A reconstruction of Turkey’s potential natural vegetation using climate indicators

  • Nussaïbah B. Raja
  • Olgu AydinEmail author
  • İhsan Çiçek
  • Necla Türkoğlu
Original Paper


Turkey, containing three of the world’s biodiversity hotspots, is a hub for genetic biodiversity. However, the vegetation cover has drastically changed in recent decades as a result of substantial transformations in land-use practices. A map of the potential natural vegetation can be used to represent the biodiversity of a country, and therefore a reference to effectively develop conservation strategies. The multinomial logistic regression is used to simulate the probability of different biomes occurring in the country using elevation, climatological data and natural vegetation data. A correlation test was applied to the climatological data to determine which predictors influence vegetation the most. These were temperature, precipitation, relative humidity and cloudiness. The Ordinary Kriging method was employed to transform the data into the format for the multinomial logistic regression model. The model showed that temperature was the most influencing factor with respect to Turkey’s vegetation and distribution follows a similar distribution as the various macroclimates. Broadleaf forests are mostly found in the Black Sea region, which is also the wettest region of the country. The Marmara region is the only other region where there are broadleaf forests. Mixed forests and shrublands are mostly located in Central Anatolia due to the region’s low humidity which favours herbaceous flora. Coniferous forests were dominant in the Aegean and Mediterranean regions, attributed to high temperatures.


Biomes Multinomial logistic regression Statistical modelling Turkey Vegetation 


  1. Anav A, Mariotti A (2011) Sensitivity of natural vegetation to climate change in the Euro-Mediterranean area. Clim Res 46(3):277–292CrossRefGoogle Scholar
  2. Atalay I (1994) Vegetation of Turkey. Aegean University Press, Izmir, pp 17–30Google Scholar
  3. Atalay I, Efe R, Öztürk M (2014) Ecology and classification of forests in Turkey. Procedia Soc Behav Sci 120:788–805CrossRefGoogle Scholar
  4. Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65(1–2):15–31CrossRefGoogle Scholar
  5. Aydin O, Çiçek İ (2015) Geostatistical interpolation of precipitation in Turkey. Lambert Academic Publishing, Saarbrucken, pp 195–197Google Scholar
  6. Bai SB, Wang J, Lü GN, Zhou PG, Hou SS, Xu SN (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology 115:23–31CrossRefGoogle Scholar
  7. Bastian O (2000) The assessment of landscape and vegetation changes: the case study upper lusatian heath and pond landscape. Probl Landsc Ecol 6:31–53Google Scholar
  8. Biltekin D (2010) Vegetation and climate of North Anatolian and North Aegean region since 7 Ma according to pollen analysis (Doctoral dissertation). Université Claude Bernard - Lyon I, France, pp 15–17Google Scholar
  9. Blasi C, Carranza ML, Frondoni R, Rosati L (2000) Ecosystem classification and mapping: a proposal for Italian landscapes. Appl Veg Sci 3(2):233–242CrossRefGoogle Scholar
  10. Bohn U, Zazanashvili N, Nakhutsrishvili G (2007) The map of the natural vegetation of Europe and its application in the Caucasus Ecoregion. Bull Ga Acad Sci 175:112–121Google Scholar
  11. Braun-Blanquet J (1928) Pflanzensoziologie: grundzüge der vegetationskunde. Springer, Berlin, p 17Google Scholar
  12. Brier GW (1950) Verification of forecasts expressed in terms of probability. Mon Weather Rev 78(1):1–3CrossRefGoogle Scholar
  13. Bryn A (2008) Recent forest limit changes in south-east Norway: effects of climate change or regrowth after abandoned utilization. Nor Geogr Tidsskr - Nor J Geogr 62:251–270CrossRefGoogle Scholar
  14. Carranza ML, Ricotta C, Fortini P, Blasi C (2003) Quantifying landscape change with actual vs. potential natural vegetation maps. Phytocoenologia 33(4):591–601CrossRefGoogle Scholar
  15. Clark WA, Hosking PL (1986) Statistical methods for geographers. Wiley, New York, pp 447–500Google Scholar
  16. Çolak AH, Rotherham ID (2006) A review of the forest vegetation of Turkey: its status past and present and its future conservation. Biol Environ 106B(3):343–354Google Scholar
  17. Cox RL, Underwood EC (2011) The importance of conserving biodiversity outside of protected areas in Mediterranean Ecosystems. PLOS 6(1):e14508CrossRefGoogle Scholar
  18. Cressie NAC (1993) Statistics for spatial data. Wiley, New York, pp 52–58Google Scholar
  19. Dahal RK, Hasegawa S, Bhandary NP, Poudel PP, Nonomura A, Yatabe R (2012) A replication of landslide hazard mapping at catchment scale. Geomat Nat Hazards Risk 3(2):161–192CrossRefGoogle Scholar
  20. del Rio S, Penas A, Perez-Romero R (2005) Potential areas of deciduous forests in Spain (Castile and Leon) according to future climate change. Plant Biosyst 139:222–233CrossRefGoogle Scholar
  21. Erinç S (1996) Klimatoloji ve Metodları [Climatology and its methods]. Istanbul University Press, Istanbul, p 538Google Scholar
  22. Evrendilek F, Berberoglu S, Karakaya N, Cilek A, Aslan G, Gungor K (2011) Historical spatiotemporal analysis of land-use/land-cover changes and carbon budget in a temperate peatland (Turkey) using remotely sensed data. Appl Geogr 31(3):1166–1172CrossRefGoogle Scholar
  23. Findell KL, Shevliakova E, Milly PCD, Stouffer RJ (2007) Modeled impact of anthropogenic land cover change on climate. J Clim 20(14):3621–3634CrossRefGoogle Scholar
  24. Fischer HS, Winter S, Lohberger E, Jehl H, Fischer A (2013) Improving transboundary maps of potential natural vegetation using statistical modeling based on environmental predictors. Folia Geobot 48(2):115–135CrossRefGoogle Scholar
  25. Flach PA (2010) ROC analysis. In: Sammut C, Webb GI (eds) Encyclopedia of Machine Learning. Springer, New York, pp 869–875Google Scholar
  26. Franklin J (2009) Mapping species distributions. Cambridge University Press, Cambridge, pp 113–203Google Scholar
  27. Gallizia Vuerich L, Poldini L, Feoli E (2001) Model for the potential natural vegetation mapping of Friuli-Venezia Giulia (NE Italy) and its application for a biogeographic classification of the region. Plant Biosyst 135:319–336CrossRefGoogle Scholar
  28. Gao X, Giorgi F (2008) Increased aridity in the Mediterranean region under greenhouse gas forcing estimated from high resolution simulations with a regional climate model. Glob Planet Change 62(3–4):195–209CrossRefGoogle Scholar
  29. Güler M, Yomralıoğlu T, Reis S (2007) Using landsat data to determine land use/land cover changes in Samsun, Turkey. Environ Monit Assess 127(1):155–167CrossRefGoogle Scholar
  30. Hemsing LO (2010) GIS-modelling of potential natural vegetation (PNV): a methodological case study from south-central Norway (Master’s thesis). Norwegian University of Life Sciences, Norway, pp 8–13Google Scholar
  31. Hemsing LO, Bryn A (2012) Three methods for modeling potential natural vegetation (PNV) compared: a methodological case study from south-central Norway. Nor Geogr Tidsskr - Nor J Geogr 66:11–29CrossRefGoogle Scholar
  32. Hoekstra JM, Boucher TM, Ricketts TH, Roberts C (2005) Confronting a biome crisis: global disparities of habitat loss and protection. Ecol Lett 8:23–29CrossRefGoogle Scholar
  33. Iyigun C, Türkeş M, Batmaz İ, Yozgatligil C, Purutçuoğlu V, Koç EK, Öztürk MZ (2013) Clustering current climate regions of Turkey by using a multivariate statistical method. Theor Appl Clim 114(1):95–106CrossRefGoogle Scholar
  34. Koçman A (1993) Climate of Turkey. Ege University Faculty for Literature Publications, Izmir, pp 56–61Google Scholar
  35. Levavasseur G, Vrac M, Roche DM, Paillard D, Guiot J (2013) An objective methodology for potential vegetation reconstruction constrained by climate. Glob Planet Change 104:7–22CrossRefGoogle Scholar
  36. Lexer MJ, Hönninger K, Scheifinger H, Matulla C, Groll N, Kromp-Kolb H, Schadauer K, Starlinger F, Englisch M (2002) The sensitivity of Austrian forests to scenarios of climatic change: a large-scale risk assessment based on a modified gap model and forest inventory data. For Ecol Manag 162:53–72CrossRefGoogle Scholar
  37. Mueller-Dombois D, Ellenberg H (1974) Aims and methods of vegetation ecology. Wiley, New York, p 422Google Scholar
  38. Nagelkerke NJD (1991) A note of a general definition of the coefficient of determination. Biometrika 78(3):691–692CrossRefGoogle Scholar
  39. Ölgen KM (2010) Türkiye’de yıllık ve mevsimsel yağış değişkenliğinin alansal dağılımı [Spatial distribution of annual and seasonal precipitation variability in Turkey]. Aegean Geogr J 19(1):85–95Google Scholar
  40. R Development Core Team (2016) The R project for statistical computing. Retrieved from Accessed 01 Dec 2016
  41. Ricotta C, Carranza ML, Avena G, Blasi C (2000) Quantitative comparison of the diversity of landscapes with actual vs. potential natural vegetation. Appl Veg Sci 3(2):157–162CrossRefGoogle Scholar
  42. Rosati L, Marignani M, Blasi C (2008) A gap analysis comparing natura 2000 vs national protected area network with potential natural vegetation. Community Ecol 9:147–154CrossRefGoogle Scholar
  43. Sen OL, Bozkurt D, Vogler JB, Fox J, Giambelluca TW, Ziegler AD (2013) Hydro-climatic effects of future land-cover/land-use change in montane mainland southeast Asia. Clim Change 118(2):213–226CrossRefGoogle Scholar
  44. Şerkercioğlu CH, Anderson S, Akçay E, Bilgin R, Can ÖE, Semiz G, Tavşanoğlu Ç, Yokeş MB, Soyumert A, İpekdal K, Sağlam İK, Yücel M, Dalfes HN (2011) Turkey’s globally important biodiversity in crisis. Biol Conserv 144(12):2752–2769CrossRefGoogle Scholar
  45. Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, Pencina MJ, Kattan MW (2010) Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21:128–138CrossRefGoogle Scholar
  46. Türkeş M (2010) Climatology and meteorology. Kriter Publishing, Istanbul, pp 379–387Google Scholar
  47. Tüxen R (1956) Die heutige potenzielle natürlich vegetations als gegenstand der vegetationskartierung [The current potential natural vegetation as the object of vegetation mapping]. Pflanzensoziologie 13:5–42Google Scholar
  48. Wald A (1941) Asymptotically most powerful tests of statistical hypotheses. Ann Math Stat 12:1–19CrossRefGoogle Scholar
  49. Zerbe S (1998) Potential natural vegetation: validity and applicability in landscape planning and nature conservation. Appl Veg Sci 1(2):165–172CrossRefGoogle Scholar

Copyright information

© Northeast Forestry University 2018

Authors and Affiliations

  • Nussaïbah B. Raja
    • 1
  • Olgu Aydin
    • 2
    Email author
  • İhsan Çiçek
    • 2
  • Necla Türkoğlu
    • 2
  1. 1.GeoZentrum Nordbayern, University Erlangen-NürnbergErlangenGermany
  2. 2.Department of Geography, Faculty of HumanitiesAnkara UniversityAnkaraTurkey

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