Predicting the time of tree death using dendrochronological data

Complex interactions of various environmental factors result in high variability of tree mortality in space and time. Tree mortality functions that are implemented in forest succession models have been suggested to play a key role in assessing forest response to climate change. However, these functions are based on theoretical considerations and are likely to be poor predictors of the timing of tree death, since they do not adequately reflect our understanding of tree mortality processes. In addition, these theoretical mortality functions and most empirical mortality functions have not been tested sufficiently with respect to the accuracy of predicting the time of tree death. We introduce a new approach to modeling tree mortality based on different growth patterns of entire tree-ring series. Dendrochronological data from Picea abies (Norway spruce) in the Swiss Alps were used to calibrate mortality models using logistic regression. The autocorrelation of the data was taken into account by a jackknife variance estimator. Model performance was assessed by two criteria for classification accuracy and three criteria for prediction error. The six models with the highest overall performance correctly classified 71-78% of all dead trees and 73-75% of all living trees, and they predicted 44-56% of all dead trees to die within 0-15 years prior to the actual year of death. For these six models, a maximum of 1.7% of all dead trees and 5% of all living trees were predicted to die >60 years prior to the last measured year. Models including the relative growth rate and a short-term growth trend as explanatory variables were most reliable with respect to inference and prediction. The generality of the mortality models was successfully tested by applying them to two independent P. abies data sets from climatologically and geologically different areas. We conclude that the methods presented improve our understanding of how tree growth and mortality are related, which results in more accurate mortality models that can ultimately be used to increase the reliability of predictions from models of forest dynamics.