Are ecological groups of species optimal for forest dynamics modelling?

The huge diversity of tree species in tropical rain-forests makes the modelling of its dynamics a difficult task. One-way to deal with it is to define species groups. A classical approach for building species groups consists in grouping species with nearby characteristics, using cluster analysis. A group of species is then characterized by the same list of attributes as a single species, and it is incorporated in the model of forest dynamics in the same way as a single species. In this paper, a new approach for building species group is proposed. It relies on the discrepancy between model predictions when all species are considered separately, and model predictions when species groups are used. An aggregation error that quantifies the bias in model predictions that results from species grouping is thus defined. We then define the optimal species grouping as the one that minimizes the aggregation error. Using data from a tropical rain-forest in French Guiana and a toy model of forest dynamics, this new method for species grouping is confronted to the classical method based on cluster analysis of the species characteristics, and to a combined method based on a cluster analysis that uses the aggregation error as a dissimilarity between species. The optimal species grouping is quite different from the classical species grouping. The ecological interpretation of the optimal groups is difficult, as there is no direct linkage between the species characteristics and the way that they are grouped. The combined approach yields species groups that are closed to the optimal ones, with much less computations. The optimal species groups are thus specific to the model of forest dynamics and lack the generality of those of the classical method, that in turn are not optimal. (C) 2003 Elsevier Science B.V. All rights reserved.