Multivariate Methods in Plant Ecology: IV. Nodal Analysis

Abstract

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On the polymorphism of cyanogenesis in Lotus corniculatus L.

“…Using the Wassenaarse Slag Road as a base line, two transects were laid down northwards parallel to the coast, one less than 500 m from the beach (coast) and the other (inland) more than 1 km from the beach. At 200 m intervals along 245 each transect, 4 m x 4 m quadrats were sampled, the quadrat size being large in relation to the size of the plants composing the vegetation (Lambert and Williams, 1962 The appropriate statistical techniques to evaluate changes from south to north on the transects were used, that is regression analyses on the quantitative data and association analyses on the categorical data. Differences between transects were estimated by analyses of variance.…”

“…In normal association analysis all differences relating to differences in abundance are eliminated and differences of richness remain; in inverse analysis richness is eliminated and differences in abundance remain. Both these analyses can then be amalgamated into a nodal analysis (Lambert and Williams, 1962) by the construction of a two-way table from which coincidence parameters are obtained and used to form noda and subnoda, basic vegetation units. In this instance the analysis was only carried as far as the two-way table, which Shimwell (1971) suggests "in many ways is a satisfactory end-point to the analysis.…”

Biogeography of Groundfish Assemblages on the Grand Bank

“…Two-way tables generated by TWINSPAN (Hill, 1979) allowed the recognition of biological features of each of the main station groupings identified by cluster analysis. This method, a refined and computerized version of a polythetic divisive method based on an ordination technique ("Indicator Species Analysis", Hill et al, 1975), gives results that fulfil requirements of non-exclusivity, i.e., an ubiquitous species can be associated with more than one cluster of stations unlike other interpretation techniques, for example nodal analysis (Lambert and Williams, 1962). The biological features included not only actual differential species (species having a clear preference for a given cluster) but also the presence or absence of a very widespread species in a cluster and anomalies in cluster richness (number of species present).…”

Stock Complexes for Fisheries Management in the Gulf of Mexico

“…The CVL data set was selected due to its high species richness, and the MRFSS data set was selected because it represented the most contrasting sector. The cells were conditionally formatted to facilitate visual identification of the dense cells, or nodes, within the data matrix in which groups of species and groups of collections coincide between the two fisheries clusters and co-occur with high frequency between species (Williams and Lambert 1961;Lambert and Williams 1962). This nodal analysis was used to identify clusters of species that were often caught together across different sectors and to suggest cluster assignment for rare species by providing a visual reference for co-occurrence with more ubiquitous or heavily exploited species.…”