The calanoid copepod Acartia ohtsukai predominates the estuarine and coastal waters of East Asia during summer. Its occurrence characteristics confer it with good potential as live prey for fish larvae through mass culture. To investigate the effect of temperature and salinity combinations on its egg production rate (EPR), hatching success (HS), and mortality rate, experiments were undertaken and repeated three times for combinations of five temperatures (10, 15, 20, 25, and 30°C) and seven salinities (10, 15, 20, 25, 27, 30, and 33 psu). EPR and HS were highest at temperatures of 25 and 30°C, respectively, with a salinity of 27 psu. Mortality rate was highest at 10°C in almost all salinity gradients, whereas it was lower at water temperature and salinity ranges of 20–30°C and 20–30 psu, respectively. These findings indicate that A. ohtsukai can inhabit wide ranges of water temperatures and salinities, and that the optimized condition for mass culture is a combination of water temperature of 25°C and salinity of 27 psu.

An in situ mesocosm experiment was performed to investigate the combined effects of ocean acidification and warming on the coastal phytoplankton standing stock and species composition of a eutrophic coastal area in the temperate-subtropical region. Experimental treatments of natural seawater included three CO₂ and two temperature conditions (present control: ~400 μatm CO₂ and ambient temperature, acidification conditions: ~900 μatm CO₂ and ambient temperature, and greenhouse conditions: ~900 μatm CO₂ and ambient temperature +3 °C). We found that increased CO₂ concentration benefited the growth of small autotrophic phytoplankton groups: picophytoplankton (PP), autotrophic nanoflagellates (ANF), and small chain-forming diatoms (DT). However, in the greenhouse conditions, ANF and DT abundances were lower compared with those in the acidification conditions. The proliferation of small autotrophic phytoplankton in future oceanic conditions (acidification and greenhouse) also increased the abundance of heterotrophic dinoflagellates (HDF). These responses suggest that a combination of acidification and warming will not only increase the small autotrophic phytoplankton standing stock but, also, lead to a shift in the diatom and dinoflagellate species composition, with potential biogeochemical element cycling feedback and an increased frequency and intensity of harmful algal blooms.

Mesozooplankton are critical components of marine ecosystems, acting as key intermediaries between primary producers and higher trophic levels by grazing on phytoplankton and influencing fish populations. They play pivotal roles in the pelagic food web and export production, affecting the biogeochemical cycling of carbon and nutrients. Therefore, accurately modeling and visualizing mesozooplankton community dynamics is essential for understanding marine ecosystem patterns and informing effective management strategies. However, modeling these dynamics remains challenging due to the complex interplay among physical, chemical, and biological factors, and the detailed parameterization and feedback mechanisms are not fully understood in theory-driven models. Graph neural network (GNN) models offer a promising approach to forecast multivariate features and define correlations among input variables. The high interpretive power of GNNs provides deep insights into the structural relationships among variables, serving as a connection matrix in deep learning algorithms. However, there is insufficient understanding of how interactions between input variables affect model outputs during training. Here we investigate how the graph structure of ecosystem dynamics used to train GNN models affects their forecasting accuracy for mesozooplankton species. We find that forecasting accuracy is closely related to interactions within ecosystem dynamics. Notably, increasing the number of nodes does not always enhance model performance; closely connected species tend to produce similar forecasting outputs in terms of trend and peak timing. Therefore, we demonstrate that incorporating the graph structure of ecosystem dynamics can improve the accuracy of mesozooplankton modeling by providing influential information about species of interest. These findings will provide insights into the influential factors affecting mesozooplankton species and emphasize the importance of constructing appropriate graphs for forecasting these species.

Global warming is affecting the composition, structure, and function of marine ecosystems. The increase in hypoxic regions due to stratification is a major environmental problem worldwide. Off the southern coast of Korea, hypoxia occurs frequently in summer, and the area of water affected is gradually expanding. In this study, we investigated the effects of hypoxia on the eggs of copepods in the order Calanoida. Data on the distribution and abundance of eggs in benthic sediments were collected from 17 stations, using a piston core sampler (64 mm internal diameter, 50 cm length), from August 1 to 7, 2012. Significant variations in the distribution of calanoid eggs and the occurrence of abnormalities in egg development were found between stations. The abundance of eggs found in the sediments ranged from 0.004 to 2.389 × 10⁶ eggs·m⁻², with higher abundances identified in hypoxic than in normoxic areas. The proportion of abnormal eggs ranged from 0 to 92.7%. In particular, there were significantly more abnormal than normal eggs in areas where hypoxia occurred (p < 0.01). These results show that hypoxia can have a lethal effect on calanoid eggs and further affect population and community dynamics.

This 20-year study (2001-2020) conducted in Jangmok Bay, Korea, assessed the intricate relationships between environmental factors and Noctiluca scintillans blooms. Granger causality tests and PCA analysis were used to assess the impact of sea surface temperature (SST), salinity, dissolved oxygen (DO) concentration, wind patterns, rainfall, and chlorophyll-a (Chl-a) concentration on bloom dynamics. The results revealed significant, albeit delayed, influences of these variables on bloom occurrence, with SST exhibiting a notable 2-month lag and salinity a 1-month lag in their impact. Additionally, the analysis highlighted the significant roles of phosphate, ammonium, and silicate, which influenced N. scintillans blooms with lags of 1 to 3 months. The PCA demonstrates how SST and wind speed during spring and summer, along with wind direction and salinity in winter, significantly impact N. scintillans blooms. We noted not only an increase in large-scale N. scintillans blooms but also a cyclical pattern of occurrence every 3 years. These findings underscore the synergistic effects of environmental factors, highlighting the complex interplay between SST, salinity, DO concentration, and weather conditions to influence bloom patterns. This research enhances our understanding of harmful algal blooms (HABs), emphasizing the importance of a comprehensive approach that considers multiple interconnected environmental variables for predicting and managing N. scintillans blooms.