Growth-dependent tree mortality models based on tree rings

Mortality is a crucial element of population dynamics. However, tree mortality is not well understood, particularly at the individual level. The objectives of this study were to (i) determine growth patterns (growth levels and growth trends) over different time windows that can be used to discriminate between dead and living Norway spruce (Picea abies (L.) Karst.) trees, (ii) optimize the selection of growth variables in logistic mortality models, and (iii) assess the impact of competition on recent growth in linear regression models. The logistic mortality model that we developed for mature stands classified an average of nearly 80% of the 119 trees from one site correctly as being dead or alive. While more than 50% of the variability of recent growth of living trees can be attributed to the influence of competition, this percentage was only 25% for standing dead trees. The predictive power of the logistic mortality model was validated successfully at two additional sites, where 29 of 41 (71%) and 34 of 42 (81%) trees were classified correctly, respectively. This supports the generality of the mortality model for Norway spruce in subalpine forests of the Alps. We conclude that growth trends in addition to the commonly used growth level significantly improve the prediction of growth-dependent tree mortality of Norway spruce.