Advertisement

Climate change effects on species of Bovidae family in Iran

  • Amir EbrahimiEmail author
  • Pourya Sardari
  • Sanaz Safavian
  • Zahra Jafarzade
  • Sadegh Bashghareh
  • Zeynab Khavari
Original Article
  • 44 Downloads

Abstract

Climate change and its effect on life is a big challenge for scientists all over the world. Global biodiversity has diminished in recent years because of climate change, human developments and some other factors. One reason for wildlife population loss is habitat degradation caused by climate change. Predicting habitat suitability can help wildlife managers to protect wildlife more effectively. Accordingly, in this study, we used present habitat suitability of five species of wild Bovidae in Iran to predict climate change effects on their habitats for a future condition over 62 years. To predict this, four RCPs and one GCM in four CC scenarios were used. Our results revealed that climate variables are important to predict suitable habitats. This study showed that at present time suitable habitats for wild goat, Urial wild sheep, Armenian wild sheep, goitered gazelle and jebeer in Iran’s total area are 5.5%, 5.8%, 5.9%, 4.9% and 5.2%, respectively. The results also reveal that in the future 59.83%, 60.89%, 59.18%, 53.57%, 69.86% of current suitable habitats will be lost for each species, respectively, over 62 years. Based on the result of our study, it seems more than 60% of suitable habitat for the studied species will be destroyed over this time period. In our opinion, wildlife managers should consider the remaining suitable habitats as some parts of a protected area before the conditions get irreversible.

Keywords

Biodiversity Global warming MaxEnt Habitat modeling Bovidae 

Notes

References

  1. Akbari H, Moradi HV, Rezaie HR, Baghestani N (2015) Seasonal changes in group size and composition of Chinkara (Gazella bennettii shikarii)(Mammalia: Bovidae) in central Iran. Ital J Zool 82(4):609–615Google Scholar
  2. Akhter S, McDonald MA, van Breugel P, Sohel S, Kjær ED, Mariott R (2017) Habitat distribution modelling to identify areas of high conservation value under climate change for Mangifera sylvatica Roxb. of Bangladesh. Land Use Policy 60:223–232Google Scholar
  3. Assessment ME (2005) Millennium ecosystem assessment. Ecosystems and human wellbeing: a framework for assessment. Island Press, Washington, DCGoogle Scholar
  4. Austin MP, Meyers JA (1996) Current approaches to modeling the environmental niche of eucalypts: implication for management of forest biodiversity. For Ecol Manage 85(1–3):95–106Google Scholar
  5. Baker RJ (1998) Bioinformatics, museums and society: integrating biological data for knowledge-based decisions. Museum of Texas Tech University. Occasional Papers, (187)Google Scholar
  6. Bashari H, Hemami MR (2013) A predictive diagnostic model for wild sheep (Ovis orientalis) habitat suitability in Iran. J Nat Conserv 21(5):319–325Google Scholar
  7. Boone RB, Krohn WB (2002) Modeling tools and accuracy assessment. In: Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, Wall WA, Samson FB (eds) Predicting species occurrences: issues of accuracy and scale. Island Press, Washington, DC, pp 265–270Google Scholar
  8. Box EO, Crumpacker DW, Hardin ED (1993) A climatic model for location of plant species in Florida, USA. J Biogeogr 629–644Google Scholar
  9. Brotons L, Thuiller W, Araújo MB, Hirzel AH (2004) Presence‐absence versus presence‐only modelling methods for predicting bird habitat suitability. Ecography 27(4):437–448Google Scholar
  10. BUSBY JR (1986) A biogeoclimatic analysis of Nothofagus cunninghamii (Hook.) Oerst. in southeastern Australia. Austral Ecol 11(1):1–7Google Scholar
  11. Carpenter G, Gillison AN, Winter J (1993) DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals. Biodiversity Conserv 2(6):667–680Google Scholar
  12. Corsi F, Duprè E, Boitani L (1999) A large-scale model of wolf distribution in Italy for conservation planning. Conserv Biol 13(1):150–159Google Scholar
  13. Ebrahimi A, Farashi A, Rashki A (2017) Habitat suitability of Persian leopard (Panthera pardus saxicolor) in Iran in future. Environ Earth Sci 76(20):697Google Scholar
  14. Elith J, Graham CH, Anderson RP, Dudík M, Ferrier S, Guisan A et al (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29(2):129–151Google Scholar
  15. Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17(1):43–57Google Scholar
  16. 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. J Appl Ecol 41(2):263–274Google Scholar
  17. Farashi A, Shariati M (2017) Biodiversity hotspots and conservation gaps in Iran. J Nat Conserv 39:37–57Google Scholar
  18. Farashi A, Kaboli M, Momeni I (2010) Habitat Suitability Modeling for Wild Goat Capra aegagrus in Kolah Ghazi National Park, Esfahan Province, pp 63–73Google Scholar
  19. Farhadinia MS, Moqanaki EM, Hosseini-Zavarei F (2014) Predator–prey relationships in a middle Asian Montane steppe: Persian leopard versus urial wild sheep in Northeastern Iran. Eur J Wildl Res 60(2):341–349Google Scholar
  20. Fertig W, Reiners WA (2002) Predicting presence/absence of plant species for range mapping: a case study from Wyoming. Predicting species occurrences: issues of accuracy and scale, pp 483–489Google Scholar
  21. Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24(1):38–49Google Scholar
  22. Firouz E (2000) A guide to the Fauna of Iran. Iran University Press, Tehran, p 50 (In Persian) Google Scholar
  23. Fleishman E, Nally RM, Fay JP, Murphy DD (2001) Modeling and predicting species occurrence using broad-scale environmental variables: an example with butterflies of the Great Basin. Conserv Biol 15(6):1674–1685Google Scholar
  24. Funk VA, Zermoglio MF, Nasir N (1999) Testing the use of specimen collection data and GIS in biodiversity exploration and conservation decision making in Guyana. Biodivers Conserv 8(6):727–751Google Scholar
  25. Hannah L, Midgley GF, Millar D (2002) Climate change-integrated conservation strategies. Glob Ecol Biogeogr 11(6):485–495Google Scholar
  26. Hayward MW, O’Brien J, Kerley GI (2007) Carrying capacity of large African predators: predictions and tests. Biol Cons 139(1–2):219–229Google Scholar
  27. Hebblewhite M, White CA, Nietvelt CG, McKenzie JA, Hurd TE, Fryxell JM, Paquet PC (2005) Human activity mediates a trophic cascade caused by wolves. Ecology 86(8):2135–2144Google Scholar
  28. Hiestand SJ, Nielsen CK, Jiménez FA (2014) Modelling potential presence of metazoan endoparasites of bobcats (Lynx rufus) using verified records. Folia Parasitol 61:401–410Google Scholar
  29. Hirzel A (2001) Linking landscape-and population ecology for large population management modelling: the case of Ibex (Capra ibex) in Switzerland. Doctoral dissertation, Université de Lausanne, Faculté des sciencesGoogle Scholar
  30. Hirzel AH, Hausser J, Chessel D, Perrin N (2002) Ecological‐niche factor analysis: how to compute habitat‐suitability maps without absence data? Ecology 83:2027–2036Google Scholar
  31. Hughes L (2000) Biological consequences of global warming: is the signal already apparent? Trends Ecol Evol 15(2):56–61Google Scholar
  32. Hughes L (2003) Climate change and Australia: trends, projections and impacts. Austral Ecol 28(4):423–443Google Scholar
  33. IPCC (2001) Climate Change 2001: The Scientific Basis. Report from Working Group I. Intergovernmental Panel on Climate Change, GenevaGoogle Scholar
  34. IUCN Standards and Petitions Subcommittee (2016) Guidelines for Using the IUCN Red List Categories and Criteria Version 2. Prepared by the Standards and Petitions Subcommittee. http://cmsdocs.s3.amazonaws.com/RedListGuidelines.pdf
  35. Kadmon R, Heller J (1998) Modelling faunal responses to climatic gradients with GIS: land snails as a case study. J Biogeogr 25(3):527–539Google Scholar
  36. Karami M, Ghadirian T, Faizolahi K (2015) The atlas of the mammals of Iran. Iran Department of the Environment, Tehran, IranGoogle Scholar
  37. Kumar S, Stohlgren TJ (2009) Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. J Ecol Nat Environ 1(4):094–098Google Scholar
  38. McCarty JP (2001) Ecological consequences of recent climate change. Conserv Biol 15(2):320–331Google Scholar
  39. McLaughlin JF, Hellmann JJ, Boggs CL, Ehrlich PR (2002) Climate change hastens population extinctions. Proc Natl Acad Sci 99(9):6070–6074Google Scholar
  40. Meinshausen M, Smith SJ, Calvin K, Daniel JS, Kainuma MLT, Lamarque JF, Thomson AGJMV (2011) The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim Change 109(1–2):213Google Scholar
  41. Morovati M, Karami M, Kaboli M (2014) Desirable areas and effective environmental factors of wild goat habitat (Capra aegagrus). Int J Environ Res 8(4):1031–1040Google Scholar
  42. Nazeri M, Madani N, Kumar L, Mahiny AS, Kiabi BH (2015) A geo-statistical approach to model Asiatic cheetah, onager, gazelle and wild sheep shared niche and distribution in Turan biosphere reserve-Iran. Ecol Inf 29:25–32Google Scholar
  43. Nicholls AO (1989) How to make biological surveys go further with generalised linear models. Biol Cons 50(1–4):51–75Google Scholar
  44. Paperş M, Gaubert P (2007) Modelling ecological niches from low numbers of occurrences: assessment of the conservation status of poorly known viverrids (Mammalia, Carnivora) across two continents. Divers Distrib 13(6):890–902Google Scholar
  45. Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature 421(6918):37–42Google Scholar
  46. Parmesan C, Ryrholm N, Stefanescu C, Hill JK, Thomas CD, Descimon H, Tennent WJ (1999) Poleward shifts in geographical ranges of butterfly species associated with regional warming. Nature 399(6736):579–583Google Scholar
  47. Pearson RG, Thuiller W, Araújo MB, Martinez-Meyer E, Brotons L, McClean C, Lees DC (2006) Model-based uncertainty in species range prediction. J Biogeogr 33(10):1704–1711Google Scholar
  48. Pearson RG, Raxworthy CJ, Nakamura M, Townsend Peterson A (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J Biogeogr 34(1):102–117Google Scholar
  49. Peterson AT (2001) Predicting species’Geographic Distributions Based on Ecological Niche Modeling. The Condor 103(3):599–605Google Scholar
  50. Peterson AT, Radocy T, Hall E, Peterhans JCK, Celesia GG (2014) The potential distribution of the Vulnerable African lion Panthera leo in the face of changing global climate. Oryx 48(4):555–564Google Scholar
  51. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190(3):231–259Google Scholar
  52. Ponder WF, Carter GA, Flemons P, Chapman RR (2001) Evaluation of museum collection data for use in biodiversity assessment. Conserv Biol 15(3):648–657Google Scholar
  53. Pounds JA, Fogden MP, Campbell JH (1999) Biological response to climate change on a tropical mountain. Nature 398(6728):611–615Google Scholar
  54. Qiao J, Yang W, Xu W, Xia C, Liu W, Blank D (2011) Social structure of goitered gazelles Gazella subgutturosa in Xinjiang, China. Pak J Zool 43(4):769–775Google Scholar
  55. Randin CF et al (2006) Are niche-based species distribution models transferable in space? J Biogeogr 33:1689–1703Google Scholar
  56. Rogelj J (2013) Long-term climate change: projections, commitments and irreversibility. In: Climate Change 2013: the physical science basis. IPCC working group I contribution to AR5—the physical science basis. IPCC working group I contribution to AR5. Cambridge University Press, Cambridge, pp 1029–1136Google Scholar
  57. Root TL, Price JT, Hall KR, Schneider SH, Rosenzweig C, Pounds JA (2003) Fingerprints of global warming on wild animals and plants. Nature 421(6918):57–60Google Scholar
  58. Scott JM, Heglund PJ, Morrison ML, Haufler JB, Raphael MG, Wall WA, Samson FB (Eds) (2002) Predicting species occurrences: issues of accuracy and scale. Washington, DC:Island Press,868 pp.Google Scholar
  59. Sindel BM, Michael PW (1992) Spread and potential distribution of Senecio madagascariensis Poir. (fireweed) in Australia. Austral Ecol 17(1):21–26Google Scholar
  60. Soberón J (1999) Linking biodiversity information sources. Trends Ecol Evol 14(7):291Google Scholar
  61. Soulé ME, Estes JA, Miller B, Honnold DL (2005) Strongly interacting species: conservation policy, management, and ethics. Bioscience 55(2):168–176Google Scholar
  62. Stockwell D, Peterson AT (2003) Comparison of resolution of methods used in mapping biodiversity patterns from point-occurrence data. Ecol Indic 3(3):213–221Google Scholar
  63. Sutherland WJ (2000) The conservation handbook: research, management and policy. Blackwell Science, Malden, MAGoogle Scholar
  64. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240(4857):1285–1293Google Scholar
  65. Thomas CD, Lennon JJ (1999) Birds extend their ranges northwards. Nature 399(6733):213–213Google Scholar
  66. Thomas CD, Bodsworth EJ, Wilson RJ, Simmons AD, Davies ZG, Musche M, Conradt L (2001) Ecological and evolutionary processes at expanding range margins. Nature 411(6837):577–581Google Scholar
  67. Thorn JS, Nijman V, Smith D, Nekaris KAI (2009) Ecological niche modelling as a technique for assessing threats and setting conservation priorities for Asian slow lorises (Primates: Nycticebus). Divers Distrib 15(2):289–298Google Scholar
  68. Walker PA, Cocks KD (1991) HABITAT: a procedure for modelling a disjoint environmental envelope for a plant or animal species. Global Ecol Biogeogr Lett 108–118Google Scholar
  69. Waltari E, Guralnick RP (2009) Ecological niche modelling of montane mammals in the Great Basin, North America: examining past and present connectivity of species across basins and ranges. J Biogeogr 36(1):148–161Google Scholar
  70. Weber TC (2011) Maximum entropy modeling of mature hardwood forest distribution in four US states. For Ecol Manag 261(3):779–788Google Scholar
  71. Wilson JB, Rapson GL, Sykes MT, Watkins AJ, Williams PA (1992) Distributions and climatic correlations of some exotic species along roadsides in South Island, New Zealand. J Biogeogr 183–193Google Scholar
  72. Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A (2008) Effects of sample size on the performance of specieGoogle Scholar
  73. Yang XQ, Kushwaha SPS, Saran S, Xu J, Roy PS (2013) Maxent modeling for predicting the potential distribution of medicinal plant, Justicia adhatoda L. in Lesser Himalayan foothills. Ecol Eng 51:83–87Google Scholar
  74. Yom TY, Kadmon R (1998) Analysis of the distribution of insectivorous bats in Israel. Divers Distrib 4(2):63–70Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Environmental Sciences, Faculty of Natural Resources and EnvironmentUniversity of BirjandBirjandIran
  2. 2.Department of Environmental Sciences, Faculty of Natural Resources and EnvironmentFerdowsi University of MashhadMashhadIran
  3. 3.Department of Environment and Energy, Science and Research branchIslamic Azad UniversityTehranIran
  4. 4.Department of Rangeland and Watershed Management, Faculty of Natural Resources and EnvironmentFerdowsi University of MashhadMashhadIran

Personalised recommendations