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Nils Lexerød (email), Trond Eid

Recruitment models for Norway spruce, Scots pine, birch and other broadleaves in young growth forests in Norway

Lexerød N., Eid T. (2005). Recruitment models for Norway spruce, Scots pine, birch and other broadleaves in young growth forests in Norway. Silva Fennica vol. 39 no. 3 article id 376. https://doi.org/10.14214/sf.376

Abstract

The objective of the present study was to develop recruitment models for Norway spruce, Scots pine, birch and other broadleaves in young growth forests in Norway. The models were developed from permanent sample plots established by the National Forest Inventory, and they will be included in a growth simulator that is part of a large-scale forestry scenario model. The modelling was therefore restricted to independent variables directly or indirectly available from inventories for practical forest management planning. A two-stage modelling approach that suited the stochastic nature of recruitment in boreal forests was used. Models predicting the probability of recruitment were estimated in a first stage, and conditional models for the number of recruits were developed in a second. The probability models as well as the conditional models were biologically realistic and logical. The goodness of fit tests revealed that the probability models fitted the data well, while the coefficients of determination for the conditional models were relatively low. No independent test data were available, but comparisons of predicted and observed number of recruits in different sub-groups of the data revealed few large deviations. The high level of large random errors was probably due to the great variability observed in number of recruits rather than inappropriate specifications of the models. Provided the generally high level of uncertainty connected to analysis performed with large-scale forestry scenario models and the stochastic nature of recruitment, the presented models seem to give satisfactory levels of accuracy.

Keywords
regeneration; growth simulators; probability models; conditional models; national forest inventories; Norway

Author Info
  • Lexerød, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway ORCID ID:E-mail nils.lexerod@umb.no (email)
  • Eid, Norwegian University of Life Sciences, Department of Ecology and Natural Resource Management, P.O. Box 5003, NO-1432 Ås, Norway. E-mail nils.lexerod@umb.no ORCID ID:

Received 7 December 2004 Accepted 5 September 2005 Published 31 December 2005

Available at https://doi.org/10.14214/sf.376 | Download PDF

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