European Journal of Forest Research

, Volume 135, Issue 1, pp 137–152 | Cite as

Climatic marginality: a new metric for the susceptibility of tree species to warming exemplified by Fagus sylvatica (L.) and Ellenberg’s quotient

  • Karl H. MellertEmail author
  • Jörg Ewald
  • Daniel Hornstein
  • Isabel Dorado-Liñán
  • Matthias Jantsch
  • Steffen Taeger
  • Christian Zang
  • Annette Menzel
  • Christian Kölling
Original Paper


In the face of climate warming, maps of potential tree species distribution can support forest management planning at coarse scales. For evaluating future suitability, conditions at the rear edge, i.e. at the meridional and lower altitudinal limits of species distribution, are of particular importance. Therefore, we present the concept of climatic marginality (distance to the rear edge) as a metric for the susceptibility against climate warming. Using a statistic niche model fitted to observed and potential beech occurrence in ICP Forests Level I monitoring plots and WorldClim data, we evaluate the modelled xeric limit of European beech based on the Ellenberg’s climate quotient involving thresholds suggested by Ellenberg and other authors. The applicability of the marginality index was tested with independent study sites. Despite the limitations of niche modelling, estimated climatic thresholds of beech were well in accordance with textbook knowledge and recent research. The regional patterns of climatic marginality were plausible and more meaningful with respect to the rear edge compared to conventional niche model outputs. In terms of climatic marginality, most regions in Central Europe are far from the xeric limit of beech. Evaluation based on independently sampled sites indicated that inclusion of soil and topography (microclimate) may permit implications at the local scale, e.g. growth potential estimations.


Ellenberg’s climate quotient Environmental niche model Climatic marginality Species selection Species distribution model Xeric limit 



The research within the project MARGINS ( is funded by the Bavarian State Forest Administration, an authority of the Ministry for Nutrition, Agriculture and Forestry. Additionally the research has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013)/ERC Grant Agreement No. [282250]. We thank ICP Forests and the involved country representatives for providing Level I data. We are deeply indebted to our colleagues Tzvetan Zlatanov, Elitsa Stoyanova and Plamen Mitov (Bulgaria), Bálint Pataki (Hungary), Mario Pellegrini (Italy), Stanislav Lazarov and Maria Teodosiu (Romania), and Tom Levanič (Slovenia) for providing us access, guidance, and support for the sampling of beech stands in their countries. We would like to thank anonymous reviewers for valuable comments to improve the paper.


  1. Alberto FJ, Aitken SN, Alía R, González-Martínez SC, Hänninen H, Kremer A, Lefèvre F, Lenormand T, Yeaman S, Whetten R, Savolainen O (2013) Potential for evolutionary responses to climate change—evidence from tree populations. Glob Change Biol 19:1645–1661CrossRefGoogle Scholar
  2. Attorre F, Francesconi F, De Sanctis M, Alfò M, Martella F, Valenti R, Vitale M (2014) Classifying and mapping potential distribution of forest types using a finite mixture model. Folia Geobot 49:313–335. doi: 10.1007/s12224-012-9139-8 CrossRefGoogle Scholar
  3. Austin MP, van Niel KP (2011) Improving species distribution models for climate change studies: variable selection and scale. J Biogeogr 38:1–8CrossRefGoogle Scholar
  4. Austin MP, Nicholls AO, Margules CR (1990) Measurement of the realised qualitative niche: environmental niches of five Eucalyptus species. Ecol Monogr 60:161–177CrossRefGoogle Scholar
  5. Berki I, Rasztovits E, Móricz N, Mátyás C (2009) Determination of the drought tolerance limit of beech forests and forecasting their future distribution in Hungary. Cereal Res Commun 37:613–616Google Scholar
  6. Bitterlich W (1984) The relascope idea. Commonwealth Agricultural Bureaux, Farnham RoyalGoogle Scholar
  7. Bohn U, Neuhäusl R, Gollub G, Hettwer C, Neuhäuslova Z, Raus T, Schlüter H, Weber H (2003) Map of the natural vegetation of Europe, scale 1:2500000. Parts 1–3. Landwirtschaftsverlag, Münster-HiltrupGoogle Scholar
  8. Bolte A, Czajkowski T, Kompa T (2007) The north-eastern distribution range of European beech—a review. Forestry 80:413–429CrossRefGoogle Scholar
  9. Bolte A, Ammer C, Löf M, Nabuurs GJ, Schall P, Spathelf P (2010) Adaptive forest management—a prerequisite of sustainable forestry in the face of climate change. In: Spathelf P (ed) Sustainable forest management in a changing world: European perspective. Managing forest ecosystems 19. Springer, Dordrecht, pp 115–139Google Scholar
  10. Booth TH (1985) A new method to assist species selection. Commonw Forest Rev 64:241–250Google Scholar
  11. Booth TH, Williams KJ (2012) Developing biodiverse plantings suitable for changing climatic conditions 1: underpinning scientific methods. Ecol Manage Restor 13:267–273CrossRefGoogle Scholar
  12. Bréda N, Huc R, Granier A, Dreyer E (2006) Temperate forest trees and stands under severe drought: a review of ecophysiological responses, adaptation processes and long-term consequences. Ann Forest Sci 63:625–644CrossRefGoogle Scholar
  13. Chen K, Dorado-Liñán I, Akhmetzyanov L, Menzel A (accepted) Climate drivers and NAO influence on beech growth at marginal sites across the Mediterranean. Clim Res Google Scholar
  14. Cools N, Vesterdal L, De Vos B, Vanguelova E, Hansen K (2014) Tree species is the major factor explaining C:N ratios in European forests. Forest Ecol Manage 311:3–16CrossRefGoogle Scholar
  15. Czúcz B, Gálhidy L, Mátyás C (2011) Present and forecasted xeric climatic limits of beech and sessile oak distribution at low altitudes in Central Europe. Ann Forest Sci 68:99–108CrossRefGoogle Scholar
  16. De Vos B, Cools N (2011) Second European forest soil condition report. Volume I: results of the BioSoil Soil Survey. INBO.R.2011.35. Research Institute for Nature and Forest, BrusselsGoogle Scholar
  17. Dobbertin M (2005) Tree growth as indicator of tree vitality and of tree reaction to environmental stress: a review. Eur J Forest Res 124:319–333CrossRefGoogle Scholar
  18. Dormann CF (2007) Promising the future? Global change projections of species distributions. Basic Appl Ecol 8:387–397CrossRefGoogle Scholar
  19. EC/JRC (2012) European Commission—Joint Research Centre, Institute for Environment and Sustainability: Glossary of Soil Terms. Accessed 23 Jan 2015
  20. Ellenberg H (1963) Vegetation Mitteleuropas mit den Alpen, 1st edn. Eugen Ulmer, StuttgartGoogle Scholar
  21. Ellenberg H (1988) Vegetation ecology of Central Europe, 1st edn. Cambridge University Press, CambridgeGoogle Scholar
  22. Ellenberg H, Leuschner C (2010) Vegetation Mitteleuropas mit den Alpen. Eugen Ulmer, StuttgartGoogle Scholar
  23. Ewald J (2012) Vegetation databases provide a close-up on altitudinal tree species distribution in the Bavarian Alps. Biodiv Ecol 4:41–48CrossRefGoogle Scholar
  24. Falk W, Hempelmann N (2013) Species favourability shift in Europe due to climate change: a case study for Fagus sylvatica L. and Picea abies (L.) Karst. Based on an ensemble of climate models. J Climatol. doi: 10.1155/2013/787250
  25. Falk W, Mellert KH (2011) Species distribution models as a tool for forest management planning under climate change: risk evaluation of Abies Alba in Bavaria. J Veg Sci 22. doi: 10.1111/j.1654-1103.2011.01294.x
  26. Fang J, Lechovicz MJ (2006) Climatic limits for the present distribution of beech (Fagus L.) species in the world. J Biogeogr 33:1804–1819CrossRefGoogle Scholar
  27. Fischer R, Lorenz M, Köhl M, Mues V, Granke O, Iost S, van Dobben H, Reinds GJ, de Vries W (2010) The Condition of Forests in Europe. 2010 executive report. ICP Forests and European Commission, HamburgGoogle Scholar
  28. Franklin J (2009) Mapping species distributions—spatial inference and prediction. University Press, CambrigeGoogle Scholar
  29. Franks SJ, Weber JJ, Aitken SN (2014) Evolutionary and plastic responses to climate change in terrestrial plant populations. Evol Appl 7:123–139PubMedPubMedCentralCrossRefGoogle Scholar
  30. Freeman EA, Moisen G (2008) PresenceAbsence: An R Package for presence absence analysis. J Stat Softw 23(11):1–31. Accessed June 2010CrossRefGoogle Scholar
  31. Gärtner S, Reif A, Xystratis F, Sayer U, Bendagha N, Matzarakis A (2008) The drought tolerance limit of Fagus sylvatica forest on limestone in southwestern Germany. J Veg Sci 19:757–768CrossRefGoogle Scholar
  32. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186CrossRefGoogle Scholar
  33. Guisan A, Zimmermann NE, Elith J, Graham C, Phillips S, Peterson AT (2007) What matters for predicting spatial distributions of trees: techniques, data, or species characteristics? Ecol Monogr 77:615–630CrossRefGoogle Scholar
  34. Hampe A (2004) Bioclimate envelope models: what they detect and what they hide. Global Ecol Biogeogr 13:469–471CrossRefGoogle Scholar
  35. Hampe A, Petit RJ (2005) Conserving biodiversity under climate change: the rear edge matters. Ecol Lett 8:461–467PubMedCrossRefGoogle Scholar
  36. Hanewinkel M, Hummel S, Cullmann D (2009) Modelling and economic evaluation of forest biome shifts under climate change in Southwest Germany. Forest Ecol Manag 259:710–719CrossRefGoogle Scholar
  37. Hanewinkel M, Cullmann D, Schelhaas MJ, Nabuurs GJ, Zimmermann NE (2013) Climate change may cause severe loss in economic value of European forestland. Nature Clim Change 3:203–207CrossRefGoogle Scholar
  38. Hanewinkel M, Cullmann DA, Michiels HG, Kändler G (2014) Converting probabilistic tree species range shift projections into meaningful classes for management. J Environ Manage 134:153–165. doi: 10.1016/j.jenvman.2014.01.010 PubMedCrossRefGoogle Scholar
  39. Hargreaves GH, Allen RG (2003) History and evaluation of Hargreaves evapotranspiration equation. J Irrig Drain E-Asce. 129:53–63CrossRefGoogle Scholar
  40. Heegaard E (2002) The outer border and central border for species/environmental relationships estimated by non-parametric generalised additive models. Ecol Model 157:131–139CrossRefGoogle Scholar
  41. Hijmans RJ, van Etten J (2012) Raster: geographic analysis and modeling with raster data. R package version 2.0-12. Accessed 23 Jan 2015
  42. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978CrossRefGoogle Scholar
  43. Hirzel A, Lay Le (2008) Habitat suitability modelling and niche theory. J Appl Ecol 45:1372–1381CrossRefGoogle Scholar
  44. Hirzel A, Hausser J, Chessel D, Perrin N (2002) Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology 83:2027–2036CrossRefGoogle Scholar
  45. Huntley B, Bartlein PJ, Prentice IC (1989) Climate control of the distribution and abundance of beech (Fagus L.) in Europe and North America. J Biogeogr 16:551–560CrossRefGoogle Scholar
  46. IPCC (2013) Climate change 2013: the physical science basis. In: Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  47. Jackson ST, Overpeck JT (2000) Responses of plant populations and communities to environmental changes of the late Quaternary. Paleobiology 26:194–220CrossRefGoogle Scholar
  48. Jump AS, Hunt JM, Penuelas J (2006) Rapid climate change-related growth decline at the southern range edge of Fagus sylvatica. Glob Change Biol 12:2163–2174CrossRefGoogle Scholar
  49. Lakatos F, Molnár M (2009) Mass mortality of beech on Southwest Hungary. Acta Silv Lignaria Hung 5:75–82Google Scholar
  50. Leathwick JR (2001) New Zealand’s potential forest pattern as predicted from current species-environment relationships. New Zeal J Bot 39:447–464CrossRefGoogle Scholar
  51. Lenoir J, Graae BJ, Aarrestad PA et al (2013) Local temperatures inferred from plant communities suggest strong spatial buffering of climate warming across Northern Europe. Global Change Biol 19:1470–1481CrossRefGoogle Scholar
  52. Liu CR, Berry PM, Dawson TP, Pearson RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28:385–393CrossRefGoogle Scholar
  53. Mátyás C, Berki I, Czúcz B, Gálos B, Moricz N, Rasztovits E (2010) Future of beech in southeast Europe from the perspective of evolutionary ecology. Acta Silv Lignaria Hung 6:91–110Google Scholar
  54. Mellert KH, Fensterer V, Küchenhoff H, Reger B, Kölling C, Klemmt HJ, Ewald J (2011) Hypothesis-driven species distribution models for tree species in the Bavarian Alps. J Veg Sci 22:635–646CrossRefGoogle Scholar
  55. Mellert KH, Deffner V, Küchenhoff H, Kölling C (2015) Modeling sensitivity to climate change and estimating the uncertainty of its impact: a probabilistic concept for risk assessment in forestry. Ecol Model 316:211–216CrossRefGoogle Scholar
  56. Miller J, Franklin J (2002) Predictive vegetation modeling with spatial dependence—vegetation alliances in the Mojave Desert. Ecol Model 57:227–247CrossRefGoogle Scholar
  57. Monserud RA, Leemans R (1992) Comparing global vegetation maps with Kappa statistic. Ecol Model 62:275–293CrossRefGoogle Scholar
  58. Morin X, Augspurger C, Chuine I (2007) Process-based modeling of species’ distributions: what limits temperate tree species’ range boundaries? Ecology 88:2280–2291PubMedCrossRefGoogle Scholar
  59. Mueller-Dombois D, Ellenberg H (1974) Aims and methods of vegetation ecology. Wiley, New YorkGoogle Scholar
  60. Nicotra AB, Atkin OK, Bonser SP, Davidson AM, Finnegan EJ, Mathesius U, Poot P, Purugganan MD, Richards CL, Valladares F, van Kleunen M (2010) Plant phenotypic plasticity in a changing climate. Trends Plant Sci 15:684–692PubMedCrossRefGoogle Scholar
  61. Nocentini S (2009) Structure and management of beech (Fagus sylvatica L.) forests in Italy. iForest 2:105–113. doi: 10.3832/ifor0499-002
  62. Pearson RG, Dawson TP (2003) Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecol Biogeogr 12:361–371CrossRefGoogle Scholar
  63. Penuelas J, Ogaya R, Boada M, Jump AS (2007) Migration, invasion and decline: changes in recruitment and forest structure in a warming-linked shift of European beech forest in Catalonia (NE Spain). Ecography 30:829–837CrossRefGoogle Scholar
  64. Peterson AT, Soberón J, Pearson RG, Anderson RP, Martínez-Meyer E, Nakamura M, Araújo MB (2011) Ecological Niches and geographic distributions. Monographs in Population Biology 49. Princeton University Press, PrincetonGoogle Scholar
  65. Piedallu C, Gégout JC, Perez V, Lebourgeois F (2013) Soil water balance performs better than climatic water variables in tree species distribution modelling. Global Ecol Biogeogr 22:470–482CrossRefGoogle Scholar
  66. Poorter H, Niklas KJ, Reich PB, Oleksyn J, Poot P, Mommer L (2012) Biomass allocation to leaves, stems and roots: meta analyses of interspecific variation and environmental control. New Phytol 19:30–50CrossRefGoogle Scholar
  67. Randin CF, Engler R, Normand S, Zappa M, Zimmermann NE, Pearman PB, Vittoz P, Thuiller W, Guisan A (2009) Climate change and plant distribution: local models predict high-elevation persistence. Global Change Biol 15:1557–1569CrossRefGoogle Scholar
  68. Rasztovits E (2011) Modelling the future distribution of beech at low-elevation xeric limits—comparison of empirical and stochastic models. Dissertation, University of West HungaryGoogle Scholar
  69. Rasztovits E, Móricz N, Berki I, Pötzelsberger E, Mátyás C (2012) Is there future for beech in Hungary? A mechanistic approach based on extreme weather and sanitary logging information. In: Pötzelsberger E, Mäkelä A, Mohren G, Palahí M, Tomé M, Hasenauer H (eds) Modelling forest ecosystems—concepts, data and application, proceedings of the COST FP0603 Spring School, May 9th–13th, 2011, Kaprun, Austria, Institute of Silviculture, University of Natural Resources and Life Sciences, Vienna, pp 143–150Google Scholar
  70. R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  71. Schober R (1987) Ertragstafeln wichtiger Baumarten. J. D Sauerländer s Verlag, Frankfurt A. MGoogle Scholar
  72. Sevanto S, McDowell NG, Dickman LT, Pangle R, Pockman WT (2014) How do trees die? A test of the hydraulic failure and carbon starvation hypotheses. Plant, Cell Environ 37:153–161CrossRefGoogle Scholar
  73. Soberón J, Peterson AT (2005) Interpretation of models of fundamental ecological niches and species distributional areas. Biodiv Info 2:1–10Google Scholar
  74. Stojanovic DB, Krzic A, Matovic B, Orlovic S, Duputie A, Djurdjevic V, Galic Z, Stojnic S (2013) Prediction of the European beech (Fagus sylvatica L.) xeric limit using a regional climate model: an example from southeast Europe. Agr Forest Meteorol 176:94–103CrossRefGoogle Scholar
  75. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293PubMedCrossRefGoogle Scholar
  76. Taeger S, Sparks TH, Menzel A (2015) Effects of temperature and drought manipulations on seedlings of Scots pine provenances. Plant Biol 17:361–372. doi: 10.1111/plb.12245 PubMedCrossRefGoogle Scholar
  77. Thuiller W, Vaydera J, Pino J et al (2003) Large-scale environmental correlates of forest tree distributions in Catalonia (NE Spain). Global Ecol Biogeogr 12:313–325CrossRefGoogle Scholar
  78. Thuiller W, Lafourcade B, Araujo M (2009) Mod operating manual for BIOMOD. Laboratoire d’Écologie Alpine, Université Joseph Fourier, Grenoble, FranceGoogle Scholar
  79. Tomassini L, Knutti R, Plattner GP, van Vuuren DP, Stocker TF, Howarth HB, Borsuk ME (2010) Uncertainty and risk in climate projections for the 21st century: comparing mitigation to non-intervention scenarios. Clim Change. doi: 10.1007/s10584-009-9763-3 Google Scholar
  80. Venables WN, Ripley BR (2002) Modern Applied Statistics with S, 4th edn. Springer, New YorkCrossRefGoogle Scholar
  81. Viro PJ (1952) Kivisyyden Määrittämisestä, On the determination of stoniness. Commun Inst For Fenniae 40:1–23Google Scholar
  82. Walentowski H, Schulze ED et al (2013) Sustainable forest management of Natura 2000 sites: a case study from a private forest in the Romanian Southern Carpathians. Ann For Res 56:217–245Google Scholar
  83. Wiens JA, Stralberg D, Jongsomjit D, Howell CA, Snyder MA (2009) Niches, models, and climate change: assessing the assumptions and uncertainties. Proc Natl Acad Sci USA 106:19729–19736PubMedPubMedCentralCrossRefGoogle Scholar
  84. World Reference Base for Soil Resources (2006) A framework for international classification, correlation and communication. World Soil Resources Reports 103. Food and Agriculture Organization of the United Nations, RomeGoogle Scholar
  85. Zimmermann NE, Jandl R, Hanewinkel et al (2013) Potential future ranges of tree species in the alps. In: Cerbu GA, Hanewinkel M, Gerosa G, Jandl R (eds) Management Strategies to adapt alpine space forests to climate change risks. InTech, Rijeka, pp 37–48Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Karl H. Mellert
    • 1
    Email author
  • Jörg Ewald
    • 2
  • Daniel Hornstein
    • 2
  • Isabel Dorado-Liñán
    • 3
  • Matthias Jantsch
    • 1
  • Steffen Taeger
    • 1
  • Christian Zang
    • 3
  • Annette Menzel
    • 3
    • 4
  • Christian Kölling
    • 1
  1. 1.Bavarian State Institute of ForestryFreisingGermany
  2. 2.Faculty of ForestryUniversity of Applied Sciences Weihenstephan TriesdorfFreisingGermany
  3. 3.EcoclimatologyTechnical University MunichFreisingGermany
  4. 4.Technical University MunichInstitute for Advanced StudyGarchingGermany

Personalised recommendations