In the early 1980s, a strategy for graphical representation of multivariate (multispecies) abundance data was introduced into marine ecology by, among others. Field, et al. (1982). A decade on, it is instructive to: (i) identify which elements of this often-quoted strategy have proved most useful in practical assessment of community change resulting from pollution impact; and (ii) ask to what extent evolution of techniques in the intervening years has added self-consistency and comprehensiveness to the approach. The pivotal concept has proved to be that of a biologically-relevant definition of similarity of two samples, and its utilization mainly in simple rank form, for example 'sample A is more similar to sample B than it is to sample C. Statistical assumptions about the data are thus minimized and the resulting non-parametric techniques will be of very general applicability. From such a starting point, a unified framework needs to encompass: (i) the display of community patterns through clustering and ordination of samples; (ii) identification of species principally responsible for determining sample groupings; (iii) statistical tests for differences in space and time (multivariate analogues of analysis of variance, based on rank similarities); and (iv) the linking of community differences to patterns in the physical and chemical environment (the latter also dictated by rank similarities between samples). Techniques are described that bring such a framework into place, and areas in which problems remain are identified. Accumulated practical experience with these methods is discussed, in particular applications to marine benthos, and it is concluded tbat they have much to offer practitioners of environmental impact studies on communities.

The method of choice for multivariate representation of community structure is often non-metric multi-dimensional scaling (MDS). This has great flexibility in accomn~odating biologically relevant (i.e. non correlation-based) definitions of similarity In species composition of 2 samples, and in preserving the rank-order relations amongst those similarities in the placing of samples in an ordination. Correlation-based techniques (such as Canonical Correlation) are then inappropriate in linking the observed biotic structure to measured environmental variables; a more natural approach is simply to compare separate sample ordinations from biotic and abiotic variables and choose that subset of environmental variables which provides a good match between the 2 configurations. In fact, the fundamental constructs here are not the ordination plots but the (rank) similarity matrices which underlie them: a suitable measure of agreement between 2 such matrices is therefore proposed and used to define an optimal subset of environmental variables w h~c h 'best explains' the biotic structure. This simple technique is illustrated wlth 3 data sets, from studles of macrobenthic, meiobenthic and diatom communities in estuarine and coastal waters.

A strategy is presented for analysing marine biological survey data and relating the biotic patterns to environmental data. To avoid circular argument, biotic and environmental data are kept separate. The strategy is illustrated by a worked example using data on the distribution of 182 nematode species in 107 samples in the River Exe estuary. Nineteen stations are grouped Into 4 main clusters using complementary classification and multi-dimensional scaling (MDS) ordination techniques. These are both based on root-root transformed abundance data with the Bray-Curtis measure of similarity. Indicator species characterising each group are extracted using information statistics. Inverse analyses give clusters of CO-occurnng species which are strongly related to the station groups. Relationships of station groups to environmental variables are revealed by superimposing data for one variable a t a time on the MDS plot, showing that some station groups differ in sediment granulometry and others in salinity, for example. Some of the other factors plotted show no difference between station groups. Similarly, physiognomic charactcrlstics of the species are superimposed on the MDS plots of the inverse analysis of species groups, revealing differences in setal length and trophic status between the species groups. Finally, the 4 major station groups and species groups are related to one another in terms of morphological adaptation to the habitat.

Statistical aspects of 'biological effects' field surveys are discussed, with particular reference to the GEEP Workshop. Recommendations are made on design criteria, for example, selection of sites and samples, and replication strategies (including formulae for sample size determination) The role of transforinations is discussed, both for univariate sub-lethal response data and the mulbvariate data arising from benthic community studies. Statistical analysis is categorised into testing methods, for establishing biological differences between field sites, and descriptive techniques, for representation of those differences. The former includes a non-parametric randomisation test for use with site-species arrays and the latter a survey of various multivariate ordination and clustering methods A final section outlines a procedure for comparison of different pollution indices, combining their power to detect specific contaminant inputs with their associated 'costs'

Summary For biological community data (species‐by‐sample abundance matrices), Warwick & Clarke (1995) defined two biodiversity indices, capturing the structure not only of the distribution of abundances amongst species but also the taxonomic relatedness of the species in each sample. The first index, taxonomic diversity (δ), can be thought of as the average taxonomic ‘distance’ between any two organisms, chosen at random from the sample: this distance can be visualized simply as the length of the path connecting these two organisms, traced through (say) a Linnean or phylogenetic classification of the full set of species involved. The second index, taxonomic distinctness (δ*), is the average path length between any two randomly chosen individuals, conditional on them being from different species. This is equivalent to dividing taxonomic diversity, δ, by the value it would take were there to be no taxonomic hierarchy (all species belonging to the same genus). δ* can therefore be seen as a measure of pure taxonomic relatedness, whereas δ mixes taxonomic relatedness with the evenness properties of the abundance distribution. This paper explores the statistical sampling properties of δ and δ*. Taxonomic diversity is seen to be a natural extension of a form of Simpson's index, incorporating taxonomic (or phylogenetic) information. Importantly for practical comparisons, both δ and δ* are shown not to be dependent, on average, on the degree of sampling effort involved in the data collection; this is in sharp contrast with those diversity measures that are strongly influenced by the number of observed species. The special case where the data consist only of presence/absence information is dealt with in detail: δ and δ* converge to the same statistic (δ+), which is now defined as the average taxonomic path length between any two randomly chosen species. Its lack of dependence, in mean value, on sampling effort implies that δ+ can be compared across studies with differing and uncontrolled degrees of sampling effort (subject to assumptions concerning comparable taxonomic accuracy). This may be of particular significance for historic (diffusely collected) species lists from different localities or regions, which at first sight may seem unamenable to valid diversity comparison of any sort. Furthermore, a randomization test is possible, to detect a difference in the taxonomic distinctness, for any observed set of species, from the ‘expected’δ+ value derived from a master species list for the relevant group of organisms. The exact randomization procedure requires heavy computation, and an approximation is developed, by deriving an appropriate variance formula. This leads to a ‘confidence funnel’ against which distinctness values for any specific area, pollution condition, habitat type, etc., can be checked, and formally addresses the question of whether a putatively impacted locality has a ‘lower than expected’ taxonomic spread. The procedure is illustrated for the UK species list of free‐living marine nematodes and sets of sa...