Species distribution potential of striped hyaena (Hyaena hyaena) in response to climate change in Iran

Document Type : Original Article


1 Environmental Sciences Institute, Shahid Beheshti University, Tehran, Iran

2 Department of Environmental Systems Science, ETH Zurich, 8092 Zurich, Switzerland


Carnivores have always been exposed to threatening processes because of their placement at the top of the food pyramid. Nowadays, approximately 65% of carnivores are listed as Critically Endangered or Vulnerable in the IUCN Red List of threatened species. On a global scale, in order Carnivora, the Hyaenidae family is the smallest with only four species. Hyaena hyaena is the only member of the Hyaenidae faimly in Iran, which is listed as “Near Threatened” (NT) in the IUCN Red List. The current study aimed to model the species distribution of the striped hyaena (Hyaena hyaena), which has a wide distribution in Iran. 
Materials and methods:
Using nineteen layers of climatic variables, the correlations between those variables were analyzed and then highly correlated variables were excluded from the modeling process. Finally, eight species distribution models from the sdm package (GLM, GAM, BRT, SVM, RF, MARS, CART, and FDA) in R software were used. Given that the output of species distribution models are often uncertain, which is an undeniable fact, one possible solution to estimate the difference between projections and reduce the uncertainty, is the use of ensemble  prediction system instead of using a single modeling method. Therefore, after determining the potential habitats of the Hyaena hyaena with those eight mentioned models and by using the ensemble  prediction system, the best regions for the distribution of this species in Iran were estimated. 
Results and discussion:
The results of this study showed that annual mean temperature, seasonal precipitation and precipitation of the warmest season have the most influence on the distribution of Hyaena hyaena. In general, FDA, GAM, BRT, CART and GLM models are fairly reliable, the MARS model is very reliable, and SVM and RF models are completely reliable. The results showed that the GLM, GAM, BRT, MARS, CART, RF models demonstrate that the suitable areas for Hyaena hyaena are generally the central regions of Iran, while the SVM and FDA models predicted the southern margin of the Caspian sea to make the best regions for the distribution of this species.
The difference in the predictions that each model makes confirms the uncertainty between models. Therefore, the necessity of using Ensemble method is revealed. The results of the Ensemble  model showed that the most suitable regions for the Hyaena hyaena species distribution are semi-arid and central steppe regions of Iran.


  1. Akhani, H. 1998., Plant biodiversity of Golestan National Park, Iran, No. 53. Stapfia.
  2. Araújo, M. B. and New, M., 2007. Ensemble forecasting of species distributions. Trends in ecology and evolution. 22(1), 42-47.
  3. Beale, C.M. and Lennon, J.J., 2012. Incorporating uncertainty in predictive species distribution modelling. Philosophical. Transactions. R. Soc. B. 367(1586), 247-258.
  4. Bosso, L., Russo, D., Di Febbraro, M., Cristinzio, G. and Zoina, A., 2016. Potential distribution of Xylella fastidiosa in Italy: A maximum entropy model. Phytopathologia Mediterranea. 55(1), 62-72.
  5. Breckle, S.W., 2002. Salt deserts in Iran and Afghanistan. Barth and Böer. Sabkha Ecosystems. 109-122.
  6. Breiman, L., 2001. Random forests. Machine learning. 45(1), 5-32.
  7. Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A., 1984. Classification and regression trees. CRC press. Brown DG, Johnson KM, Loveland TR, Theobald DM., 2005. Rural land‐use trends in the conterminous United States, 1950–2000. Ecological Applications. 15(6), 1851-63.
  8. Brown, C. A., Jackson, G. A., Holt, S. A., & Holt, G. J., 2005. Spatial and temporal patterns in modeled particle transport to estuarine habitat with comparisons to larval fish settlement patterns.
  9. Estuarine, Coastal and Shelf Science, 64(1), 33-46.
  10. Channell, R. and Lomolino, M.V., 2000. Trajectories to extinction: spatial dynamics of the contraction of geographical ranges. Journal of Biogeography. 27, 169–179.
  11. Clemens, R. S., Herrod, A., and Weston, M. A., 2014. Lines in the mud; revisiting the boundaries if important shorebird areas. Journal of Nature Conservation. 22, 59–67.
  12. Clemens, R. S., Weston, M. A., Haslem, A., Silcocks, A., and Ferris, J., 2010. Identification of significant shorebird areas: Thresholds and criteria. Diversity and Distribution. 16, 229–242.
  13. Elith, J., Phillips, S. J., Hastie, T., Dudik, M., Chee, Y. E. and Yates, C. J., 2011. A statistical explanation of MaxEnt for ecologists. Diversity and distributions. 17(1), 43-57.
  14. Falcucci, A., Maiorano, L. and Boitani, L., 2007. Changes in land-use/land-cover patterns in Italy and their implications for biodiversity conservation. Landscape ecology. 22(4), 617-631.
  15. Franklin, J., 2010. Mapping species distributions: spatial inference and prediction. Cambridge University Press.
  16. Friedman, J. H., 2001. Greedy function approximation: A gradient boosting machine. – Annals of Statistics. 29, 1189–1232.
  17. Gage GS, Brooke MD, Symonds MRE, Wege D., 2004. Ecological correlates of the threat of extinction in Neotropical bird species. Animal Conservation. 7, 161–168.
  18. Giovanelli, J. G., de Siqueira, M. F., Haddad, C. F. 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. 221(2), 215-224.
  19. Hampe, A., and Petit, R. J., 2005. Conserving biodiversity under climate change: the rear edge matters. Ecology letters. 8(5), 461-467.
  20. Hanski, I., 1998. Metapopulation dynamics. Nature. 396, 41–49.
  21. Harris G, Pimm SL., 2007. Range size and extinction risk in forest birds. Conservation Biology. DOI: 10.1111/j.1523-1739.2007. 00798.
  22. Hastie, T., and Tibshirani, R. J., 1990. Generalised additive models Chapman and Hall. London, England.
  23. Hastie, T., Tibshirani, R., and Buja, A., 1994. Flexible discriminant analysis by optimal scoring. Journal of the American statistical association. 89(428), 1255-1270.
  24. Houghton McNab BK., 2003. Metabolism ecology shapes bird bioenergetics. Nature. 426, 620–621. http://www.iucnredlist.org
  25. Wood, C., Sullivan, B., Iliff, M., Fink, D. and Kelling, S., 2011. eBird: engaging birders in science and conservation. PLoS biology. 9(12), 101-105.
  26. IPCC., 2013. Summary for Policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.
  27. Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung, J., and Midgley, P. M., 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 1535 pp.
  28. Jafari, M. K., Asghari, A. and Rahmani, I., 1997. Empirical correlation between shear wave velocity (Vs) and SPT-N value for south of Tehran soils. In Proceedings of the 4th international conference on civil engineering, Tehran, Iran.
  29. Jetz W, C Rahbek RK, Colwell., 2004. The coincidence of rarity and richness and the potential signature of history in centers of endemism. Ecology Letters. 7, 1180–1191.
  30. Karami, M., Ghadirian, T and Faizolahi, K., 2012. The Atlas of Mammals of Iran. Department of Environment of Iran. (In Persian with English abstract).
  31. Karami, M., Riazi, B, and Kalani, N., 2008. Investigating the Seasonal Dispersion of Hyaena hyaena hyaena in In Khojir National Park. Journal of Environmental Sciences and Technology.10, 99-104.
  32. Ko, C. Y., Murphy, S. C., Root, T. L. and Lee, P. F., 2014. An assessment of the efficiency of protection status through determinations of biodiversity hotspots based on endemic bird species, Taiwan. Journal for Nature Conservation, 22, 570-576. Lambin EF, Geist HJ, Lepers E., 2003. Dynamics of land-use and land-cover change in tropical regions. Annual review of environment and resources. 28(1), 205-41.
  33. Lee, S., and Oh, H. J., 2012. Ensemble-based landslide susceptibility maps in Jinbu area, Korea. In Terrigenous Mass Movements (pp. 193-220). Springer Berlin Heidelberg. Lepers, E, Lambin, E.F., Janetos, A.C, DeFries, R., Achard, F., Ramankutty, N. and Scholes, R.J., 2005. A synthesis of information on rapid land-cover change for the period 1981–2000. AIBS Bulletin. 55(2), 115-24.
  34. Malekian, M. and Bagheri., R., 2015. Investigating birds' diversity and richness of Kohgiluyeh and Boyer Ahmad protected areas and the influence of area size and shape on diversity and richness. Natural Environment Journal. 67(3), 343-354. (In Persian with English abstract). Martin, P. R., and Martin, T. E., 2001. Ecological and fitness consequences of species coexistence: a removal experiment with wood warblers. Ecology, 82(1), 189-206. Matson, P.A., Parton, W.J., Power, A.G. and Swift, M.J., 1997. Agricultural intensification and ecosystem properties. Science. 277, 504-9.
  35. Maurer, B.A. and Taper, M.L., 2002. Connecting geographical distributions with population processes. Ecology Letters. 5, 223–231.
  36. McCullagh, P. and Nelder, J. A., 1989. Generalized linear models. Chapman and Hall.
  37. McNab BK., 2003. Metabolism ecology shapes bird bioenergetics. Nature. 426, 620–621.
  38. Miller SP, Whalen MW, Cofer DD., 2010. Software model checking takes off. Communications of the ACM. 53(2), 58-64.
  39. Naimi, B. and Araújo, M. B., 2016. sdm: a reproducible and extensible R platform for species distribution modelling. Ecography. 39(4), 368-375.
  40. Parmesan, C. and Yohe, G., 2003. A globally coherent fingerprint of climate change impacts across natural systems. Nature. 421, 37–42.
  41. Phillips, S.J., Anderson, R.P. and Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecological modelling. 190, 231-259.
  42. Pimm, S.L. and Lawton, J.H., 1998. Planning for biodiversity. Science. 279, 2068-20699. Pimm, S., Raven, P., Peterson, A., Sekercioglu, CH. and Ehrlich, P.R., 2006. Human impacts on the rates of recent, present, and future bird extinctions. Proceedings of the National Academy of Sciences of the United States of America. 103, 10941–10946.
  43. Platts, P. J., McClean, C. J., Lovett, J. C. and Marchant, R., 2008. Predicting tree distributions in an East African biodiversity hotspot: model selection, data bias and envelope uncertainty. Ecological Modelling. 218(1), 121-134.
  44. Pounds, J.A, Bustamante, M.R., Coloma, L.A, Consuegra, J.A., Fogden, M.P., Foster, P.N., La Marca, E., Masters, KL., Merino-Viteri, A., Puschendorf, R. and Ron, S.R., 2006. Widespread amphibian extinctions from epidemic disease driven by global warming. Nature. 439, 161–167.
  45. Pounds, J.A, Fogden, M.P.L. and Campbell, J.H., 1999. Biological response to climate change on a tropical mountain. Nature. 398, 611–615.
  46. Pulliam, H.R., 1988. Sources, sinks and population regulation. The American Naturalist. 132, 652–661.
  47. Pulliam, H.R., 2000. On the relationship between niche and distribution. Ecology Letters. 3, 349–361.
  48. Renner, I. W. and Warton, D.I., 2013. Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology. Biometrics. 69(1), 274-281.
  49. Ricklefs, R.E. and Schluter, D., 1993. Species diversity in ecological communities. Historical and geographical perspectives. University of Chicago Press, Chicago, IL. Root TL, Price JT, Hall KR, Schneider SH., 2003. Fingerprints of global warming on wild animals and plants. Nature. 421. 57–60.
  50. Rokach, L., 2010. Ensemble-based classifiers. Artificial Intelligence Review, 33(1-2), 1-39.
  51. Royle, J. A., Chandler, R. B., Yackulic, C., and Nichols, J. D., 2012. Likelihood analysis of species occurrence probability from presence‐only data for modelling species distributions. Methods in Ecology and Evolution. 3(3), 545-554. Sala, O.E., Chapin, F.S., Armesto, J.J., Berlow, E., Bloomfield, J., Dirzo, R., Huber-Sanwald, E., Huenneke, L.F., Jackson, R.B., Kinzig, A. and Leemans, R., 2000. Global biodiversity scenarios for the year 2100. Science. 287, 5459. 1770-4. Sanderson, E.W., Jaiteh, M., Levy, M.A., Redford, K.H., Wannebo, A.V. and Woolmer, G., 2002. The human footprint and the last of the wild. BioScience. 52(10), 891-904.
  52. Scharlemann, J.P., Green, R.E. and Balmford, A., 2004. Land‐use trends in Endemic Bird Areas: global expansion of agriculture in areas of high conservation value. Global Change Biology. 10(12), 2046-51.
  53. Shoo, L.P, Williams, S.E. and Hero, J.M., 2005. Climate warming and the rainforest birds of the Australian Wet Tropics: using abundance data as a sensitive predictor of the change in total population size. Biological Conservation. 125, 335–343.
  54. Smeraldo, S., Di Febbraro, M., Ćirović, D., Bosso, L., Trbojević, I. and Russo, D., 2017. Species distribution models as a tool to predict range expansion after reintroduction: A case study on Eurasian beavers (Castor fiber). Journal for Nature Conservation.
  55. Sodhi, N. S., Koh, L. P., Brook, B. W., and Ng, P. K., 2004. Southeast Asian biodiversity: an impending disaster. Trends in ecology & evolution, 19(12), 654-660.
  56. Svenning, J.C. and Skov, F., 2007. Could the tree diversity pattern in Europe be generated by postglacial dispersal limitation? Ecology Letters. 10, 453–460.
  57. Vapnik, V., 1995. The nature of statistical learning theory. Springer.
  58. Williams, S.E., Bolitho, E.E. and Fox, S., 2003. Climate change in Australian tropical rainforests: An impending environmental catastrophe. Proceedings of the Royal Society of London Series B. 270, 1887–1892.
  59. Yackulic, C.B., Chandler, R., Zipkin, E.F., Royle, J.A., Nichols, J.D., Campbell Grant, E. H., and Veran, S., 2013. Presence‐only modelling using MAXENT: when can we trust the inferences?.
  60. Methods in Ecology and Evolution. 4(3), 236-243.