𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Two-step vegetation analysis based on very large data sets

✍ Scribed by Maarel, Eddy ;Espejel, Ileana ;Moreno-Casasola, Patricia


Book ID
104625914
Publisher
Springer-Verlag
Year
1987
Tongue
English
Weight
396 KB
Volume
68
Category
Article
ISSN
1573-5052

No coin nor oath required. For personal study only.

✦ Synopsis


A two-step method for the classification of very large phytosociological data sets is demonstrated. Stratification of the set is suggested either by area in the case of a large and geographically heterogeneous region, or by vegetation type in the case of a set covering all the plant communities of an area. First, cluster analysis is performed on each subset. The resulting basic clusters are summarized by calculating a 'synoptic coverabundance value' for each species in each cluster. All basic clusters are then subjected to the same procedure. Second order clusters are interpreted as community types. The synoptic value proposed reflects both frequency and average cover-abundance. It is emphasized that a species should have a high frequency to be used as a diagnostic species.

The method is demonstrated with a set of 1138 relev6s and 250 species of coastal sand dune vegetation in Yucatan treated with the programs TWINSPAN and TABORD. Some problems and perspectives of the approach are discussed in the light of hierarchy theory and classification theory.


πŸ“œ SIMILAR VOLUMES


Fast principal component analysis of lar
✍ F. Vogt; M. Tacke πŸ“‚ Article πŸ“… 2002 πŸ› John Wiley and Sons 🌐 English βš– 349 KB πŸ‘ 2 views

## Abstract Principal component analysis (PCA) and principal component regression (PCR) are routinely used for calibration of measurement devices and for data evaluation. However, their use is hindered in some applications, e.g. hyperspectral imaging, by excessive data sets that imply unacceptable