Taxonomy and morphology of Calciopappus curvus sp. nov. (Syracosphaeraceae, Prymnesiophyceae), a novel appendage-bearing coccolithophore.

Based on scanning electron microscopy observations, a new species of the coccolithophore genus Calciopappus (Syracosphaeraceae, Prymnesiophyceae) is described from the surface waters off Bergen and from the lower photic zone of sub-tropical and tropical waters. Morphological, coccolith rim structure and biometric analyses strongly support separation of this morphotype from the two described Calciopappus species, but inclusion of it within the genus. The new form differs from the other species in being noticeably smaller and in morpho-structural details of each of the three coccolith types that form the coccosphere: (1) the body coccoliths have an open central area; (2) the whorl coccoliths have a wide central opening and two thumb-like protrusions; and (3) the appendage coccoliths are curved. On this basis, the species is formally described as Calciopappus curvus sp. nov., its systematic affinity is discussed and compared with other extant coccolithophores.

A rapid technique for classifying phytoplankton fluorescence spectra based on self-organizing maps.

Fluorescence spectroscopy has been demonstrated to be a powerful tool for characterizing phytoplankton communities in marine environments. Using different fluorescence spectra techniques, it is now possible to discriminate the major phytoplankton groups. However, most of the current techniques are based on fluorescence excitation measurements, which require stimulation at different wavelengths and thus considerable time to obtain the complete spectral profile. This requirement may be an important constraint for several mobile oceanographic platforms, such as vertical profilers or autonomous underwater vehicles, which require rapid-acquisition instruments. This paper presents a novel technique for classifying fluorescence spectra based on self-organizing maps (SOMs), one of the most popular artificial neural network (ANN) methods. The method is able to achieve phytoplankton discrimination using only fluorescence emission spectra (single wavelength excitation), thus reducing the acquisition time. The discrimination capabilities of SOM using excitation and emission spectra are compared. The analysis shows that the SOM has a good performance using excitation spectra, whereas data preprocessing is required in order to obtain similar discrimination capabilities using emission spectra. The final results obtained using emission spectra indicate that the discrimination is properly achieved even between algal groups, such as diatoms and dinoflagellates, which cannot be discriminated with previous methods. We finally point out that although techniques based on excitation spectra can achieve a better taxonomic accuracy, there are some applications that require faster acquisition processes. Acquiring emission spectra is almost instantaneous, and techniques such as SOM can achieve good classification performance using appropriately preprocessed data.