Hybrid model for ecological vulnerability assessment in Benin

Abstract 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 stud...

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Autores principales:	Jacqueline Fifame Dossou, Xu Xiang Li, Mohammed Sadek, Mohamed Adou Sidi Almouctar, Eman Mostafa
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2907d50f9cd04554846486751d759005
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spelling	oai:doaj.org-article:2907d50f9cd04554846486751d7590052021-12-02T13:24:07ZHybrid model for ecological vulnerability assessment in Benin10.1038/s41598-021-81742-22045-2322https://doaj.org/article/2907d50f9cd04554846486751d7590052021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81742-2https://doaj.org/toc/2045-2322Abstract 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 km2 and 36,450 km2 of stable areas, respectively, for a difference of 12,150 km2, and 3,729 km2 and 3,007 km2 of vulnerable areas, for a difference of 722 km2. Using Moran’s I and automatic linear modeling, we improved the accuracy of the established systems. In the composite system, increases of 11,669 km2 in the potentially vulnerable area and 1,083 km2 in the highly vulnerable area were noted in addition to a decrease of 4331 km2 in the potential area; while in the additive system, an increase of 3,970 km2 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.Jacqueline Fifame DossouXu Xiang LiMohammed SadekMohamed Adou Sidi AlmouctarEman MostafaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jacqueline Fifame Dossou Xu Xiang Li Mohammed Sadek Mohamed Adou Sidi Almouctar Eman Mostafa Hybrid model for ecological vulnerability assessment in Benin
description	Abstract 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 km2 and 36,450 km2 of stable areas, respectively, for a difference of 12,150 km2, and 3,729 km2 and 3,007 km2 of vulnerable areas, for a difference of 722 km2. Using Moran’s I and automatic linear modeling, we improved the accuracy of the established systems. In the composite system, increases of 11,669 km2 in the potentially vulnerable area and 1,083 km2 in the highly vulnerable area were noted in addition to a decrease of 4331 km2 in the potential area; while in the additive system, an increase of 3,970 km2 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	article
author	Jacqueline Fifame Dossou Xu Xiang Li Mohammed Sadek Mohamed Adou Sidi Almouctar Eman Mostafa
author_facet	Jacqueline Fifame Dossou Xu Xiang Li Mohammed Sadek Mohamed Adou Sidi Almouctar Eman Mostafa
author_sort	Jacqueline Fifame Dossou
title	Hybrid model for ecological vulnerability assessment in Benin
title_short	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_sort	hybrid model for ecological vulnerability assessment in benin
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2907d50f9cd04554846486751d759005
work_keys_str_mv	AT jacquelinefifamedossou hybridmodelforecologicalvulnerabilityassessmentinbenin AT xuxiangli hybridmodelforecologicalvulnerabilityassessmentinbenin AT mohammedsadek hybridmodelforecologicalvulnerabilityassessmentinbenin AT mohamedadousidialmouctar hybridmodelforecologicalvulnerabilityassessmentinbenin AT emanmostafa hybridmodelforecologicalvulnerabilityassessmentinbenin
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Hybrid model for ecological vulnerability assessment in Benin

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