GEP  Vol.6 No.4 , April 2018
Ecological Niche Modeling of Zebra Species within Laikipia County, Kenya
Abstract: Wildlife conservation is essential, especially for countries like Kenya which rely on tourism as a major earner of foreign exchange. Conservation of species with minimal ecological information such as Grevy’s zebra, though a challenge, is critical to enable the future survival of such species. Grevy’s and Plains zebra have been classified as endangered and near-threatened by International Union for Conservation of Nature and Natural Resources (IUCN) respectively, with Grevy’s zebra found mostly in Northern Kenya and Ethiopia. This has been due to habitat degradation from livestock grazing, local hunting and development of resorts. Six prediction variables i.e. rainfall, temperature, land use, population, NDVI and cattle occurrence were used in Maxent algorithm to produce a habitat prediction map for both species. Both prediction maps had an AUC > 0.75, which is adequate for conservation planning. Niche similarity based on Warren’s I index (I = 0.78) indicates that both zebra species are identical based on their occupied niche environments, suggesting that similar conversation strategies can be adopted for both species.
Cite this paper: Mwangi, T. , Waithaka, H. and Boitt, M. (2018) Ecological Niche Modeling of Zebra Species within Laikipia County, Kenya. Journal of Geoscience and Environment Protection, 6, 264-276. doi: 10.4236/gep.2018.64016.

[1]   WTTC (2017) Travel & Tourism Economic Impact 2017.

[2]   Okello, M.M. (2014) Economic Contribution, Challenges and Way Forward for Wildlife-Based Tourism Industry in Eastern African Countries Moses M. Journal of Tourism & Hospitality, 3, 1.

[3]   Kerley, G.I.H., Geach, B.G.S. and Vial, C. (2003) Jumbos or Bust: Do Tourists’ Perceptions Lead to an Under-Appreciation of Biodiversity? South African Journal of Wildlife Research, 33, 13-21.

[4]   Rubenstein, D., Low Mackey, B., Davidson, Z., Kebede, F. and King, S.R.B. (2016) The IUCN Red List of Threatened Species. IUCN Global Species Programme Red List Unit.

[5]   King, S.R.B. and Moehlman, P. (2016) Equus quagga. The IUCN Red List of Threatened Species. IUCN Global Species Programme Red List Unit.

[6]   Parker, G., Sundaresan, S. and Chege, G. (2011) Using Sample Aerial Surveys to Estimate the Abundance of the Endangered Grevy’s Zebra in Northern Kenya. African Journal of Ecology, 49, 56-61.

[7]   Kigen, C., et al. (2013) Modeling the Spatial Impact of Climate Change on Grevy’S Zebra (Equusgrevyi) Niche in Kenya. Elixir International Journal, 62, 11761-17608.

[8]   Kebede, F., Bekele, A., Moehlman, P.D. and Evangelista, P.H. (2012) Endangered Grevy’s Zebra in the Alledeghi Wildlife Reserve, Ethiopia: Species Distribution Modeling for the Determination of Optimum Habitat. Endangered Species Reserach, 17, 237-244.

[9]   Caravaggi, A., et al. (2017) Niche Overlap of Mountain Hare Subspecies and the Vulnerability of Their Ranges to Invasion by the European Hare; the (Bad) Luck of the Irish. Biological Invasions, 19, 655-674.

[10]   Republic of Kenya (2012) Laikipia County: First County Development Integrated Development Plan 2013-2017. Nairobi, Kenya.

[11]   Kumar, S. and Stohlgren, T.J. (2009) Maxent Modeling for Predicting Suitable Habitat for Threatened and Endangered Tree Canacomyrica monticola in New Caledonia. Journal of Ecology and Natural Environment, 1, 94-98.

[12]   Lu, G.Y. and Wong, D.W. (2008) An Adaptive Inverse-Distance Weighting Spatial Interpolation Technique. Computers & Geosciences, 34, 1044-1055.

[13]   Chen, F.-W. and Liu, C.-W. (2012) Estimation of the Spatial Rainfall Distribution Using Inverse Distance Weighting (IDW) in the Middle of Taiwan. Paddy and Water Environment, 10, 209-222.

[14]   Dormann, C.F., et al. (2013) Collinearity: A Review of Methods to Deal with It and a Simulation Study Evaluating Their Performance. Ecography, 36, 27-46.

[15]   Bolboaca, S.-D. and Jantschi, L. (2006) Pearson versus Spearman, Kendall’s Tau Correlation Analysis on Structure-Activity Relationships of Biologic Active Compounds. Leonardo Journal of Science, No. 9, 179-200.

[16]   Moore, D.S., McCabe, G.P. and Craig, B.A. (2009) Introduction to the Practice of Statistics. WH Freeman, New York.

[17]   Baldwin, R.A. (2009) Use of Maximum Entropy Modeling in Wildlife Research. Entropy, 11, 854-866.

[18]   Mwendera, N.Y. (2015) Modelling the Distribution of the Cheetah (Acinonyx jubatus) in Namibia. University of Twente Faculty of Geo-Information and Earth Observation (ITC).

[19]   Navarro-Cerrillo, R.M., Hernández-Bermejo, J.E. and Hernández-Clemente, R. (2011) Evaluating Models to Assess the Distribution of Buxus balearica in Southern Spain. Applied Vegetation Science, 14, 256-267.

[20]   Morales, N.S., Fernández, I.C., Carrasco, B. and Orchard, C. (2015) Combining Niche Modelling, Land-Use Change, and Genetic Information to Assess the Conservation Status of Pouteria splendens Populations in Central Chile. International Journal of Ecology, 2015, Article ID: 612194.

[21]   Phillips, S.J., Anderson, R.P. and Schapire, R.E. (2006) Maximum Entropy Modeling of Species Geographic Distributions. Ecological Modelling, 190, 231-259.

[22]   Merow, C., Smith, M.J. and Silander, J.A. (2013) A Practical Guide to MaxEnt for Modeling Species’ Distributions: What It Does, and Why Inputs and Settings Matter. Ecography, 36, 1058-1069.

[23]   Franklin, J. and Miller, J.A. (2009) Mapping Species Distributions: Spatial Inference and Prediction. Cambridge University Press, Cambridge.

[24]   Fielding, A.H. and Bell, J.F. (1997) A Review of Methods for the Assessment of Prediction Errors in Conservation Presence/Absence Models. Environmental Conservation, 24, 38-49.

[25]   Lobo, J.M., Jiménez-Valverde, A. and Real, R. (2008) AUC: A Misleading Measure of the Performance of Predictive Distribution Models. Global Ecology and Biogeography, 17, 145-151.

[26]   Yackulic, C.B., et al. (2013) Presence-Only Modelling using MAXENT: When Can We Trust the Inferences? Methods in Ecology and Evolution, 4, 236-243.

[27]   Warren, D.L., Glor, R.E. and Turelli, M. (2008) Environmental Niche Equivalency versus Conservatism: Quantitative Approaches to Niche Evolution. Evolution, 62, 2868-2883.

[28]   Schoener, T.W. (1968) The Anolis Lizards of Bimini: Resource Partitioning in a Complex Fauna. Ecology, 49, 704-726.

[29]   der Vaart, A.W. (1998) Asymptotic Statistics. Vol. 3, Cambridge University Press, Cambridge.

[30]   Legendre, P. and Gallagher, E.D. (2001) Ecologically Meaningful Transformations for Ordination of Species Data. Oecologia, 129, 271-280.