The Method

The ABC method as originally formulated involves the plotting of separate ¿-dominance curves (see ¿-Dominance Curves) for species abundances and species biomasses on the same graph and comparing the forms of the two curves relative to each other. The species are ranked in order of importance in terms of abundance or biomass on the x-axis on a logarithmic scale, with percentage dominance on the j-axis on a cumulative scale. Of course the species ordering is unlikely to be the same for abundance and biomass. In undisturbed assemblages a few large species are dominant in terms of biomass but not abundance, resulting in the elevation of the biomass curve relative to the abundance curve throughout its length (Figure 1a). Perturbed assemblages, however, have a few species with very high abundance but small body size so that they do not dominate the biomass and the abundance curve lies above the biomass curve (Figure 1c). Under moderate perturbation the large competitive dominants are eliminated but there is no population explosion of small opportunists, so that the inequality in size between the numerical and biomass dominants is reduced and the biomass and abundance curves are closely coincident and may cross over each other one or more times (Figure 1b).

The contention is that these three conditions (unperturbed, moderately perturbed, or grossly perturbed) should be recognizable without reference control samples in time or space, the two curves acting as an internal control against each other and providing a snapshot of the condition of the assemblage at any one time or place.


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5 10 Species rank (log scale)

5 10

Figure 1 Hypothetical k-dominance curves for species biomass and abundance, showing unperturbed, moderately perturbed, and grossly perturbed conditions.

Of course, confirmatory comparisons with spatial or temporal reference samples are still highly desirable. A prerequisite of the method is adequate sample size or replication because the large biomass dominants are often rare and liable to a higher sampling error than the numerical dominants.

The evaluation of ABC curves involves their visual inspection, and can be cumbersome if many sites, times, or replicates are involved. In such cases it is convenient to reduce each plot to a single summary statistic. If the abundance (A) values are subtracted from the biomass (B) values for each species rank in the ABC curve, the sum of the B — A values across the ranks will be strongly positive in the unperturbed case (Figure 1a), near zero in the case where the curves are closely coincident (Figure 1b), and strongly negative where the curves are transposed (Figure 1c). The summation needs to be standardized to a common scale so that comparisons can be made between samples with differing numbers of species (S), the most widely used form being the W (for Warwick) statistic:

For replicated samples, the W statistic also provides an obvious route for hypothesis testing, using standard uni-variate ANOVA.

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