Spatio-temporal variations in bloom of the red-tide dinoflagellate Karenia mikimotoi in Imari Bay, Japan, in 2014: Factors controlling horizontal and vertical distribution.

A massive bloom of the dinoflagellate Karenia mikimotoi appeared in 2014 in Imari Bay, Japan. Bloom dynamics and hydrographical conditions were examined by field survey. The bloom initially developed in the eastern area of Imari Bay, subsequently after rainfall during the neap tides, cell density exceeded over 10,000cellsml. Vertical distribution of K. mikimotoi was primarily controlled by the light intensity and secondarily by the water quality during the daytime. Almost all cell-density maxima occurred in depths with weak daytime light intensities of <300µmolm-2s-1. In some cases of weak light intensity, cell-density maxima occurred in depths with favorable hydrodynamic conditions for the growth. Spatially classified areas were identified by cluster analysis using the growth rate calculated from seawater temperature and salinity. This study quantitatively evaluated the environmental factors of the eastern area, where the bloom initially occurred, during the development of the bloom.

Massively parallel sequencing-based survey of eukaryotic community structures in Hiroshima Bay and Ishigaki Island.

In this study, we compared the eukaryote biodiversity between Hiroshima Bay and Ishigaki Island in Japanese coastal waters by using the massively parallel sequencing (MPS)-based technique to collect preliminary data. The relative abundance of Alveolata was highest in both localities, and the second highest groups were Stramenopiles, Opisthokonta, or Hacrobia, which varied depending on the samples considered. For microalgal phyla, the relative abundance of operational taxonomic units (OTUs) and the number of MPS were highest for Dinophyceae in both localities, followed by Bacillariophyceae in Hiroshima Bay, and by Bacillariophyceae or Chlorophyceae in Ishigaki Island. The number of detected OTUs in Hiroshima Bay and Ishigaki Island was 645 and 791, respectively, and 15.3% and 12.5% of the OTUs were common between the two localities. In the non-metric multidimensional scaling analysis, the samples from the two localities were plotted in different positions. In the dendrogram developed using similarity indices, the samples were clustered into different nodes based on localities with high multiscale bootstrap values, reflecting geographic differences in biodiversity. Thus, we succeeded in demonstrating biodiversity differences between the two localities, although the read numbers of the MPSs were not high enough. The corresponding analysis showed a clear seasonal change in the biodiversity of Hiroshima Bay but it was not clear in Ishigaki Island. Thus, the MPS-based technique shows a great advantage of high performance by detecting several hundreds of OTUs from a single sample, strongly suggesting the effectiveness to apply this technique to routine monitoring programs.

An assessment of phytoplankton primary productivity in the Arctic Ocean from satellite ocean color/in situ chlorophyll-a based models.

We investigated 32 net primary productivity (NPP) models by assessing skills to reproduce integrated NPP in the Arctic Ocean. The models were provided with two sources each of surface chlorophyll-a concentration (chlorophyll), photosynthetically available radiation (PAR), sea surface temperature (SST), and mixed-layer depth (MLD). The models were most sensitive to uncertainties in surface chlorophyll, generally performing better with in situ chlorophyll than with satellite-derived values. They were much less sensitive to uncertainties in PAR, SST, and MLD, possibly due to relatively narrow ranges of input data and/or relatively little difference between input data sources. Regardless of type or complexity, most of the models were not able to fully reproduce the variability of in situ NPP, whereas some of them exhibited almost no bias (i.e., reproduced the mean of in situ NPP). The models performed relatively well in low-productivity seasons as well as in sea ice-covered/deep-water regions. Depth-resolved models correlated more with in situ NPP than other model types, but had a greater tendency to overestimate mean NPP whereas absorption-based models exhibited the lowest bias associated with weaker correlation. The models performed better when a subsurface chlorophyll-a maximum (SCM) was absent. As a group, the models overestimated mean NPP, however this was partly offset by some models underestimating NPP when a SCM was present. Our study suggests that NPP models need to be carefully tuned for the Arctic Ocean because most of the models performing relatively well were those that used Arctic-relevant parameters.