Shift in algal blooms from micro- to macroalgae around China with increasing eutrophication and climate change

Dynamic causes contribute to the increasing trend of red tides in the east China sea during 2020-2022

Red tide is a marine phenomenon caused by the excessive growth of microscopic algae in the ocean. This study aims to analyze the development trends of red tides in the past 20 years and the dynamic external causes that induce red tides based on existing satellite remote sensing and numerical simulation data. And the changes in dominant species of red tides in different seasons are analyzed. The results show significant temperature fluctuations within the week before the red tide occurs, with an average increase of 1.42 °C. In contrast, the change in salinity is relatively small. Meanwhile, ocean fronts are areas in the ocean where different water masses meet and form boundaries. The average strength of ocean fronts increased by 3.7%, indicating enhanced ocean mixing over a short period of time. Under the combined influence of these factors, the probability of a red tide outbreak in the East China Sea increases rapidly. Therefore, this study has important reference value for further research on the causes of red tides and their response to ocean dynamic changes.

Evolutionary trends and analysis of the driving factors of Ulva prolifera green tides: A study based on the random forest algorithm and multisource remote sensing images

Understanding the prolonged spatiotemporal evolution and identifying the underlying causes of Ulva prolifera green tides play pivotal roles in managing such occurrences, restoring water ecology, and fostering sustainable development in marine ecosystems. Satellite remote sensing represents the primary choice for monitoring Ulva prolifera green tides due to its capability for extensive, long-term ocean monitoring. Based on multi-source remote sensing images, ecological and environmental datasets, and machine learning algorithms, therefore, this study focused on "remote sensing modelling - evolution history - change trends - mechanism analysis" to elucidate both the remote sensing monitoring models and the underlying driving factors governing the spatiotemporal evolution of Ulva prolifera green tides in the highly impacted South Yellow Sea of China. With the use of GOCI Ⅰ/Ⅱ images, an hybrid remote sensing extraction model merging the robustness of the random forest (RF) model and the optical algae cloud index (ACI) was established to map Ulva prolifera distribution patterns. The ACI-RF method exhibited exceptional performance, with an F1 score surpassing 0.95, outperforming alternative methods such as the support vector machine (SVM) and K-nearest neighbour (KNN) methods. On the basis, we analysed the evolutionary trends and the driving factors determining these distribution patterns using meteorological data, runoff data, and data on various water quality parameters (SST, ocean current speed, wind speed, precipitation, DO, PAR, Si, NO3-, PO43-and N/P). Over the period from 2011 to 2022, excluding 2021, there was a notable decline in the area of Ulva prolifera green tides, varying between 397 and 2689.9 km2, with an average annual reduction rate of 3%. The maximum annual biomass varied between 0.12 and 15.9 kt. Notably, more than 75% of the area of Ulva prolifera green tides exhibited northward drift, which was significantly influenced by northern currents and wind fields. The analysis of driving factors indicates that factors such as average sea surface temperature, eastward wind speed, northward wind speed, precipitation, PO43- and N/P/Si significantly influence the biological growth rate of Ulva prolifera. Furthermore, coastal land use change and surface runoff, particularly surface runoff in June, significantly impacted the growth rate of Ulva prolifera, with Pearson correlation coefficients of 0.74 and 0.67, respectively. Against the background of global warming and severe deterioration in the marine environment, Ulva prolifera blooms persist. Consequently, two distinct management strategies were proposed based on the distribution patterns and cause analysis results for addressing Ulva prolifera green tides: establishing a continuous protection framework for rivers, lakes, and nearshore areas to mitigate pollutant inputs and implementing precise environmental monitoring measures in urban expansion areas and farmlands to combat overgrowth-induced green tides. This methodology could be applied in other regions affected by marine ecological disasters, and the criteria for selecting influencing factors offer a valuable reference for designing tailored and proactive measures aimed at controlling Ulva prolifera green tides.

Metatranscriptomic insights into the microbial metabolic activities during an Ulva prolifera green tide in coastal Qingdao areas

Green tide, a typical marine environmental disaster that profoundly influenced the coastal areas, has been occurred consecutively in the South Yellow Sea of China since 2007. Herein, the active microbial community structure and metabolic pathways in Qingdao offshore during an Ulva prolifera green tide were investigated by using metatranscriptomic approach. The dominant active microbial taxa at the outbreak phase were primarily a functional group that can utilize organic matters derived from U. prolifera, such as Lentibacter, Polaribacter and Planktomarina. While the taxa involved in biogeochemical cycles, including Phaeobacter, Pseudomonas and Marinobacterium, dominated the active microbial communities at the decline phase. The expression level of enzymes involved in U. prolifera polysaccharides degradation was significantly higher at the outbreak phase compared to the decline phase. At the same time, the main players Glaciecola and Polarbacter showed similar trends, suggesting that the low competitiveness for nutrients of related microorganisms at this phase made them degrade more U. prolifera polysaccharides to meet their own nutrient needs, thereby accelerating the degradation of U. prolifera. According to KEGG annotation, the biogeochemical pathways including nitrogen cycle, sulfur cycle and methane oxidation altered during the green tide, with thiosulfate oxidation and methane oxidation probably being the crucial pathways at the outbreak and the decline phase respectively. The coupling of sulfide oxidation and denitrification was also observed in this study. Furthermore, the green tide in Qingdao offshore might impact the greenhouse effects induced by CH4 and N2O through influencing the related microbial processes.