Regional Environmental Change

, Volume 19, Issue 5, pp 1495–1506 | Cite as

The response of English yew (Taxus baccata L.) to climate change in the Caspian Hyrcanian Mixed Forest ecoregion

  • Seyed Jalil AlaviEmail author
  • Kourosh Ahmadi
  • Seyed Mohsen Hosseini
  • Masoud Tabari
  • Zahra Nouri
Original Article


The Hyrcanian climate in the northern parts of Iran has warmed over the past 50 years, but the impacts on plant species are unknown. As the longest-lived tree in the Hyrcanian forest, English yew, Taxus baccata L., is a rare and endangered species in the forests along the Iranian coasts of the Caspian Sea, which is likely affected by climate change. This paper explores the current and future distribution of this species, using four species distribution models. In order to project the effect of climate change on the distribution of English yew by 2050 and 2070, output from the HadGEM2-ES climate model was used for two RCPs scenarios (2.6 and 8.5). The results showed a good accuracy of all the models for the distribution of this species with a mean area under the receiver operating curve (AUC) of 0.92. Using ensemble forecasting as an algorithm for reducing the uncertainty in species distribution modeling shows that the suitable habitats for this species is about 6000 km2 for the current climate conditions in the study area. Range size analysis indicates that in 2050, in the most optimistic scenario (RCP 2.6), only 17% of the habitats will retain their suitability, while in the most pessimistic scenario (RCP 8.5), this amount will decrease to 2%. In 2070, in the most optimistic scenario, only 10% of the currently suitable habitats will retain their suitability, while in the RCP 8.5, no stable suitable habitats will be left. It is strongly recommended that the impacts of climate change on English yew should be considered in the management decisions and conservation plans in the Hyrcanian forests.


Habitats suitability Species distribution models Rare species English yew Bioclimatic variables 



We acknowledge the efforts by Prof. Jafar Seyfabadi for carefully going through the manuscript. Thanks also go to Ghasemali Parad, Younes Geravand, Salman Zalekani, and Kambiz Ahmadi for field data sampling.

Funding information

The research leading to these results has received funding from the Iran National Science Foundation (INSF) under grant agreement no 95826133 (project title: “ecological niche of endangered species (Taxus baccata L.) and effect of climate change on its distribution in Hyrcanian forest (north of Iran)”).


  1. Aertsen W, Kint V, Van Orshoven J, Özkan K, Muys B (2010) Comparison and ranking of different modelling techniques for prediction of site index in Mediterranean mountain forests. Ecol Model 221:1119–1130. CrossRefGoogle Scholar
  2. Aguirre-Gutiérrez J, Carvalheiro LG, Polce C, van Loon EE, Raes N, Reemer M, Biesmeijer JC (2013) Fit-for-purpose: species distribution model performance depends on evaluation criteria–Dutch hoverflies as a case study. PLoS One 8(5):e63708. CrossRefGoogle Scholar
  3. Ahmadi K, Alavi SJ, Tabari Kouchaksaraei M, Aertsen W (2013) Non-linear height-diameter models for oriental beech (Fagus orientalis Lipsky) in the Hyrcanian forests, Iran. Biotechnol Agron Soc Environ 17:431–440Google Scholar
  4. Akhani H, Djamali M, Ghorbanalizadeh A, Ramezani E (2010) Plant biodiversity of Hyrcanian relict forests, N Iran: an overview of the flora, vegetation, palaeoecology and conservation. Pak J Bot 42:231–258Google Scholar
  5. Araújo MB, New M (2007) Ensemble forecasting of species distributions. Trends Ecol Evol 22:42–47. CrossRefGoogle Scholar
  6. Attarod P, Kheirkhah F, Khalighi Sigaroodi S, Sadeghi SMM, Dolatshahi A, Bayramzadeh V (2017) Trend analysis of meteorological parameters and reference evapotranspiration in the Caspian region. Iranian Journal of Forest 9:171–185 (in Persian)Google Scholar
  7. Babaeian I, Najafi Nik Z, Zabol Abbasi F, Habeibei M, Adab H, malbisei S (2010) Estimation of climate change during the period of 2010-2039 in Iran using downscaled data of the general circulation model ECHO-G. Geography and Development 7:135–152 (in Persian)Google Scholar
  8. Breiman L (2001) Random forests. Mach Learn 45:5–32CrossRefGoogle Scholar
  9. Breiman L (2017) Classification and regression trees. Routledge, 358pGoogle Scholar
  10. Chala D, Brochmann C, Psomas A, Ehrich D, Gizaw A, Masao CA, Zimmermann NE (2016) Good-bye to tropical alpine plant giants under warmer climates? Loss of range and genetic diversity in Lobelia rhynchopetalum. Ecol Evol 6:8931–8941. CrossRefGoogle Scholar
  11. Cheaib A, Badeau V, Boe J, Chuine I, Delire C, Dufrêne E, Thuiller W (2012) Climate change impacts on tree ranges: model intercomparison facilitates understanding and quantification of uncertainty. Ecol Lett 15:533–544. CrossRefGoogle Scholar
  12. Crimmins SM, Dobrowski SZ, Greenberg JA, Abatzoglou JT, Mynsberge AR (2011) Changes in climatic water balance drive downhill shifts in plant species’ optimum elevations. Science (80- ) 331:324–327. CrossRefGoogle Scholar
  13. Dunk JR, Zielinski WJ, Preisler HK (2004) Predicting the occurrence of rare mollusks in northern California forests. Ecol Appl 14:713–729CrossRefGoogle Scholar
  14. Duque-Lazo J, van Gils H, Groen TAA, Navarro-Cerrillo RM (2016) Transferability of species distribution models: the case of Phytophthora cinnamomi in Southwest Spain and Southwest Australia. For Ecol Manag 106:62–70. Google Scholar
  15. Elith J, Graham CH (2009) Do they? How do they? WHY do they differ? On finding reasons for differing performances of species distribution models. Ecography (Cop) 32:66–77. CrossRefGoogle Scholar
  16. Engler R, Randin CF, Thuiller W, ullinger S, Zimmermann NE, Araújo MB, Choler P (2011) 21st century climate change threatens mountain flora unequally across Europe. Glob Chang Biol 17:2330–2341CrossRefGoogle Scholar
  17. Fatemi Azarkhavarani SS, Rahimi M, Tarkesh M, Ravanbakhsh H (2017) Prediction of Juniperus excelsa M.Bieb. Geographical distribution using by climate data under the conditions of current and future in Semnan Province. Iranian Journal of Forest 9:233–248 (in Persian)Google Scholar
  18. Fourcade Y, Engler JO, Besnard AG, Rödder D, Secondi J (2013) Confronting expert-based and modelled distributions for species with uncertain conservation status: a case study from the corncrake (Crex crex). Biol Conserv 167:161–171. CrossRefGoogle Scholar
  19. Franklin J (2010) Mapping species distributions: spatial inference and prediction. Cambridge University PressGoogle Scholar
  20. Freeman EA, Moisen G (2008) Presence Absence: an R package for presence absence analysis. J Stat Software 23 31 pGoogle Scholar
  21. Gallardo B, Aldridge DC (2013) Evaluating the combined threat of climate change and biological invasions on endangered species. Biol Conserv 160:225–233. CrossRefGoogle Scholar
  22. Gelviz-Gelvez SM, Pavón NP, Illoldi-Rangel P, Ballesteros-Barrera C (2015) Ecological niche modeling under climate change to select shrubs for ecological restoration in Central Mexico. Ecol Eng 74:302–309. CrossRefGoogle Scholar
  23. Guisan A, Theurillat J-P (2001) Assessing alpine plant vulnerability to climate change: a modeling perspective. Integr Assess 1:307–320. CrossRefGoogle Scholar
  24. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186. CrossRefGoogle Scholar
  25. Guisan A, Thuiller W, Zimmermann N (2017) Habitat suitability and distribution models - with applications in RGoogle Scholar
  26. Guo C, Lek S, Ye S, Li W, Liu J, Li Z (2015) Uncertainty in ensemble modelling of large-scale species distribution: effects from species characteristics and model techniques. Ecol Model 306:67–75. CrossRefGoogle Scholar
  27. Haidarian Aghakhani M, Tamartash R, Jafarian Z, Tarkesh M, Tatian MR (2017) Predicting the impacts of climate change on Persian oak (Quercus brantii) using species distribution modelling in central Zagros for conservation planning. Journal of Environmental Studies 43:497–511. (in Persian)Google Scholar
  28. Harrell Jr FE (2015) Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. SpringerGoogle Scholar
  29. Harte J, Shaw R (1995) Shifting dominance within a montane vegetation community: results of a climate-warming experiment. Science (80- ) 267:876–880.
  30. Hastie TJ, Tibshirani RJ (1990) Generalized additive models, volume 43 of Monographs on Statistics and Applied ProbabilityGoogle Scholar
  31. Heikkinen RK, Marmion M, Luoto M (2012) Does the interpolation accuracy of species distribution models come at the expense of transferability? Ecography (Cop) 35:276–288CrossRefGoogle Scholar
  32. Hijmans RJ, Elith J (2013) Species distribution modeling with R. R Packag version 08-11Google Scholar
  33. Hijmans RJ, Phillips S, Leathwick J, Elith J, Hijmans MRJ (2017) Package ‘dismo.’ Circles 9:Google Scholar
  34. Hof AR, Jansson R, Nilsson C (2012) The usefulness of elevation as a predictor variable in species distribution modelling. Ecol Model 246:86–90. CrossRefGoogle Scholar
  35. IPCC (2007) Mitigation of climate change: contribution of working group III to the fourth assessment report of the intergovernmental panel on climate change. Intergov Panel Clim Chang 10:851.
  36. Jafari M (2008) Investigation and analysis of climate change factors in Caspian Zone forests for last fifty years. Iranian Journal of Forest and Poplar Research 16:314–326 (in Persian)Google Scholar
  37. Koo KA, Madden M, Patten BC (2014) Projection of red spruce (Picea rubens Sargent) habitat suitability and distribution in the Southern Appalachian Mountains, USA. Ecol Model 293:91–101. CrossRefGoogle Scholar
  38. Koralewski TE, Wang HH, Grant WE, Byram TD (2015) Plants on the move: assisted migration of forest trees in the face of climate change. For Ecol Manag 344:30–37. CrossRefGoogle Scholar
  39. Kramer K, Degen B, Buschbom J, Hickler T, Thuiller W, Sykes MT, de Winter W (2010) Modelling exploration of the future of European beech (Fagus sylvatica L.) under climate change-range, abundance, genetic diversity and adaptive response. For Ecol Manag 259:2213–2222. CrossRefGoogle Scholar
  40. Kumar S, Spaulding SA, Stohlgren TJ, Hermann KA, Schmidt TS, Bahls LL (2009) Potential habitat distribution for the freshwater diatom Didymosphenia geminata in the continental US. Front Ecol Environ 7:415–420. CrossRefGoogle Scholar
  41. Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174CrossRefGoogle Scholar
  42. Laughlin DC, Fulé PZ, Huffman DW, Crouse J, Laliberté E (2011) Climatic constraints on trait-based forest assembly. J Ecol 99:1489–1499. CrossRefGoogle Scholar
  43. Lenoir J, Marquet PA, De Ruffray P, Brisse H (2008) A significant upward shift in plant species optimum elevation during the 20th century, vol 320, pp 1768–1771Google Scholar
  44. Linares JC (2013) Shifting limiting factors for population dynamics and conservation status of the endangered English yew (Taxus baccata L., Taxaceae). For Ecol Manag 291:119–127. CrossRefGoogle Scholar
  45. Manzoor SA, Griffiths G, Lukac M (2018) Species distribution model transferability and model grain size-finer may not always be better. Sci Rep 8:1–9. CrossRefGoogle Scholar
  46. Marvie Mohadjer MR (2005) Silviculture. University of Tehran. (in Persian)Google Scholar
  47. McCullagh P, Nelder JA (1989) Generalized linear models. CRC pressGoogle Scholar
  48. Moradi H, Naqinezhad A, Siadati S, Yousefi Y, Attar F, Etemad V, Reif A (2016) Elevational gradient and vegetation-environmental relationships in the central Hyrcanian forests of northern Iran. Nord J Bot 34:1–14. CrossRefGoogle Scholar
  49. Mossadegh A (1971) Stands of Taxus baccata in Iran. Revue forestière française 23(6):645–648CrossRefGoogle Scholar
  50. Naimi B (2015) Usdm: uncertainty analysis for species distribution models. R package version 1.1–15Google Scholar
  51. Nogués-Bravo D, Araújo MB, Errea MP, Martínez-Rica JP (2007) Exposure of global mountain systems to climate warming during the 21st century. Glob Environ Chang 17:420–428. CrossRefGoogle Scholar
  52. Noroozi J, Pauli H, Grabherr G, Breckle SW (2011) The subnival-nival vascular plant species of Iran: a unique high-mountain flora and its threat from climate warming. Biodivers Conserv 20:1319–1338. CrossRefGoogle Scholar
  53. Oladi R, Pourtahmasi K, Eckstein D, Bräuning A (2011) Seasonal dynamics of wood formation in Oriental beech (Fagus orientalis Lipsky) along an altitudinal gradient in the Hyrcanian forest, Iran. Trees 25:425–433. CrossRefGoogle Scholar
  54. 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 33:1704–1711CrossRefGoogle Scholar
  55. Penuelas J, Boada M (2003) A global change-induced biome shift in the Montseny mountains (NE Spain). Glob Chang Biol 9:131–140. CrossRefGoogle Scholar
  56. Perrin PM, Mitchell FJG (2013) Effects of shade on growth, biomass allocation and leaf morphology in European yew (Taxus baccata L.). Eur J For Res 132:211–218. CrossRefGoogle Scholar
  57. Peterson AT, Azim NH, Subki A, Yusof ZNB (2003) Predicting the geography of species’ invasions via ecological niche modeling. Q Rev Biol 78:419–433. CrossRefGoogle Scholar
  58. Qiao H, Soberón J, Peterson AT (2015) No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation. Methods Ecol Evol 6:1126–1136. CrossRefGoogle Scholar
  59. R Core Team (2018) R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Austria, 2015Google Scholar
  60. Remya K, Ramachandran A, Jayakumar S (2015) Predicting the current and future suitable habitat distribution of Myristica dactyloides Gaertn. Using MaxEnt model in the Eastern Ghats, India. Ecol Eng 82:184–188. CrossRefGoogle Scholar
  61. Rouhi-Moghaddam E, Hosseini SM, Ebrahimi E, Tabari M, Rahmani A (2008) Comparison of growth, nutrition and soil properties of pure stands of Quercus castaneifolia and mixed with Zelkova carpinifolia in the Hyrcanian forests of Iran. For Ecol Manag 255:1149–1160. CrossRefGoogle Scholar
  62. Ruprecht H, Dhar A, Aigner B, Oitzinger G, Klumpp R, Vacik H (2010) Structural diversity of English yew (Taxus baccata L.) populations. Eur J For Res 129:189–198. CrossRefGoogle Scholar
  63. Sagheb Talebi K, Sajedi T, Pourhashemi M (2016) Forests of Iran: a treasure from the past, a hope for the future. SpringerGoogle Scholar
  64. Scharnweber T, Rietschel M, Manthey M (2007) Degradation stages of the Hyrcanian forests in southern Azerbaijan. Arch Nat schutz Landsch forsch 46:133–156Google Scholar
  65. Schirone B, Ferreira RC, Vessella F, chirone A, Piredda R, Simeone MC (2010) Taxus baccata in the Azores: a relict form at risk of imminent extinction. Biodivers Conserv 19:1547–1565. CrossRefGoogle Scholar
  66. Scott JM, Heglund PJ, Morrison ML (2002) Predicting species occurrences: issues of scale and accuracy. Island pressGoogle Scholar
  67. Shirk AJ, Cushman SA, Waring KM, Wehenkel CA, Leal-Sáenz A, Toney C, Lopez-Sanchez CA (2018) Southwestern white pine (Pinus strobiformis) species distribution models project a large range shift and contraction due to regional climatic changes. For Ecol Manag 411:176–186. CrossRefGoogle Scholar
  68. Sousa-Silva R, Alves P, Honrado J, Lomba A (2014) Improving the assessment and reporting on rare and endangered species through species distribution models. Glob Ecol Conserv 2:226–237. CrossRefGoogle Scholar
  69. Taleshi H, Jalali SG, Alavi SJ, Hosseini SM, Naimi B (2018) Climate change impacts on the distribution of oriental beech (Fagus orientalis Lipski) in the Hyrcanian forests of Iran. Iranian Journal of Forest 10:251–266 (in Persian)Google Scholar
  70. Taylor S, Kumar L (2013) Potential distribution of an invasive species under climate change scenarios using CLIMEX and soil drainage: a case study of Lantana camara L. in Queensland, Australia. J Environ Manag 114:414–422. CrossRefGoogle Scholar
  71. Thomas PA, Garcia-Martí X (2015) Response of European yews to climate change: a review. For Syst 24:1–11. Google Scholar
  72. Thuiller W (2007) Climate change and the ecologist. Nature 448:550–552. CrossRefGoogle Scholar
  73. Thuiller W, Georges D, Engler R, Breiner F (2016) Biomod2: ensemble platform for species distribution modeling. R package version 3.3–7Google Scholar
  74. Vieilledent G, Cornu C, Cuní Sanchez A, Pock-Tsy JML, Danthu P (2013) Vulnerability of baobab species to climate change and effectiveness of the protected area network in Madagascar: towards new conservation priorities. Biol Conserv 166:11–22. CrossRefGoogle Scholar
  75. Wang H, mei SX, Jiang Y, Fang XQ, Wu SH (2013) The impacts of climate change on the radial growth of Pinus koraiensis along elevations of Changbai Mountain in northeastern China. For Ecol Manag 289:333–340. CrossRefGoogle Scholar
  76. Wang J, Wang H, Cao Y, Bai Z, Qin Q (2016) Effects of soil and topographic factors on vegetation restoration in opencast coal mine dumps located in a loess area. Sci Rep 6:1–11. CrossRefGoogle Scholar
  77. Watling JI, Brandt LA, Bucklin DN, Fujisaki I, Mazzotti FJ, Romañach SS, Speroterra C (2015) Performance metrics and variance partitioning reveal sources of uncertainty in species distribution models. Ecol Model 309–310:48–59. CrossRefGoogle Scholar
  78. Yousefpour R, Temperli C, Jacobsen JB, Thorsen BJ, Meilby H, Lexer M, Ray D (2017) A framework for modeling adaptive forest management and decision making under climate change. Ecol Soc 22:.
  79. Yun JH, Nakao K, Tsuyama I, Matsui T, Park CH, Lee BY, Tanaka N (2018) Vulnerability of subalpine fir species to climate change: using species distribution modeling to assess the future efficiency of current protected areas in the Korean Peninsula. Ecol Res 33:341–350. CrossRefGoogle Scholar
  80. Zare H (2001) Introduced and native conifers in Iran. Research Institute of Forest and Rangelands Press, Tehran. (in Persian)Google Scholar
  81. Zhang Z, Capinha C, Weterings R, McLay CL, Xi D, Lü H, Yu L (2018) Ensemble forecasting of the global potential distribution of the invasive Chinese mitten crab, Eriocheir sinensis. Hydrobiologia 0123456789.

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

Authors and Affiliations

  1. 1.Department of Forestry, Faculty of Natural Resources and Marine SciencesTarbiat Modares UniversityTehranIran
  2. 2.Department of Forestry, Faculty of Natural ResourcesUniversity of TehranTehranIran

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