Assessing the performance of theoretical and empirical tree mortality models using tree-ring series of Norway spruce

Successional dynamics of forests under current and changed climate are often investigated using gap models, a subset of forest succession models that simulate establishment, growth, and mortality of trees. However, the mortality submodels of gap models are largely based on theoretical assumptions, and have not been tested in detail. In the present study, we compared the performance of a range of theoretical mortality functions (TMFs) that are commonly used in gap models with several empirical mortality functions (EMFs) that were derived using logistic regression from growth patterns of tree-ring series as predictor variables. Data from dead and living Norway spruce (Picea abies (L.) Karst.) trees from subalpine forests at three study sites in Switzerland were used to this end. Three of the four EMFs consistently performed better at all three sites, while three of the four TMFs performed worse than the remaining mortality functions. At one site, these three EMFs correctly classified 71-78% of the dead trees (48-72% for the three TMFs) and 73% (49-64%) of the living trees. 44-54% (21-25%) of the dead trees were predicted to die within 15 years prior to death. 0-2% (7-10%) of the dead trees and 5% (19-31%) of the living trees were predicted to die more than 60 years prior to the last measured year. We conclude that, unless the parameters of the TMFs are optimized for individual species, the TMFs are not appropriate to predict the time of tree death, in spite of their widespread use. A substantial change in simulated forest succession is to be expected if the currently implemented TMFs in gap models are replaced by species-specific EMFs.