Environmental Monitoring and Assessment

, Volume 68, Issue 3, pp 273–295 | Cite as

Exploratory Analysis and a Stochastic Model for Humusdisintegration



Ulrich (1981) supposes in the hypothesis of humusdisintegrationthat the balance between polymerisation and breakdown of organicmaterial may be disturbed in chemically well buffered Europeanforest soils. This new aspect of aluminium toxicity may causenitrogen exceedance in forest ecosystems and subsequent seasonalnitrate output (Eichhorn and Hütterman, 1999).In a research program the substances in the seepage water aremonitored in a small woodland in central Germany. We explorethese multivariate data for examining possible influences on theprocess of humusdisintegration and its temporal evolution. As aresult, a regression model for carbon is developed, whichincludes covariables, i.e., other substances, as well as spatialand temporal terms describing systematic variability. Especiallyiron and aluminium turn out to be very influential in the model.So far our work is a basic step for monitoring the seepage waterdata by means of stochastic modelling.

forest damage multivariate regression spatial-temporal data statistical modelling 


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  1. Besag, J. E.: 1974. ‘Spatial Interaction and the Statistical Analysis of Lattice Systems’, Journal of the Royal Statistical Society, Series B 36, pp. 192–225.Google Scholar
  2. Box, G. E. P. and Cox, D. R.: 1964, ‘An Analysis of Transformations’, Journal of the Royal Statistical Society, Series B 26, pp. 211–243.Google Scholar
  3. Box, G. E. P., Jenkins, G. M. and Reinsel, G. C.: 1994, Time series analysis, forecasting and control, Third edition, Pretice Hall, Englewood Cliffs.Google Scholar
  4. Brillinger, D. R.: 1996, ‘Remarks Concerning Graphical Models for Time Series and Point Processes’, Revista De Econometria 16, pp. 1–23.Google Scholar
  5. Christensen, R.: 1987, Plant Answers to Complex Questions. The Theory of Linear Models, Springer, New York.Google Scholar
  6. Cook, R. D. and Weisberg, S.: 1982, Residuals and Influence in Regression, Chapman and Hall, New York.Google Scholar
  7. Cressie, N. A. C.: 1993, Statistics for Spatial Data, John Wiley & Sons, New York.Google Scholar
  8. Dahlhaus, R.: 2000, ‘Graphical Interaction Models for Multivariate Time Series’, to appear in Metrika.Google Scholar
  9. Draper, N. and Smith, H.: 19981, Applied Regression Analysis, Second Edition, John Wiley & Sons, New York.Google Scholar
  10. Eichhorn, J.: 1995, ‘Stickstoffsättigung und ihre Auswirkungen auf das Buchenwaldökosystem der Fallstudie Zierenberg’, Habilitationsschrift, Bericht des Forschungszentrums Waldökosysteme Reihe A 124, Göttingen.Google Scholar
  11. Eichhorn, J. and Hüttermann, A.: 1999, ‘Mechanisms of Humus Dynamics and Nitrogen Mobilization’, in: N. Rastin, and J. Bauhus (eds.): Going Underground, Ecological Studies in Forest Soils, Research Signpost, Trivandrum, India, pp. 239–277.Google Scholar
  12. Englund, E. and Sparks, A.: 1991, ‘Geo-EAS 1.2.1 User's Guide’, EPA Report 600/8–91/008, EPAEMSL, Las Vegas, U.S.A.; Geo-EAS is available via http://curie.ei.jrc.it/software/geoeas.html.Google Scholar
  13. Ferrándiz, J., López, A., Llopis, A., Morales, M. and Tejerizo, M. L.: 1995, ‘Spatial Interaction between Neighbouring Counties: Cancer Mortality Data in Valéncia (Spain)’, Biometrics 51, pp. 665–678.Google Scholar
  14. Fried, R.: 1999a, Räumlich-zeitliche Modellierung der Kohlenstoffkonzentration im Waldboden-Sickerwasser zur Untersuchung der Hypothese der Humusdisintegration, PhD-thesis, Department of Mathematics, Darmstadt University of Technology, Germany, and Logos, Berlin.Google Scholar
  15. Fried, R.: 1999b, Coupled Linear Models For Repeated Measurements Considering Missing Data, Preprint, Department of Mathematics, Darmstadt University of Technology, Germany.Google Scholar
  16. Gelfand, A. E., Ghosh, S. K., Knight, J. R. and Sirmans, C. F.: 1998, ‘Spatio-Temporal Modeling of Residential Sales Data’, J. Bus, Econ, Stat. 16, pp. 312–321.Google Scholar
  17. Good, I. J.: 1981, ‘Some Logic and History of Hypothesis Testing’, in: J. C. Pitt (ed.) Philosophical Foundations of Economics, Reidel, Dordrecht, reprinted more briefly in: I. J. Good, Good Thinking, University of Minnesota press, Minneapolis, pp. 22–55.Google Scholar
  18. Hampel, F. R., Ronchetti, E. M., Rousseeuw, P. J. and Stahel, W. A.: 1986, Robust Statistics, John Wiley & Sons, New York.Google Scholar
  19. Hodges, J. L. and Lehmann, E. L.: 1954, ‘Testing the Approximate Validity of Statistical Hypotheses’, Journal of the Royal Statistical Society, Series B 16, 259–268.Google Scholar
  20. Journal, A. G. and Huijbregts, C. J.: 1978, Mining Geostatistics, Academic Press, London.Google Scholar
  21. Lauritzen, S. L.: 1996, Graphical Models, Oxford: Clarendon.Google Scholar
  22. Paar, U.: 1994, ‘Untersuchungen zum Einfluss von Ammonium und Nitrat auf wurzelphysiologische Reaktionsmuster der Buche’, Berichte des Forschungszentrums Waldökosysteme, Reihe A 115, University of Göttingen, Germany.Google Scholar
  23. Pfeifer, P. E. and Deutsch, S. J.: 1980, ‘A Three-Stage Iterative Procedure for Space-Time Modelling’, Technometrics 22, pp. 35–47.Google Scholar
  24. Rousseeuw, P. J.: 1997, ‘Introduction to Positive-Breakdown Methods’, in G. S. Maddala and C. R. Rao (eds) Handbook of Statistics 15, 101–121.Google Scholar
  25. Rousseeuw, P. J. and Van Zomeren, B. C.: 1990, 'Unmasking Multivariate Outliers and Leverage Points (with discussion). Journal of the American Statistical Association 85, 633–651.Google Scholar
  26. Sen, A. and Srivastava, M.: 1990, Regression Analysis: Theory, Methods and Applications, Springer, New York.Google Scholar
  27. Ulrich, B.: 1981, ‘Theoretische Betrachtung des Ionenkreislaufs in Waldökosystemen’, Z. Pflanzenernährung Bodenkunde, pp. 647–659.Google Scholar
  28. Wermuth, N. and Lauritzen, S. L.: 1990, ‘On Substantive Research Hypotheses, Conditional Independence Graphs and Graphical Chain Models’, Journal of the Royal Statistical Society, Series B 52, pp. 21–50.Google Scholar
  29. Wolpert, R. L. and Ickstadt, K.: 1998, ‘Poisson/gamma random field models for spatial statistics’, Biometrika 85, pp. 251–267.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of DortmundGermany
  2. 2.Forest Research and Forest EcologyHessian Agency of Forest Management PlanningHannoversch MündenGermany

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