Deriving functional types for rain-forest trees
Article first published online: 24 FEB 2009
1999 IAVS - the International Association of Vegetation Science
Journal of Vegetation Science
Volume 10, Issue 5, pages 641–650, October 1999
How to Cite
Gitay, H., Noble, I.R. and Connell, J.H. (1999), Deriving functional types for rain-forest trees. Journal of Vegetation Science, 10: 641–650. doi: 10.2307/3237079
- Issue published online: 24 FEB 2009
- Article first published online: 24 FEB 2009
- Received 16 October 1998; Revision received 7 June 1999; Accepted 14 June 1999.
- Cluster analysis;
- Deductive classification;
- Successional species;
- Vital attribute
- Floyd (1989)
Abstract. A common goal in functional type research is to find a useful classification that defines the dynamic behaviour of groups of species in relation to environmental variation. Long-term data sets on the dynamics of forests are difficult to obtain; thus, it would be useful if more readily available data, such as that on morphological and life history characters, could be used to derive groups that reflect the dynamics of the species. We used a 30-yr data set on the dynamics of subtropical rainforests in Australia to derive classification based on the dynamics of the species and compared this classification with groups of species derived by other approaches. Functional types were derived for ca. 80 tree species using subjective, deductive and data-driven approaches. The subjective classification used was a pioneer to late successional grouping. The deductive classification was an extension of the vital attribute approach.
Two data sets were used for the data-based classifications, one based on morphological, life history and phenological characters (morphological data) readily available from taxonomic descriptions and another based on long-term observations on the establishment, growth and death of all individuals on permanent plots (dynamic data).
SAHN (Sequential, Agglomerative, Hierarchical and Nested) clustering techniques were used for the numerical classifications. There was some similarity between the classification based on dynamic characters and the subjective and deductive classifications. The classification based on the readily available morphological characters showed less similarity with other classifications. However, the morphological data could be used to predict group membership in the dynamic classification using discriminant analysis with 87% accuracy. Thus, it appears that surrogate classifications might be found to describe the dynamics of the subtropical rainforest site. Further exploration and testing at other sites is required, especially to link the functional classification to specific perturbations.