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Harmful Algal Blooms (HABs) are a growing environmental concern due to their adverse impacts on aquatic ecosystems, human health, and economic activities. These blooms are driven by a combination of factors, including nutrient enrichment, environmental factors, and hydrological conditions, leading to the excessive growth of algae. HABs produce toxins that threaten aquatic biodiversity, contaminate drinking water, and cause economic losses in fisheries and tourism. The causes of HABs are multifaceted, involving interactions between environmental factors such as temperature, light availability, and nutrient levels. Agricultural runoff, wastewater discharge, and industrial pollution introduce excessive nitrogen and phosphorus into water bodies, fueling bloom formation. Climate change further exacerbates the problem by altering precipitation patterns, increasing water temperatures, and intensifying coastal upwelling events, all of which create favorable conditions for HAB proliferation. This review explores the causes, ecological consequences, and potential mitigation strategies for HABs. Effective monitoring and detection methods, including satellite remote sensing, molecular biotechnology, and artificial intelligence-driven predictive models, offer promising avenues for early intervention. Sustainable management strategies such as nutrient load reductions, bioremediation, and regulatory policies can help mitigate the adverse effects of HABs. Public awareness and community involvement also play a crucial role in preventing and managing HAB events by promoting responsible agricultural practices, reducing waste discharge, and supporting conservation efforts. By examining existing literature and case studies, this study underscores the urgent need for comprehensive and interdisciplinary approaches to regulate HABs.

Availability of operational regional hydrodynamic models and near real time Harmful Algal Bloom (HAB) alerts from monitoring stations and remote sensing products have allowed the proliferation of short term advective HAB forecasts. However, their predictive ability in simulating HAB transport needs to be continuously evaluated in events of different HAB species to assess their applicability to different domains and the impacts of the choices made in model setup. Here we review the performance of three different modelling systems which were part of the PRIMROSE project against historical bloom events in different regions in the European Atlantic Area. The objectives are to understand their predictive ability and to demonstrate some aspects of Lagrangian model setup that are relevant to HAB early warning systems; in particular the use of advection-diffusion only models (without a biological component) and the effects of model configuration, especially model resolution. Hindcast and forecast simulations have been run in examples of high biomass blooms detected in satellite imagery; in the western English Channel, several events of potentially toxic species like Karenia mikimotoi and Prorocentrum cordatum (minimum) were simulated and in Western France a bloom of Mesodinium rubrum , prey of the toxic Dinophysis spp. Additionally, some simulations for studying the evolution of low biomass Dinophysis spp. blooms in Galicia-North Portugal were undertaken with models of different setup. Several metrics have been used to quantify the model performance and to compare the results of the different model configurations, showing that differences in hydrodynamical model configuration (initiation, resolution, forcing, and simulation domain) result in differences in the predicted transport of HABs. We find that advection only is a reasonable approximation but that it may do worse in an early (onset) phase than later on, and we find transport is generally increases with increasing resolution. Our results confirm that Lagrangian particle tracking tools can be integrated operationally in HAB early warning systems providing useful information on potential HAB evolution to users.

Harmful algal blooms (HABs) cause harm to human health or hinder sustainable use of the marine environment in Blue Economy sectors. HABs are temporally and spatially variable and hence their mitigation is closely linked to effective early warning. The European Union (EU) Interreg Atlantic Area project “PRIMROSE”, Predicting Risk and Impact of Harmful Events on the Aquaculture Sector, was focused on the joint development of HAB early warning systems in different regions along the European Atlantic Area. Advancement of the existing HAB forecasting systems requires development of forecasting tools, improvements in data flow and processing, but also additional data inputs to assess the distribution of HAB species, especially in areas away from national monitoring stations, usually located near aquaculture sites. In this contribution, we review different novel technologies for acquiring HAB data and report on the experience gained in several novel local data collection exercises performed during the project. Demonstrations include the deployment of autonomous imaging flow cytometry (IFC) sensors near two aquaculture areas: a mooring in the Daoulas estuary in the Bay of Brest and pumping from a bay in the Shetland Islands to an inland IFC; and several drone deployments, both of Unmanned Aerial Vehicles (UAV) and of Autonomous Surface vehicles (ASVs). Additionally, we have reviewed sampling approaches potentially relevant for HAB early warning including protocols for opportunistic water sampling by coastguard agencies. Experiences in the determination of marine biotoxins in non-traditional vectors and how they could complement standard routine HAB monitoring are also considered.

Among the organisms that spread into and flourish in Arctic waters with rising temperatures and sea ice loss are toxic algae, a group of harmful algal bloom species that produce potent biotoxins. Alexandrium catenella, a cyst-forming dinoflagellate that causes paralytic shellfish poisoning worldwide, has been a significant threat to human health in southeastern Alaska for centuries. It is known to be transported into Arctic regions in waters transiting northward through the Bering Strait, yet there is little recognition of this organism as a human health concern north of the Strait. Here, we describe an exceptionally large A. catenella benthic cyst bed and hydrographic conditions across the Chukchi Sea that support germination and development of recurrent, locally originating and self-seeding blooms. Two prominent cyst accumulation zones result from deposition promoted by weak circulation. Cyst concentrations are among the highest reported globally for this species, and the cyst bed is at least 6× larger in area than any other. These extraordinary accumulations are attributed to repeated inputs from advected southern blooms and to localized cyst formation and deposition. Over the past two decades, warming has likely increased the magnitude of the germination flux twofold and advanced the timing of cell inoculation into the euphotic zone by 20 d. Conditions are also now favorable for bloom development in surface waters. The region is poised to support annually recurrent A. catenella blooms that are massive in scale, posing a significant and worrisome threat to public and ecosystem health in Alaskan Arctic communities where economies are subsistence based.

Translational value of mouse models of neuropsychiatric disorders depends heavily on the accuracy with which they replicate symptoms observed in the human population. In mouse models of autism spectrum disorder (ASD) these include, among others, social affiliation, and communication deficits as well as impairments in understanding and perception of others. Most studies addressing these issues in the BTBR T+ Itpr3tf/J mouse, an idiopathic model of ASD, were based on short dyadic interactions of often non‐familiar partners placed in a novel environment. In such stressful and variable conditions, the reproducibility of the phenotype was low. Here, we compared physical conditions and the degree of habituation of mice at the time of testing in the three chambered social affiliation task, as well as parameters used to measure social deficits and found that both the level of stress and human bias profoundly affect the results of the test. To minimize these effects, we tested social preference and network dynamics in mice group‐housed in the Eco‐HAB system. This automated recording allowed for long‐lasting monitoring of differences in social repertoire (including interest in social stimuli) in BTBR T+ Itpr3tf/J and normosocial c57BL/6J mice. With these observations we further validate the BTBR T+ Itpr3tf/J mouse as a model for ASD, but at the same time emphasize the need for more ecological testing of social behavior within all constructs of the Systems for Social Processes domain (as defined by the Research Domain Criteria framework). The data shown here challenges the classical view that social deficits in mouse models of neurodevelopmental disorders should be adressed with brief, dyadic tests of social affiliation. They encourage taking a more ethological approach, employing group testing of animals in semi‐natural environments over periods of multiple days. Such testing conditions are not only less stressful for the animal, but also permit unfolding of a full repertoire of social behaviors, which in turn allows for a more thorough search for impairments across multiple constructs of the Systems for Social Processes Domain of the RDoC.

Global trends in the occurrence, toxicity and risk posed by harmful algal blooms to natural systems, human health and coastal economies are poorly constrained, but are widely thought to be increasing due to climate change and nutrient pollution. Here, we conduct a statistical analysis on a global dataset extracted from the Harmful Algae Event Database and Ocean Biodiversity Information System for the period 1985–2018 to investigate temporal trends in the frequency and distribution of marine harmful algal blooms. We find no uniform global trend in the number of harmful algal events and their distribution over time, once data were adjusted for regional variations in monitoring effort. Varying and contrasting regional trends were driven by differences in bloom species, type and emergent impacts. Our findings suggest that intensified monitoring efforts associated with increased aquaculture production are responsible for the perceived increase in harmful algae events and that there is no empirical support for broad statements regarding increasing global trends. Instead, trends need to be considered regionally and at the species level.

Harmful algal blooms (hereafter HABs) pose significant threats to aquatic health and environmental safety. Although satellite remote sensing can monitor HABs at a large-scale, it is always a challenge to achieve both high spatial and high temporal resolution simultaneously with a single earth observation system (EOS) sensor, which is much needed for aquatic environment monitoring of inland lakes. This study proposes a multi-source remote sensing-based approach for HAB monitoring in Chaohu Lake, China, which integrates Terra/Aqua MODIS, Landsat 8 OLI, and Sentinel-2A/B MSI to attain high temporal and spatial resolution observations. According to the absorption characteristics and fluorescence peaks of HABs on remote sensing reflectance, the normalized difference vegetation index (NDVI) algorithm for MODIS, the floating algae index (FAI) and NDVI combined algorithm for Landsat 8, and the NDVI and chlorophyll reflection peak intensity index (ρchl) algorithm for Sentinel-2A/B MSI are used to extract HAB. The accuracies of the normalized difference vegetation index (NDVI), floating algae index (FAI), and chlorophyll reflection peak intensity index (ρchl) are 96.1%, 95.6%, and 93.8% with the RMSE values of 4.52, 2.43, 2.58 km2, respectively. The combination of NDVI and ρchl can effectively avoid misidentification of water and algae mixed pixels. Results revealed that the HAB in Chaohu Lake breaks out from May to November; peaks in June, July, and August; and more frequently occurs in the western region. Analysis of the HAB’s potential driving forces, including environmental and meteorological factors of temperature, rainfall, sunshine hours, and wind, indicated that higher temperatures and light rain favored this HAB. Wind is the primary factor in boosting the HAB’s growth, and the variation of a HAB’s surface in two days can reach up to 24.61%. Multi-source remote sensing provides higher observation frequency and more detailed spatial information on a HAB, particularly the HAB’s long-short term changes in their area.

The frequency and intensity of harmful algal blooms (HABs) and phytoplankton community shifts toward toxic species have increased worldwide. Although most research has focused on eutrophication as the cause of this trend, many other global- and regional-scale anthropogenic influences may also play a role. Ocean acidification (high pCO₂/low pH), greenhouse warming, shifts in nutrient availability, ratios, and speciation, changing exposure to solar irradiance, and altered salinity all have the potential to profoundly affect the growth and toxicity of these phytoplankton. Except for ocean acidification, the effects of these individual factors on harmful algae have been studied extensively. In this review, we summarize our understanding of the influence of each of these single factors on the physiological properties of important marine HAB groups. We then examine the much more limited literature on how rising CO₂ together with these other concurrent environmental changes may affect these organisms, including what is possibly the most critical property of many species: toxin production. New work with several diatom and dinoflagellate species suggests that ocean acidification combined with nutrient limitation or temperature changes may dramatically increase the toxicity of some harmful groups. This observation underscores the need for more in-depth consideration of poorly understood interactions between multiple global change variables on HAB physiology and ecology. A key limitation of global change experiments is that they typically span only a few algal generations, making it difficult to predict whether they reflect likely future decadal- or century-scale trends. We conclude by calling for thoughtfully designed experiments and observations that include adequate consideration of complex multivariate interactive effects on the long-term responses of HABs to a rapidly changing future marine environment.

Harmful algal blooms (HABs) in marine environments have significant adverse effects on public health, aquaculture and recreational activities. Surges of certain phytoplanktonic toxin-producing microalgae (mostly dinoflagellates or diatoms species) can induce Amnesic, Diarrhetic or Paralytic Shellfish Poisoning (ASP, DSP and PSP). Among HAB species, the genus Dinophysis leads to DSP in human consumers; this being the most recurrent problem in the Iberian Peninsula with the biggest economic impact on clam production and harvesting. While complete elimination of HABs is not feasible, timely implementation of appropriate measures can prevent their negative consequences. This is critical for aquaculture. Research on D. acuminata (dominant Dinophysis species in the North Atlantic) has been focused on ecophysiology and population dynamics, although with few modelling attempts. Weekly monitoring along the Portuguese coast since 2006 has revealed that D. acuminata thrives under spring/summer photosynthetically active radiation (PAR) coupled with water temperatures below 20°C, which typically coincide with the local upwelling regime. In order to advance this knowledge numerically, we developed a demographic model linking D. acuminata growth rate to PAR and sea surface temperature (SST). The 13-year (1-Jan-2006 to 31-Dec-2018) time-series of observations was closely fit by model forecasts. However, the model demonstrated limitations in issuing timely warnings of harmful proliferation of D. acuminata, failing to do so in 50% of cases, and issuing incorrect warnings in 5% of the cases. Furthermore, improving the odds of emitting timely warnings always worsened the odds of emitting false warnings, and vice-versa. To simultaneously improve both aspects, the modelling results clearly indicated the need of implementing both census/ projection intervals smaller than 7 days and a laboratory detection limit below 20 cell/L. The time resolution of the census and of the model proved to be the most limiting factor that must be addressed in order to improve numerical forecasting of HABs.

In the last decade, there has been a steady stream of information on the methods and techniques available for detecting harmful algae species. The conventional approaches to identify harmful algal bloom (HAB), such as microscopy and molecular biological methods are mainly laboratory-based and require long assay times, skilled manpower, and pre-enrichment of samples involving various pre-experimental preparations. As an alternative, biosensors with a simple and rapid detection strategy could be an improvement over conventional methods for the detection of toxic algae species. Moreover, recent biosensors that involve the use of nanomaterials to detect HAB are showing further enhanced detection limits with a broader linear range. The improvement is attributed to nanomaterials’ high surface area to volume ratio, excellent biological compatibility with biomolecules, and being capable of amplifying the electrochemical signal. Hence, this review presents the potential usage of biosensors over conventional methods to detect HABs. The methods reported for the detection of harmful algae species, ranging from conventional detection methods to current biosensor approaches will be discussed, along with their respective advantages and drawbacks to indicate the future prospects of biosensor technology for HAB event management.