1. Alemi, M. H., Azari, A. S., Nielson, D. R. 1980. Kriging and univariate modeling of a spatial correlate data. Soil Technology, 1: 133-147. 2. Engler, R., Guisan, A., Rechsteiner, L. 2004. An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data. Journal of Applied Ecology, 41: 263-274 3. Gibson, L. A., Wilson, B.A., Cahill, D. M., Hill, J. 2003. Modeling Habitat suitability of the swamp Antechinus (Antechinus minimus maritimus) in the costal heathlands of southern Victoria, Australia. International Journal of Biological Conservation, 117: 143-150. 4. Guisan, A., Zimmermanm, N.E. 2000. Predictive habitat Distribution Models in Ecology. J. Ecological Modeling, 147-186. 5. Field, A. 2009. Discovering statistics using SPSS, 3rd Edition, Sage Publications Ltd., London, 821p. 6. Hegazy, A.K., Demerdash, M.A.EI., Hosni, H.A. 1998. Vegetation, species diversity, and floristic relations along an altitudinal gradient in sourth-west Saudi Arabia. Journal of Arid Environments, 38: 3-13. 7. Hengel, T., Sierdsema, H., Radovi, A., Dilo, A. 2009. Spatial prediction of species distribution from occurrence-only records: combining point pattern analysis, ENFA and regression-kriging. Ecological Modeling, 220: 3499-3511. 8. Hirzel, A.H., Hausser, J., Chessel, D., Perrin. N. 2002. Ecological Niche Factor Analysis: How to compute habitat-suitability maps without absence data?. Ecology, 73 (22): 2027-2036. 9. Hosmer, D.W., Lemeshow, S. 2000. Applied Llogistic Regression. Wiley, New York. 307p. 10. Http:// hamshahrionline. Ir/ details/ 84024, 2009. 11. Jianbing, W., Boucher, A., Zhang, T. 2008. A SGEMS code for pattern simulation of continuous and categorical variables: FILTERSIM. computers & Geosciences, 12: 1863-1876. 12. Jongman, R.H.G., Ter Break, C.J.F., Van Tongeren, O.F.R. 1987. Data Analysis in Community and Landscape Ecology. Cambridge university press, Wageningen. 299p. 13. Zare Chahouki, M.A., Khalasi Ahvazi,L. 2012. Predicting potential distributions of Zygophyllum eurypterum by three modeling techniques (ENFA, ANN and logistic) in North East of Semnan, Iran. Range Mgmt. & Agroforestry, 33 (2): 123-128. 14. Muir, S. 2001. Managing Rangeland. In URL: http:// rangelands West. Org/ az/ Monitoring technical. Htmh. 15. Nicholls, A. O. 1989. How to make biological surveys go further with generalized linear models. Biol. Conserv, 50: 51–75. 16. Pearce, J., Ferrier, S. 2000. An evaluation of alternative algorithms for fitting species distribution models using logistic regression. Ecological Modelling, 128: 127-147. 17. Rushton, S.P., Ormerod, S.J., Kerby, G. 2004. New paradigms for modelling species distributions?. Journal of Applied Ecology, 41(2):193-200. 18. Stephenson, C., MacKenzie, M., Edwards, C., Travis, J. 2006. Modelling establishment probabilities of an exotic plant, Rhododendron ponticum, invading a heterogeneous, woodland landscape using logistic regression with spatial autocorrelation. Ecol. Model, 193 (3-4):747-758. 19. Thomaes. A., Kervynb, T., Maes,D. 2008. Applying species distribution modelling for the conservation of the threatened Saproxylic Stag Beetle (Lucanus cervus). Biological Conservation. 141: 1400-1410. 20. Trethowan, P., Robertson, D., Mcconnachiec, A.J. 2011. Ecological nich modelling of an imvasive alien plant and its potential biological control egents. South African journal of Botany, 77: 137-146. 21. Wolmarans, R., Robertson, M.P., Van Rensburg, B.j. 2010. Predicting invasive alien plant distributions. How geographical bias in occurrence records influences model performance. Journal of Biogeography, 37(9): 1629-1834.
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