Guide to the coastal and surface zooplankton of the south-western Indian Ocean. Occasional Publication of the Marine Biological Association 15

A guide compiled as an aid to researchers in the identification of the coastal and shallow water, south-western Indian Ocean pelagic zooplankton, as much of the identification literature covering this area of amazing biodiversity is currently spread through the scientific literature and not accessible without extensive library resources. Most zooplankton groups, except fish larvae and eggs, have been covered, but some specialist groups have not yet been dealt with in great detail. However, a selection of representative members of most groups have been given, so that organisms can at least be assigned to perhaps a particular genus within the main group. The species list is based on zooplankton sampling carried out round the coastal areas of the islands of Mahé and Aldabra (Seychelles), Rodrigues (Mauritius), Madagascar and from a sampling transect between Seychelles and Rodrigues. The guide therefore includes a high proportion of the island-coastal and surface water zooplankton of the whole Indian Ocean. The location where a particular species has been sampled has been noted and some species that have not been sampled, but are known to occur in the region, have been included. Comprehensive taxonomic information has not been presented, but sufficient information should be given to identify each species. Keys have not yet been included for genera, as further species will be added. A bibliography of relevant plankton references has also been included.

Automatic classification of field-collected dinoflagellates by artificial neural network

Automatic taxonomic categorisation of 23 species of dinoflagellates was demonstrated using field-collected specimens. These dinoflagellates have been responsible for the majority of toxic and noxious phytoplankton blooms which have occurred in the coastal waters of the European Union in recent years and make severe impact on the aquaculture industry. The performance by human 'expert' ecologists/taxonomists in identifying these species was compared to that achieved by 2 artificial neural network classifiers (multilayer perceptron and radial basis function networks) and 2 other statistical techniques, k-Nearest Neighbour and Quadratic Discriminant Analysis. The neural network classifiers outperform the classical statistical techniques. Over extended trials, the human experts averaged 85% while the radial basis network achieved a best performance of 83%, the multilayer perceptron 66%, k-Nearest Neighbour 60%, and the Quadratic Discriminant Analysis 56%.