Taxonomic surrogacy in monitoring of tropical polychaete communities along the West African continental margin

Abstract

Efficacy tests of surrogacy measures are a very important element of marine benthic monitoring studies that may be used not only in pollution assessment and protection management but also in conservation planning. This approach is very important in the Large Marine Ecosystems that are influenced by numerous environmental factors and threatened by human activities associated with oil excavation and other types of industry. Here we analysed polychaete communities in the Gulf of Guinea (a poorly studied but highly important centre of marine biodiversity in the tropical East Atlantic) at species, genus and family levels. We demonstrated good efficacy of surrogates (genus- and family-level data) in the assessment of richness, diversity and evenness (Shannon, Simpson, Margalef, Pielou indices) along a 25–1000 m depth gradient. The lowest usefulness of higher taxa surrogates was demonstrated in the analysis of faunistic similarity, such as clustering and Similarity Percentage (SIMPER), based on the Bray-Curtis formula. Canonical Correspondence Analysis (CCA) and generalised linear models (GLM) based on Poisson distribution were also used, and demonstrated that genus-level patterns were relatively similar to those at species level. We recorded a substantial loss of information at the family level (as expressed by the modification of eigenvalues and the statistical significance of the axes, as well as the number of most parsimonious models and the smaller weight of each model). Higher taxa analysis at both genus and family levels failed to identify pollution indicator taxa. Since practical aspects of surrogacy require fast identification of the material, family-level data are the most desirable surrogates. Nevertheless, our data demonstrated that, in the case of clustering and ordination based on large numbers of environmental variables, their usefulness is doubtful and they should be used cautiously in order to avoid reaching biased conclusions.