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The Bio-Geographical Regions Division of Global Terrestrial Animal by Multivariate Similarity Clustering Analysis Method 被引量:3
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作者 Qi Shen Jiqi Lu +3 位作者 Shujie Zhang Zhixing You Yingdang Ren Xiaocheng Shen 《Open Journal of Ecology》 2022年第3期236-255,共20页
A novel multivariate similarity clustering analysis (MSCA) approach was used to estimate a biogeographical division scheme for the global terrestrial fauna and was compared against other widely used clustering algorit... A novel multivariate similarity clustering analysis (MSCA) approach was used to estimate a biogeographical division scheme for the global terrestrial fauna and was compared against other widely used clustering algorithms. The faunal dataset included almost all terrestrial and freshwater fauna, a total of 4631 families, 141,814 genera, and 1,334,834 species. Our findings demonstrated that suitable results were only obtained with the MSCA method, which was associated with distinct hierarchies, reasonable structuring, and furthermore, conformed to biogeographical criteria. A total of seven kingdoms and 20 sub-kingdoms were identified. We discovered that the clustering results for the higher and lower animals did not differ significantly, leading us to consider that the analysis result is convincing as the first zoogeographical division scheme for global all terrestrial animals. 展开更多
关键词 Global Animal multivariate Similarity clustering Analysis BIOGEOGRAPHY REGIONALIZATION
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Geo-Hazard Susceptibility Assessment and Its Impacts on Livelihoods in Kerio Valley, Kenya 被引量:1
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作者 Mark Boitt John Gathoni 《International Journal of Geosciences》 2022年第3期199-243,共45页
Geohazards are a recurrent issue in the Kerio River catchment of Kenya, which usually results in life and property loss. This research focuses on mapping geo-hazard risk zones of the region. The risk zones were develo... Geohazards are a recurrent issue in the Kerio River catchment of Kenya, which usually results in life and property loss. This research focuses on mapping geo-hazard risk zones of the region. The risk zones were developed from a combination of land use land cover maps, agroecological zones maps and soil erosion maps using the Analytical Hierarchy Process method of multi-criteria analysis. The final results depict the geohazard risk maps which show the susceptibility of different areas in the catchment (classified as risk zones) to hazards. The zones range from no risk zones to very high-risk zones. The results showed that the lowlands are most susceptible to hazards as they were classified as high-risk zones. These risk zone areas have impacts on the socio-economic development hence negatively impacting livelihoods in the area. 展开更多
关键词 Kerio Valley Basin Land Use Land Cover Moisture Zones Agroecological Zones Soil Erosion RUSLE Model Geohazard Risk Zones multivariate clustering Analytical Hierarchy Process
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