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 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.

Automated summary

Assessing ecosystem vulnerability in Benin revealed that the composite system identified more stable and vulnerable areas compared to the additive system. Southern Benin was consistently identified as vulnerable, while Donga Province was stable.