© 1995 Heron Publishing—Victoria, Canada
Nonlinear regression-typological analysis of ecophysiological states of vegetation: a pilot study with small data sets
V. T. Perekrest (1), T. V. Khachaturova (1), I. B. Beresneva (1), N. M. Mitrofanova (2), E. Künstle (3), E. Wagner (4) and L. Fukshansky (4, 5)
1. Economic-Mathematical Institute, Russian Academy of Science, 1 Chaikovskistreet, St. Petersburg, Russia / 2. Technical University of St. Petersburg, 29 Polytechnic Street, St. Petersburg, Russia / 3. Institut für Waldwachstum, University of Freiburg, D-78 Freiburg, Germany / 4. Institute of Biology II, University of Freiburg, Schänzlestrasse 1, D-78 Freiburg, Germany / 5. Author to whom correspondence should be addressed / Received August 11, 1993
Summary
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.
Keywords:
forest decline, functional multidimensional scaling, interdependence of the effects of environmental factors, multicausal
syndrome.
