Introduction

An integral part of determining ecological integrity is the measurement of biological integrity, typically emphasizing analyses of plankton, benthos, macroalgae, and fish. In the development of protocols for evaluating biological integrity, benthic macroinvertebrate communities are the most consistently emphasized biotic component of aquatic ecosystems. Many methodologies with hundreds of indices, metrics, and evaluation tools are presently available and, in particular, benthic infaunal communities have been used to a great extent as indicators of environmental status in the marine environment. Benthos react predictably to a range of types of natural and anthropogenic stress, as numerous studies have established. The potential of benthos for high exposure to stress is one of the many characteristics that make them useful indicators. Benthic organisms have limited mobility and are incapable of escaping adverse conditions, and are therefore exposed to contaminants accumulated in sediments. Consequently, benthic assemblages reflect local environmental conditions, as distinct from most pelagic fauna.

Benthic infaunal communities are also useful as biological indicators because of their taxonomic diversity: undoubtedly, benthic organisms have a wide variety of physiological tolerances, feeding modes, and trophic interactions, which cause them to be susceptible to a broad range of environmental stressors. However, an interpretation of this diversity of responses can be challenging. Usually, benthic scientists are extremely persistent in quantifying variations of the abundance of species over time or space, but then tend to use subjective approaches in order to gauge whether the sum extent of these variations across species are indicative of an improving or a deteriorating environment. The most difficult challenge in index development is selecting and combining metrics in a manner that is complex enough to capture the dynamics of essential ecological processes but not so complex that its meaning is obscured.

Benthic condition indices can be grouped into three classes, based upon their complexity, information content, and method of metric combination.

In order to summarize data, univariate individual-species data, or community structure measures can be used. Even if, in given circumstances, these measures may be constructive, there is evidence to suggest that benthos respond to the stress of pollution in stages, so that different measures are required in order to capture different levels of response.

A second methodology is the multimetric index. This unites multiple measures of community response into a single index, in order to more effectively capture the various types of response that transpire at distinct levels of stress.

The third approach directly utilizes species composition information. This typically occurs through the description of the assemblage patterns in a comparative multivariate space describing the assemblages pattern, including modeling. Research has proposed that multi-variate approaches provide greater sensitivity in the assessment of perturbation than methods based upon assemblage metrics. Nevertheless, the simple conveyance to managers of the execution of multivariate approaches and the assessment of their output is often too complex. Information regarding individual species has also been utilized in numerous indices through the assignment of pollution tolerance scores to different members of the community, with the successive calculation of a mean pollution tolerance score of the species found at a site. The benthic response index (BRI) belongs to multivariate indices category and it represents the abundance-weighted average pollution tolerance of species occurring in a sample, and is comparable to the weighted average approach that is used in gradient analysis. BRI aims at combining the ease of communication of the tolerance score approach with the analytical rigor of multivariate statistics. The average position of species i (p) on the pollution gradient defined in the ordination space was computed as follows:

utilized as pollution tolerance scores in eqn [2] in order to calculate the index values:

where t is the number of samples to be used in the sum, with only the highest ti species abundance values included in the sum. The gj is the position of species i on the pollution gradient in the ordination space for sample j (i.e., g is the projection onto the direction vector representing the pollution gradient.) Equation [1] is the arithmetic average of the pollution gradient positions of the stations, at which species i occurs, with only the stations corresponding to the ti highest abundance values of species i used in the average. The pi values that have been computed in eqn [1] are

[2l where Is is the index value for sample s, n is the number of species for sample s, pi is the position for species i on the pollution gradient (pollution tolerance score), and asi is the abundance of species i in sample s. Species in the sample without pi values are ignored. The cube root of abundance was determined to be the optimal weighting factor based upon an optimization procedure. The determination of the pollution tolerance score (pi) for the species involves four steps: (1) the assembling of a calibration infaunal data set; (2) the conduction of an ordination analysis to place each sample in the calibration data set on a pollution gradient; (3) the computation of the average position of each species along the gradient; and (4) the standardization and scaling of the positions in order to achieve comparability across depth zones.

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