Identifying habitat patches and suitability for roe deer, Capreolus capreolus as a protected species in Iran

Document Type: Research Paper


1 Department of Environmental Sciences, Faculty of Natural Resources, University of Tehran, Karaj, Iran

2 Agriculture Biotechnology Research Institute of Iran (ABRII), Tehran, Iran

3 Department of Environmental Sciences, Faculty of Marine Natural Resources, Khorramshahr University of Marine Science and Technology, Khorramshahr, Iran



Species distribution models (SDMs) are a tool for the management of wildlife including the roe deer, Capreolus capreolus, as an elusive and national protected herbivore in Iran. Habitat suitability modeling can be one of the most important steps to protect this species. This study was carried out to evaluate the potential distribution of the roe deer in the north and northwest of Iran and to identify the important habitat patches for this species. The habitat suitability modeling was applied 95 presence points and nine environmental variables by MaxEnt®. Thereafter, we focused on the extraction of important habitat patches based on presence points. The land cover, as the most important variable on the habitat suitability model of roe deer and its highest probability presence, is classified as the high and moderate densities in the forest. Habitat patches covered an area of about 4467.81 km2 (i.e., 6.04%of the study area). The largest habitat patch, covering an area about 4022 km2, created a continuous patch in the east of the study area. There were several inter-connected small patches in the most western part of the study area in Arasbaran forests. Actually, Habitat patches should be taken into consideration in the conservation of the roe deer.


Allouche, O, Tsoar, A & Kadmon, R 2006, Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43: 1223-1232.

Almasieh, K, Kaboli, M & Beier, P 2016, Identifying habitat cores and corridors for the Iranian black bear in Iran. Ursus, 27: 18-30.

Anderson, RP & Raza, A 2010, The effect of the extent of the study region on GIS models of species geographic distributions and estimates of niche evolution: preliminary tests with montane rodents (genus Nephelomys) in Venezuela. Journal of Biogeography, 37: 1378‐1393.

Barbet‐Massin, M, Jiguet, F, Albert, CH & Thuiller, W 2012, Selecting pseudoabsences for species distribution models: how, where and how many. Methods in Ecology and Evolution, 3: 327–338.

DoE 2018, Department of the Environment of Iran. Accessed 1 October 2018.

Elith, J 2002, Quantitative methods for modeling species habitat: Comparative performance and an application to Australian plants. In S, Ferson and M, Burgman (Eds.). Quantitative methods for conservation biology, Springer-Verlag, New York, pp. 39-58.

Elith, J, Graham, CH, Anderson, R.P. et al 2006, Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29: 129-151.

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.

Fick, SE & Hijmans, RJ 2017. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37: 4302-4315.

Firooz, A 1999, Iranian wildlife. University Publishing Center with the collaboration of Green Circle Publishing, Tehran, Iran.P:389-390

Franklin, J 2009, Mapping species distributions: spatial inference and prediction. Cambridge University Press, Cambridge, UK.p:615-615.

Ferretti, F, Bertoldi, G, Sforzi, A & Fattorini, L 2011, Roe and fallow deer: are they compatible neighbors? European Journal of Wildlife Research, 57: 775-783.

FRWMO 2010, Iranian Forests, Range and Watershed Management Organization National Land use/Land cover map. Iranian Forest, Range and Watershed Management Organization, Tehran, Iran,

Gibson, LA,Wilson, BA, Cahill, DM & Hill, J 2004, Modeling habitat suitability of the swamp antechinus (Antechinus minimus maritimus) in the coastal heathlands of southern Victoria, Australia. Biological Conservation, 117: 143-150.

Graham, CH, Ferrier, S, Huettman, F, Moritz, C & Peterson, AT 2004, New developments in museum-based informatics and applications in biodiversity analysis. Trends in Ecology and Evolution, 19: 497-503.

Guisan, A & Zimmermann, N.E 2000, Predictive habitat distribution models in ecology. Ecological Modelling, 135: 147-186.

Heidari Safari Kouchi, A, Moradian Fard, F, Eskandari, A & Rostami Shahraji, T 2015, Investigation of some quantitative and qualitative characteristics of Persian oak (Quercus brantii Lindl) in Bazoft forests of Chahar Mahal and Bakhtiari Province. Zagros Forest Researches, 2: 75-91.

Henareh Khalyani, A, Mayer, AL & Falkowski, MJ 2011, Effects of protection on amount and structure of forest cover at two scales in Bozin and Marakhil protected area, Iran. 1st World Sustain. Forum Conference. Basel, Switzerland,1-6

Hijmans, RJ, Cameron, SE, Parra, JL, Jones, PG & Jarvis, A 2005, Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25: 1965-1978.

Ineichen, P 2015, Habitat selection of roe deer (Capreolus capreolus) in a landscape of fear shaped by human recreation. MSc. Dissertation, Department of Environmental Systems Science (D-USYS). Swiss Federal Institute of Technology(ETH), Zurich, Switzerland, p:1-34

Jiang, G, Zhang, M & Ma, J 2008, Habitat use and separation between red deer Cervus elaphus xanthopygus and roe deer Capreolus pygargus bedfordi in relation to human disturbance in the Wandashan Mountains, Northeastern China. Wildlife Biology, 14: 92-100.

Jimenez-Valverde, A & Lobo, JM 2007, Threshold criteria for conversion of probability of species presence to either-or presence-absence. Acta Oecologica, 31: 361-369.

Kabiri, H. R., Rezaee, H.R & Naderi, S 2017, Genetic diversity of roe deer in Golestan and Mazandaran provinces based on mitochondrial and D-Loop gene sequences. Journal of Animal Ecology, 9: 49-56

Karami, M, Ghadirian, T & Faizolahi, K 2015. The atlas of the mammals of Iran, Iran Department of the Environment, Tehran, Iran, P:181-181

Linnel, JDC, Nilsen, EB & Andersen, R 1999, Selection of bed-sites by roe deer Capreolus capreolus fawns in an agricultural landscape. Acta Theriologica, 49: 103-111.

Lobo, JM, Jime´nez-Valverde, A & Hortal, J 2010, The uncertain nature of absences and their importance in species distribution modeling. Ecography, 33: 103-114.

Lovari, S, Serrao, G & Mori, E 2017, Woodland features determining the home range size of roe deer. Behavioral Processes, 140: 115-120

Morellet, N, Van Moorter, B, Cargnelutti, B, Angibault, JM, Lourtet, B, Merlet, J, Ladet, S & Hewison, AJM 2011, Landscape composition influences roe deer habitat selection at both home range and landscape scales. Landscape Ecology, 26: 999-1010.

Myers, N, Mittermeier, RA, Mittermeier, CG, da Fonseca, GAB & Kent, J 2000, Biodiversity hotspots for conservation priorities. Nature, 403: 853-858.

Mysterud, A & Ostbye, E 1999, Cover as a habitat element for temperate ungulates: effects on habitat selection and demography. Wildlife Society Bulletin, 27: 385-394.

Radeloff, VC, Pidgeon, AM & Hostert, P 1999, Habitat and population modelling of roe deer using an interactive geographic information system. Ecological Modelling, 114: 287-304.

Pellerin, M, Calenge, C, Saïd, S, Gaillard, JM, Fritz, H, Duncan, P & Van Laere, G 2010, Habitat use by female western roe deer (Capreolus capreolus): influence of resource availability on habitat selection in two contrasting years. Canadian Journal of Zoology, 88: 1052-1062.

Phillips, SJ, Anderson, R.P & Schapire, R.E 2006. Maximum entropy modeling of species geographic distributions. Ecological Model, 190: 231-259.

Phillips, SJ, Dudík, M & Schapire, RE 2017, Maxent software for modeling species niches and distributions (Version 3.4.1), (accessed on July 15, 2018).

Sedighi, F, Taheri abkenar, K & Heidari Safari kouchi, A 2020, Effect of physiographic factors on quantitative characteristics of cypress (Juniperus excelsa M. Bieb) trees (case study: Spiro cypress habitat–Damghan), Journal of Forest Research and Development, 6: 29-42.

Soofi, M, Ghoddousi, A, Hamidi, AK, Ghasemi, B, Egli, L 2017, Precision and reliability of indirect population assessments for the Caspian red deer Cervus elaphus maral. Wildlife Biology, doi: 10.2981/wlb.00230p:1-8

Swet, JA 1988, Measuring the accuracy of diagnostic systems. Science, 240: 1285-1293.

Titeux, N, Dufrene, M, Radoux, J, Hirzel, A.H & Defourny, P 2007, Fitness-related parameters improve presence-only distribution modeling for conservation practice: The case of the red-backed shrike. Biological Conservation, 138: 207-223.

Yost, AC, Peterson, S­L, Gregg, M & Miller, R 2008, Predictive modeling and mapping sage grouse (Centrocercus urophasianus) nesting habitat using Maximum Entropy and a long-term dataset from Southern Oregon. Ecological Informatics, 3: 375-386.