Habitat suitability modeling of Goitered gazelle (Gazella subgutturosa): A Maximum Entropy approach from Samelghan plain, Iran


  • Abbas Naqibzadeh Environmental Science, School of Natural Resources and Desert, Yazd University, Yazd, Iran
  • Jalil Sarhangzadeh Environmental Science, School of Natural Resources and Desert, Yazd University, Yazd, Iran
  • Ahad Sotoudeh Environmental Science, School of Natural Resources and Desert, Yazd University, Yazd, Iran
  • Mohammad Javad Jafari Expert in cultural heritage, crafts, and tourism of North Khorasan, Bojnourd, Iran




Goitered gazelle, MaxEnt, Samelghan plain, SDMs


The spatial distribution modeling can simulate the suitability of species habitats on different spatial scales, based on species records and site characteristics to gain insight into ecological, and evolutionary drivers or help predict habitat suitability across large scales. Species distribution models (SDMs) based on presence-absence or presence-only data are widely used in biogeography to indicate the ecological niche and predict the geographical distribution of species' habitats. Although presence-absence data is generally of higher quality, it is also less common than presence-only data because it requires more rigorous planning to visit a set of pre-determined sites. Among the algorithms available, the MaxEnt approach is one of the most widely used methods of developing habitat modeling. The MaxEnt uses maximum entropy to generalize specific observations of presence-only data and does not require data where the species is absent within the theoretical framework. The purpose of this study is to predict the suitable habitat for Goitered gazelle (Gazella subgutturosa) in the Samelghan plain in northeastern Iran. The results showed that the variables of the Mediterranean climate classes, slope 0-5% class, and semi-dense pastures with type Acantholimon-Astragalus are more important than other environmental variables used in modeling. The area under the curve (AUC), Receiver Operating Characteristic (ROC), and the classification threshold illustrate the model performance. Based on the ROC (AUC=0.99) results in this study, it was found that Maxent's performance was very good.


Alatawi, S. A., Gilbert, F., & Reader, T. (2020). Modelling terrestrial reptile species richness, distributions and habitat suitability in Saudi Arabia. J Arid Environments 178:104153. https://doi.org/10.1016/j.jaridenv.2020.104153.

Alcala-Canto, Y., Figueroa-Castillo, A. J., Ibarra-Velarde, F., Vera-Montenegro, Y., Cervantes-Valencia, E. M., Salem, M. Z. A., & Cuéllar-Ordaz, A. J. (2018). Development of the first georeferenced map of Rhipicephalus (Boophilus) spp. in Mexico from 1970 to date and prediction of its spatial distribution. Geospat. Health 13(1):624. https://doi.org/10.4081/gh.2018.624.

Crooks, R. K., Burdett, L. C., Theobald, M. D., King, R. S., Marco, D. M., Rondinini, C., & Boitani, L. (2017). Quantification of Habitat Fragmentation Reveals Extinction Risk in Terrestrial Mammals. PNAS 1-6. https://doi.org/10.1073/pnas.1705769114.

Dormann, C. F., Schymanski, S. J., Cabral, J., Chuine, I., Graham, C., Hartig, F., Kearney, M., Morin, X., Römermann, C., Schröder, B., & Singer, A. (2012). Correlation and process in species distribution models: bridging a dichotomy. J Biogeography 39(12):2119-2131. https://doi.org/10.1111/j.1365-2699.2011.02659.x.

Elith, J., & Leathwick, J. R. (2009). Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology and Systematics 40:677-697. https://doi.org/10.1146/annurev.ecolsys.110308.120159.

Elith, J., Phillips, S. J., Hastie, T., Dudık, M., Chee, Y. E., & Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17(1):43-57. https://doi.org/10.1111/j.1472-4642.2010.00725.x.

Evcin, O., Kucuk, O., & Akturk, E. (2019). Habitat suitability model with maximum entropy approach for European roe deer (Capreolus capreolus) in the Black Sea Region. Environmental monitoring and assessment 191,669. https://doi.org/10.1007/s10661-019-7853-x.

Fand, B. B., Kumar, M., & Kamble, L. A. (2014). Predicting the potential geographic distribution of cotton mealybug Phenacoccus solenopsis in India based on MAXENT ecological niche model. Journal of Environmental Biology 35:973–982.

Fielding, A. H., & Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24(1):38-49. https://doi.org/10.1017/S0376892997000088.

Franklin, J. (2013). Species distribution models in conservation biogeography: developments and challenges. Diversity and Distributions 19(10):1217-1223. https://doi.org/10.1111/ddi.12125.

Fronczak, L. D., Andersen, E. D., Hanna, E. E., & Cooper, R. T. (2017). Distribution and migration chronology of eastern population sandhill cranes. J Wildlife Manage 81(6):1021-1032. https://doi.org/10.1002/jwmg.21272.

Ghanbarian, G., Raoufat, R. M., Pourghasemi, R. H., & Safaeian, R. (2019). Habitat suitability mapping of Artemisia aucheri boiss based on the GLM model in R. Spatial Modeling in GIS and R for Earth and Environmental Sciences 213-227. https://doi.org/10.1016/B978-0-12-815226-3.00009-0.

Giovanelli, J. F., Siqueira, F. M., Haddad, B. F. C., & 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 Modeling 221(2):215-224. https://doi.org/10.1016/j.ecolmodel.2009.10.009.

Gormley, A. M., Forsyth, D. M., Griffioen, P., Lindeman, M., Ramsey, D. S. L., Scroggie, M. P., & Woodford, L. (2011). Using presence-only and presence–absence data to estimate the current and potential distributions of established invasive species. Journal of Applied Ecology 48:25–34. doi: 10.1111/j.1365-2664.2010.01911.x.

Guisan, A., & Thuiller, W. (2005). Predicting species distribution: offering more than simple habitat models. Ecology Letters 8(9):993-1009. https://doi.org/10.1111/j.1461-0248.2005.00792.x.

Hernandez, A. P., Graham, H. C., Master, L. L., & Albert, L. D. (2006). The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29(5):773-785. https://doi.org/10.1111/j.0906-7590.2006.04700.x.

Hirzel, A. H., L, G., Helfer, V., Randin, C., & Guisan, A. (2006). Evaluating the ability of habitat suitability models to predict species presences. Ecological Modelling, 199(2):142-152. https://doi.org/10.1016/j.ecolmodel.2006.05.017.

Ito, H., Hayakawa, K., Ooba, M., & Fujii, T. (2020). Analysis of habitat area for endangered species using maxent by urbanization in Chiba, Japan. International Journal of GEOMATE. 18(68): 94-100. https://doi.org/10.21660/2020.68.5721.

IUCN SSC Antelope Specialist Group. (2017). Gazella subgutturosa. The IUCN Red List of Threatened

Species2017. http://dx.doi.org/10.2305/IUCN.UK.2017-2.RLTS.T8976A50187422.en

Jiménez-Valverde, A. (2012). Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modeling. Global Ecology Biogeography 21(4):498-507. https://doi.org/10.1111/j.1466-8238.2011.00683.x.

Jiménez‐Valverde, A., Ortuño, M. V., & Lobo, M. J. (2007). Exploring the distribution of Sterocorax Ortuño, 1990 (Coleoptera, Carabidae) species in the Iberian Peninsula. Journal of Biogeography (J. Biogeogr.) 34(8):1426-1438. https://doi.org/10.1111/j.1365-2699.2007.01702.x.

Kamyo, T., & Asanok, L. (2020). Modeling habitat suitability of Dipterocarpus alatus (Dipterocarpaceae) using MaxEnt along the Chao Phraya River in Central Thailand. J Forest Science and Technology 16(1):1-7. https://doi.org/10.1080/21580103.2019.1687108.

Kozak, K. H., Graham, C. H., & Wiens, J. J. (2008). Integrating GIS-based environmental data into evolutionary biology. Trends in Ecology and Evolution 23(3):141-148. https://doi.org/10.1016/j.tree.2008.02.001.

Kramer-Schadt, S., Niedballa, J., Pilgrim, D. J., Schroder, B., Lindenborn, J., Reinfelder, V., Stillfried, M., Heckmann, I., Scharf, K. N., Augeri, M. D., Cheyne, M. S., Hearn, J. A., Ross, J., Macdonald, W. D., Mathai, J., Eaton, J., Marshall, J. A., Semiadi, G., Rustam, R., Bernard, H., Alfred, R., Samejima, H., Duckworth, W. J., Breitenmoser-Wuersten, C., Belant, L. J., Hofer, H., & Wilting, A. (2013). The importance of correcting for sampling bias in MaxEnt species distribution models. Diversity and Distributions, (Diversity Distrib.) 19(11):1366-1379. https://doi.org/10.1111/ddi.12096.

Kuloba, M. B., Gils, V. H., Duren, V. I., Muya, M. S., & Ngene, M. S. (2015). Modeling Cheetah Acinonyx jubatus fundamental niche in Kenya. International Journal of Environmental Monitoring and Analysis 3(5):317-330. doi:10.11648/j.ijema.20150305.22.

Leung, B., Lodge, M. D., Finnoff, D., Shogren, F. J., Lewis, A. M., & Lamberti, G. (2002). An ounce of prevention or a pound of cure: bioeconomic risk analysis of invasive species. The Royal Society. Biol Sci 269(1508):2407-2413. https://doi.org/10.1098/rspb.2002.2179.

Li, J., Fan, G., & He, Y. (2020). Predicting the current and future distribution of three Coptis herbs in China under climate change conditions, using the MaxEnt model and chemical analysis. Science of the Total Environment 698:134-141. https://doi.org/10.1016/j.scitotenv.2019.134141.

Lobo, J. M. (2008). More complex distribution models or more representative data?. Biodiversity Informatics 5:14-19. https://doi.org/10.17161/bi.v5i0.40.

Long, R. P., Zefania, S., Ffrench-Constant, H. R., & Székely, T. (2008). Estimating the population size of an endangered shorebird, the Madagascar plover, using a habitat suitability model. Animal Conservation 11(2):118-127. https://doi.org/10.1111/j.1469-1795.2008.00157.x.

Mashkour, M. (2001). Paleoenvironmental Investigations in the Qazvin Plain (Iran) in Proceedings of the First International Congress of Archaeology of the Near East (ICAANE -May 1998), Rome. 2:967-982.

Mashkour, M. (2013b). Sociétés pastorales et économies de subsistance au Nord Est de l’Iran et au Sud du Turkménistan. in Bendezu-Sarmiento, J. (dir.), Archéologie française en Asie centrale. Nouvelles recherches et enjeux socioculturels, Cahiers d’Asie Centrale, n° 21/22 pp.533-544.

Mashkour, M., Fontugne, M., & Hatté, C. (1999). Investigations on the Evolution of Subsistence Economy in the Qazvin Plain (Iran) from the Neolithic to the Iron Age. Antiquity 73(279):65-76. doi:10.1017/S0003598X00087846.

Mashkour. M. (2013a). Section C. Specialist contributions, Chp. Animal Bones 20.3 Animal exploitation during the Iron Age to Achaemenid, Sasanian and Early Islamic periods along the Gorgan Wall. In Sauer, E., Omrani Rekavandi, H., Wilkinson, T. and Nokandeh J., Persia’s Imperial Power in Late Antiquity: The Great Gorgan Wall and the Frontier Landscapes of Sasanian Iran. British Institute of Persian Studies monograph. British Academy. Oxbow Books. pp:548−580 (bibliography from pp:642-667. ISBN-13:978-1-84217-519-4, ISBN-10:1-84217-519-X.

Merow, C., Smith, J. M., & Silander, A. J. (2013). A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter. Ecography 36(10):1058-1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x.

Merow, C., Smith, J. M., Edwards, C. T., Guisan, A., Mcmahon, M. S., Normand, S., Thuiller, W., Wüest, O. R., Zimmermann, E. N., & Elith. J. (2014). What do we gain from simplicity versus complexity in species distribution models?. Ecography 37(12):1267-1281. https://doi.org/10.1111/ecog.00845.

Mi, C., Falk, H., & Guo, Y. (2016). Climate envelope predictions indicate an enlarged suitable wintering distribution for Great Bustards (Otis tarda dybowskii ) in China for the 21st century. Peer J, 4:e1630. https://doi.org/10.7717/peerj.1630.

Moreno, R., Zamora, R., Molina, R. J., Vasquez, A., & Herrera, A. M. (2011). Predictive modeling of microhabitats for endemic birds in South Chilean temperate forests using Maximum entropy (Maxent). Ecological Informatics 6(6):364-370. https://doi.org/10.1016/j.ecoinf.2011.07.003.

Mousazade, M., Ghorbanian, G., Pourghasemi, R. H., Safaeian, R., & Cerda, A. (2019). Maxent data mining technique and its comparison with a bivariate statistical model for predicting the potential distribution of Astragalus Fasciculifolius Boiss. in Fars, Iran. Sustainability 11(12):3452, https://doi.org/10.3390/su11123452.

Muscarella, R., Galante, J. P., Soley-Guardia, M., Boria, A. R., Kass, M. J., Uriarte, M., & Anderson, P. R. (2014). ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for M AXENT ecological niche models. Methods in Ecology and Evolution 5(11):1198-1205. https://doi.org/10.1111/2041-210X.12261.

Naimi, B., Hamm, A. S. N., Groen, A. T., Skidmore, K. A., & Toxopeus, G. A. (2014). Where is positional uncertainty a problem for species distribution modelling?. Ecography 37(2):191–203. https://doi.org/10.1111/j.1600-0587.2013.00205.x.

Naqibzadeh, A., Sarhangzadeh, J., & Sayedi, N. (2021b). Habitat desirability modeling of Goitered Gazelle (Gazella subgutturosa) by Ecological Niche Factor Analysis in the Bidouyeh Protected Area, Iran. Journal of wildlife and Biodiverity 5(4):15-27. 10.22120/JWB.2021.528662.1223.

Naqibzadeh, A., Sarhangzadeh, J., Sotoudeh, A., Mashkour, M., & Thomalsky, J. (2021a). Habitat Suitability Modeling for Wildlife Management Objectives by Using Maximum Entropy Method. J Human, Earth and Futur 2(4):371-381. 10.28991/HEF-2021-02-04-05.

Pearce, J. L., & Boyce, M. S. (2006). Modelling distribution and abundance with presence-only data. J. Appl. Ecol 43(3):405-412. https://doi.org/10.1111/j.1365-2664.2005.01112.x.

Pearce, J., & Ferrier, S. (2000). Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model 133(3):225-245. https://doi.org/10.1016/S0304-3800(00)00322-7.

Pearson, G. R., Raxworthy, J. C., Nakamura, M., & Peterson, T. A. (2007). Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeography 34(1):102–117. https://doi.org/10.1111/j.1365-2699.2006.01594.x.

Pédarros, E., Goetzee, T., Fritz, H., & Guerbois, C. (2020). Rallying citizen knowledge to assess wildlife occurrence and habitat suitability in anthropogenic landscapes. Biological Conservation 242:108407. https://doi.org/10.1016/j.biocon.2020.108407.

Peterson, A. T., Soberon, J., Pearson, R. G., Anderson, R. P., Martınez-Meyer, E., Nakamura, M., & Araujo, M. B. (2011). Ecological niches and geographic distributions. Princeton University Press, Princeton, NJ.

Phillipes, S. J. (2012). A brief tutorial on Maxent, versions. 3.3.3. Available online: http://www.cs. princeton.edu /~schapire/maxent/.

Phillips, J. S., & Dudik, M. (2008). Modeling of species distributions with MaxEnt: new extensions and a comprehensive evaluation. Ecography 31(2):161-175. https://doi.org/10.1111/j.0906-7590.2008.5203.x.

Phillips, J. S., Anderson, P. R., & Schapire, E. R. (2006), Maximum entropy modeling of species geographic distributions. Ecological Modelling 190(3-4):231-259. https://doi.org/10.1016/j.ecolmodel.2005.03.026.

Phillips, J. S., Anderson, P. R., Dudík, M., Schapire, E. R., & Blair, E. M. (2017). Openingthe black box: An open source release of Maxent. Ecography 40. https://doi.org/10.1111/ecog.03049.

Phillips, L. S., Dudík, M., & Schapire, R. (2004). A Maximum Entropy Approach to Species Distribution Modeling. Proceedings of the 21th International Conference on Machine Learning. Banff, Canada. https://doi.org/10.1145/1015330.1015412.

Purohit, S., & Rawat, N. (2021). MaxEnt modeling to predict the current and future distribution of Clerodendrum infortunatum L. under climate change scenarios in Dehradun district, India. Modeling Earth System and Environment. https://doi.org/10.1007/s40808-021-01205-5.

Qin, A., Jin, K., Batsaikhan, E. M., Nyamjav, J., Li, G., Li, J., Xue, Y., Sun, G., Wu, L., Indree, T., Shi, Z., & Xiao, W. (2020). Predicting the current and future suitable habitats of the main dietary plants of the Gobi Bear using MaxEnt modeling. Global Ecology and Conservation 22:e01032. https://doi.org/10.1016/j.gecco.2020.e01032.

Qin, A., Liu, B., Guo, Q., Bussmann, R. W., Ma, F., Jian, Z., Xu, G., & Pei, S. (2017). Maxent modeling for predicting impacts of climate change on the potential distribution of Thuja sutchuenensis Franch., an extremely endangered conifer from southwestern China. Glob. Ecol. Conserv 10:139–146. https://doi.org/10.1016/j.gecco.2017.02.004.

Radosavljevic, A., & Anderson, P. R. (2014). Making better MAXENT models of species distributions: complexity, overfitting and evaluation. Journal of Biogeography (J.Biogeogr.) 41(4):629-643. https://doi.org/10.1111/jbi.12227.

Renner, W. I., Elith, J., Baddeley, A., Fithian, W., Hastie, T., Phillips, J. S., Popovic, G., & Warton, D. I. (2015). Point process models for presence-only analysis. Methods in Ecology and Evolution 6(4):366-379. https://doi.org/10.1111/2041-210X.12352.

Ruete, A., & Leynaud, C. G. (2015). Goal-oriented evaluation of species distribution models’ accuracy and precision: True skill statistic profile and uncertainty maps. PeerJ PrePrints. https://dx.doi.org/10.7287/peerj.preprints.1208v1.

Saupe, E. E., Qiao, H., Hendricks, R. J., Portell, W. R., Hunter, J. S., Soberón, J., & Lieberman, S.B. (2015). Nichebreadthand geographic Range size as determinants of species survival on geological time scales. Global Ecology and Biogeography 24(10):1159-1169. https://doi.org/10.1111/geb.12333.

Signorini, M. R., Cassini, M., Drigo, A., Frangipane di Regalbono, M., Pietrobelli, F., & Montarsi, A. S.

(2014). Ecological niche model of Phlebotomus perniciosus, the main vector of canine leishmaniasis in north-eastern Italy. Geospat. Health 9(1):193–201. https://doi.org/10.4081/gh.2014.16.

Thomas, L., Buckland, S. T., Rexstad, E. A., Laake, J. L., Strindberg, S., Hedley, S. L., Bishop, J. R. B., Marques, T. A., & Burnham, K. P. (2010). Distance software: design and analysis of distance sampling surveys for estimating population size. J Applied Ecology 47:5-14. https://doi.org/10.1111/j.1365-2664.2009.01737.x.

Toor, L. M., Jaberg, C., & Safi, K. (2011). Integrating sex-specific habitat use for conservation using habitat suitability models. Animal Conservation 14(5):512-520. https://doi.org/10.1111/j.1469-1795.2011.00454.x.

Tourne, M. C. D., Ballester, R. V. M., James, A. M. P., Martorano, G. L., Guedes, C. M., & Thomas, E. (2019). Strategies to optimize modeling habitat suitability of Bertholletia excelsa in the Pan‐Amazonia. Ecology and Evolution 9(22):12623-12638. https://doi.org/10.1002/ece3.5726.

Traill, W. L., & Bigalke, C. R. (2007). A presence-only habitat suitability model for large grazing African ungulates and its utility for wildlife management. Afr. J. Ecol 45:347-354.

Van Strien, J. A., Van Swaay, M. A. C., & Termaat, T. (2013). Opportunistic citizen science data of animal species produce reliable estimates of distribution trends if analysed with occupancy models. J. Applied Ecology 50(6):1450-1458. https://doi.org/10.1111/1365-2664.12158.

Waltert, M., Meyer, B., Shanyangi, M. W., Balozi, J. J., Kitwara, O., Qolli, S., Krischke, H., & Muehlenberg, M. (2008). Foot surveys of large mammals in woodlands of western Tanzania. Journal of Wildlife Management 72(3):603-610. https://doi.org/10.2193/2006-456.

Wan, J. Z., Wang, C. J., Yu, J. H., Nie, S. M., Han, S. J., Liu, J. Z., Zu, Y. G., & Wang, Q. G. (2016). Developing conservation strategies for Pinus koraiensis and Eleutherococcus senticosus by using model-based geographic distributions. J Forestry Research 27:389-400. https://doi.org/10.1007/s11676-015-0170-5.

Wan, J., Wang, C., & Yu, F. (2019). Effects of occurrence record number, environmental variable number, and spatial scales on MaxEnt distribution modelling for invasive plants. Biologia 74:757-766. https://doi.org/10.2478/s11756-019-00215-0.

Wang, J., Liu, H., Li, Y., & Zhang, H. (2019). Habitat quality of overwintering red-crowned cranes based on ecological niche modeling. Arabian Journal of Geosciences 12:750. https://doi.org/10.1007/s12517-019-4932-9.

Warren, L. D., Glor, E. R., & Turelli, M. (2008). Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution 62(11):2868–2883. https://doi.org/10.1111/j.1558-5646.2008.00482.x.

Warton, I. D., & Shepherd, C. L. .(2010). Poisson point process models solve the “pseudo-absence problem” for presence-only data in ecology. The Annals of Applied Statistics 4(3):1383-1402. doi:10.1214/10-AOAS331.

Yackulic, B. C., Chandler, R., Zipkin, F. E., Royle, A.J., Nichols, D. J., Grant, C. H. E., & Veran ,S. (2013). Presence-only modelling using MAXENT: when can we trust the inferences?. Methods in Ecology and Evolution 4(3):236-243. https://doi.org/10.1111/2041-210x.12004.

Yost, A. C., Petersen, S. L., 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 Information 3(6):375-386. https://doi.org/10.1016/j.ecoinf.2008.08.004.

Yuan, H. S., Wei, Y. L., & Wang, X. G. (2015). Maxent modelling for predicting the potential distribution of Sanghuang, an important group of medicinal fungi in China. Fungal Ecology 17:140-145. https://doi.org/10.1016/j.funeco.2015.06.001.

Zhang, J., Jiang, F., Li, G., Qin, W., Li, S., Gao, H., Cai, Z., Lin, G., & Zhang, T. (2019). ,Maxent modeling for predicting the spatial distribution of three raptors in the Sanjiangyuan National Park, China. Ecology and Evolution, 9(11): 6643-6654. https://doi.org/10.1002/ece3.5243.

Zhang, K., Yao, L., Meng, J., & Tao, J. (2018). Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Science of the Total Environment 634:1326-1334. https://doi.org/10.1016/j.scitotenv.2018.04.112.




How to Cite

Naqibzadeh, A., Sarhangzadeh, J. ., Sotoudeh, A., & Jafari, M. J. (2022). Habitat suitability modeling of Goitered gazelle (Gazella subgutturosa): A Maximum Entropy approach from Samelghan plain, Iran. Scientific Reports in Life Sciences, 3(3), 11–28. https://doi.org/10.5281/zenodo.7058808