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The presence of nutrients such as nitrate and phosphate can have a positive impact on phytoplankton growth. However, if the nutrient concentration is very high in the waters, it can cause an explosion in the phytoplankton population or Harmful Algae Blooms (HABs) in the waters. Phytoplankton explosions in waters have a negative impact on the surrounding ecosystem, marine biota, and humans. This research aimed to analyze the spatial distribution and structure of phytoplankton communities that can potentially cause HABs in the waters of Laikang Bay, South Sulawesi. This research was conducted in June 2021. The research found 20 species of phytoplankton HABs from 2 classes, namely Bacillariophyceae (8 types) and Dinophyceae (12 species). The Bacillariophyceae class has a higher percentage, 93%, and Dinophyceae at 7%. The abundance status of phytoplankton HABs is classified as not blooming, with the highest abundance found at Station 4 (control), namely 210 ± 80 cells/L. The HABs phytoplankton diversity index (H') is moderate with a value of 1.576 – 2.332, the uniformity index (E) of Station 1 (west) and Station 4 (control) is moderate with a value of 0.568 and 0.582 while Station 2 (north) and Station 3 (south) is classified as high, namely 0.807 and 0.823, the dominance index (D) is classified as low with a value of 0.135 – 0.357. Spatially, phytoplankton HABs based on the station can be divided into five groups. The results of the PCA analysis show that substations 1.A, 1.B, 1.C, 2.A, 2.B, and 2. C are characterized by high temperature and salinity parameters. Substations 3.A, 3.B, and 3.C are characterized by high nitrate and phosphate parameters. Meanwhile, substations 4.A, 4.B, and 4.C are characterized by high parameters of brightness, current speed, and pH and are associated with a high abundance of phytoplankton HABs.

We have developed probabilistic models to estimate the likelihood of harmful algae presence and outbreaks along the Norwegian coast, which can help optimization of the national monitoring program and the planning of mitigation actions. We employ support vector machines to calibrate probabilistic models for estimating the presence and harmful abundance (HA) of eight toxic algae found along the Norwegian coast, including Alexandrium spp., Alexandrium tamarense, Dinophysis acuta, Dinophysis acuminata, Dinophysis norvegica, Pseudo-nitzschia spp., Protoceratium reticulatum, and Azadinium spinosum. The inputs are sea surface temperature, photosynthetically active radiation, mixed layer depth, and sea surface salinity. The probabilistic models are trained with data from 2006 to 2013 and tested with data from 2014 to 2019. The presence models demonstrate good statistical performance across all taxa, with R (observed presence frequency vs. predicted probability) ranging from 0.69 to 0.98 and root mean squared error ranging from 0.84% to 7.84%. Predicting the probability of HA is more challenging, and the HA models only reach skill with four taxa (Alexandrium spp., A. tamarense, D. acuta, and A. spinosum). There are large differences in seasonal and geographical variability and sensitivity to the model input of different taxa, which are presented and discussed. The models estimate geographical regions and periods with relatively higher risk of toxic species presence and HA, and might optimize the harmful algae monitoring. The method can be extended to other regions as it relies only on remote sensing and model data as input and running national programs of toxic algae monitoring.

Environmental mitigation measures are being undertaken as a proactive approach to prevent the adverse consequences associated with Harmful Algal Blooms (HABs). Presently, there is a lack of comprehensive information regarding the occurrence of HABs in the waters surrounding Bintan Island, and routine monitoring to assess the potential for eutrophication is absent. This research should begin by gathering information about potential sources that contribute to the emergence of HABs, the specific types of algae with the capacity to trigger HABs The primary objective of this study is to identify the harmful algae species present in the coastal waters of Bintan Island. The research was conducted in August 2023, with the sampling process taking place at four specific locations within the coastal waters of Bintan Island. These sampling stations were selected deliberately to capture the varying activities predominant in each respective area. The microalgae found consisted of four groups, namely Bacillariophyta, Cyanophyta, Dinophyta, and Chlorophyta. Bacillariophyta is a group of microalgae with the highest abundance proportion at all observation stations. Harmfull Algae found in the coastal waters of Bintan Island generally come from the Dinophyta group of the types Ceratium sp., Peridinium sp., Prorocentrum sp., and Dinophysis sp. Harmful microalgae from the Dinophyta group were found at all research stations, with the highest proportion of abundance at Station 2 (6%) and the lowest at Station 3 (1%). The difference in these findings is thought to be due to the dynamics of the hydrosanographic parameters of the waters, especially nutrient concentrations. Keywords: Bintan Ceratium Dinophyta Harmful Microalgae

Harmful algal blooms (HABs) can negatively affect coastal water quality, aquatic fauna, and human health from the consumption of toxin-contaminated seafood. Estuaries are especially prone to the incidence of these noxious events and sensitive to the associated damage. This study represents a comprehensive overview of previous investigations reporting the occurrence of harmful algae and/or phycotoxins in the Paranaguá Estuarine Complex (CEP), in Southern Brazil. Secondary data from 2002 to 2021 were combined with new results obtained from widely spaced sampling campaigns performed from 2018 to 2019 to assess the periods and estuarine sectors at greater risk for the incidence of HABs. In total, about 600 water/sediment samples containing harmful microalgae and 675 tissue samples of phycotoxin-contaminated fauna were analyzed. The most frequent and abundant species of potentially toxic microalgae were dinoflagellates belonging to the species complexes Dinophysis acuminata and Prorocentrum lima, producers of diarrhetic shellfish toxins (DSTs), and diatoms of the genus Pseudo-nitzschia, producers of amnesic shellfish toxins (ASTs). Okadaic acid, a DST, was by far the most common toxin in the fauna, reaching higher levels in primary consumers such as bivalve mollusks (mussels, oysters, and clams), zooplankton, and suspension-feeding crustaceans (ghost shrimps), and moderate to low levels in cephalopods, fishes, gastropods, echinoderms, sea turtles, seabirds, and cetaceans. Less rainy periods (winter-early spring) were associated with the highest incidence of DSTs and blooms of their producing algae P. lima and Dinophysis spp., mainly in the middle region of Paranaguá Bay and on the shallow continental platform adjacent to the estuarine mouth. However, certain HAB-forming species may be more successful in inner estuarine areas (Prorocentrum cordatum) and during different periods, such as late summer--early fall (Pseudo-nitzschia spp.). Continuous monitoring of harmful algae and toxins is therefore highly recommended to mitigate the effects of HABs in the entire estuary. [ABSTRACT FROM AUTHOR]

The evaluation of water quality is commonly conducted by measuring physicochemical parameters. However, to enable identification of water quality changes of aquatic ecosystems, such as lakes, requires long time series data sampling. In this study, we performed water quality assessments by utilising the presence of potential harmful algae presence in Lake Ranu Grati, Indonesia. We conducted water sampling to evaluate 13 water quality parameters (in-situ and ex-situ) and identified the phytoplankton community structure of the lake at seven sampling sites from December 2018 to February 2019. To assess the water quality of the lake, we used two approaches. First, calculated the classical water quality index using the STORET method. Second, identified the potential harmful algae from the phytoplankton community structure of Lake Ranu Grati, and then determined the water quality factors related to its occurrence using canonical correspondence analysis. The results showed that water quality condition of Lake Ranu Grati was optimum except for COD, BOD and ammonia. The STORET index also classified the seven sites in Lake Ranu Grati as lightly polluted and moderately polluted. Considering the presence of potentially harmful algae from the Cyanophyta and Euglenophyta divisions, it was suggested that Sites 6 and 7 were the most polluted areas of Lake Ranu Grati, which corresponded to the high concentration of COD and BOD. This finding indicates that agricultural and aquaculture activities around the lake need to be controlled to maintain the sustainability of the water quality condition of Lake Ranu Grati.