Cargando…

Hybrid model for ecological vulnerability assessment in Benin

Identifying ecologically fragile areas by assessing ecosystem vulnerability is an essential task in environmental conservation and management. Benin is considered a vulnerable area, and its coastal zone, which is subject to erosion and flooding effects, is particularly vulnerable. This study assesse...

Descripción completa

Detalles Bibliográficos
Autores principales: Dossou, Jacqueline Fifame, Li, Xu Xiang, Sadek, Mohammed, Sidi Almouctar, Mohamed Adou, Mostafa, Eman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7844054/
https://www.ncbi.nlm.nih.gov/pubmed/33510209
http://dx.doi.org/10.1038/s41598-021-81742-2
_version_ 1783644258901688320
author Dossou, Jacqueline Fifame
Li, Xu Xiang
Sadek, Mohammed
Sidi Almouctar, Mohamed Adou
Mostafa, Eman
author_facet Dossou, Jacqueline Fifame
Li, Xu Xiang
Sadek, Mohammed
Sidi Almouctar, Mohamed Adou
Mostafa, Eman
author_sort Dossou, Jacqueline Fifame
collection PubMed
description Identifying ecologically fragile areas by assessing ecosystem vulnerability is an essential task in environmental conservation and management. Benin is considered a vulnerable area, and its coastal zone, which is subject to erosion and flooding effects, is particularly vulnerable. This study assessed terrestrial ecosystems in Benin by establishing a hybrid ecological vulnerability index (EVI) for 2016 that combined a composite model based on principal component analysis (PCA) with an additive model based on exposure, sensitivity and adaptation. Using inverse distance weighted (IDW) interpolation, point data were spatially distributed by their geographic significance. The results revealed that the composite system identified more stable and vulnerable areas than the additive system; the two systems identified 48,600 km(2) and 36,450 km(2) of stable areas, respectively, for a difference of 12,150 km(2), and 3,729 km(2) and 3,007 km(2) of vulnerable areas, for a difference of 722 km(2). Using Moran’s I and automatic linear modeling, we improved the accuracy of the established systems. In the composite system, increases of 11,669 km(2) in the potentially vulnerable area and 1,083 km(2) in the highly vulnerable area were noted in addition to a decrease of 4331 km(2) in the potential area; while in the additive system, an increase of 3,970 km(2) in the highly vulnerable area was observed. Finally, southern Benin was identified as vulnerable in the composite system, and both northern and southern Benin were identified as vulnerable in the additive system. However, regardless of the system, Littoral Province in southern Benin, was consistently identified as vulnerable, while Donga Province was stable.
format Online
Article
Text
id pubmed-7844054
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-78440542021-01-29 Hybrid model for ecological vulnerability assessment in Benin Dossou, Jacqueline Fifame Li, Xu Xiang Sadek, Mohammed Sidi Almouctar, Mohamed Adou Mostafa, Eman Sci Rep Article Identifying ecologically fragile areas by assessing ecosystem vulnerability is an essential task in environmental conservation and management. Benin is considered a vulnerable area, and its coastal zone, which is subject to erosion and flooding effects, is particularly vulnerable. This study assessed terrestrial ecosystems in Benin by establishing a hybrid ecological vulnerability index (EVI) for 2016 that combined a composite model based on principal component analysis (PCA) with an additive model based on exposure, sensitivity and adaptation. Using inverse distance weighted (IDW) interpolation, point data were spatially distributed by their geographic significance. The results revealed that the composite system identified more stable and vulnerable areas than the additive system; the two systems identified 48,600 km(2) and 36,450 km(2) of stable areas, respectively, for a difference of 12,150 km(2), and 3,729 km(2) and 3,007 km(2) of vulnerable areas, for a difference of 722 km(2). Using Moran’s I and automatic linear modeling, we improved the accuracy of the established systems. In the composite system, increases of 11,669 km(2) in the potentially vulnerable area and 1,083 km(2) in the highly vulnerable area were noted in addition to a decrease of 4331 km(2) in the potential area; while in the additive system, an increase of 3,970 km(2) in the highly vulnerable area was observed. Finally, southern Benin was identified as vulnerable in the composite system, and both northern and southern Benin were identified as vulnerable in the additive system. However, regardless of the system, Littoral Province in southern Benin, was consistently identified as vulnerable, while Donga Province was stable. Nature Publishing Group UK 2021-01-28 /pmc/articles/PMC7844054/ /pubmed/33510209 http://dx.doi.org/10.1038/s41598-021-81742-2 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Dossou, Jacqueline Fifame
Li, Xu Xiang
Sadek, Mohammed
Sidi Almouctar, Mohamed Adou
Mostafa, Eman
Hybrid model for ecological vulnerability assessment in Benin
title Hybrid model for ecological vulnerability assessment in Benin
title_full Hybrid model for ecological vulnerability assessment in Benin
title_fullStr Hybrid model for ecological vulnerability assessment in Benin
title_full_unstemmed Hybrid model for ecological vulnerability assessment in Benin
title_short Hybrid model for ecological vulnerability assessment in Benin
title_sort hybrid model for ecological vulnerability assessment in benin
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7844054/
https://www.ncbi.nlm.nih.gov/pubmed/33510209
http://dx.doi.org/10.1038/s41598-021-81742-2
work_keys_str_mv AT dossoujacquelinefifame hybridmodelforecologicalvulnerabilityassessmentinbenin
AT lixuxiang hybridmodelforecologicalvulnerabilityassessmentinbenin
AT sadekmohammed hybridmodelforecologicalvulnerabilityassessmentinbenin
AT sidialmouctarmohamedadou hybridmodelforecologicalvulnerabilityassessmentinbenin
AT mostafaeman hybridmodelforecologicalvulnerabilityassessmentinbenin