Radial tree-growth modelling with fuzzy regression

A so-called fuzzy linear regression is used in dendroecology to model empir ically tree growth as a function of a bioclimatic index representing the wa ter stress, i.e., the ratio of actual evapotranspiration to potential evapo transpiration. The response function predicts tree growth as (fuzzy) interv als, narrow in the domain where the bioclimatic index is most limiting and becoming progressively larger elsewhere. The method is tested with a popula tion of Pinus pinea L. from the Provence region in France. It is shown that fuzzy linear regression gives results comparable with those obtained using a linear response function. The interval of credibility given by the fuzzy regression suggests that more precise expected growth is obtained for high water stress, which is typical of Mediterranean climate. Fuzzy linear regr ession can be also a method to test different hypotheses on several potenti al predictors when any further experimental approach is quite impossible as it is for trees in their natural environment. To sum up, fuzzy regression could be a first step before the construction of a kind of growth simulator adapted to different environments of a given species. In environmental sci ences, the fuzzy response function thus appears to be an approach between t he mechanistic and the statistical descriptive approaches.