Relationship of Spatial Phytoplankton Variability during Spring with Eutrophic Inshore and Oligotrophic Offshore Waters in the East Sea, Including Dokdo, Korea

The area near the subpolar front of the East Sea has high primary productivity during the spring season. We conducted two surveys, one in early spring and another in late spring, to assess environmental factors that influence phytoplankton community structure during these times. During early spring, vertical mixing supplied abundant nutrients to the surface. Due to the well-mixed water column, there were high nutrient levels, but total phytoplankton abundances and diversity were relatively low and were dominated by the diatom Chaetoceros spp. During late spring, the water column gradually stratified, with relatively high levels of nutrients in the surface layers near the coastal areas. Phytoplankton abundance and diversity at that time were higher, and there were diatoms (Pseudo-nitzschia spp. and Chaetoceros spp.), cryptophytes, and small flagellates. Pseudo-nitzschia spp. were especially abundant in re-sampled areas. The presence of a stratified and stable water mass and sufficient nitrate led to high phytoplankton growth, even in the open sea during late spring. These findings provide a better understanding of how phytoplankton population dynamics in the East Sea depend on water column stability during both early and late spring seasons.

Neural Network Approaches to Reconstruct Phytoplankton Time-Series in the Global Ocean

Phytoplankton plays a key role in the carbon cycle and supports the oceanic food web. While its seasonal and interannual cycles are rather well characterized owing to the modern satellite ocean color era, its longer time variability remains largely unknown due to the short time-period covered by observations on a global scale. With the aim of reconstructing this longer-term phytoplankton variability, a support vector regression (SVR) approach was recently considered to derive surface Chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) from physical oceanic model outputs and atmospheric reanalysis. However, those early efforts relied on one particular algorithm, putting aside the question of whether different algorithms may have specific behaviors. Here, we show that this approach can also be applied on satellite observations and can even be further improved by testing performances of different machine learning algorithms, the SVR and a neural network with dense layers (a multi-layer perceptron, MLP). The MLP outperforms the SVR to capture satellite Chl (correlation of 0.6 vs. 0.17 on a global scale, respectively) along with its seasonal and interannual variability, despite an underestimated amplitude. Among deep learning algorithms, neural network such as MLP models appear to be promising tools to investigate phytoplankton long-term time-series.

Influence of coastal upwelling on micro-phytoplankton variability at Valparaíso Bay (~33ºS), Central Chile

In this work 10 years of data (1986-1996) from a fixed station located in the northern part of Valparaíso Bay (33º00’S; 71º35’W) were analysed to study the influence of coastal upwelling activity on the temporal variation of micro-phytoplankton (20-200 μm) and their relationship with oceanographic conditions. The upwelling activity at the bay was associated to semi-annual wind regime with an intensification of upwelling-favourable S-SW winds from September to March followed by a decrease and the occurrence of downwelling events from April to August. Oceanographic conditions showed the ascent of cold, nutrient-rich salty water in spring (September-November). However, during summertime under highest upwelling index, thermal stratification conditions were registered. This stratification might be associated to either the solar radiation or the presence of an upwelling shadow area in the bay. The upwelling period had the highest micro-phytoplankton abundance mainly dominated by diatoms. This period was associated with an increase in biomass and richness in the bay. Meanwhile during non-upwelling period —under homogenous conditions of temperature, salinity and nutrients— an increase in diversity (but low abundance and richness) associated to dinoflagellates and silicoflagellates was noted. Therefore, the results suggest the presence of a bi-modal regime of micro-phytoplankton in the bay in response to changes in oceanographic conditions related to local wind forcing and mixing/stratification.