Abstract
The aim of this study is to develop climate-sensitive single-tree growth models, to be used in stand based prediction systems of managed forest in Switzerland. Long-term observations from experimental forest management trials were used, together with retrospective climate information from 1904 up to 2012. A special focus is given to the role of transformation of modelling basal area increment, helping to normalize the random error distribution. A nonlinear model formulation was used to describe the basic relation between basal area increment and diameter at breast height. This formulation was widely expanded by groups of explanatory variables, describing competition, stand development, site, stand density, thinning, mixture, and climate. The models are species-specific and contain different explanatory variables per group, being able to explain a high amount of variance (on the original scale, up to 80% in the case of Quercus spec.). Different transformations of the nonlinear relation where tested and based on the mean squared error, the square root transformation performed best. Although the residuals were homoscedastic, they were still long-tailed and not normal distributed, making robust statistics the preferred method for statistical inference. Climate is included as a nonlinear and interacting effect of temperature, precipitation and moisture, with a biological meaningful interpretation per tree species, e.g., showing better growth for Abies alba in warm and wet climates and good growing conditions for Picea abies in colder and dryer climates, being less sensitive on temperature. Furthermore, a linear increase in growth was found to be present since the 1940s. Potentially this is an effect of the increased atmospheric CO2 concentration or changed management in terms of reduced nutrient subtractions from forest ground, since industrialization lowered the demand of residue and slash uptake.
Highlights
Projections of tree growth support management decisions and are the basis for forest policy.Pro-active and adaptive managers [1] are interested in effects on future productivity under a changing climate [2]
The final models were refitted with the different transformations
Weaker transformations generally showed better results
Summary
Projections of tree growth support management decisions and are the basis for forest policy. Pro-active and adaptive managers [1] are interested in effects on future productivity under a changing climate [2]. Single tree stand simulators (e.g., Prognosis model [3], Prognaus [4], Silva [5], BWinPro [6], SwissStandSim [7]) are useful tools and the basis of forestry decision support systems [8]. In the development of future management strategies. One of the core processes of tree simulators is tree growth [3]. Tree growth is a complex process, depending on multiple and interacting factors. Some single tree growth models share analogies to the model developed from
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