New Forests

, Volume 32, Issue 3, pp 265–283 | Cite as

Predicting regeneration establishment in Norway spruce plantations using a multivariate multilevel model

  • Jari MiinaEmail author
  • Timo Saksa


This study predicts the regeneration establishment on 3-year-old Norway spruce (Picea abies (L.) Karst.) plantations in southern Finland using regeneration survey data. Regeneration establishment was described by seven response variables: number of planted spruces, natural Scots pines (Pinus sylvestris L.), natural spruces, natural seed-origin birches (Betula pubescens Ehrh. and B. pendula Roth.) and other broadleaves (i.e. sprout-origin birches and other broadleaves than birch), as well as height of crop-tree spruce and dominant height of broadleaves. Due to the multivariate (several responses for each plot) and multilevel (plot, stand, municipality, forest centre) structure, regeneration establishment was modelled by fitting a multivariate multilevel model with explanatory variables such as temperature sum, site fertility, soil quality and method of site preparation. In the model, the numbers of tree seedlings were modelled using over-dispersed Poisson distributed equations, and the tree heights were modelled using normally distributed linear equations. The estimated fixed and random parameters of the equations were logical, and there was no serious bias in predicting the regeneration establishment in the independent test data set. This modelling approach can be used to predict the regeneration establishment stochastically by taking into account the large unexplained variation in regeneration models.


Generalized linear mixed models Picea abies Poisson model Regeneration survey data Simultaneous models 


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  1. Cajander A.K. (1926) The theory of forest types. Acta For. Fenn. 29(3):108Google Scholar
  2. Ferguson D.E. 1997. Regeneration models for FVS variants. In: Teck R., Moeur M. and Adams J. (eds), Proceedings: Forest Vegetation Simulator Conference, Fort Collins, CO, February 3–7, 1997. USDA, Forest Service, General Technical Report INT-GTR-373, pp. 43-49Google Scholar
  3. Ferguson D.E. and Carlson C.E. 1993. Predicting regeneration establishment with the Prognosis model. USDA, Forest Service, Research Paper INT-467, 54 ppGoogle Scholar
  4. Ferguson D.E., Stage A.R., Boyd R.J. 1986. Predicting Regeneration in the Grand Fir-cedar-hemlock Ecosystem of the Northern Rocky Mountains. For. Sci. Monograph 26, 41 ppGoogle Scholar
  5. Finnish Statistical Yearbook of Forestry. 2003. The Finnish Forest Research Institute. SVT, Agriculture, Forestry and Fishery 45, 385 ppGoogle Scholar
  6. Goldstein H. 1996. Multilevel Statistical Models. Kendall’s Library of Statistics 3, 2nd ed., p. 178Google Scholar
  7. Hasenauer H., Kindermann G. (2002) Methods for assessing regeneration establishment and height growth in uneven-aged mixed species stands. Forestry 75: 385-394CrossRefGoogle Scholar
  8. Hynynen J., Ojansuu R., Hökkä H., Siipilehto J., Salminen H. and Haapala P. 2002. Models for predicting stand development in MELA System. Finnish Forest Research Institute, Research papers 835, 116 ppGoogle Scholar
  9. Kozlowski T.T. (2002) Physiological ecology of natural regeneration of harvested and disturbed forest stands: implications for forest management. For. Ecol. Manage. 158: 195–221CrossRefGoogle Scholar
  10. Laine J. and Vasander H. 1993. Suotyypit. 3rd ed. Kirjayhtymä, Helsinki, 80 ppGoogle Scholar
  11. Luonnonläheinen metsänhoito. Metsänhoitosuositukset. 1994. Metsäkeskus Tapion julkaisuja 6/1994, 72 ppGoogle Scholar
  12. McCullagh P. and Nelder J.A. 1989. Generalized Linear Models, 2nd ed. Chapman and Hall, University Press, Cambridge, 511 ppGoogle Scholar
  13. Monserud R.A., Sterba H. and Hasenauer H. 1997. The single-tree stand growth simulator PROGNAUS. In: Teck R., Moeur M. and Adams J. (eds), Proceedings: Forest Vegetation Simulator Conference, Fort Collins, CO, February 3–7, 1997. USDA, Forest Service, General Technical Report INT-GTR-373, pp. 50–56Google Scholar
  14. Ojansuu R., Henttonen H. (1983) Estimation of local values of monthly mean temperature, effective temperature sum and precipitation sum for the measurements made by the Finnish Meteorological Office. Silva Fenn. 17:143–160Google Scholar
  15. Rasbash J., Browne W., Goldstein H., Yang M., Plewis I., Healy M., Woodhouse G., Draper D., Langford I. and Lewis T. 2000. A User’s Guide to MLwiN Version 2.1.a. University of London, UK, 278 ppGoogle Scholar
  16. Ripley B.D. 1981. Spatial Statistics. Wiley, New York, 252 ppGoogle Scholar
  17. Ripley B.D. 1987. Stochastic Simulation. Wiley, New York, 237 ppGoogle Scholar
  18. Saarenmaa L. (1990) Choice of reforestation method based on an expert system in Finnish Lapland. Folia For. 762:49Google Scholar
  19. Saksa T. 1992. Development of Scots pine plantations in prepared reforestation areas. Dissertation. Finnish Forest Research Institute, Research papers 418, 48 ppGoogle Scholar
  20. Saksa T., Särkkä-Pakkala K., Smolander H. (2002) Työkalu metsänuudistamisen laatutyöhön. Metsätieteen aikakauskirja 1: 29–34Google Scholar
  21. Schweiger J., Sterba H. (1997) A model describing natural regeneration recruitment of Norway spruce (Picea abies (L.) Karst.) in Austria. For. Ecol. Manage. 97: 107–118CrossRefGoogle Scholar
  22. Searle S.R., Casella G. and McCulloch C.E. 1992. Variance Components. Wiley, New York, 501 ppGoogle Scholar
  23. Snijders T. and Bosker R. 1999. Multilevel Analysis. An Introduction to Basic and Advanced Multilevel Modeling. SAGE Publications Inc., London, 266 ppGoogle Scholar
  24. Wilson G.F. and Maguire D.A. 1996. Simulation of early regeneration processes in mixed-species forests of Maine, USA: Germination, survival, and height growth. In: Skovsgaard J.P. and Johannsen V.K. (eds), Modelling Regeneration Success and Early Growth of Forest Stands. Proceedings from the IUFRO Conference, held in Copenhagen, 10–13 June 1996. Danish Forest and Landscape Research Institute, Hørsholm, pp. 530–539Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  1. 1.The Finnish Forest Research Institute, Joensuu Research UnitJoensuuFinland
  2. 2.The Finnish Forest Research Institute, Suonenjoki Research UnitSuonenjokiFinland

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