___

• Anderson SC (1999). The Lizards of Iran. Oxford, UK: Society for the Study of Amphibians and Reptiles.Araújo MB, Alagador D, Cabeza M, Nogues-Bravo D, Thuiller W (2011). Climate change threatens European conservation areas. Ecol Lett 14: 484–492.
• Araújo MB, Lobo JM, Moreno JC (2007). The effectiveness of Iberian protected areas in conserving terrestrial biodiversity. Conserv Biol 21: 1423–1432.
• Araújo MB, New M (2007). Ensemble forecasting of species distributions. Trends Ecol Evol 22: 42–47.
• Araújo MB, Thuiller W, Pearson RG (2006). Climate warming and the decline of amphibians and reptiles in Europe. J Biogeogr 33: 1712–1728.
• Araújo MB, Whittaker RJ, Ladle RJ, Erhard M (2005). Reducing uncertainty in projections of extinction risk from climate change. Glob Change Biol 14: 529–538.
• Bellard C, Bertelsmeier C, Leadley P, Thuiller W Courchamp F (2012). Impacts of climate change on the future of biodiversity. Ecol Lett 15: 365–377.
• Breiman L (2001). Random forests. Mach Learn 45: 5–32.
• Cayuela L, Golicher DJ, Newton AC, Kolb M, Alburquerque FS, Arets EJMM, Alkemade JRM, Pérez AM (2009). Species distribution modeling in the tropics: problems, potentialities, and the role of biological data for effective species conservation. Trop Conserv Sci 2: 319–352.
• Chen IC, Hill JK, Ohlemüller R, Roy DB, Thomas CD (2011). Rapid range shifts of species associated with high levels of climate warming. Science 333:1024–1026.
• Crossman ND, Bass DA (2008). Application of common predictive habitat techniques for post-border weed risk management. Divers Distrib 14: 213–224.
• Dawson TP, Jackson ST, House JI, Prentice IC, Mace GM (2011). Beyond predictions: biodiversity conservation in a changing climate. Science 332: 53–58.
• Domínguez-Vega H, Monroy-Vilchis O, Balderas-Valdivia CJ, Gienger CM, Ariano-Sánchez D (2012). Predicting the potential distribution of the beaded lizard and identification of priority areas for conservation. J Nat Conserv 20: 247–253.
• Duckett PE, Wilson PD, Stow AJ (2013). Keeping up with the neighbours: using a genetic measurement of dispersal and species distribution modelling to assess the impact of climate change on an Australian arid zone gecko (Gehyravariegata). Diversity Distrib 19: 964–976.
• Elith J, Graham CH, Anderson RP, Dudík M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A et al. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29: 129–151.
• Elith J, Leathwick J (2007). Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines. Divers Distrib 13: 265–275.
• Elith J, Leathwick JR (2009). Species distribution models: ecological explanation and prediction across space and time. Annu Rev Ecol Evol Syst 40: 677–697.
• Elith J, Phillips SJ, Hastie T, Dudik M, Chee YE, Yates CJ (2011). A statistical explanation of MaxEnt for ecologists. Divers Distrib 17: 43–57.
• 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. Appl Ecol 41: 263–274.
• Fourcade Y, Engler JO, Besnard AG, Rödder D, Secondi J (2013). Confronting expert-based and modelled distributions for species with uncertain conservation status: a case study from the corncrake (Crexcrex). Biol Conserv 167: 161–171.
• Friedman J (1991). Multivariate adaptive regression splines. Ann Stat 19: 1–141.
• Grenouillet G, Buisson L, Casajus N, Lek S (2011). Ensemble modelling of species distribution: the effects of geographical and environmental ranges. Ecography 34: 9–17.
• Guisan A, Thuiller W (2005). Predicting species distribution: offering more than simple habitat models. Eco Lett 8: 993–1009.
• Guo QH, Kelly M, Graham CH (2005). Support vector machines for predicting distribution of sudden oak death in California. Ecol Model 182: 75–90.
• Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005). Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25: 1965–1978.
• Kafash A, Kaboli M, Köhler G (2014a). Predicting the impacts of climate change on the Mesopotamian Spiny-tailed Lizard (Saara loricata): using maximum entropy algorithm and Bioclim. J Animal Bio 7: 75–82.
• Kafash A, Yousefi M, Ahmadi M, Köhler G, Kaboli M (2013). Predicting the impacts of climate change on the desert dwelling reptiles of Iran (Case study Saara loricata). In: The 3rd International Conference on Environmental Planning and Management;29–30 October 2013; University of Tehran, Tehran, Iran.
• Kafash A, Yousefi M, Rezie A, Köhler G, Kaboli M (2014b). The role of vegetation in burrow site selection by Mesopotamian spiny-tailed lizard, Saara loricata (Blanford, 1875) in Mish Dagh Protected Area, Khuzestan province. J Animal Environ 2: 31–38.
• Kalle R, Ramesh T, Qureshi Q, Sankar K (2013). Predicting the distribution pattern of small carnivores in response to environmental factors in the Western Ghats. PLoS One 8: e79295.
• Latifi M (2000). The Snakes of Iran. Tehran, Iran: Department of the Environment.
• Liu X, Guo Z, Ke Z, Wang S, Li Y (2011). Increasing potential risk of a global aquatic invader in Europe in contrast to other continents under future climate change. PLoS One 6: e18429.
• Lomba A, Pellissier L, Randin C, Vicente J, Moreira F, Honrado J, Guisan A (2010). Overcoming the rare species modelling paradox: a novel hierarchical framework applied to an Iberian endemic plant. Biol Conserv 143: 2647–2657.
• Maclean IMD, Wilson RJ (2011). Recent ecological responses to climate change support predictions of high extinction risk. P Natl Acad Sci USA 108: 12337–12342.
• Madjnoonian H, Kiabi BH, Danesh M (2005). Readings in Zoogeography of Iran. Tehran, Iran: Department of Environment.
• Manel S, Dias JM, Buckton ST, Ormerod SJ (1999). Alternative methods for predicting species distribution: an illustration with Himalayan river birds. Appl Ecol 36: 734–747.
• McCullagh P, Nelder JA (1989). Generalized Linear Models. London, UK: Chapman and Hall.
• McKenna JE, Carlson DM, Payne-Wynne ML (2013). Predicting locations of rare aquatic species’ habitat with a combination of species-specific and assemblage-based models. Divers Distrib 19: 503–517.
• McMahon SM, Harrison SP, Armbruster WS, Bartlein PJ, Beale CM, Edwards ME, Kattge J, Midgley G, Morin X, Prentice IC (2011). Improving assessment and modelling of climate change impacts on global terrestrial biodiversity. Trends Ecol Evol 26: 249–259.
• Moisen GG, Frescino TS (2002). Comparing five modelling techniques for predicting forest characteristics. Ecol Model 157: 209–225.
• Monge-Nájera J (2008). Ecological biogeography: a review with emphasis on conservation and the neutral model. Gayana 72: 102–112.
• Papenfuss T, Shafiei Bafti S, Sharifi M, Anderson S C (2009). Saara loricata. The IUCN Red List of Threatened Species 2009. Gland, Switzerland: IUCN.
• Parmesan C, Yohe G (2003). A globally coherent fingerprint of climate change impacts across natural systems. Nature 421: 37–42.
• Peterson AT, Soberón J, Pearson RG, Anderson RP, Martínez-Meyer E, Nakamura M, Araújo MB (2011). Ecological Niches and Geographic Distributions. Monographs in Population Biology, No. 49. Princeton, NJ, USA: Princeton University Press.
• Phillips SJ, Anderson RP, Schapire RE (2006). Maximum entropy modeling of species geographic distributions. Ecol Model 190: 231–259.
• Rastegar-Pouyani N, Rastegar-Pouyani E, Jawaheri M (2006). Field Guide to the Reptiles of Iran. Kermanshah, Iran: Razi University Press.
• R Development Core Team (2011). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.Ridgeway G (1999). The state of boosting. Comput Sci Stat 31: 172–181.
• Rodriguez JP, Brotons L, Bustamante J, Seoane J (2007). The application of predictive modelling of species distribution to biodiversity conservation. Divers Distrib 13: 243–251.
• Solomon IS, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (2007). Climate Change: The Physical Science Basis. Contribution of Working Group 1 to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 1e18). Cambridge, UK: Cambridge University Press.
• Sousa-Silva R, Alves P, Honrado J, Lomba A (2014). Improving the assessment and reporting on rare and endangered species through species distribution models. Glob Eco Conserv 2: 226–237.
• Spitz F, Lek S (1999). Environmental impact prediction using neural network modelling. An example in wildlife damage. Appl Ecol 36: 317–326.
• Thuiller W (2007). Biodiversity – climate change and the ecologist. Nature 448: 550–552.
• Thuiller W, Lafourcade B, Engler R, Araújo MB (2009). BIOMOD – A platform for ensemble forecasting of species distributions. Ecography 32: 369–373.
• Velásquez-Tibatá J, Salaman P, Graham CH (2012). Effects of climate change on species distribution, community structure, and conservation of birds in protected areas in Colombia. Reg Environ Change 13: 235–248.
• Whittaker RJ, Araújo MB, Paul J, Ladle RJ, Watson JE, Willis KJ (2005). Conservation biogeography: assessment and prospect. Divers Distrib 11: 3–23.
• Williams JN, Seo C, Thorn J, Nelson JK, Erwin S, O’Brien JM, Schwartz MW (2009). Using species distribution models to predict new occurrences for rare plants. Divers Distrib 15: 565–576.
• Wilms T (2005). Uromastyx, Natural History, Captive Care and Breeding. Offenbach am Main, Germany: Herpeton Verlag.
• Wilms T, Böhme W (2007). A review of the taxonomy of the spiny-tailed lizards of Arabia (Reptilia: Agamidae: Leiolepidinae: Uromastyx). Fauna of Arabia 23: 435–468.
• Zhang Y, Richardson JS, Pinto X (2009). Catchment-scale effects of forestry practices on benthic invertebrate communities in Pacific coastal streams. J Appl Eco 46: 1292–1303.