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The authors present evidence of the difficulties facing human taxonomists/ecologists in identifying marine dinoflagellates. This is especially important for work on harmful algal blooms in marine aquaculture. It is shown that it is difficult for people to categorise specimens from species with significant morphological variation, perhaps with morphologies overlapping with those of other species. Trained personnel can be expected to achieve 67 to 83% self-consistency and 43% consensus between people in an expert taxonomic labelling task. Experts who are routinely engaged in particular discriminations can return accuracies in the range of 84 to 95%. In general, neither human nor machine can be expected to give highly accurate or repeatable labelling of specimens. It is also shown that automation methods can perform as well as humans on these complex categorisations.

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Autonomous underwater vehicles (AUVs) are playing an ever-growing role in modern subocean operations, generating a demand for faster, more manoeuvrable designs capable of deployments of increasingly longer durations. In order to meet these demands, vehicle developers have been looking to biological aquatic animals for inspiration. After evolving for millions of years, fish and cetaceans have developed fast efficient locomotion techniques, with levels of manoeuvrability that far outperform conventional engineered marine locomotion systems. This paper aims to give a brief introduction into some of the biologically inspired propulsion mechanisms that have been developed, to explain their strengths, their weaknesses, and the motivation behind them, and then finally to predict future trends in biomimetic AUV propulsion design.

In the future, if marine science is to achieve any progress in addressing biological diversity of ocean plankton, then it needs to sponsor development of new technology. One requirement is the development of high-resolution sensors for imaging field-collected and in situ specimens in a non-invasive manner. The rapid automatic categorisation of species must be accompanied by the creation of very large distributed databases in the form of high-resolution 3D rotatable images of species, which could become the standard reference source for automatic identification. These 3D images will serve as classification standards for field applications, and (in adjusted optical quality) as training templates for image analysis systems based on statistical and other pattern-matching processes. This paper sets out the basic argument for such developments and proposes a long-term solution to achieve these aims.