|Wunder, J; Reineking, B; Matter, J-F; Bigler, C; Bugmann, H: Predicting tree death for Fagus sylvatica and Abies alba using permanent plot data, Journal of Vegetation Science, 18, 525-534 (2007)|
|Key words: LOGISTIC-REGRESSION ANALYSIS; MAXIMUM-LIKELIHOOD; GROWTH-PATTERNS; CLIMATIC-CHANGE; HABITAT MODELS; EUROPEAN ALPS; MORTALITY; FOREST; STANDS; SENSITIVITY|
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 weil 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.