Department of Environmental Sciences, Faculty of Natural Resources and Environment science, Malayer University. Iran
Abstract
Knowledge of effective factors on species habitat suitability and range of suitable habitats have essential importance in planning in order to wildlife protecting and managing. Dena protected area is 93820 ha located that is located in 36 km far from Yasouj County in Kohgilouia and boyerahmad province. Wild goat is indicator species of Dena protected area. Propose of this study is analyzing of wild goat's dispersion in Dena region and eastern Dena. Species presence points' data were collected in spring and divided into two clusters randomly of Training data and Test data. Then other habitat variables ran in MaxEnt model. The result of Jackknife sensitivity indicated that distance from rocky regions, distance from forest, and roughness are most important effective variables in habitat suitability. The result of study demonstrated that suitable habitat for this species is located in rocky zones far from man-made regions and incline to sparse plant covered region with high elevation.
Baldwin, R.A., 2009: Use of maximum entropy modeling in wildlife research. Entropy. Vol. 11, pp: 854-866
Farhadinia, M.S.; Akbari, H.; Mousavi, S.J.; Eslami, M.; Azizi, M.; Shokouhi, J.; Gholikhani, M. and Hosseini Zavarei, F., 2013. Exceptionally long movements of the Asiatic cheetah Acinonyx jubatus venaticus across multiple arid reserves in central Iran. Oryx. Vol. 47, pp: 427-430.
Flory, A.R.; Kumar, S.; Stohlgren, T.J. and Cryan, P.M., 2012. Environmental conditions associated withbat white nose syndrome mortality in the north eastern United States. Journal of Applied Ecology. Vol. 49, No.3, pp: 680-689.
Giovanelli, J.G.R.; DeSiqueira, M.F.; Haddad, C.F.B. and Alexandrino, J., 2010. Modeling a spatially restricted distribution in the Neotropics: how the size of calibration area affects the performance of five presence-only methods, Ecological Modelling. Vol. 221, pp: 215-224.
Hirzel, A.H.; Helfer, V. and Metral, F., 2001.Assessing habitat suitability models with a virtualspecies. Ecological Modelling. Vol. 145, pp: 111-121.
Hoffman, J.D.; Narumalani, S.; Mishra, D.R.; Merani, P. and Wilson, R.G.,2008. Predicting potential occurrence and spread of invasive plant species along the North Platte River, Nebraska. Invasive Plant Science and Management. Vol. 1, No. 4, pp: 359-367.
Jepsen, J.U.; Madsen, A.B.; Karlsson, M.D. and Groth, R., 2005. Predicting distribution and density of European badger (Melesmeles) setts in Denmark, Biodiversity and Conservation. Vol. 14, pp: 3235-3253.
Jimenez-Valverde, A. and Lobo, J.M., 2007: Threshold criteria for conversion of probability of species presence to either or presence absence. A cta O ecologica. Vol. 31, pp: 361-369.
Katsaounis, C.H., 2012. Habitat use of the endangeredand endemic Cretan Capricorn and impact of domestic goats, Thesis submitted for Master of Science. Faculity of Geo Information Science and Earth Observation, University of Twente.
Khan, B.; Ablimit, A.; Khan, G.; Jasra, A.W.; Ali, H.; Ali, R.; Ahmad, E. and Ismail, M., 2015. Abundance, distribution and conservation status of Siberian ibex, Marco Polo and Blue Sheep in Karakoram Pamir mountain area. Journal of King Saud University Science.
Liu, C.; White, M. and Newell, G., 2009: Measuring the accuracy of species distribution models: a review. 18th W orld IMA CS/MODSIM Congress, Carins, A ustralia.
Malekian, M., 2007. Effects of habitat fragmentation onthe genetic diversity and population structure Petaurubrevicepsspecies in southeastAustralia. The NationalBiotechnology Congress of Iran
Nazeri, M.; Jusoff, K.; Madani, N.; Mahmud, A.R.; Bahman, A.R. and Kumar, L., 2012. Predicting modeling and mapping of Malayan sun bear (Helarctos malayanus) distribution using maximum entropy. PLos ONE. Vol. 7, No. 10. e48104.
Olivier, F. and Wotherspoon, S., 2006. Modelling habitat selection using presence only data: Case study of a colonial hollow nesting bird, the snow petrel. Ecological modeling. Vol. 195, pp: 187-204.
P Anderson, R.; Dudík, M.; Ferrier, S.; Guisan, A.J.; Hijmans, R.; Huettmann, F. and Zimmermann, E.N., 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography. Vol. 29, No. 2, pp: 129-151.
Pearson, R.G., 2007. Species distribution modeling forconservation educators and practitioners. American Museum of Natural History. pp: 1-50.
Peterson, A.T.; Papes, M. and Eaton, M., 2007. Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent, Ecography. Vol. 30, pp: 550-560.
Phillips, S.J. and Dudik, M., 2008. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography. Vol. 31, pp: 161-175.
Phillips, S.J.; Anderson, R.P. and Schapire, R.E., 2006: Maximum entropy modeling ofspecies geographic distributions. Ecological modelling. Vol. 190, pp: 231-259.
Phillips, S.J.; Anderson, R.P. and Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling. Vol. 190, pp: 231-259.
Rahim, M., 2016. Influence of Environmental Variables on Distribution of Wild Goat (Capra aegagrus), in Iraq by Maxent. American Scientific Research Journal for Engineering, Technology, and Sciences (ASRJETS). Vol. 18, No. 1, pp: 97-107.
Rushton, J.P.; Skuy, M. and Bons, T.A., 2004.Construct Validity of Raven's Advanced Progressive Matrices for African and Non‐African Engineering Students in South Africa. International Journal ofSelection and Assessment. Vol. 12, No. 3, pp: 220-229.
Bagchi, S. and Ritchie, M.E., 2010. Herbivore ects on above and belowground plant production and soil nitrogen availability in the Trans-Himalayan shrub steppe, Oecologia. Vol. 164, pp: 1075-1082.
Sarhangzadeh, J.; Yavari, A.R.; Hemami, M.R.; Jafari, H.R. and Shams Esfandabad, B., 2013: Habitatsuitability modeling for wild goat (Capra aegagrus) in a mountainous arid area, central Iran Caspian Journal ofEnvironmental Science. Vol. 11, pp: 41-51.
Thorn, J.S.; Nijman, V.; Smith, D. and Nekaris, K.A.I., 2009. Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates: Nycticebus). Diversity and Distributions. Vol. 15, pp: 289-298.
Williams, A.K., 2003. The influence of probability of detection when modeling species occurrence using GIS and survey data: Virginia Polytechnic Institute and State University. 281 p.
Wilting, A.; Cord, A.; Hearn, A.J.; Hesse, D.; Mohamed, A.;Traeholdt, C.; Cheyne, S.M.; Sunarto, S.; Jayasilan, M.A.; Ross, J.; Shapiro, A.C.; Sebastian, A.; Dech, S.; Breitenmoser, C.; Sanderson, J.;Duckworth, J.W. and Hofer, H., 2010. Modelling the species distribution of Flat headed cat (Prionailurus planiceps), an endangered south-east Asian Small Felid, PLos ONE. Vol. 5, No. 3. e9612.
Mirsanjari, M. M., & Sokhango, F. (2018). Habitat suitability modeling for Wild goat in Dena protected area by Maximum Entropy (MAXENT) model. Journal of Animal Environment, 10(2), 23-30.
MLA
Mir Mehrdad Mirsanjari; Farangis Sokhango. "Habitat suitability modeling for Wild goat in Dena protected area by Maximum Entropy (MAXENT) model". Journal of Animal Environment, 10, 2, 2018, 23-30.
HARVARD
Mirsanjari, M. M., Sokhango, F. (2018). 'Habitat suitability modeling for Wild goat in Dena protected area by Maximum Entropy (MAXENT) model', Journal of Animal Environment, 10(2), pp. 23-30.
VANCOUVER
Mirsanjari, M. M., Sokhango, F. Habitat suitability modeling for Wild goat in Dena protected area by Maximum Entropy (MAXENT) model. Journal of Animal Environment, 2018; 10(2): 23-30.
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