Explore the vast range of titles available.

Harmful Algal Blooms: A Compendium Desk Reference provides basic information on harmful algal blooms (HAB) and references for individuals in need of technical information when faced with unexpected or unknown harmful algal events. Chapters in this volume will provide readers with information on causes of HAB, successful management and monitoring programs, control, prevention, and mitigation strategies, economic consequences of HAB, associated risks to human health, impacts of HAB on food webs and ecosystems, and detailed information on the most common HAB species.    Harmful Algal Blooms: A Compendium Desk Reference will be an invaluable resource to managers, newcomers to the field, those who do not have easy or affordable access to scientific literature, and individuals who simply do not know where to begin searching for the information needed, especially when faced with novel and unexpected HAB events.  Edited by three of the world's leading harmful algal bloom researchers and with contributions from leading experts, Harmful Algal Blooms: A Compendium Desk Reference will be a key source of information for this increasingly important topic.

Freshwater blooms of phytoplankton affect public health and ecosystem services globally 1 , 2 . Harmful effects of such blooms occur when the intensity of a bloom is too high, or when toxin-producing phytoplankton species are present. Freshwater blooms result in economic losses of more than US$4 billion annually in the United States alone, primarily from harm to aquatic food production, recreation and tourism, and drinking-water supplies 3 . Studies that document bloom conditions in lakes have either focused only on individual or regional subsets of lakes 4 – 6 , or have been limited by a lack of long-term observations 7 – 9 . Here we use three decades of high-resolution Landsat 5 satellite imagery to investigate long-term trends in intense summertime near-surface phytoplankton blooms for 71 large lakes globally. We find that peak summertime bloom intensity has increased in most (68 per cent) of the lakes studied, revealing a global exacerbation of bloom conditions. Lakes that have experienced a significant ( P  < 0.1) decrease in bloom intensity are rare (8 per cent). The reason behind the increase in phytoplankton bloom intensity remains unclear, however, as temporal trends do not track consistently with temperature, precipitation, fertilizer-use trends or other previously hypothesized drivers. We do find, however, that lakes with a decrease in bloom intensity warmed less compared to other lakes, suggesting that lake warming may already be counteracting management efforts to ameliorate eutrophication 10 , 11 . Our findings support calls for water quality management efforts to better account for the interactions between climate change and local hydrological conditions 12 , 13 . Analyses show that the peak intensity of summertime phytoplankton blooms has increased in 71 large lakes globally over the past three decades, revealing a worldwide exacerbation of bloom conditions.

The rapid growth of phytoplankton and microalgae has presented considerable environmental and societal challenges to the sustainable development of human society. Given the inherent limitations of satellite-based algal bloom detection techniques that rely on chlorophyll and fluorescence methods, this study proposes a method that employs hyperspectral data to calculate water chromatic indices (WCIs), including hue, saturation (S), dominant wavelength (λd), and integrated apparent visual wavelength (IAVW), to identify algal blooms. A global in situ hyperspectral dataset was constructed, comprising 13,110 entries, of which 9595 were for normal waters and 3515 for algal bloom waters. The findings of our investigation indicate statistically significant discrepancies in chromaticity parameters between normal and algal bloom waters, with a p-value of 0.05. It has been demonstrated that different algal blooms exhibit distinct chromatic characteristics. For algae of the same type, the chromaticity parameters increase exponentially with chlorophyll concentration for hue and λd, while S shows low correlation and IAVW displays a good linear relationship with chlorophyll concentration. The application of this method to the Bohai Sea (coastal) and Taihu Lake (inland water) for the extraction of algal blooms revealed a clear separation in chromaticity parameters between normal and algal bloom waters. Moreover, the method can be applied to satellite data, offering an alternative approach for the detection of algal blooms based on satellite data. The indices can serve as ground truth values for colorimetric indices and provide a benchmark for the validation of satellite chromatic products.

Harmful algal blooms (HABs) are overgrowths of toxic strains of algae (diatoms, green) and cyanobacteria (blue-green algae). While occurring naturally, human-induced environmental changes have resulted in more frequent occurrences of such blooms worldwide. Meantime, the ecotoxicological risk of HABs is rarely evaluated by means of standard test methods. For the first time, the genotoxic potential of the HAB event 2020 was assessed using two different Tradescantia-based test systems (Trad-SHM and Trad-MN, 24-h exposure). An integrated analysis of biological (algal abundance) and ecotoxicological (testing) data revealed linkages among algal proliferation, changes in Tradescantia stamen hairs (mutations and suppressed growth) and chromosomal aberrations during microsporogenesis (appearance of micronuclei) that were likely to be caused by toxic algal groups. Green alga Botryococcus braunii and the cyanobacterial species Anabaena and Oscillatoria could suppress stamen hair growth; Cyanobacteria Phormidium and Aphanothece sp. could trigger mutations in stamen hairs (appearance of pink and colorless cells); and Oscillatoria sp. could be responsible for the occurrence of chromosomal damage. Diatom proliferation in the spring was not related to the genotoxic response in Tradescantia. Both tests, the Trad-SHM and Trad-MN, are suitable for the evaluation of the toxic potential of HABs.

Harmful algal blooms (HABs) are a serious threat to ecosystems and human health. The accurate prediction of HABs is crucial for their proactive preparation and management. While mechanism-based numerical modeling, such as the Environmental Fluid Dynamics Code (EFDC), has been widely used in the past, the recent development of machine learning technology with data-based processing capabilities has opened up new possibilities for HABs prediction. In this study, we developed and evaluated two types of machine learning-based models for HABs prediction: Gradient Boosting models (XGBoost, LightGBM, CatBoost) and attention-based CNN-LSTM models. We used Bayesian optimization techniques for hyperparameter tuning, and applied bagging and stacking ensemble techniques to obtain the final prediction results. The final prediction result was derived by applying the optimal hyperparameter and bagging and stacking ensemble techniques, and the applicability of prediction to HABs was evaluated. When predicting HABs with an ensemble technique, it is judged that the overall prediction performance can be improved by complementing the advantages of each model and averaging errors such as overfitting of individual models. Our study highlights the potential of machine learning-based models for HABs prediction and emphasizes the need to incorporate the latest technology into this important field.

In recent decades, the locally extinct sea otter (Enhydra lutris lutris) has been recolonizing the coast of eastern Hokkaido. Their diet includes benthic invertebrates such as bivalves, sea urchins, snails, and chitons. In the fall of 2021, a harmful algal bloom (HAB) of Karenia selliformis occurred across Hokkaido's northern and eastern coasts, leading to a massive mortality of sea urchins. This dinoflagellate produces a neurotoxin (gymnodimine) implicated in shellfish poisoning. To determine the effect of the HAB on the marine community, we conducted benthic surveys using SCUBA and visually monitored the prey items of the sea otters in the affected area from 2020 to 2023. Following the HAB, we observed an 82% decrease in benthic sea urchin density (number m.sup.2 ), leading to their complete absence from the diet of sea otters. Conversely, bivalve density increased six-fold, accompanied by a nearly two-fold rise in their percentage in the sea otters' diet. Minimal changes were observed in the density of chitons and snails, with no significant alteration in the sea otters' diet. Despite these changes, the impact of the HAB on otters' dietary preferences was temporary, as the percentage of dietary sea urchins began recovering one year later. Sea otters augmented their diet with bivalves to compensate for the reduced availability of sea urchins during the HAB with no apparent effects on the number of sea otters or their health. Our results highlight the adaptability of sea otters to adjust their diet according to prey availability.

Cyanobacterial harmful algal blooms (CyanoHABs) are an increasingly common feature of large, eutrophic lakes. Non-N2-fixing CyanoHABs (e.g., Microcystis) appear to be proliferating relative to N2-fixing CyanoHABs in systems receiving increasing nutrient loads. This shift reflects increasing external nitrogen (N) inputs, and a > 50-year legacy of excessive phosphorus (P) and N loading. Phosphorus is effectively retained in legacy-impacted systems, while N may be retained or lost to the atmosphere in gaseous forms (e.g., N2, NH3, N2O). Biological control on N inputs versus outputs, or the balance between N2 fixation versus denitrification, favors the latter, especially in lakes undergoing accelerating eutrophication, although denitrification removal efficiency is inhibited by increasing external N loads. Phytoplankton in eutrophic lakes have become more responsive to N inputs relative to P, despite sustained increases in N loading. From a nutrient management perspective, this suggests a need to change the freshwater nutrient limitation and input reduction paradigms; a shift from an exclusive focus on P limitation to a dual N and P co-limitation and management strategy. The recent proliferation of toxic non-N2-fixing CyanoHABs, and ever-increasing N and P legacy stores, argues for such a strategy if we are to mitigate eutrophication and CyanoHAB expansion globally.

Satellite remote sensing has been widely used to monitor the water quality of inland and coastal environments. Using satellite data from the Landsat 5 Thematic Mapper (L5TM), Ho et al.1 showed an increase in peak summertime bloom intensity in 68% of the 71 large lakes worldwide from 1982 to 2012. However, we question the veracity of their find ing for at least two reasons: (1) satellite-derived reflectance in a single near-infrared (NIR) band is not a reliable proxy for bloom strength, and (2) the infrequent satellite observations from L5TM make it difficult to draw statistically meaningful conclusions.

Harmful algal blooms (HABs) are naturally occurring phenomena, and cyanobacteria are the most commonly occurring HABs in freshwater systems. Cyanobacteria HABs (cyanoHABs) negatively affect ecosystems and drinking water resources through the production of potent toxins. Furthermore, the frequency, duration, and distribution of cyanoHABs are increasing, and conditions that favor cyanobacteria growth are predicted to increase in the coming years. Current methods for mitigating cyanoHABs are generally short-lived and resource-intensive, and have negative impacts on non-target species. Cyanophages (viruses that specifically target cyanobacteria) have the potential to provide a highly specific control strategy with minimal impacts on non-target species and propagation in the environment. A detailed review (primarily up to 2020) of cyanophage lifecycle, diversity, and factors influencing infectivity is provided in this paper, along with a discussion of cyanophage and host cyanobacteria relationships for seven prominent cyanoHAB-forming genera in North America, including: Synechococcus, Microcystis, Dolichospermum, Aphanizomenon, Cylindrospermopsis, Planktothrix, and Lyngbya. Lastly, factors affecting the potential application of cyanophages as a cyanoHAB control strategy are discussed, including efficacy considerations, optimization, and scalability for large-scale applications.