A classification-ordination analysis of a Belgian mixed forest in the transition zone of two phytogeographical districts. II. Spring data

Abstract

From 115 quadrats, laid out in the same way and on the same coordinates as described in a first paper, cover-abundance and height data were collected in april 1976 on 58 taxa, belonging to the upper and lower herb layer, on the moss layer as a whole and on the litter. They were submitted to four classification methods, namely Unweighted pair-group centroid technique (UPGC), normal Association analysis (NAA) , agglomerative normal and inverse Information analysis (NIA and IIA), Minimum variance clustering (MVC), and to four ordination methods, namely Beals' Polar ordination (POS), Simple ordination (SO), Optimized polar ordination (OPO) and Position vectors ordination (PVO). The only divisive method (NAA) produces more than sufficient results: they are slightly better with the 1%-stopping rule especially when no species reduction is involved. NAA and NIA give quite similar results. From the three agglomerative techniques, the greatest number of vegetation- clusters (7), recognizable in the field, has been obtained with MVC, the poorest picture however by UPGC. With regard to the forest-structure, the better results are with NIA, followed by MVC. Moreover. NIA applied in a quantitative way, is less appropriate. The outcome of species classification is not interpretable. The ordination results obtained by OPO and PVO are better as compared to those from POB and SO, as well in discovering gradients as clusters, the gradients particularly reflect changes in cover-abundance and height. In the ordination of vegetation quadrats, the total of the efficiency ratios extracted for the first three axes in PVO and GPO are respectively four and three times higher than with SO. Concerning the structure quadrats, the percentage extraction values are very high and very alike. Comparing the spring with the summer forest, UPGC distinguishes more identifiable vegetation types in the summer data, NAA on the contrary in the spring data.