Multivariate analysis applied to agglomerated macrobenthic data from an unpolluted estuary

Abstract

We agglomerated species into higher taxonomic aggregations and functional groups to analyse environmental gradients in an unpolluted estuary. We then applied non-metric Multidimensional Scaling and Redundancy Analysis (RDA) for ordination of the agglomerated data matrices. The correlation between the ordinations produced by both methods was generally high. However, the performance of the RDA models depended on the data matrix used to fit the model. As a result, salinity and total nitrogen were only found significant when aggregated data matrices were used rather than species data matrix. We used the results to select a RDA model that explained a higher percentage of variance in the species data set than the parsimonious model. We conclude that the use of aggregated matrices may be considered complementary to the use of species data to obtain a broader insight into the distribution of macrobenthic assemblages in relation to environmental gradients.