs could be corrected for spatial and temporal heterogeneities in only 1 of the 4 species we examined, highlighting the need to consider all potential biases when modeling species distributions. Finally, we show that model reliability and performance also depend on the interaction between the incompleteness of the data and species niches, with the distribution of narrow-niche species being less sensitive to data gaps than species occupying wider niches. Article in Journal/Newspaper Antarc* Antarctic Southern Ocean Université de Nantes: HAL-UNIV-NANTES Antarctic Southern Ocean Kerguelen Marine Ecology Progress Series 594 149 164 |
institution |
Open Polar |
collection |
Université de Nantes: HAL-UNIV-NANTES |
op_collection_id |
ftunivnantes |
language |
English |
topic |
Species distribution modeling Model performance Historical datasets Kerguelen Plateau Presence-only data [SDE.BE]Environmental Sciences/Biodiversity and Ecology [SDV.EE.ECO]Life Sciences [q-bio]/Ecology environment/Ecosystems |
spellingShingle |
Species distribution modeling Model performance Historical datasets Kerguelen Plateau Presence-only data [SDE.BE]Environmental Sciences/Biodiversity and Ecology [SDV.EE.ECO]Life Sciences [q-bio]/Ecology environment/Ecosystems Guillaumot, Charlène Martin, Alexis Eléaume, Marc Saucède, Thomas Methods for improving species distribution models in data-poor areas: example of sub-Antarctic benthic species on the Kerguelen Plateau. |
topic_facet |
Species distribution modeling Model performance Historical datasets Kerguelen Plateau Presence-only data [SDE.BE]Environmental Sciences/Biodiversity and Ecology [SDV.EE.ECO]Life Sciences [q-bio]/Ecology environment/Ecosystems |
description |
16 pages International audience Species distribution models (SDMs) are essential tools to aid conservation biologists in evaluating the combined effects of environmental change and human activities on natural habitats and for the development of relevant conservation plans. However, modeling species distributions over vast and remote regions is often challenging due to poor and heterogeneous data sets, and this raises questions regarding the relevance of the modeling procedures. In recent years, there have been many methodological developments in SDM procedures using virtual species and broad data sets, but few solutions have been proposed to deal with poor or heterogeneous data. In the present work, we address this methodological challenge by studying the performance of different modeling procedures based on 4 real species, using presence-only data compiled from various oceanographic surveys on the Kerguelen Plateau (Southern Ocean). We followed a practical protocol to test for the reliability and performance of the models and to correct for limited and aggregated data, as well as accounting for spatial and temporal sampling biases. Our results show that producing reliable SDMs is feasible as long as the amount and quality of available data allow testing and correcting for these biases. However, we found that SDMs could be corrected for spatial and temporal heterogeneities in only 1 of the 4 species we examined, highlighting the need to consider all potential biases when modeling species distributions. Finally, we show that model reliability and performance also depend on the interaction between the incompleteness of the data and species niches, with the distribution of narrow-niche species being less sensitive to data gaps than species occupying wider niches. |
author2 |
Laboratoire de Biologie Marine Université libre de Bruxelles (ULB) Biologie des Organismes et Ecosystèmes Aquatiques (BOREA) Université de Caen Normandie (UNICAEN) Normandie Université (NU)-Normandie Université (NU)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université des Antilles (UA) Institut de Systématique, Evolution, Biodiversité (ISYEB ) Muséum national d'Histoire naturelle (MNHN)-Université Pierre et Marie Curie - Paris 6 (UPMC)-École pratique des hautes études (EPHE) Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS) Biogéosciences UMR 6282 (BGS) Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS) |
format |
Article in Journal/Newspaper |
author |
Guillaumot, Charlène Martin, Alexis Eléaume, Marc Saucède, Thomas |
author_facet |
Guillaumot, Charlène Martin, Alexis Eléaume, Marc Saucède, Thomas |
author_sort |
Guillaumot, Charlène |
title |
Methods for improving species distribution models in data-poor areas: example of sub-Antarctic benthic species on the Kerguelen Plateau. |
title_short |
Methods for improving species distribution models in data-poor areas: example of sub-Antarctic benthic species on the Kerguelen Plateau. |
title_full |
Methods for improving species distribution models in data-poor areas: example of sub-Antarctic benthic species on the Kerguelen Plateau. |
title_fullStr |
Methods for improving species distribution models in data-poor areas: example of sub-Antarctic benthic species on the Kerguelen Plateau. |
title_full_unstemmed |
Methods for improving species distribution models in data-poor areas: example of sub-Antarctic benthic species on the Kerguelen Plateau. |
title_sort |
methods for improving species distribution models in data-poor areas: example of sub-antarctic benthic species on the kerguelen plateau. |
publisher |
HAL CCSD |
publishDate |
2018 |
url |
https://hal.archives-ouvertes.fr/hal-01806930 https://doi.org/10.3354/meps12538 |
geographic |
Antarctic Southern Ocean Kerguelen |
geographic_facet |
Antarctic Southern Ocean Kerguelen |
genre |
Antarc* Antarctic Southern Ocean |
genre_facet |
Antarc* Antarctic Southern Ocean |
op_source |
ISSN: 0171-8630 EISSN: 1616-1599 Marine Ecology Progress Series https://hal.archives-ouvertes.fr/hal-01806930 Marine Ecology Progress Series, Inter Research, 2018, 594, pp.149-164. ⟨10.3354/meps12538⟩ https://www.int-res.com/abstracts/meps/v594/p149-164 |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.3354/meps12538 hal-01806930 https://hal.archives-ouvertes.fr/hal-01806930 doi:10.3354/meps12538 |
op_doi |
https://doi.org/10.3354/meps12538 |
container_title |
Marine Ecology Progress Series |
container_volume |
594 |
container_start_page |
149 |
op_container_end_page |
164 |
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1766270299027800064 |