Recently Updated Pages
5.9 Example: Okura estuary macrofauna
describe macrofauna samples from the Okura estuary {O}, on the northern fringes of urban Aucklan...
5.8 Further nMDS/mMDS developments
Higher dimensional solutions MDS solutions can be sought in higher dimensions and we noted previo...
5.7 MDS strengths and weaknesses
MDS strengths MDS is simple in concept. The numerical algorithm is undeniably complex, but it ...
5.6 Example: Amoco-Cadiz oil spill, Morlaix
Benthic macrofaunal abundances of 251 species were sampled by at 21 times between April 1977 and ...
5.5 Example: Celtic Sea zooplankton
In situations where the samples are strongly grouped, as in Figs. 5.4 and 5.5, both clustering an...
5.4 EXAMPLE: Dosing experiment, Solbergstrand
The nematode abundance data from the dosing experiment {D} at the GEEP Oslo Workshop was previous...
5.3 Diagnostics: Adequacy of MDS representation
Is the stress value small? By definition, stress reduces with increasing dimensionality of the ...
5.2 Non-metric multidimensional scaling (MDS)
The method of non-metric MDS was introduced by and , for application to problems in psychology; ...
5.1 Other ordination methods
Principal Co-ordinates Analysis The two main weaknesses of PCA, identified at the end of Chapter ...
4.5 Example: Dosing experiment, Solbergstrand mesocosm
An example of this final point for a real data set can be seen in Fig. 4.2. This is of nematode ...
4.4 PCA for environmental data
The above example makes it clear that PCA is an unsatisfactory ordination method for biological d...
4.3 Example: Garroch Head macrofauna
Fig. 4.1 shows the result of applying PCA to square-root transformed macrofaunal biomass data fro...
4.2 Principal components analysis
The starting point for PCA is the original data matrix rather than a derived similarity matrix (t...
4.1 Ordinations
An ordination is a map of the samples, usually in two or three dimensions, in which the placement...
3.7 k-R clustering (non-hierarchical)
Another major class of clustering techniques is non-hierarchical, referred to above as flat clust...
3.6 Binary divisive clustering
All discussion so far has been in terms of hierarchical agglomerative clustering, in which sample...
3.5 Similarity profiles (SIMPROF)
Given the form of the dendrogram in Fig. 3.3, with high similarities in apparently tightly define...
3.4 Recommendations
Hierarchical clustering with group-average linking, based on sample similarities or dissimilari...
3.3 Example: Bristol Channel zooplankton
perform hierarchical cluster analyses of zooplankton samples, collected by double oblique net ha...
3.2 Hierarchical agglomerative clustering
The most commonly used clustering techniques are the hierarchical agglomerative methods. These u...