Distribution Of Harmful Algal Blooms Research Articles

Evaluation of metrics and thresholds for use in national-scale river harmful algal bloom assessments

The spatiotemporal distribution of harmful algal blooms (HABs) in rivers remains poorly understood, and there is an urgent need to develop a consistent set of metrics to better document HAB occurrences and forecast future events. Using data from seven sites in the Illinois River Basin, we computed metrics focused on HAB conditions related to excess algal growth and hypoxia. Daily mean chlorophyll and dissolved oxygen (DO) concentrations, gross primary productivity (GPP), and net ecosystem productivity (NEP) rates, focused on water quality status, identifying the timing of the transition from a clear-water to an algal dominated state. Early warning indicators (EWIs), the first-order autoregressive process (Ar1) and standard deviation (SD) of chlorophyll concentrations, focused on future events, forecasting blooms. Metrics were compared to either literature-derived or statistical-based thresholds and were normalized by total number of daily samples for an exceedance rate. Exceedances of a daily mean chlorophyll concentration averaged 50 % across all sites using a 10 µg L−1 threshold but increasing the threshold to 50 μg L−1 reduced the average exceedance rate to 5 %. The average exceedance rate for GPP (∼8 g O2 m2d−1 threshold) was 15 %, similar to the daily amplitude DO concentration (∼3 mg L−1 threshold), but the average for NEP (0 g O2 m2 d−1 threshold) was higher, at 28 %. The number of days with at least 1 continuous DO concentration below the threshold of 5, 3, or 2 mg L−1, had basin wide exceedance rates of 9 %, 3 %, and 2 %, respectively. Thresholds for EWIs, Ar1 and SD, were exceeded at 5 of the 7 sites with high chlorophyll concentrations and GPP rates. The correlation between proxies for algal biomass (chlorophyll concentration) and productivity (GPP) was strongest for sites in the middle region of the basin, with R2 values between 0.54 and 0.74. Although, cyanotoxin concentrations are the most commonly used metrics by states to define an inland water HAB, there is a paucity of publicly available data. The wider availability of chlorophyll and oxygen data combined with the results from this study suggest that biomass and productivity state and event-based metrics may be a promising way to assess and predict the vulnerability of rivers to some of the deleterious effects of HABs at broad spatial scales.

Spatio-temporal distribution of harmful algal blooms and their correlations with marine hydrological elements in offshore areas, China

Harmful algal blooms (HABs) occur frequently in Chinese coastal areas, causing major economic losses and seriously threatening coastal marine environments. HAB research using remote sensing technology focuses on detecting and monitoring HABs. However, no study has explored the occurrence of HABs in response to hydrological factors based on remote sensing. This study used 30 years (1990–2019) of data to analyze the spatial and temporal characteristics of Chinese coastal HABs. The development and change trends of marine hydrological factors (sea surface salinity (SSS), sea surface temperature (SST), and chlorophyll (Chl) concentration) during the occurrence and disappearance of HABs were clarified, and correlations between marine hydrological parameters and HABs were described (Ahn and Shanmugam, 2006). From 1990 to 2019, 1,557 HABs occurred in coastal areas of China, and 961 HABs occurred from 2001 to 2012, accounting for 62.00% of the total. From 1998 to 2019, the total of 1393 HABs occurred, and 813, 249, 198 and 133 HABs occurred in the East China Sea, South China Sea, Bohai Sea, and Yellow Sea, respectively. From 2009 to 2019, the total of 612 HABs occurred, and 536 HABs occurred from April to September, accounting for 87.58% (Anderson et al., 2021). Spatially, kernel density estimation showed that most HABs were concentrated in the Bohai Bay and Yangtze and Pearl River deltas (Bao and Ren, 2014). During HABs, SST increased by 0.72–6.41 °C, Chl increased by 11.00–95.36 mg × m−3, and SSS decreased by 0.10–4.34 PSU. When HABs disappeared, SST decreased by 1.14–6.01 °C, Chl decreased by 4.50–96.46 mg × m−3, and SSS increased by 0.08–3.431 PSU. The SSS of a HAB year was relatively low, while the Chl was relatively high. SST between 8.00 °C and 28.00 °C induced HABs. The research illustrates the spatiotemporal characteristics of HABs in coastal areas of China and their sensitivity to marine hydrological factors. These findings are of great importance in regard to the impacts on human health and ecological damage caused by HABs.

The underappreciated role of biotic factors in controlling the bloom ecology of potentially harmful microalgae in the Hudson-Raritan Bay

Despite widespread distribution of harmful algal blooms (HABs) and new and improved methods for detecting and quantifying them, no unifying ecological explanation has been found. Improved understanding depends upon local, ecological studies that include analysis of phytoplankton species data in relation to both abiotic and biotic parameters. Ecological network analysis was used to detect co-occurrence patterns among abiotic and biotic parameters in a long-term monitoring dataset (i.e., 2010–2021) from the eutrophic Hudson-Raritan Estuary (HRE) between the states of New York and New Jersey. The regular co-occurrence of potentially harmful bloom-forming species with companion species observed through microscopy was supported by the results of ecological network analysis, which showed that there were far more associations between HAB species and biotic parameters (∼95%) than abiotic parameters (∼5%). Temperature was the environmental variable that was most associated with HAB species throughout the estuary. The numerous network associations of HAB species with one another and with diatoms, dinoflagellates, and zooplankton highlight the complexity of planktonic food webs and interactions. Results also suggest that some taxa may play a central role in structuring the HRE plankton communities. These findings demonstrate that biotic associations play an underappreciated role in plankton structure and the value of examining the ecology of HAB species within the breadth of their biological communities. While network analysis does not fully explain and confirm complex associations among species, it does provide fresh insights and testable hypotheses to strengthen understanding and improve prediction.

Integrated workflow for interpretation of satellite imageries using machine learning to assess and monitor algal blooms in Utah Lake, USA

An integrated workflow is developed to estimate the spatial distribution of harmful algal blooms, especially cyanobacteria concentrations in inland water bodies. The methodology comprises satellite data extraction and preprocessing for atmospheric and water surface corrections, identifying feature importance, in-situ sample collection, training and testing of machine learning algorithms, and prediction. Six input bands are selected using feature importance algorithms from 12 original bands of Sentinel-2 satellite imagery. In-situ sample data that are synchronous with Sentinel-2 image capture time were obtained from a public database. These models are evaluated and compared using spider plots of different error calculations. The workflow developed in this study and the predicted spatial concentration of harmful algal blooms across the lake can be used to improve warning and advisory systems for the public and avoid exposure. The incorporation of other parameters such as water temperature, nutrient concentrations, and surface wind speed could improve the machine-learning models.

Diversity and Distribution of Harmful Algal Bloom Species from Seamount to Coastal Waters in the South China Sea.

Mount Xianbei is one of the largest shallow seamounts located in the middle of the South China Sea (SCS), which might play a role in shaping the biodiversity of surrounding continental coastal waters, particularly the diversity of phytoplankton species causing frequent harmful algal blooms (HABs) in northern SCS. However, the diversity, composition, and distribution of phytoplankton species in the seamount regions of Xianbei remain largely unexplored. In this study, samples around and outside the seamount regions were collected during a late summer cruise of 2021 to test whether seamounts play a role in HAB species propagation. In total, we identified 19 HAB species across all samples using the ASV-based DNA metabarcoding approach, 6 of which had not been reported previously in the SCS, suggesting a diverse HAB species in the SCS. Specifically, 16 HAB species were found in the seamount region of Xianbei, and 5 of them were also found in the coastal waters, indicating a close connection between seamount and coastal waters. This study was the first attempt to explore HAB species' spatial diversity and vertical distribution in the seamount region of Xianbei at single-nucleotide resolution, which provides a novel explanation for the coastal HAB occurrence in the northern SCS. IMPORTANCE There are a number of seamounts under the water of the South China Sea (SCS). The seamounts might play a role in shaping the biodiversity of surrounding continental coastal waters. However, there is no direct evidence revealing the relationship of the biodiversity of phytoplankton between seamounts and coastal waters in the SCS, especially those species having the potential to form harmful algal blooms (HABs). Some HAB species might proliferate in certain geographic locations, while others may be broadly distributed across oceanic provinces. In this study, we provided a detailed analysis of phytoplankton composition and molecular detection of HAB species from seamount to coastal waters in the SCS, which suggested a strong interaction in the HAB species between the two areas. This finding provides new insights into the diversity and distribution of HABs in seamounts and their role in shaping the composition and the occurrence of HABs in coastal water.

Relationship between freshwater harmful algal blooms and neurodegenerative disease incidence rates in South Korea

BackgroundDue to anthropogenic activities and global warming, the severity and distribution of harmful algal blooms (HABs) have been increasing steadily worldwide, including in South Korea (S. Korea). Previous studies reported that exposure to HABs could increase the risk of HAB-related diseases. However, very few studies examined the linkage between HABs and disease occurrence, particularly in S. Korea. The objective of this study was to evaluate the potential impact of HABs on neurodegenerative diseases (NDs), including Alzheimer’s disease, Parkinson’s disease, and motor neuron disease, at a population level.MethodsThirteen-year data (2005–2017) for chlorophyll-a (chl-a) concentrations as a bloom-related parameter, annual numbers of NDs, and population information were collected. First, the entire area of S. Korea was divided into a grid of 1 km, and the population number in each 1-km grid was collected using the Statistical Geographic Information Service Plus system. Cross-sectional time series data were analyzed with two statistical models, a generalized linear mixed model and a generalized linear model.ResultsThe results show a general trend of increasing chl-a concentration and NDs year by year. We observed positive correlations between HAB intensity and the incidence rate of NDs. Particularly, HABs seem to have the most long-term carry-over effect on Parkinson’s disease. Another key finding was that a 5-km radius from the HAB location was the boundary that showed the most significant associations with three NDs.ConclusionsThis study provides statistical evidence that supports the potential risk of NDs from the exposure to HAB. Thus, it is recommended to monitor a broad spectrum of cyanotoxins, including neurotoxins, in bloom-affected regions in S. Korea and epidemiological studies in the future.

Morphologic and phylogenic characterization of two bloom-forming planktonic Prorocentrum (Dinophyceae) species and their potential distribution in the China Sea

The genus Prorocentrum includes many species that are responsible for harmful algal blooms globally, which have serious economic impacts on marine aquaculture, fisheries, and tourism, as well as major environmental and human health impacts. In this study, two bloom-forming planktonic Prorocentrum species, P. cordatum and P. sigmoides, were isolated from the coastal waters of China. Their morphology was examined in detail using light microscopy (LM) and scanning electron microscopy (SEM). The features of their periflagellar area platelet pattern, important taxonomic criteria, were revealed, and the distinctions in their morphology were compared with other closely related species. Both partial large subunit ribosomal DNA (LSU rDNA) and internal transcribed spacer region (ITS1-5.8S-ITS2) sequence-based phylogeny revealed three clades within planktonic Prorocentrum, referred to as P. micans-like (where the P. sigmoides included), P. cordatum-like, and P. triestinum-like groups. These groups showed consistencies in their thecal plate ornamentation and periflagellar area platelet pattern characteristics. Suitable habitats of P. cordatum and P. sigmoides along the coast of China were predicted with combined maximum entropy (MaxEnt) and random forest (RF) models. Highly or very highly suitable P. cordatum habitats were mainly located in the coastal waters of central to northern China. The habitat suitability range of P. sigmoides was narrower than that of P. cordatum, and concentrated near the Yangtze River Estuary. The main environmental drivers were identified by the variable importance evaluation procedure in BIOMOD 2. Mean sea surface temperature (SST) was the main distribution driver of both P. cordatum and P. sigmoides, and mean sea surface salinity (SSS), distance from coastline, and phosphorus concentrations also influenced their distribution. Ocean warming, eutrophication, and re-oligotrophication could potentially expand the distribution range of bloom-forming species. These results showed that machine learning models can play a useful role in the study of the potential distribution of harmful algal blooms.

A theoretical modeling framework for motile and colonial harmful algae

Climate change is leading to an increase in severity, frequency, and distribution of harmful algal blooms across the globe. For many harmful algae species in eutrophic lakes, the formation of such blooms is controlled by three factors: the lake hydrodynamics, the vertical motility of the algae organisms, and the ability of the organisms to form colonies. Here, using the common cyanobacterium Microcystis aeruginosa as an example, we develop a model that accounts for both vertical transport and colony dynamics. At the core of this treatment is a model for aggregation. For this, we used Smoluchowski dynamics containing parameters related to Brownian motion, turbulent shear, differential setting, and cell‐to‐cell adhesion. To arrive at a complete description of bloom formation, we place the Smoluchowski treatment as a reaction term in a set of one‐dimensional advection‐diffusion equations, which account for the vertical motion of the algal cells through molecular and turbulent diffusion and self‐regulating buoyant motion. Results indicate that Smoluchowski aggregation qualitatively describes the colony dynamics of M. aeruginosa. Further, the model demonstrates wind‐induced mixing is the dominant aggregation process, and the rate of aggregation is inversely proportional to algal concentration. Because blooms of Microcystis typically consist of large colonies, both of these findings have direct consequences to harmful algal bloom formation. While the theoretical framework outlined in this manuscript was derived for M. aeruginosa, both motility and colony formation are common among bloom‐forming algae. As such, this coupling of vertical transport and colony dynamics is a useful step for improving forecasts of surface harmful algal blooms.

Monoculture-based consumer-resource models predict species dominance in mixed batch cultures of dinoflagellates

Global change will disturb the frequency, scale and distribution of harmful algal blooms (HABs), but we are unable to predict future HABs due to our limited understanding of how physicochemical changes in the environment affect interspecific competition between dinoflagellates. Trait-based mechanistic modelling is an important tool to unravel and quantify various direct and indirect interactions between species. The present study explores whether MacArthur's consumer-resource model can be used as a viable base model to predict dinoflagellate growth in closed multispecies systems. To this end, two batch culture experiments (294 cultures in total) with monocultures and multispecies cultures of Alexandrium minutum, Prorocentrum lima, P. micans, Protoceratium reticulatum and Scrippsiella trochoidea were performed. Despite changes to the relative (different nitrate concentrations) and absolute nutrient availability (dilutions of L1 medium), P. micans outcompeted all other species in mixed cultures. Consumer-resource modelling parameterized using monoculture growth correctly predicted this species dominance (R² between 0.80 and 0.95). Parameter estimates revealed that P. micans had a faster uptake of nitrogen when compared to its competitors, but did not differ in resource efficiency and natural mortality rate. Yet, while the model accurately predicted community dynamics during the growth phase, it was not able to predict their dynamics beyond the point of quiescence. Consumer-resource modelling was shown to differentiate the roles of resource assimilation, resource efficiency, and natural mortality rates in batch culture experiments with minimal data requirements beyond common measurements. The results suggest that consumer-resource models provide a promising basis for trait-based modelling of interspecific competition between (harmful) algae.

Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir

The remote sensing of algal pigments is essential for understanding the temporal and spatial distribution of harmful algal blooms (HABs). In particular, the vertical distribution of cyanobacterial pigment (e.g., phycocyanin (PC)) is critical factor in remote sensing because the diel vertical migration of cyanobacteria may affect the spectral signals according to observational time. Although numerous studies have been conducted on the remote sensing of algal bloom using pigments, few studies considered the vertical distribution of the pigments for the remote sensing of cyanobacteria in inland waters. In this regard, the objective of this study was to develop an improved bio-optical remote-sensing method using in-situ remote-sensing reflectance (Rrs) at different water depths and cumulative PC and Chlorophyll-a (Chl-a) concentrations, which was cumulated from the surface to a 5-m water depth. The results showed that the bio-optical algorithm using surface Rrs and surface pigment concentration was more accurate than that using the subsurface Rrs and surface pigments. The bio-optical algorithm using subsurface Rrs showed the highest R-squared (R2) values (0.87–0.94) in each regression with the cumulative PC concentration from surface to each depth. The regressions between drone-based surface reflectance and cumulative PC concentration for each depth indicated a better performance than those between the reflectance and surface PC concentration; the highest R2 value of 0.82 was obtained from a bio-optical algorithm using drone-based reflectance and a 1.0-m cumulative PC concentration, which was the best-performing algorithm. The PC maps developed using the best bio-optical algorithm accurately described the spatial and temporal distributions of the PC concentrations in the reservoir. This study demonstrates that the application of vertical cumulative pigment concentration and subsurface Rrs measurement in bio-optical algorithms can improve their performance in estimating pigments, and that drone-based hyperspectral imagery is an efficient tool for the remote sensing of cyanobacterial pigments over a wide area.

IDENTIFICATION OF HIGH POTENTIAL BAYS FOR HABs OCCURRENCE IN PENINSULAR MALYSIA USING PALSAR REMOTE SENSING DATA

Abstract. Increasing frequency, intensity, and geographic distribution of Harmful algal blooms (HABs) poses a serious threat to the coastal fish/shellfish aquaculture and fisheries in Malaysian bays. Rising in sea level, shoreline erosion, stresses on fisheries, population pressure, interference of land-use and lack of institutional capabilities for integrated management make major challenges. Recent investigations and satellite observations indicate HABs originated from specific coast that have favourable geographic, geomorphic and coastal geology conditions to bring the green macro algae from the coast offshore. Therefore, the identification of high HABs frequented bays using remote sensing and geology investigations in Malaysian waters is required to reduce future challenges in this unique case. This research implemented comprehensive geomorphic and coastal geology investigations combined with remote sensing digital image processing approach to identify Malaysian bays frequented with HABs occurrence in Malaysian waters territory. The landscape and geomorphological features of the Malaysian bays were constructed from the Phased Array type L-band Synthetic Aperture Radar (PALSAR) remote sensing satellite data combined with field observations and surveying. The samples for laboratory analysis were collected from the sediment stations with different distance across shorelines features and watersheds of the Johor Bahru estuary. This research identified that semi-enclosed bays such as Kuala Lumpur and Johor Bahru bays with connection to estuaries have high potential to be frequented with HABs occurrence.

Remote-Sensing Applications for Environmental Health Research

More than 1,000 manmade satellites currently orbit our planet.1 Some are near the edge of the Earth’s atmosphere just a few hundred kilometers up. Others are tens of thousands of kilometers above us.2 They aid in communication, navigation, defense, and science. A small number3,4 play a critical and quickly expanding role: monitoring the Earth’s surface and atmosphere to track environmental conditions that are intimately tied to human health. A number of new Earth-observing missions are planned for the next decade, including Sentinel-5 aboard the European Space Agency’s MetOp Second Generation satellites (pictured).48 In the meantime researchers are finding new uses for the satellite ... Researchers and government agencies worldwide already use satellite data to monitor air pollutants, infectious disease epidemics, harmful algal blooms (HABs), climate change, and more. But as current research indicates, that’s only the beginning of what we can do with the technology, broadly referred to as “remote sensing.” In the coming years, new satellites will offer higher-resolution imagery in conjunction with more robust and precise algorithms to process the data they deliver. As a result, researchers expect to dramatically expand their ability to view and understand Earth’s land, water, and air, from its remotest ocean waters to its largest cities. The National Aeronautics and Space Administration (NASA) launched its first satellite in 1958,5 and TIROS-1, the country’s first meteorological satellite, came 2 years later.6 Within a few decades members of the epidemiological and public health communities began actively looking at satellite data, says John Haynes, program manager of the NASA Applied Sciences Health and Air Quality Applications Program. In recent years interest in remote-sensing data has soared, with newer avenues being developed and fine-tuned, including air-quality measurements and vector-borne disease projections. “There’s really been a paradigm shift in the use of remote sensing for public health issues,” Haynes says. “Every year there seems to be more and more interest.” Indeed, by March 2015 NASA will have launched 6 Earth-observing missions in 12 months,7 more than in any year in at least a decade.8 New launches include a “global precipitation observatory” that will make frequent global measurements of rain and snowfall, plus one satellite designed to measure soil moisture and another that will measure how carbon moves through the Earth’s atmosphere, land, and oceans. In addition, the International Space Station will receive three new instruments, one that will observe how winds behave around the world, one that will measure clouds and aerosols (particles suspended in the atmosphere)—two variables that remain difficult to predict in climate-change models—and one that will take global, long-term measurements of key components of the Earth’s atmosphere, including aerosols and ozone.9 The momentum will carry through at least the next 8 or so years, with NASA and other space agencies in Europe and Asia planning to launch new satellites that will provide even higher-resolution snapshots of the Earth. Along with technological and scientific advances, a third development is leading to new and improved applications of satellite data: NASA and the National Oceanic and Atmospheric Administration (NOAA) have made their satellite data available free of charge, Haynes says, while the European Space Agency (ESA) has reduced prices and promised to provide free access to data from its next generation of instruments. “More people use the data, and you get more out of it than when you try to restrict it,” says Raphael Kudela, an oceanographer at the University of California, Santa Cruz, who uses satellite imagery to study HABs. This free sharing of data has been instrumental in his field, allowing researchers at institutions around the world to study HABs from above and to improve systems to track and predict them.

Harmful algal blooms in the Australian region

In the past two decades, there has been an apparent increase in the frequency, intensity and geographical distribution of harmful algal blooms in Australian coastal, estuarine and fresh waters. Wild and cultured fish kills have been associated with blooms of the dinoflagellates Scrippsiella trochoidea (through the generation of anoxic conditions), Cochlodinium cf. helix, Gymnodinium cf. galatheanum, Gymnodinium mikimotoi and the golden-brown flagellate Prymnesium parvum (most likely through the production of substances affecting the gills of fish). Contamination of shellfish products with algal toxins has been caused by the diatoms Rhizosolenia cf. chunii (bitter-tasting compound), the dinoflagellates Alexandrium catenella, A. minutum and Gymnodinium catenatum (paralytic shellfish poisons) and, to a lesser extent, the dinoflagellates Dinophysis acuminata and D. fortii (diarrhetic shellfish poisons). Poisoning of cattle and wildlife or contamination of drinking water supplies by blue-green algal toxins from Nodularia spumigena (brackish water), Anabaena circinalis and Microcystis aeruginosa (freshwater) is also an increasing problem. The management of nutrient discharges to inland and coastal waterways is crucial to arrest the increasing impact of harmful algal blooms.