Folia Geobotanica

, Volume 49, Issue 3, pp 385–405 | Cite as

Regionalizing Indicator Values for Soil Reaction in the Bavarian Alps – from Averages to Multivariate Spectra

  • Tim Häring
  • Birgit Reger
  • Jörg Ewald
  • Torsten Hothorn
  • Boris Schröder


We present an approach to produce maps of Ellenberg values for soil reaction (R-value) in the Bavarian Alps. Eleven meaningful environmental predictors covering GIS-derived information on climatic, topographic and soil conditions were used to predict R-values. As dependent variables, Ellenberg indicator values for soil reaction were queried from plot records in the vegetation database WINALPecobase. We used an additive georegression model, which combines complex prediction models and the increased prediction accuracy of a boosting algorithm. In addition to environmental predictors we included spatial effects into the model to account for spatial autocorrelation. As we were particularly interested in the usefulness of averaged R-values for spatial prediction, we applied two different models: (1) a geo-additive regression model that estimates mean R-values and (2) a proportional odds model predicting the probability distribution over R-values 1 to 9. We found meaningful dependencies between the R-value and our predictors. Both models produced the same spatial pattern of predictions. Spatial effects had an impact only in the first model. The main drawback of mean R-values is the oversimplification of complex conditions of soil reaction, which is entailed by averaging and regression to mean values. Therefore, regionalized average indicator values provide only limited information on site-ecological characteristics. Model 1 failed to predict the range and shapes of original indicator spectra precisely. In contrast, the second model provided a more sophisticated picture of soil reaction. To make the multivariate output of model 2 comparable to that of model 1, we propose to plot the distribution in a three-dimensional color-space. In addition, comparison of both models based on a multiple linear regression model resulted in a R 2 of 0.93. The proportional odds model is a promising approach also for other indicator values and different regions as well as for other ordinal-scaled ecological parameters.


Boosting Geo-additive regression Proportional odds model Spatial effect Species distribution modeling 



The research presented here forms part of the project “Forest Information System for the Northern Alps” (, which is funded by the European Fund for Regional Development (EFRE) within the “INTERREG Bayern – Österreich 2007–2013” program, the Bavarian Forest Administration and the Bavaria State Forest Enterprise (BaySF). We are indebted to the Bavarian State Institute of Forestry’s “Maps for the future” crew. The digital elevation model was obtained from the Bavarian Topographical Survey (LVG), soil maps were obtained from the Bavarian State Agency for the Environment (LfU) and climate data were obtained from German Meteorological Service (DWD).

Supplementary material

12224_2013_9157_MOESM1_ESM.pdf (63 kb)
ESM 1 (PDF 62 kb)


  1. Bochter R (1984) Böden naturnaher Bergwaldstandorte auf carbonatreichen Substraten. Forschungsbericht 6, Nationalpark Berchtesgaden, BerchtesgadenGoogle Scholar
  2. Böcker R, Kowarik I, Bornkamm R (1983) Untersuchungen zur Anwendung der Zeigerwerte nach Ellenberg. Verh Ges Oekol 11:35–56Google Scholar
  3. Böhner J, Köthe R, Conrad O, Ringeler A, Selige T (2002) Soil regionalisation by means of terrain analysis and process parameterisation. In Micheli E, Nachtergaele F, Montanarella L (eds) Soil classification 2001. EUR 20398 EN, European Soil Bureau, Research Report No. 7, Luxembourg, pp 213–222Google Scholar
  4. Dengler J, Jansen F, Glöckler F, Peet RK, De Cáceres M, Chytrý M, Ewald J, Oldeland J, Lopez-Gonzalez G, Finckh M, Mucina L, Rodwell JS, Schaminée JHJ, Spencer N (2011) The Global Index of Vegetation-Plot Databases (GIVD): a new resource for vegetation science. J Veg Sci 22:582–597CrossRefGoogle Scholar
  5. Diekmann M (2003) Species indicator values as an important tool in applied plant ecology – a review. Basic Appl Ecol 4:493–506CrossRefGoogle Scholar
  6. Dormann CF, McPherson JM, Araújo MB, Bivand R, Bolliger J, Carl G, Davies RG, Hirzel A, Jetz W, Kissling D, Kühn I, Ohlemüller R, Peres-Neto PR, Reineking B, Schröder B, Schurr FM, Wilson R (2007) Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30:609–628CrossRefGoogle Scholar
  7. Elith J, Leathwick, JR (2009) Species distribution models: Ecological explanation and prediction across space and time. Annual Rev Ecol Evol Syst 40:677–697CrossRefGoogle Scholar
  8. Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77:802–813PubMedCrossRefGoogle Scholar
  9. Ellenberg H, Weber HE, Düll R, Wirth V, Werner W (2001) Zeigerwerte von Pflanzen in Mitteleuropa. Verlag Erich Goltze KG, GöttingenGoogle Scholar
  10. Ewald J (1999) Relationships between floristic and microsite variability in coniferous forests of the Bavarian Alps. Phytocoenologia 29:327–344CrossRefGoogle Scholar
  11. Ewald J (2003) A critique for phytosociology. J Veg Sci 14:291–296CrossRefGoogle Scholar
  12. Ewald J (2007) Bimodal spectra of nutrient indicators reveal abrupt eutrophication of pine forests. Preslia 79:391–400Google Scholar
  13. Foody GM (2004) Spatial nonstationarity and scale-dependency in the relationship between species richness and environmental determinants for the sub-Saharan endemic avifauna. Global Ecol Biogeogr 13:315–320CrossRefGoogle Scholar
  14. Franklin J (2010) Mapping species distributions: spatial inference and prediction. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  15. Graham CH, Elith J, Hijmans RJ, Guisan A, Peterson AT, Loiselle BA (2008) The influence of spatial errors in species occurrence data used in distribution models. J Appl Ecol 45:239–247CrossRefGoogle Scholar
  16. Guisan A, Harrell FE (2000) Ordinal response regression models in ecology. J Veg Sci 11:617–626CrossRefGoogle Scholar
  17. Guisan A, Spehn E, Körner C (2007) MRI Newsletter 8: Georeferenced biological databases – a tool for understanding mountain biodiversity. Mount Res Developm 27:80–81CrossRefGoogle Scholar
  18. Häring T, Reger B, Ewald J, Hothorn T, Schröder B (2013) Predicting Ellenberg’s soil moisture indicator value in the Bavarian Alps using additive georegression. Appl Veg Sci 16:110–121CrossRefGoogle Scholar
  19. Hastie TJ, Tibshirani RJ, Friedman JH (2009) The elements of statistical learning: data mining, inference, and prediction. Ed. 2. Springer Verlag, New YorkGoogle Scholar
  20. Hawkes JC, Pyatt DG, White IMS (1997) Using Ellenberg values to assess soil quality in British forests from ground vegetation: a pilot study. J Appl Ecol 34:375–387CrossRefGoogle Scholar
  21. Hengl T, Toomanian N, Reuter HI, Malakouti MJ (2007) Methods to interpolate soil categorical variables from profile observations: lessons from Iran. Geoderma 140:417–427CrossRefGoogle Scholar
  22. Hera U, Rötzer T, Zimmermann L, Schulz C, Maier H, Weber H, Kölling C (2012) Klima en détail. Neue, hochaufgelöste Klimakarten bilden wichtige Basis zur klimatischen Regionalisierung Bayerns. LWF Aktuell 86:34–37Google Scholar
  23. Hothorn T, Bühlmann P, Kneib T, Schmid M, Hofner B (2010) Model-based boosting 2.0. J Machine Learning Res 11:2109–2113Google Scholar
  24. Hothorn T, Müller J, Schröder B, Kneib T, Brandl R (2011) Decomposing environmental, spatial and spatio-temporal components of species distributions. Ecol Monogr 81:329–347CrossRefGoogle Scholar
  25. Käfer J, Witte JPM (2004) Cover-weighted averaging of indicator values in vegetation analyses. J Veg Sci 15:647–652CrossRefGoogle Scholar
  26. Kellomäki S, Leinonen S (eds) (2005) Management of European forests under changing climatic conditions. Tiedonantoja/Research Notes No. 163, University of Joensuu, JoensuuGoogle Scholar
  27. Ketterer K, Ewald J (1999) Waldgesellschaften und Standorte auf dem Eibsee-Bergsturz bei Garmisch-Partenkirchen. Hoppea 60:627–690Google Scholar
  28. Kneib T, Hothorn T, Tutz G (2009) Variable selection and model choice in geoadditive regression models. Biometrics 65:626–634PubMedCrossRefGoogle Scholar
  29. Kowarik I, Seidling W (1989) Zeigerwertberechnungen nach Ellenberg. Zu Problemen und Einschränkungen einer sinnvollen Methode. Landschaft und Stadt 21:132–143Google Scholar
  30. Kühn I (2007) Incorporating spatial autocorrelation may invert observed patterns. Diversity Distrib 13:66–69Google Scholar
  31. Legendre P, Legendre L (1998) Numerical ecology. Ed. 2. Elsevier, AmsterdamGoogle Scholar
  32. Lennon JJ (2000) Red-shifts and red herrings in geographical ecology. Ecography 549:101–113CrossRefGoogle Scholar
  33. Lichstein JW, Simons TR, Shriner SA, Franzreb KE (2002) Spatial autocorrelation and autoregressive models in ecology. Ecol Monogr 72:445–463CrossRefGoogle Scholar
  34. Maloney KO, Schmid M, Weller DE (2011) Applying additive modelling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages. Methods Ecol Evol 3:116–128CrossRefGoogle Scholar
  35. Maracchi G, Sirotenko O, Bindi M (2005) Impacts of present and future climate variability on agriculture and forestry in the temperate regions: Europe. Climatic Change 70:117–135CrossRefGoogle Scholar
  36. McCullagh P (1980) Regression models for ordinal data. J Roy Statist Soc Ser B 42:109–142Google Scholar
  37. Miller, JA, Hanham RQ (2011) Spatial nonstationarity and the scale of species-environment relationships in the Mojave Desert, California, USA. Int J Geogr Inform Sci 25:423–438CrossRefGoogle Scholar
  38. Miller JA, Franklin J, Aspinall R (2007) Incorporating spatial dependence in predictive vegetation models. Ecol Modelling 202:225–242CrossRefGoogle Scholar
  39. Pakeman RJ, Reid CL, Lennon JJ, Kent M (2008) Possible interactions between environmental factors in determining species optima. J Veg Sci 19:201–208CrossRefGoogle Scholar
  40. Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) (2007) Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  41. Reger B, Kölling C, Ewald J (2011) Modelling effective thermal climate for mountain forests in the Bavarian Alps: Which is the best model? J Veg Sci 22:677–687CrossRefGoogle Scholar
  42. Reger B, Schüpferling R, Beck J, Dietz E, Morovitz D, Schaller R, Wilhelm G, Ewald J (2012) WINALPecobase – Ecological database of mountain forests in the Bavarian Alps. Biodiversity Ecol 4:167–171CrossRefGoogle Scholar
  43. Reger B, Häring T, Ewald J (2014) The TRM-model of potential natural vegetation in mountain forests. Folia Geobot (this issue). doi: 10.1007/s12224-013-9158-0
  44. Schaetzl RJ, Krist FJJ, Miller BA (2012) A taxonomically based ordinal estimate of soil productivity for landscape-scale analyses. Soil Sci 177:288–299CrossRefGoogle Scholar
  45. Schaffers AP, Sýkora KV (2000) Reliability of Ellenberg indicator values for moisture, nitrogen and soil reaction: a comparison with field measurements. J Veg Sci 11:225–244CrossRefGoogle Scholar
  46. Schmid M, Hothorn T, Maloney KO, Weller DE, Potapov S (2011) Geoadditive regression modeling of stream biological condition. Environm Ecol Statist 18:709–733CrossRefGoogle Scholar
  47. Seidling W, Rohner MS (1993) Zusammenhänge zwischen Reactions-Zeigerwerten und bodenchemischen Parametern am Beispiel von Waldbodenvegetation. Phytocoenologia 23:301–317CrossRefGoogle Scholar
  48. Zevenbergen LW, Thorne CR (1987) Quantitative analysis of land surface topography. Earth Surface Processes and Landforms 12:47–56CrossRefGoogle Scholar

Copyright information

© Institute of Botany, Academy of Sciences of the Czech Republic 2013

Authors and Affiliations

  • Tim Häring
    • 1
    • 2
  • Birgit Reger
    • 3
  • Jörg Ewald
    • 3
  • Torsten Hothorn
    • 4
  • Boris Schröder
    • 2
  1. 1.BASF SE, Environmental Fate – ModellingLimburgerhofGermany
  2. 2.Landscape EcologyTechnische Universität MünchenFreisingGermany
  3. 3.Faculty of ForestryUniversity of Applied Sciences Weihenstephan-TriesdorfFreisingGermany
  4. 4.Institut für Sozial- und PräventivmedizinUniversität ZürichZürichSwitzerland

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