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All statistics were performed using Minitab version 10.0, except for the Kolmogorov-
Smirnov test which was carried out by hand using the formula given by Zar (1984)
and the tables given by Smirnov (1948).
Multivariate analyses were carried out on the faunal data to assess (dis)similarities
between community assemblages. All multivariate analyses were performed using the
PRIMER (Plymouth Routines In Multivariate Ecological Research) package, version
4.0 (see Warwick and Clarke, 1994). Dendrograms were produced by hierarchical
agglomerative clustering with group-average linking from the Bray-Curtis similarity
matrices. The raw community data were square-root transformed, this was chosen a
priori as a compromise between no transformation in which different community
assemblages may result from the variability in the most common taxa, and a strong
transformation, such as -JAI or log(x+1), in which the rarer species have very strong
influences on community (dis)similarities (Warwick and Clarke, 1994).
Non-metric Multi-Dimensional Scaling (or MDS) was carried out from which an
ordination plot was produced. In ordination plots, the relative distances apart of the
samples reflect relative similarity in species composition. Since the MDS ordination
represents a multi-dimensional ordination in 2 dimensions, each algorithm has an
associated stress value, the influence of which on the reliability of ordination plots is
discussed by Warwick and Clarke (1994). The final MDS ordination in each analysis
was that with the lowest associated stress value out of 9 iterations. The MDS
procedure was repeated 10 times for each analysis to minimise the chance of
producing MDS plots with only 'local minimum' stress functions (Warwick and
Clarke, 1994), i.e., increasing the number of starting configurations of points in the
ordination plots increases the chance of producing the most optimum MDS plot.
Testing for significance between patch and non-patch communities was performed
using a One-way ANOSIM test (analysis of similarities) in which the null hypothesis
(Ho) in each case was that there were no significant community differences between
the two plot types. The ANOSIIVI test can be regarded as a non-parametric equivalent
of the MANOVA test (e.g., Mardia et al., 1979) in which few, if any, assumptions
about the data are made. Benthic community data are usually far from normally
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