Nonlinear regression-typological analysis of ecophysiological states of vegetation: a pilot study with small data sets

The interactions of environmental factors associated with forest decline were analyzed by a modified multidimensional scaling method. The method subdivides the entire data set into homogeneous classes; linear regression is then applied within each single class. A nonlinear picture of the interdependence of the effects of different factors is developed as a composite of the contributions from each single class. The analysis was performed on a restricted data set, and the results compared with some expected effects and with results obtained by standard linear regression. Even with the limited data set, multidimensional scaling not only explained expected effects but also revealed new information. We conclude that the method will be useful for analyzing complex time series data because it is able to detect complex interactions between environmental variables that affect physiological parameters.