Tracking Alexandrium catenella from seed-bed to bloom on the southern coast of Korea

Metabarcoding and co-occurrence network reveal significant effects of mariculture on benthic eukaryotic microalgal community: A case study in Daya Bay of the South China Sea

Benthic eukaryotic microalgae were analyzed by metabarcoding the partial 18S rRNA gene in Daya Bay bi-monthly in 2021. Altogether 941 eukaryotic microalgal OTUs were detected, belonging to 27 classes of 8 phyla. Dinophyta and Chlorophyta were the dominant phyla. Microalgal community in the mariculture zone differed significantly from those in non-mariculture zone, reflected by low alpha diversity indexes and increasing abundance and richness of chlorophytes and correspondingly decreasing of dinoflagellates. The abundant occurrences of the pico- and nano-sized taxa such as the chlorophyte Picochlorum in the mariculture zone suggested that nutrient enrichment might result in the miniaturization of the benthic eukaryotic microalgae. The co-occurrence network suggested more negative interactions between taxa in the mariculture zone. A total of 41 algal bloom and/or harmful algal bloom (HAB) species were detected in this study, suggesting a high potential risk of HABs in Daya Bay, especially for the recurrent bloom species Scrippsiella acuminata.

Quantification of Alexandrium catenella (Group I) using sxtA4-based digital PCR for screening of paralytic shellfish toxins in Jinhae-Masan Bay, Korea

We employed a detection method to quantify Alexandrium catenella (Group I), one of the causative species for paralytic shellfish poisoning (PSP) in Jinhae-Masan Bay, Korea, targets sxtA4, via chip-based digital PCR. Additionally, we explored the dynamics of Alexandrium during the spring of 2022 using an rDNA-based quantitative PCR (qPCR) assay to enhance the performance of the dPCR assay. In matching dPCR results with PSP monitoring reports, we optimized a cell regulatory threshold of 102 cells L-1, the maximum cell density when shellfish harvesting was permitted, for the dPCR assay. This threshold functioned similar to the PST threshold used in mouse bioassays (MBAs). Furthermore, we validated a total concordance rate of 83.8 % between the two assays for 2020-2022, reaching a maximum of 96.2 % in 2020. Thus, the result of dPCR could complement MBAs, facilitating the early detection of PSP outbreaks.

Seasonal change and subniche dynamics of three Alexandrium species in the Korea Strait

Some members of the dinoflagellate genus Alexandrium produce toxins responsible for paralytic shellfish poisoning, which causes environmental impacts and large economic losses worldwide. The Outlying Mean Index (OMI) and the Within Outlying Mean Index (WitOMI) were used to examine the ecological niches of three Alexandrium species identifying factors affecting their population dynamics in the Korea Strait (KS). Species niches were divided into seasonal subniches based on species' temporal and spatial patterns, with A. catenella being highest in the spring, A. pacificum in the summer, and A. affine in the autumn. These shifts in abundance are likely due to changes in their habitat preferences and resource availability, as well as the effects of biological constraints. A subniche-based approach, which considers the interactions between the environment and the biological characteristics of a species, was useful in understanding the factors shaping the population dynamics of the individual species. Additionally, a species distribution model was used to predict the phenology and biogeography of the three Alexandrium species in the KS and their thermal niches on a larger scale. The model predicted that, in the KS, A. catenella exists on the warm side of the thermal niche, while A. pacificum and A. affine exist on the cold side, indicating that these species may respond differently to increases in water temperature. However, the predicted phenology was incongruent with the abundance of the species as measured by droplet digital PCR. Overall, the WitOMI analysis and species distribution model can provide valuable insights into how population dynamics are influenced by the integrated interplay of biotic and abiotic processes.

Bloom development of toxic dinoflagellate Alexandrium catenella (Group I) in Jinhae-Masan Bay, Korea: Germination strategy of resting cysts in relation to temperature and salinity

To better understand the role of resting cysts in the outbreak of paralytic shellfish poisoning and bloom dynamics in Jinhae-Masan Bay, Korea, this study investigated the germination features of ellipsoidal Alexandrium cysts isolated from sediments collected in winter and summer under different combinations of temperature and salinity. Morphology and phylogeny of germling cells revealed that the ellipsoidal Alexandrium cysts belong to Alexandrium catenella (Group I). The cysts could germinate across a wide range of temperature (5-25 °C) with germination success within 5 days, indicating that continuous seeding for the maintenance of vegetative cells in the water column may occur through the year without an endogenous clock to regulate germination timing. In addition, the cyst germination of A. catenella (Group I) was not controlled by seasonal salinity changes. Based on the results, this study provides a schematic scenario of the bloom development of A. catenella (Group I) in Jinhae-Masan Bay, Korea.

Formation mechanism and environmental drivers of Alexandrium catenella bloom events in the coastal waters of Qinhuangdao, China

In the last 5 years, paralytic shellfish toxins (PSTs) have been recurrently detected in mollusks farmed in the mussel culture area of Qinhuangdao city, along with the occurrence of toxic outbreaks linked to dinoflagellate species of the Alexandrium genus. To understand the formation mechanism and variation of these events, continuous and comprehensive PSTs monitoring was carried out between 2017 and 2020. Through the analysis of both phytoplankton and cysts via light microscopy and quantitative polymerase chain reaction, it was shown that Alexandrium catenella was responsible for the production of PSTs, which consisted mainly of gonyautoxins 1,4 (GTX1/4, 87%) and GTX2/3 (13%). During bloom events in 2019, mussels accumulated the highest PSTs value (929 μg STX di-HCl eq·kg^-1) in conjunction with the peak of cell abundances, and toxin profiles were consistent with high distributions of GTX1/4, GTX2/3, and Neosaxitoxin. Toxin metabolites vary in different substances and mainly transferred to a stable proportion of α-epimer: β-epimers 3:1. The environmental drivers of Alexandrium blooms included the continuous rise of water temperature (>4 °C) and calm weather with low wind speed and no significant precipitation. By comparing toxin profiles and method sensitivity, it was found that dissolved toxins in seawater are more useful for early warning. These results have important implications for the effective monitoring and management of paralytic shellfish poisoning outbreaks.

An investigation on bloom dynamics of Alexandrium catenella and A. pacificum and toxin accumulation in shellfish along the coast of Qinhuangdao, China

The dinoflagellate genus Alexandrium comprises most of the toxic bloom-forming species producing paralytic shellfish toxins (PSTs) in the sea. Recently, repeated paralytic shellfish poisoning episodes have been recorded in Qinhuangdao located at the west coast of the Bohai Sea. To elucidate the relationship between toxic Alexandrium blooms and the poisoning episodes, a year-round investigation was carried out in this region from July 2020 to July 2021. Two qPCR assays were used to detect A. catenella and A. pacificum, and LC-MS/MS was applied to analyze PSTs in phytoplankton and shellfish samples. The blooms of A. catenella and A. pacificum were found in April and July, respectively, and PST content in three bivalves exhibited notable increase following the bloom of A. catenella. The results revealed bloom dynamics of the two toxic Alexandrium species in the Bohai Sea for the first time, and further confirmed A. catenella as the causative agent of poisoning episodes.

Projecting Future Climate Change-Mediated Impacts in Three Paralytic Shellfish Toxins-Producing Dinoflagellate Species

Toxin-producing microalgae present a significant environmental risk for ecosystems and human societies when they reach concentrations that affect other aquatic organisms or human health. Harmful algal blooms (HAB) have been linked to mass wildlife die-offs and human food poisoning episodes, and climate change has the potential to alter the frequency, magnitude, and geographical extent of such events. Thus, a framework of species distribution models (SDMs), employing MaxEnt modeling, was used to project changes in habitat suitability and distribution of three key paralytic shellfish toxin (PST)-producing dinoflagellate species (i.e., Alexandrium catenella, A. minutum, and Gymnodinium catenatum), up to 2050 and 2100, across four representative concentration pathway scenarios (RCP-2.6, 4.5, 6.0, and 8.5; CMIP5). Despite slightly different responses at the regional level, the global habitat suitability has decreased for all the species, leading to an overall contraction in their tropical and sub-tropical ranges, while considerable expansions are projected in higher latitudes, particularly in the Northern Hemisphere, suggesting poleward distributional shifts. Such trends were exacerbated with increasing RCP severity. Yet, further research is required, with a greater assemblage of environmental predictors and improved occurrence datasets, to gain a more holistic understanding of the potential impacts of climate change on PST-producing species.

Metabarcoding of harmful algal bloom species in sediments from four coastal areas of the southeast China

In the past three decades, harmful algal blooms (HAB) have become more frequent and widespread in southeast Chinese sea areas. Resting stages are regarded as the "seed bank" of algal blooms, and play an important role in initiating HABs. The distribution of resting stages in sediments especially those of HAB species can make good predictions about the potential risk of future blooms, however with limited reports. In this study, surface sediment samples were collected in the four sea areas along the southeast Chinese coasts, including Dafeng Port (DF) in the southern Yellow Sea, Xiangshan Bay (XS), Funing Bay (FN), and Dongshan Bay (DS) in the East China Sea. Diversity and community structure of eukaryotic microalgae in surface sediments were assessed by metabarcoding V4 region of the 18S rDNA, focusing on the distribution of HAB species. Biogenic elements including total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), biogenic silicon (BSi), and moisture content (MC) were analyzed. A total of 454 eukaryotic algal OTUs were detected, which belonged to 31 classes of 9 phyla. Altogether 149 algal species were detected in this study, and 59 taxa have been reported to form resting stages. Eukaryotic algal community was similar in XS, FN and DS of the East China Sea, which were predominated by dinoflagellates. However, algal community was different in DF of the Yellow Sea, and characterized by the dominance of chrysophytes and low OTU richness. The distribution of most abundant HAB species showed positive correlations with TN, BSi, and TOC, suggesting that eutrophication and consequent increase in diatom productivity may have a significant influence on the distribution of HAB species and facilitate the occurrence of HABs. Furthermore, HAB species occurred more abundantly and widely in FN. Our results suggest high potential risks of HABs in the southeast Chinese coast especially in Funing Bay.

Temporal Prediction of Paralytic Shellfish Toxins in the Mussel Mytilus galloprovincialis Using a LSTM Neural Network Model from Environmental Data

Paralytic shellfish toxins (PSTs) are produced mainly by Alexandrium catenella (formerly A. tamarense). Since 2000, the National Institute of Fisheries Science (NIFS) has been providing information on PST outbreaks in Korean coastal waters at one- or two-week intervals. However, a daily forecast is essential for immediate responses to PST outbreaks. This study aimed to predict the outbreak timing of PSTs in the mussel Mytilus galloprovincialis in Jinhae Bay and along the Geoje coast in the southern coast of the Korea Peninsula. We used a long-short-term memory (LSTM) neural network model for temporal prediction of PST outbreaks from environmental data, such as water temperature (WT), tidal height, and salinity, measured at the Geojedo, Gadeokdo, and Masan tidal stations from 2006 to 2020. We found that PST outbreaks is gradually accelerated during the three years from 2018 to 2020. Because the in-situ environmental measurements had many missing data throughout the time span, we applied LSTM for gap-filling of the environmental measurements. We trained and tested the LSTM models with different combinations of environmental factors and the ground truth timing data of PST outbreaks for 5479 days as input and output. The LSTM model trained from only WT had the highest accuracy (0.9) and lowest false-alarm rate. The LSTM-based temporal prediction model may be useful as a monitoring system of PSP outbreaks in the coastal waters of southern Korean.

Alexandrium catenella (Group I) and A. pacificum (Group IV) cyst germination, distribution, and toxicity in Jinhae-Masan Bay, Korea

To better understand the outbreaks of paralytic shellfish poisoning and bloom dynamics caused by Alexandrium species in Jinhae-Masan Bay, Korea, the germination and distributions of ellipsoidal Alexandrium cysts were investigated, and paralytic shellfish toxins (PSTs) profiles and contents were determined using strains established from germling cells. The phylogeny and morphological observations revealed that the germinated vegetative cells from ellipsoidal cysts collected from the surface sediments in Jinhae-Masan Bay belong to Alexandrium catenella (Group I) and A. pacificum (Group IV) nested within A. tamarense species complex. Cyst germinations of A. catenella (Group I) were observed at only 10 °C, whereas cysts of A. pacificum (Group IV) could germinate at temperature ranges of 10 to 25 °C. Maximum germination success (85%) for isolated cysts occurred at 15 °C, and the germling cells were A. pacificum (Group IV). The results indicate that the variation in water temperature in Jinhae-Masan Bay can control the seasonal variations in germination of cysts of A. catenella (Group I) and A. pacificum (Group IV). The germination rates of ellipsoidal Alexandrium cysts were different among sampling sites in Jinhae-Masan Bay, probably because of differences in distribution and abundance of A. catenella (Group I) and A. pacificum (Group IV) in the sediments. The ellipsoidal Alexandrium cyst concentrations were much higher in February than in August, however the distributions were similar. Gonyautoxins 3 and 4 (GTX-3 and GTX-4) contributed a large proportion (>90%) of the toxins produced by strains A. catenella (Group I) and A. pacificum (Group IV) established from germling cells, and the total cellular contents were higher in A. catenella (Group I) than in A. pacificum (Group IV).

Geographic distribution and historical presence of the resting cysts of Karenia mikimotoi in the seas of China

The toxic dinoflagellate Karenia mikimotoi frequently forms harmful algal blooms (HABs) and thus causes massive kills of fish and shellfish in worldwide coastal waters, which has led to intensive investigations on multiple facets of the species. Following our recent discovery of K. mikimotoi forming resting cyst, a very possible mechanism for the inoculation of blooms and geographic expansion for this and many other HABs-causing species, here we report our detection of K. mikimotoi resting cysts in 125 surface sediment samples collected from the coastal waters (covering a latitude range from 18.29°N to 39.85°N) and 3 sediment cores (accumulated in 70‒100 years) collected from the East China Sea where are adjacent to the frequent blooming areas of K. mikimotoi. Via applications of quantitative real-time PCR (LSU rDNA-targeted), species-specific fluorescence in situ hybridization (FISH), and nested-PCR-and-sequencing to both types of the sediment samples that were pretreated with sodium polytungstate solution (SPT), we demonstrated that 1) K. mikimotoi cysts are widely present in surface sediments of the China seas (Bohai Sea (BS), Yellow Sea (YS), East China Sea (ECS), and South China Sea (SCS)), 2) the abundance of cysts is generally low (0 to 33 cysts in 32 g wet sediment), with that in the ECS and the SCS being higher than that in the YS and the BS, and the highest abundance was observed in sites of the ECS (e.g., Ningde, Fujian province) where the blooms of the species occurred frequently, as quantified by both methods, and 3) the cysts of K. mikimotoi have been present in the sediments of the ECS since 1970s, a short time prior to the first recorded bloom of K. mikimotoi in the SCS at 1980s. Our results not only demonstrated the wide geographic distribution of resting cyst of K. mikimotoi along the coast of China, but also proved a 50 years preservation of the cysts in the sediments of coastal area prone to forming frequent blooms. We consider our results have provided critical insights into the mechanisms of frequent bloom outbreaks and global distribution of K. mikimotoi in general, and particularly into the historical origin of K. mikimotoi in China. Further investigations are suggested to focus on on-site surveys for the cyst production and germination rates.

Identification of influencing factors of A. catenella bloom using machine learning and numerical simulation

Alexandrium catenella (A. catenella) is a notorious algal species known to cause paralytic shellfish poisoning (PSP) in Korean coastal waters. There have been numerous studies on its temporal and spatial blooms in Korea. However, its bloom dynamics have not been fully understood because of the complexity in physical, chemical, and biological environments. This study aims to identify the factors that influence A. catenella blooms by applying a numerical model and machine learning. Intensive monitoring of A. catenella was conducted to investigate temporal variations in its population and its spatial distribution in the area with frequent occurrences of PSP bloom initiation. Moreover, a numerical model was built to analyze the ocean physical factors related to the bloom of A. catenella. Based on the information obtained from the monitored and simulated results, the decision tree (DT) method was applied to identify factors that caused the bloom. The outbreak of A. catenella was observed in the eastern coastal water of Geoje Island in 2017, recording a peak density of 4 × 10⁴ (cell L^-1). Retention time and particle scattering demonstrated that the physical force in 2017 was weaker than that in 2018, as shown by the smaller effects of advection and dispersion in 2017. The decision tree model showed that (1) water temperature below 17.21 °C was ideal for the growth of A. catenella, (2) phosphate influenced the growth of the species, and (3) cell density was accelerated with increasing retention time. The results from DT can contribute to the prediction of A. catenella blooms by determining the conditions that cause bloom initiation. Further, they can be used as a practical approach for mitigating HABs. Thus, machine learning and numerical simulation in this study can be a potential approach for effectively managing the bloom of A. catenella.