Predicting tree death for Fagus sylvatica and Abies alba using permanent plot data
Version of Record online: 24 FEB 2009
2007 IAVS - the International Association of Vegetation Science
Journal of Vegetation Science
Volume 18, Issue 4, pages 525–534, August 2007
How to Cite
Wunder, J., Reineking, B., Matter, J.-F., Bigler, C. and Bugmann, H. (2007), Predicting tree death for Fagus sylvatica and Abies alba using permanent plot data. Journal of Vegetation Science, 18: 525–534. doi: 10.1111/j.1654-1103.2007.tb02567.x
- Issue online: 24 FEB 2009
- Version of Record online: 24 FEB 2009
- Received 21 March 2006; Accepted 19 October 2006
- European beech;
- Forest ecology;
- Forest inventory;
- Forest reserve;
- Mortality model;
- Restricted cubic spline;
- Silver fir;
- Tree mortality
Question: How well can mortality probabilities of deciduous trees(Fagus sylvatica) and conifers (Abies alba) be predicted using permanent plot data that describe growth patterns, tree species, tree size and site conditions?
Location: Fagus forests in the montane belt of the Jura folds (Switzerland).
Method: Permanent plot data were used to develop and validate logistic regression models predicting survival probabilities of individual trees. Backward model selection led to a reduced model containing the growth-related variable ‘relative basal area increment’ (growth-dependent mortality) and variables not directly reflecting growth such as species, size and site (growth-independent mortality).
Results: The growth-mortality relationship was the same for both species (growth-dependent mortality). However, species, site and tree size also influenced mortality probabilities (growth-independent mortality). The predicted survival probabilities of the final model were well calibrated, and the model showed an excellent discriminatory power (area under the receiver operating characteristic curve = 0.896).
Conclusion: Mortality probabilities of Fagus sylvatica and Abies alba can be predicted with high discriminatory power using a well calibrated logistic regression model. Extending this case study to a larger number of tree species and sites could provide species- and site-specific tree mortality models that allow for more realistic projections of forest succession.