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Spatial autocorrelation analysis assesses whether the abundance of a species from one
sample is significantly dependent on the abundances in neighbouring samples using
Moran's 'V and/or Geary's 'c' coefficients, or the Mantel coefficient for the
multivariate situation (Sokal, 1986; Sokal and Thomson, 1987; Legendre and Fortin,
1989; Wartenberg, 1989). Correlograms, or graphs of these autocorrelation
coefficients for various distance classes, allow inferences to be made on a variety of
spatial patterns, heterogeneity in abundance, patch size and density gradients (Sokal
and Oden, 1978; Sokal, 1979). Oden and Sokal (1986) and Sokal (1986) suggested
that since correlograms describe the underlying spatial relationships of a pattern rather
than its appearance, they may be closer guides to some of the processes that have
generated these patterns than the patterns themselves. Consequently, spatial
autocorrelation analysis has been widely used in many areas of ecology (Legendre and
Trousellier, 1988; Leduc et al., 1992; Hinch et al., 1993; Diniz-Filho and Bini, 1994;
Burgman and Williams, 1995) as well as in marine benthic studies (Jumars, 1978;
Eckman, 1979; McArdle and Blackwell, 1989; Thrush et al., 1989; Lawrie, 1996;
Hewitt et al., 1997) to describe the spatial pattern exhibited by macrobenthic
populations and to help elucidate the processes producing them. The pattern observed
depends upon the grain, lag and the extent and, therefore, there is a limit to the scales
of patterns detected within any one survey.
Since correlograms may only ambiguously correspond to a single type of spatial
structure (Sokal and Oden, 1978) their interpretation should be complemented by
maps representing the spatial variation of the variable(s) of interest (Legendre, 1993).
The easiest way to obtain a contour map of a single variable is to use inverse-square
distance or other interpolation methods, such as kriging. However, interpretation of
interpolation plots may be subjective: the patterns produced depend to a certain extent
on the interpolation method and the distance classes chosen.
This study involved a detailed investigation into the spatial patterns exhibited by
macrobenthic invertebrate species on an intertidal sandflat using some of the above
described numerical techniques including the variance to mean ratio, Morisita index
and standardised Morisita index, mapping with kriging and spatial autocorrelation
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