Genomic Sequence and Analysis of EhV-99B1, a New Coccolithovirus from the Norwegian Fjords

Coccolithoviruses are giant dsDNA viruses that infect Emiliania huxleyi, the most ubiquitous marine microalga. Here, we present the genome of the latest coccolithovirus strain to be sequenced, EhV-99B1, and compare it with two other coccolithovirus genomes (EhV-86 and EhV-163). EhV-99B1 shares a pairwise nucleotide identity of 98% with EhV-163 (the two strains were isolated from the same Norwegian fjord but in different years), and just 96.5% with EhV-86 (isolated in the same spring as EhV-99B1 but in the English Channel). We confirmed and extended the list of relevant genomic differences between these EhVs from the Norwegian fjord and EhVs from the English Channel, namely the removal/insertions of: a phosphate permease, an endonuclease, a transposase, and two specific tRNAs. As a whole, this study provided new clues and insights into the diversity and mechanisms driving the evolution of these large oceanic viruses, in particular those processes involving selfish genetic elements.

Long-term variability and environmental preferences of calycophoran siphonophores in the Bay of Villefranche (North-Western Mediterranean)

Long-term variability of the main calycophoran siphonophores was investigated between 1974 and 1999 in a coastal station in the north-western Mediterranean. The data were collected at weekly frequency using a macroplankton net (680 μm mesh size) adapted to quantitatively sample delicate gelatinous plankton. A 3-year collection (1967–1969) of siphonophores from offshore waters using the same methodology showed that the patterns of variability observed inshore were representative of siphonophores’ changes at a regional scale. The aims of the study were: (i) to investigate the patterns of variability that characterised the dominant calycophoran species and assemblages; (ii) to identify the environmental optima that were associated with a significant increase in the dominant siphonophore species and (iii) to verify the influence of hydroclimatic variability on long-term changes of siphonophores. Our results showed that during nearly 3 decades the standing stock of calycophoran siphonophores did not show any significant change, with the annual maximum usually recorded in spring as a result of high densities of the dominant species Lensia subtilis, Muggiaea kochi and Muggiaea atlantica. Nevertheless, major changes in community composition occurred within the calycophoran population. Since the middle 1980s, M. kochi, once the most dominant species, started to decrease allowing other species, the congeneric M. atlantica and Chelophyes appendiculata, to increasingly dominate in spring and summer–autumn, respectively. The comparison of environmental and biotic long-term trends suggests that the decrease of M. kochi was triggered by hydrological changes that occurred in the north-western Mediterranean under the forcing of large-scale climate oscillations. Salinity, water stratification and water temperature were the main hydroclimatic factors associated with a significant increase of siphonophores, different species showing different environmental preferences.

Satellite discrimination of Karenia mikimotoi and Phaeocystis harmful algal blooms in European coastal waters: Merged classification of ocean colour data

The detection of dense harmful algal blooms (HABs) by satellite remote sensing is usually based on analysis of chlorophyll-a as a proxy. However, this approach does not provide information about the potential harm of bloom, nor can it identify the dominant species. The developed HAB risk classification method employs a fully automatic data-driven approach to identify key characteristics of water leaving radiances and derived quantities, and to classify pixels into “harmful”, “non-harmful” and “no bloom” categories using Linear Discriminant Analysis (LDA). Discrimination accuracy is increased through the use of spectral ratios of water leaving radiances, absorption and backscattering. To reduce the false alarm rate the data that cannot be reliably classified are automatically labelled as “unknown”. This method can be trained on different HAB species or extended to new sensors and then applied to generate independent HAB risk maps; these can be fused with other sensors to fill gaps or improve spatial or temporal resolution. The HAB discrimination technique has obtained accurate results on MODIS and MERIS data, correctly identifying 89% of Phaeocystis globosa HABs in the southern North Sea and 88% of Karenia mikimotoi blooms in the Western English Channel. A linear transformation of the ocean colour discriminants is used to estimate harmful cell counts, demonstrating greater accuracy than if based on chlorophyll-a; this will facilitate its integration into a HAB early warning system operating in the southern North Sea.