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An annotated checklist of mammals of Kenya

Zoological Research 2019年第1期40卷 3-52页

作者： Simon Musila ara monadjem Paul W.Webala Bruce D.Patterson Rainer Hutterer Yvonne A.De Jong Thomas M.Butynski Geoffrey Mwangi Zhong-Zheng Chen Xue-Long Jiang Mammalogy Section Department of Zoology National Museums of Kenya Department of Biological Sciences University of Swaziland Mammal Research Institute Department of Zoology & Entomology University of Pretoria Department of Forestry and Wildlife Management Maasai Mara University Integrative Research Center Field Museum of Natural History Zoologisches Forschungsmuseum Alexander Koenig Leibniz-Institut für Biodiversit?t der Tiere Eastern Africa Primate Diversity and Conservation Program School of Natural Resources and Environmental Studies Karatina University Sino-African Joint Research Center Chinese Academy of Sciences Kunming Institute of Zoology Chinese Academy of Sciences

Kenya has a rich mammalian fauna. We reviewed recently published books and papers including the six volumes of Mammals of Africa to develop an up-to-date annotated checklist of all mammals recorded from Kenya. A total of 390 species have been identified in the country, including 106 species of rodents,104 species of bats, 63 species of even-toed ungulates(including whales and dolphins), 36 species of insectivores and carnivores, 19 species of primates,five species of elephant shrews, four species of hyraxes and odd-toed ungulates, three species of afrosoricids, pangolins, and hares, and one species of aardvark, elephant, sirenian and hedgehog. The number of species in this checklist is expected to increase with additional surveys and as the taxonomic status of small mammals(e.g., bats, shrews and rodents) becomes better understood.

关键词： Checklist Rodents Bats Ungulate Carnivores Shrews Kenya

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Using potential distributions to explore environmental correlates of bat species richness in southern Africa： Effects of model selection and taxonomy

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Current Zoology 2013年第3期59卷 279-293页

作者： M. Corrie SCHOEMAN F. P. D. (Woody) COTTERILL Peter J. TAYLOR ara monadjem School of Life Sciences University of KwaZulu-Natal Westville Campus Durban 4000 South Africa Africa Earth Observatory Network (AEON) Geoecodynamics Research Hub c/o Department of Botany and Zoology University of Stellenbosch Stellenbosch 7602 South Africa Department of Ecology and Resource Management School of Environmental Sciences University of Venda Thohoyandou 0950 South Africa All Out Africa Research Unit Department of Biological Sciences University of Swaziland Private Bag 4 Kwaluseni Swaziland Mammal Research Institute Department of Zoology & Entomology University of Pretoria Private Bag 20 Hatfield 0028 Pretoria South Africa

We tested the prediction that at coarse spatial scales, variables associated with climate, energy, and productivity hy- potheses should be better predictor（s） of bat species richness than those associated with environmental heterogeneity. Distribution ranges of 64 bat species were estimated with niche-based models informed by 3629 verified museum specimens. The influence of environmental correlates on bat richness was assessed using ordinary least squares regression （OLS）, simultaneous autoregressive models （SAR）, conditional autoregressive models （CAR）, spatial eigenvector-based filtering models （SEVM）, and Classification and Regression Trees （CART）. To test the assumption of stationarity, Geographically Weighted Regression （GWR） was used. Bat species richness was highest in the eastern parts of southern Africa, particularly in central Zimbabwe and along the western border of Mozambique. We found support for the predictions of both the habitat heterogeneity and climate/productivity/energy hypothe- ses, and as we expected, support varied among bat families and model selection. Richness patterns and predictors of Miniopteridae and Pteropodidae clearly differed from those of other bat families. Altitude range was the only independent variable that was sig- nificant in all models and it was most often the best predictor of bat richness. Standard coefficients of SAR and CAR models were similar to those of OLS models, while those of SEVM models differed. Although GWR indicated that the assumption of stationa- rity was violated, the CART analysis corroborated the findings of the curve-fitting models. Our results identify where additional data on current species ranges, and future conservation action and ecological work are needed.

关键词： Chiroptera Macroecology Niche-based models Spatial models Species richness

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An annotated checklist of mammals of Kenya

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动物学研究 2019年第1期

作者： Simon Musila ara monadjem Paul W.Webala Bruce D.Patterson Rainer Hutterer Yvonne A.De Jong Thomas M.Butynski Geoffrey Mwangi Zhong-Zheng Chen Xue-Long Jiang

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