The management of vegetation classifications with fuzzy clustering

Questions Does fuzzy clustering provide an appropriate numerical framework to manage vegetation classifications? What is the best fuzzy clustering method to achieve this? Material We used 531 releves from Catalonia (Spain), belonging to two syntaxonomic alliances of mesophytic and xerophytic montane pastures, and originally classified by experts into nine and 13 associations, respectively. Methods We compared the performance of fuzzy C-means (FCM), noise clustering (NC) and possibilistic C-means (PCM) on four different management tasks: (1) assigning new releve data to existing types; (2) updating types incorporating new data; (3) defining new types with unclassified releves; and (4) reviewing traditional vegetation classifications. Results As fuzzy classifiers, FCM fails to indicate when a given releve does not belong to any of the existing types; NC might leave too many releves unclassified; and PCM membership values cannot be compared. As unsupervised clustering methods, FCM is more sensitive than NC to transitional releves and therefore produces fuzzier classifications. PCM looks for dense regions in the space of species composition, but these are scarce when vegetation data contain many transitional releves. Conclusions All three models have advantages and disadvantages, although the NC model may be a good compromise between the restricted FCM model and the robust but impractical PCM model. In our opinion, fuzzy clustering might provide a suitable framework to manage vegetation classifications using a consistent operational definition of vegetation type. Regardless of the framework chosen, national/regional vegetation classification panels should promote methodological standards for classification practices with numerical tools.