Bayesian classification of vegetation types with Gaussian mixture density fitting to indicator values
Abstract:Question: Is it possible to mathematically classify relevés into vegetation types on the basis of their average indicator values, including the uncertainty of the classification?
Location: The Netherlands.
Method: A large relevé database was used to develop a method for predicting vegetation types based on indicator values. First, each relevé was classified into a phytosociological association on the basis of its species composition. Additionally, mean indicator values for moisture, nutrients and acidity were computed for each relevé. Thus, the position of each classified relevé was obtained in a three-dimensional space of indicator values. Fitting the data to so called Gaussian Mixture Models yielded densities of associations as a function of indicator values. Finally, these density functions were used to predict the Bayesian occurrence probabilities of associations for known indicator values. Validation of predictions was performed by using a randomly chosen half of the database for the calibration of densities and the other half for the validation of predicted associations.
Results and Conclusions: With indicator values, most relevés were classified correctly into vegetation types at the association level. This was shown using confusion matrices that relate (1) the number of relevés classified into associations based on species composition to (2) those based on indicator values. Misclassified relevés belonged to ecologically similar associations. The method seems very suitable for predictive vegetation models.
Document Type: Research Article
Publication date: 2007-08-01
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- The Journal of Vegetation Science publishes original articles, short notes and review articles in the field of vegetation science, both methodological and theoretical studies, and descriptive and experimental studies of plant communities and plant populations. The Journal is the Official Organ of the International Association for Vegetation Science (IAVS).
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