Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-22T18:11:20.989Z Has data issue: false hasContentIssue false

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

Published online by Cambridge University Press:  18 September 2013

K. Robert Clarke*
Affiliation:
Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, PL1 3DH, UK Centre for Fish and Fisheries Research, Murdoch University, South St Murdoch, Perth, WA 6150, Australia
James R. Tweedley
Affiliation:
Centre for Fish and Fisheries Research, Murdoch University, South St Murdoch, Perth, WA 6150, Australia
Fiona J. Valesini
Affiliation:
Centre for Fish and Fisheries Research, Murdoch University, South St Murdoch, Perth, WA 6150, Australia
*
Correspondence should be addressed to: K.R. Clarke, Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, PL1 3DH, UK email: [email protected]

Abstract

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.

Type
Research Article
Copyright
Copyright © Marine Biological Association of the United Kingdom 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Anderson, M.J. (2001) A new method for non-parametric multivariate analysis of variance. Austral Ecology 26, 3246.Google Scholar
Box, G.E.P. and Cox, D.R. (1964) An analysis of transformations. Journal of the Royal Statistical Society, Series B 26, 211252.Google Scholar
Bray, J.R. and Curtis, J.T. (1957) An ordination of the upland forest communities of Southern Wisconsin. Ecological Monographs 27, 325349.CrossRefGoogle Scholar
Brearley, A. (2005) Ernest Hodgkin's Swanland. Perth: University of Western Australia Press.Google Scholar
Buckley, B.W., Sullivan, W., Chan, P. and Leplastrier, M. (2010) Two record breaking Australian hail storms: storm environments, damage characteristics and rarity. In 25th Conference on Severe Local Storms. 11–14 October 2010 Denver: American Meteorological Society. Available at: https://ams.confex.com/ams/pdfpapers/175723.pdf (accessed 16 August 2013).Google Scholar
Chatfield, C. and Collins, A.J. (1980) Introduction to multivariate analysis. London: Chapman & Hall.CrossRefGoogle Scholar
Clarke, K.R. (1993) Non-parametric multivariate analyses of changes in community structure. Australian Journal of Ecology 18, 117143.CrossRefGoogle Scholar
Clarke, K.R. and Ainsworth, M. (1993) A method of linking multivariate community structure to environmental variables. Marine Ecology Progress Series 92, 205219.CrossRefGoogle Scholar
Clarke, K.R. and Gorley, R.N. (2006) PRIMER v.6: user manual/tutorial. Plymouth: PRIMER-E.Google Scholar
Clarke, K.R. and Green, R.H. (1988) Statistical design and analysis for a ‘biological effects' study. Marine Ecology Progress Series 46, 213226.CrossRefGoogle Scholar
Clarke, K.R. and Warwick, R.M. (1998) Quantifying structural redundancy in ecological communities. Oecologia 113, 278289.CrossRefGoogle ScholarPubMed
Clarke, K.R. and Warwick, R.M. (2001) Change in marine communities: an approach to statistical analysis and interpretation, 2nd edition. Plymouth: PRIMER-E.Google Scholar
Clarke, K.R., Chapman, M.G., Somerfield, P.J. and Needham, H.R. (2006a) Dispersion-based weighting of species counts in assemblage analyses. Marine Ecology Progress Series 320, 1127.CrossRefGoogle Scholar
Clarke, K.R., Somerfield, P.J. and Chapman, M.G. (2006b) On resemblance measures for ecological studies, including taxonomic dissimilarities and a zero-adjusted Bray–Curtis coefficient for denuded assemblages. Journal of Experimental Marine Biology and Ecology 330, 5580.CrossRefGoogle Scholar
Clarke, K.R., Warwick, R.M. and Brown, B.E. (1993) An index showing breakdown of seriation, related to disturbance, in a coral-reef assemblage. Marine Ecology Progress Series 102, 153160.CrossRefGoogle Scholar
Clifford, H.T. and Stephenson, W. (1975) An introduction to numerical classification. New York: Academic Press.Google Scholar
Faith, D.P., Minchin, P.R. and Belbin, L. (1987) Compositional dissimilarity as a robust measure of ecological distance. Vegetatio 69, 5768.CrossRefGoogle Scholar
Field, J.G., Clarke, K.R. and Warwick, R.M. (1982) A practical strategy for analysing multispecies distribution patterns. Marine Ecology Progress Series 8, 3752.CrossRefGoogle Scholar
Gower, J.C. (1971) A general coefficient of similarity and some of its properties. Biometrics 27, 857872.CrossRefGoogle Scholar
Gray, J.S., Aschan, M., Carr, M.R., Clarke, K.R., Green, R.H., Pearson, T.H., Rosenberg, R. and Warwick, R.M. (1988) Analysis of community attributes of the benthic macrofauna of Frierfjord/Langesundfjord and in a mesocosm experiment. Marine Ecology Progress Series 46, 151165.CrossRefGoogle Scholar
Hallett, C.S., Valesini, F.J. and Clarke, K.R. (2012) A method for selecting health index metrics in the absence of independent measures of ecological condition. Ecological Indicators 19, 240252.CrossRefGoogle Scholar
Kruskal, J.B. (1964) Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29, 127.CrossRefGoogle Scholar
Legendre, P. and Legendre, L. (1998) Numerical ecology. 2nd English edition. Amsterdam: Elsevier.Google Scholar
Mantel, N. (1967) The detection of disease clustering and a generalized regression approach. Cancer Research 27, 209220.Google Scholar
McArdle, B.H. and Anderson, M.J. (2001) Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 82, 290297.CrossRefGoogle Scholar
Mumby, P.J., Clarke, K.R. and Harborne, A.R. (1996) Weighting species abundance estimates for marine resources assessment. Aquatic Conservation: Marine and Freshwater Ecosystems 6, 115120.3.0.CO;2-T>CrossRefGoogle Scholar
Olsgard, F. and Somerfield, P.J. (2000) Surrogates in marine benthic investigations—which taxonomic unit to target? Journal of Aquatic Ecosystem Stress and Recovery 7, 2542.CrossRefGoogle Scholar
Olsgard, F., Somerfield, P.J. and Carr, M.R. (1997) Relationships between taxonomic resolution and data transformations in analyses of a macrobenthic community along an established pollution gradient. Marine Ecology Progress Series 149, 173181.CrossRefGoogle Scholar
Olsgard, F., Somerfield, P.J. and Carr, M.R. (1998) Relationships between taxonomic resolution, macrobenthic community patterns and disturbance. Marine Ecology Progress Series 172, 2536.CrossRefGoogle Scholar
Pearson, K. (1901) On lines and planes of closest fit to systems of points in space. Philosophical Magazine 2, 559572.Google Scholar
R Core Team (2013) R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing.Google Scholar
Sneath, P. (1957) The application of computers to taxonomy. Journal of General Microbiology 17, 201226.Google ScholarPubMed
Somerfield, P.J. and Clarke, K.R. (1995) Taxonomic levels, in marine community studies, revisited. Marine Ecology Progress Series 127, 113119.CrossRefGoogle Scholar
Tweedley, J.R. and Hallett, C.S. (2013) Monitoring the effects of artificial oxygenation on biota. Perth: Swan River Trust, Perth, 62 pp.Google Scholar
Tweedley, J.R., Warwick, R.M., Valesini, F.J., Platell, M.E. and Potter, I.C. (2012) The use of benthic macroinvertebrates to establish a benchmark for evaluating the environmental quality of microtidal, temperate southern hemisphere estuaries. Marine Pollution Bulletin 64, 12101221.CrossRefGoogle ScholarPubMed
Valesini, F.J., Potter, I.C. and Clarke, K.R. (2004) To what extent are the fish compositions at nearshore sites along a heterogeneous coast related to habitat type? Estuarine, Coastal and Shelf Science 60, 737754.CrossRefGoogle Scholar
Valesini, F.J., Hourston, M., Wildsmith, M.D., Coen, N.J. and Potter, I.C. (2010) New quantitative approaches for classifying and predicting local-scale habitats in estuaries. Estuarine, Coastal and Shelf Science 86, 645664.CrossRefGoogle Scholar
Valesini, F.J., Tweedley, J.R., Clarke, K.R. and Potter, I.C. (in press) The importance of local, system-wide and regional spatial scales in structuring temperate estuarine fish communities. Estuaries and Coasts.Google Scholar
Warton, D.I., Wright, S.T. and Wang, Y. (2012) Distance-based multivariate analyses confound location and dispersion effects. Methods in Ecology and Evolution 3, 89101.CrossRefGoogle Scholar
Whittaker, R.H. (1952) A study of summer foliage insect communities in the Great Smoky Mountains. Ecological Monographs 22, 144.CrossRefGoogle Scholar
Wildsmith, M.D., Rose, T.H., Potter, I.C., Warwick, R.M. and Clarke, K.R. (2011) Benthic macroinvertebrates as indicators of environmental deterioration in a large microtidal estuary. Marine Pollution Bulletin 62, 525538.CrossRefGoogle Scholar
Wilkinson, L. (1994) Advanced Applications: Systat for DOS version 6. Chicago, IL: SYSTAT Inc.Google Scholar
Wilkinson, L. and Friendly, M. (2009) The history of the cluster heat map. The American Statistician 63, 179184.CrossRefGoogle Scholar