A method for classifying commercial tree species of an uneven-aged mixed species tropical forest for growth and yield model construction

Abstract

The sparse distribution of timber species in the tropical rainforest has been a major obstacle in the construction of timber growth and yield models for forest management planning. Models for individual species usually have biased parameter estimates because of the sparse representation of those species ( Vanclay, 1991). We present a technique for pooling species with similar growth characteristics into groups to minimize the level of bias in the estimation of model parameters. Principal component, canonical discriminant, and approximate covariance estimation for clustering procedures ( Milligan and Cooper, 1985) were used to transform and improve the sphericity and separation of multivariate diameter increment data. The k-th nearest neighbor clustering technique ( Wong and Lane, 1983) was used to evaluate the most likely number of species clusters within the population covered by the data. The average linkage, complete linkage, and Ward's minimum variance hierarchical clustering procedures ( Milligan, 1981) were separately applied to the transformed data to identify the characteristics of inherent structures as the basis for classifying similar species into groups. An objective reclassification criterion was used to evaluate the performance of the three clustering methods. Optimal species groupings were attained, with 86.3% cluster recovery, by applying Ward's minimum variance method to data transformed by the canonical discriminant procedure. Consequently, six species growth classes were formed on the basis of the similarities of species growth increment patterns. This objective procedure is guaranteed to produce optimal species groups, with minimal internal variations, suitable for developing unbiased estimates of growth model parameters. The analytic procedure should be applicable to most tropical forest ecological types similar to the moist semi-deciduous forest.