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Simple shade plots aid better long-term choices of data pre-treatment in multivariate assemblage studies

  • K. Robert Clarke (a1) (a2), James R. Tweedley (a2) and Fiona J. Valesini (a2)


Shade plots, simple visual representations of abundance matrices from multivariate species assemblage studies, are shown to be an effective aid in choosing an overall transformation (or other pre-treatment) of quantitative data for long-term use, striking an appropriate balance between dominant and less abundant taxa in ensuing resemblance-based multivariate analyses. Though the exposition is entirely general and applicable to all community studies, detailed illustrations of the comparative power and interpretative possibilities of shade plots are given in the case of two estuarine assemblage studies in south-western Australia: (a) macrobenthos in the upper Swan Estuary over a two-year period covering a highly significant precipitation event for the Perth area; and (b) a wide-scale spatial study of the nearshore fish fauna from five divergent estuaries. The utility of transformations of intermediate severity is again demonstrated and, with greater novelty, the potential importance seen of further mild transformation of all data after differential down-weighting (dispersion weighting) of spatially ‘clumped’ or ‘schooled’ species. Among the new techniques utilized is a two-way form of the RELATE test, which demonstrates linking of assemblage structure (fish) to continuous environmental variables (water quality), having removed a categorical factor (estuary differences). Re-orderings of sample and species axes in the associated shade plots are seen to provide transparent explanations at the species level for such continuous multivariate patterns.


Corresponding author

Correspondence should be addressed to: K.R. Clarke, Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, PL1 3DH, UK email:


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Simple shade plots aid better long-term choices of data pre-treatment in multivariate assemblage studies

  • K. Robert Clarke (a1) (a2), James R. Tweedley (a2) and Fiona J. Valesini (a2)


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