Download (20MB) - Repository@Napier

The results show that at the smaller scale, i.e., lm survey, P. elegans distribution was
not correlated with any species nor with any sediment variable. The results from the
8m survey suggest that at this scale, P. elegans was positively correlated with M.
balthica and all the sediment variables, i.e., % organic carbon, % silt/clay, Md 0 and
sorting coefficient, and negatively correlated with A. marina. At the larger scale, i.e.,
40m survey, P. elegans was only positively correlated with M. balthica and all the
sediment variables, except Md (1). The significant correlations of P. elegans
abundances with those of A. marina and the sediment variables must be treated with
caution because of the differences in sampling protocols (see Methods).
Some of the correlations which were significant even after the Bonferroni correction,
which Legendre and Legendre (1997) suggested was overly conservative and often led
to rejecting too few hypotheses, appeared only weakly correlated when their scatter
plots were examined. This was because the numbers of degrees of freedom (i.e., 64)
were so high that even relatively low values of the coefficient, e.g., 0.401 for %
organic carbon content from 40m survey, were significant. Fowler and Cohen (1990)
suggested that this correlation coefficient should be used only when the number of
sampling units is 30 or less. Therefore, caution is needed in the interpretation of these
correlation results and thus it is important to examine the contour maps when
interpreting them.
lm survey - spatial patterns
The contour maps presented in Figures 2.5(i-vii) display the distributions of the most
abundant species from the lm survey which had significantly non-random
distributions, and Figures 2.6(i-v) present the significant correlograms from spatial
autocorrelation analysis. The distance classes 1-6 in Figures 2.6(i-ix) represent those
samples that are 0-1m, 1-1.5m, 1.5-2m, 2-2.5m, 2.5-3m and 3-3.5m apart respectively.
These were chosen instead of larger intervals so that a more accurate estimate of patch
size could be achieved.
Sokal (1979) described the inferences which can be made about the spatial
distributions of biological populations from autocorrelation analysis and provided
some useful examples. Low-order (short-distance) positive autocorrelation, where
36