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Maps produced by kriging and other suitable interpolation methods were essentially
similar.
Spatial autocorrelation analysis was carried out using the Spatial Autocorrelation
Analysis Program (SAAP) version 4.3 (Wartenberg, 1989). This package produces
correlograms of autocorrelation coefficient values against each distance class.
Moran's `i' and Geary's 'c' autocorrelation coefficients (Cliff and Ord, 1973) were
calculated in the present study since these are the most commonly used and have been
recommended for use in ecological applications (Sokal, 1979; Hubert et al., 1981;
Legendre and Fortin, 1989). The SAAP program gives i values in the range 1 to -1
and c values in the range 0-2; while low values of i correspond to negative
autocorrelation and high values correspond to positive autocorrelation, the opposite
applies for values of c. Each coefficient is sensitive to slightly different behaviours of
the data (Jumars et al., 1977). For example, i bears a close resemblance to a Pearson
product moment correlation coefficient and is therefore most sensitive to extreme
values in the data set while c, which is a distance-type function, is most sensitive to
the proximity of similar or dissimilar values, regardless of their departure from the
mean (Jumars eta!., 1977).
Following Oden (1984) the overall significance of each spatial autocorrelogram was
assessed by checking that the most significant spatial autocorrelation coefficient found
in a correlogram was significant at a Bonferroni-corrected significance level a', where
a'=a/k, k being the number of autocorrelation coefficients (i.e., the number of
distance classes) in the correlogram (Sokal, 1986). Spatial autocorrelation analysis
allowed the nature of patterns and estimates of patch sizes to be made in this study.
The spatial characteristics of patches indicated by autocorrelation analysis were only
accepted if the contour plots were consistent with these patterns.
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