Mapping of coastal surface chlorophyll-a concentration by multispectral reflectance measurement from unmanned aerial vehicles

In subtropical coastal waters, the explosive growth of phytoplankton under favorable conditions can lead to water discolouration and massive fish kills. Manual field sampling and laboratory analysis of chlorophyll-a concentration (Chl-a) as an indicator to algal biomass, is resources intensive and time consuming, delaying responses to disastrous harmful algal blooms. Cloudy weather often precludes the use of satellite images for water quality and algal bloom monitoring. This study aims at developing an estimator algorithm for quantitative mapping of surface Chl-a for coastal waters, based on surface reflectance measurement from an Unmanned Aerial Vehicle (UAV) with a five-band multispectral camera. The surface reflectance is obtained from calibrated multispectral images which are radiometric-corrected against incoming solar radiation. It is found that Chl-a has an inverse correlation with the Normalized Green-Red Difference Index (NGRDI). A regression estimator model for Chl-a from NGRDI is developed, showing excellent performance for fish farms in coastal waters with different characteristics. The technology is demonstrated for mapping the spatial and temporal variation of Chl-a during an algal bloom, offering a useful complement to traditional field monitoring for fisheries management and emergency response.     

Simulation of harmful algal blooms using a deterministic Lagrangian particle separation-based method

Algal blooms (red tide) are often observed in Hong Kong's coastal waters. These algal blooms can cause discoloration of the marine water, and may result in severe dissolved oxygen depletion and fish kills; most harmful algal blooms (HAB) are caused by diatoms and dinoflagellates. Diatoms are non-motile algae relying on water turbulence for suspension and nutrient supply. Dinoflagellates, on the other hand, can undergo diel vertical migration. At night, the algal cells swim down the water column to uptake nutrient and store it as internal nutrient reserve (cell quota). During daytime, they ascend to the water surface to carry out photosynthesis using the nutrient reserve. Diel vertical migration is an important adaptive strategy of dinoflagellates to form blooms in stratified waters.In this paper, the vertical migration behaviour of dinoflagellates is modelled using a simple deterministic Lagrangian model based on a NEighbourhood Separation Technique (NEST). The method is based on relative diffusion concepts, and simulates the diffusion process via an equivalent macroscopic motion; it uses far less number of particles than that required in random walk methods. The Lagrangian cell quota based algal dynamics is incorporated in a one-dimensional model to predict the vertical structure of water quality. Dinoflagellates are represented by a number of particles, with algal growth dependent on its nutrient reserve and the available light intensity. Swimming behaviour is simulated by the corresponding advective translocation of the particle. The model is applied to study species competition, resulting in a simple bloom prediction criterion based on nutrient availability and vertical diffusivity. In addition, the changes in water quality during an observed dinoflagellate bloom in Hong Kong coastal waters are well supported by field data; the role of stratification and diel vertical migration on the bloom formation and the signature of dissolved oxygen are discussed.     

An analysis of satellite-derived chlorophyll and algal bloom indices on Lake Winnipeg

Lake Winnipeg has experienced dramatic increases in nutrient loading and phytoplankton biomass over the last few decades, accompanied by a marked shift in community composition towards the dominance of cyanobacteria. Comprehensive lake-wide observations of algal blooms are critical to assessing the lake's health status, its response to nutrient management practices, and an improved understanding of the processes driving blooms. We present an analysis of the spatial and temporal variability of algal blooms on Lake Winnipeg using satellite-derived chlorophyll and indices for algal bloom intensity, spatial extent, severity, and duration over the period of ESA's MERIS mission (2002–2011). Imagery documented extensive blooms covering as much as 93% of the lake surface. Bloom conditions were analysed in the context of in-lake and watershed processes to gain further insight on the drivers of bloom events. Day to day bloom variability was driven primarily by intermittent wind mixing events, with quiescent periods leading to the formation of dense surface blooms. Seasonal bloom distribution was consistent with light limitation in the south basin and lake circulation transporting bloom material towards the north-east shore. Inter-annual variability in average bloom severity was related to both total phosphorus (TP) loadings and summer lake surface temperatures. Results provide a valuable historical time series of bloom conditions to which ongoing observations from Sentinel-3's OLCI sensor can be added for longer term monitoring and change detection.     

A novel method for tracking western Lake Erie Microcystis blooms, 2002–2011

After a period of improvement from the late 1970s through the mid 1990s, western Lake Erie has returned to eutrophic conditions and harmful algal blooms now dominated by the cyanobacterium Microcystis aeruginosa. The detection of long-term trends in Microcystis blooms would benefit from a convenient method for quantifying Microcystis using archived plankton tows. From 2002 to 2011, summer Microcystis blooms in western Lake Erie were quantified using plankton tows (N = 649). A flotation separation method was devised to quantify Microcystis biovolume in the tows, and the method was tested against whole water cell counts. Floating Microcystis biovolume (mL) in preserved tows was highly correlated with total Microcystis cells (R² = 0.84) and biomass (R² = 0.95) in whole water samples. We found that Microcystis annual biovolume was highly variable among years; the 2011 bloom was 2.4 times greater than the second largest bloom (2008) and 29.0 times greater than the smallest bloom (2002). Advantages of the method include use of archived samples, high sampling volume, and low effort and expense. Limitations include specificity for cyanobacterial blooms dominated by large Microcystis colonies and the need for site-specific validation. This study indicates that the flotation method can be used to rapidly assess past and present Microcystis in western Lake Erie and that there was high variability in the timing, duration, and intensity of the annual Microcystis blooms over a 10-year period. The data made possible by this method will aid further investigations into the underlying causal factors of blooms.     

The impact of phytoplankton community composition on optical properties and satellite observations of the 2017 western Lake Erie algal bloom

Since the early 2000s Lake Erie has seen a dramatic increase in phytoplankton biomass, manifested in particular by the rise in the severity of cyanobacteria blooms and the prevalence of potentially toxic taxa such as Microcystis. Satellite remote sensing has provided a unique capacity for the synoptic detection of these blooms, enabling spatial and temporal trends in their extent and severity to be documented. Algorithms for satellite detection of Lake Erie algal blooms often rely on a single consistent relationship between algal or cyanobacterial biomass and spectral indices such as the Maximum Chlorophyll Index (MCI) or Cyanobacteria Index (CI). Blooms, however, are known to vary significantly in community composition over space and time. A suite of phytoplankton and optical property measurements during the western Lake Erie algal bloom of 2017 showed highly diverse bloom composition with variable absorption and backscatter properties. Elevated backscattering coefficients were observed in the Maumee Bay, likely due to phytoplankton cell morphology and buoyancy regulating gas vacuoles, compared with typically Planktothrix dominated blooms in Sandusky Bay. MCI and CI calibrated to historical chlorophyll observations and applied to Sentinel 3's OLCI sensor accurately captured the 2017 bloom in Maumee Bay but underestimated the Sandusky Bay bloom by nearly 80%. The phycoerythrin-rich picocyanobacteria Aphanothece and Synechococcus were found in abundance throughout the western and central basins, resulting in substantial biomass underestimations using blue to green ratio-based algorithms. Potential misrepresentation of bloom severity resulting from phytoplankton optical properties should be considered in assessments of bloom conditions on Lake Erie.     

Algal bloom transport in Lake Erie using remote sensing and hydrodynamic modelling: Sensitivity to buoyancy velocity and initial vertical distribution

In this study, we simulate three-dimensional transport of algal blooms in Lake Erie using a combination of remote sensing and hydrodynamic modelling. The remote sensing algorithms use data from the Sentinel-3 OLCI satellite sensor to derive chlorophyll-a concentration from cyanobacteria blooms in Lake Erie. The derived chlorophyll-a concentration initializes an algal bloom transport model driven by the lake component of the Water Cycle Prediction System for the Great Lakes, a system of coupled atmosphere-lake-hydrological models operated out of Environment and Climate Change Canada. The bloom is modelled as Microcystis aeruginosa, a buoyant species that is often dominant in harmful algal blooms in western Lake Erie. Short-term (a few days) predictions of algal bloom transport from July 27 to October 8, 2017 are modelled in both Eulerian and Lagrangian frameworks. The Eulerian framework is used to evaluate the sensitivity of model results to the initial vertical distribution of the bloom. In this work, the Lagrangian framework is limited to two-dimensional surface confined particles. We use several error metrics to evaluate model predictions. We find that results are sensitive to the buoyancy velocity for cases where the bloom was initially distributed over a large portion of the water column. An initial vertical distribution selected from modelled chlorophyll-a half depth shows the highest accuracy for the entire range of buoyancy velocities tested. We also find that the Pierce skill score is difficult to interpret, particularly in cases where bloom intensity is greatly overpredicted by the model.     

Determining remote sensing spatial resolution requirements for the monitoring of harmful algal blooms in the Great Lakes

Harmful algal blooms (HABs) have become a major health and environmental concern in the Great Lakes. In 2014, severe HABs prompted the State of Ohio to request NASA Glenn Research Center (GRC) to assist with monitoring algal blooms in Lake Erie. The most notable species of HAB is Microcystis aeruginosa, a hepatotoxin producing cyanobacteria that is responsible for liver complications for humans and other fauna that come in contact with these blooms. NASA GRC conducts semiweekly flights in order to gather up-to-date imagery regarding the blooms' spatial extents and concentrations. Airborne hyperspectral imagery is collected using two hyperspectral imagers, HSI-2 and HSI-3. Hyperspectral imagery is necessary in order to conduct experiments on differentiation of algal bloom types based on their spectral reflectance. In this analysis, imagery from September 19, 2016 was utilized to study the subpixel variability within the footprint of arbitrary sized pixels using several analysis techniques. This particular data set is utilized because it represents a worst case scenario where there is significant potential for public health concern due to high concentrations of microcystin toxin found in the water on this day and the concurrent observational challenges to accurately measure the algal bloom concentration variability with a remote sensing system due to the blooms high spatial variability. It has been determined that the optimal spatial resolution to monitor algal blooms in the Great Lakes is at most 50 m, and for much lower error 25 m, thus allowing for greater ease in identifying high concentration blooms near the surface. This resolution provides the best sensitivity to high concentration areas that are of significant importance in regard to human health and ecological damage.     

Limnological characteristics, eutrophication and cyanobacterial blooms in an inland reservoir, Australia

Ben Chifley Reservoir, the only potable water supply for Bathurst, New South Wales, Australia, has been experiencing recurrent cyanobacterial bloom problems since 1991. A study was undertaken from June 1998 to July 1999 to assess the limnological characteristics pertinent to eutrophication and the associated cyanobacterial blooms. From January–May 1999, the reservoir exhibited significant numbers of cyanobacterial cells, totalling > 9000 cells mL^?1. The highest number of cells (> 27 000 cells mL^?1) was recorded during April 1999. The water quality characteristics of the reservoir, and the river inflow and climatic data, were grouped into three distinct periods; before, during and after cyanobacterial blooms. High water temperature (15–22°C), thermal stratification (ΔT = 2.7–2.8°C), depletion of dissolved oxygen and high nutrient concentrations, all of which are conducive to enhanced cyanobacterial blooms, were evident before and during the bloom periods. Based on its nitrogen to phosphorus molar ratio, Ben Chifley Reservoir can be considered as being phosphorus‐deficient, in contrast to nitrogen, which is readily available from a number of sources in its drainage basin, including atmospheric fixation. Thus, it is recommended that adopting management strategies to reduce the quantity of bioavailable phosphorus in the reservoir would be the most effective way to minimize the occurrence of algal blooms.     

Cyanobacterial blooms in the central basin of Lake Erie: Potentials for cyanotoxins and environmental drivers

Lake Erie western basin (WB) cyanobacterial blooms are a yearly summer occurrence; however, blooms have also been reported in the offshore waters of the central basin (CB), and very little is known about what drives these blooms or their potential for cyanobacterial toxins. Cyanobacteria Index was quantified using MODIS and MERIS data for the CB between 2003 and 2017, and water samples were collected between 2013 and 2017. The goals were to 1) quantify cyanobacteria, 2) determine environmental drivers of CB blooms, and 3) determine the potential for cyanobacterial toxins in the CB. Dolichospermum (Anabaena) occurred in the CB during July before the onset of the WB bloom, and then in August and September, the cyanobacteria community shifted towards Microcystis. The largest Dolichospermum blooms (2003, 2012, 2013, and 2015) were associated with reduced water clarity (Secchi disk depth?<?4?m), whereas large CB Microcystis blooms (2011 and 2015) were associated with large WB blooms. Dolichospermum blooms occurred in high nitrate concentrations (>20?μmol/L) and high nitrogen-to?phosphorus ratios (>100), which indicate nutrient concentrations or ratios did not select for Dolichospermum. Additionally, the sxtA gene, but not mcyE or microcystins, were detected in the CB during July 2016 and 2017. The mcyE gene and microcystins were detected in the CB during August 2016 and 2017. The results indicate the CB's potential for cyanotoxins shifts from saxitoxins to microcystins throughout the summer. Continued monitoring of cyanobacteria and multiple cyanobacterial toxins is recommended to ensure safe drinking water for CB coastal communities.     

Future increase in harmful algal blooms in the North Sea due to climate change.

In temperate seas such as the North Sea harmful (toxic) algal blooms will probably increase as a result of climate change. This conclusion was reached after investigating the projected effect of climate change for the year 2100 in Dutch coastal waters (4 degrees C temperature rise and increased water column stratification) on the growth rates of six harmful and two non-harmful phytoplankton species. Micro algae form the basis of the marine food chain. However, toxin-producing species may seriously disrupt the food web and lead to fish kills and human intoxication. Two species with estimated doubled growth rates in 2100, F. japonica and C. antiqua, entered Europe via ship's ballast water or shellfish imports. This stresses the need to legally regulate such invasion routes in order to prevent the import of novel species. Future toxic phytoplankton blooms may further devaluate ecosystem deliverables such as fish production or recreational use. This devaluation can be estimated by monetary value assessments that are needed in cost-benefit analyses for policy guidance. The lack of understanding of future climate, ecosystem functioning and its response to climate change calls for a scientific effort to improve our knowledge on present day coastal ecosystem functioning and its resilience.     

The MERIS Maximum Chlorophyll Index; its merits and limitations for inland water algal bloom monitoring

Satellite remote sensing methods adopting wavelengths in the red and near infra-red have been shown to be superior to the standard blue to green ratio based approaches in the detection of algal blooms under turbid, eutrophic conditions. Here, the MERIS Maximum Chlorophyll Index (MCI) has been explored as a tool for monitoring algal blooms in North America's inland waters where waters range from optically complex, turbid, eutrophic conditions, to low chlorophyll and oligotrophic conditions. Assessment of the MERIS MCI product is made for intense blooms of cyanobacteria in Lake of the Woods, algal blooms in turbid waters of Lake Erie, and low chlorophyll conditions in Lake Ontario. The MCI product is shown to be a versatile tool in monitoring intense surficial algal blooms with chlorophyll concentrations in the 10–300 mg m^? 3 range, while limited in its application to low-biomass conditions as observed in Lake Ontario. Wavelength shifts in the position of the MCI peak for different chlorophyll concentration ranges, as well as variations in the inherent optical properties of water colouring constituents, are anticipated to account for regional variations in MCI–chlorophyll relationships and potentially hinder a universally applicable quantitative MCI product.     

Status,causes and controls of cyanobacterial blooms in Lake Erie

The Laurentian Great Lakes are among the most prominent sources of fresh water in the world. Lake Erie's infamous cyanobacterial blooms have, however, threatened the health of this valuable freshwater resource for decades. Toxic blooms dominated by the cyanobacterium Microcystis aeruginosa have most recently been one of primary ecological concerns for the lake. These toxic blooms impact the availability of potable water, as well as public health and revenues from the tourism and fishery industries. The socioeconomic effects of these blooms have spurred research efforts to pinpoint factors that drive bloom events. Despite decades of research and mitigation efforts, these blooms have expanded both in size and duration in recent years. However, through continued joint efforts between the Canadian and United States governments, scientists, and environmental managers, identification of the factors that drive bloom events is within reach. This review provides a summary of historical and contemporary research efforts in the realm of Lake Erie's harmful cyanobacterial blooms, both in terms of experimental and management achievements and insufficiencies, as well as future directions on the horizon for the lake's research community.     

三峡水库大宁河春季水华藻类分布及影响因子

以三峡水库支流大宁河2010年3月中旬的水华调查数据为依据,分析浮游藻类分布规律,并探讨其影响因子。结果表明,在容易暴发水华的库湾开阔地带之外,狭窄的峡谷地带亦会暴发严重水华,并且持续时间更长;河口区域的浮游藻类则表现出比其他区域更好的群落稳定性和生物均匀性。在此次水华的前、中期,浮游藻类的群落稳定性随生物量增大而降低;后期则随其增大而增加。通过对样品的定量分析,共鉴定浮游藻类7门24属,主要为绿藻和甲藻,第一优势种为拟多甲藻,占到总藻类的38%,其次为小球藻和衣藻。观察到藻类群体有垂直迁移现象,可能因藻类有趋光性所致。拟多甲藻水华的暴发会降低水体氮磷比,而随着水华的消退,水体氮磷比会较迅速地恢复到一个较高水平。在水华前期,总磷与浮游藻类群落稳定性呈现高度负相关,在中、后期其相关性减弱。     

An optical tool for quantitative assessment of phycocyanin pigment concentration in cyanobacterial blooms within inland and marine environments

Quantitative assessment of the pigment phycocyanin (PC) in cyanobacterial blooms is essential to assess their abundance and distribution and consequently aid their management in many recreational waters within inland and coastal environments. In contrast to the open-ocean waters, these water bodies are very complex with a pronounced heterogeneity of their optical properties, and hence accurate retrieval of the water-leaving radiances and PC concentration from satellite observations is notoriously difficult with existing algorithms. In the present study, a new inversion algorithm is developed as a rapid cyanobacteria bloom assessment method and its retrievals of PC are compared with in-situ and satellite observations and those from a previously reported inversion algorithm. The new algorithm estimates PC concentration on the basis of the unique absorption feature of phycocyanin at 620 nm which is isolated from the total pigment absorption by taking advantage of the well-recognized absorption and reflectance features in the red and near-infrared (NIR) wavelengths (less impacted by the influences of the overlapping absorption signatures of the mixture constituents and pigment packaging). The by-products of this work include chl-a concentration and predictions from reflectance data to monitor the cyanobacterial component and non-cyanobacterial component of the phytoplankton assemblage and to evaluate PC:Chl-a pigment weight ratios for specific water types. Initial validation of the algorithm was performed using in-situ field data in turbid productive waters dominated by phycocyanin and other pigments, yielding coefficients of determination and slope close to unity and mean errors less than a few percent. These results suggest that the algorithm could be used as a rapid assessment tool for the remote-sensing assessment of the spatial distribution and relative abundance of cyanobacterial blooms in many regional water bodies.     

三峡水库支流水华机理及其调控技术研究进展

三峡水库自2003年蓄水以来,水库干支流水环境状况及支流水华问题已成为广泛关注的问题,国内外不少科研单位及学者对此进行了大量的研究。本文在介绍三峡水库蓄水以来水库水质状态及支流水华情势的基础上,系统总结了关于三峡水库干支流水动力特征及其环境效应、水华机理及其调控措施的研究发现,并提出了有待进一步研究的内容。主要研究发现包括:(1)三峡水库蓄水后支流库湾普遍存在分层异重流现象,产生的原因是干支流温度差及泥沙浓度差引起的水体密度差,其中水体温度差是主要因素;(2)在分层异重流的驱动下,支流库湾水体呈现"双混斜"及"半U"型特殊水体分层模式,支流库湾营养盐也主要来自于水库干流倒灌;(3)流速变缓只是支流暴发水华的表观原因,分层异重流驱动下的混合层(Zm)与临界层(ZCr)的关系变化才是决定水华生消的关键;(4)水库水位升降可通过影响分层异重流的形态、改变支流水体分层状态进而调控支流水华的生消过程,基于此提出了防控支流水华的"潮汐式"生态调度方法。如何将上述新发现上升为具有三峡水库特色的系统理论与方法,实现支流水华的精准预测预报,开展能够协调水库传统效益和防控支流水华等生态效益的三峡及上游梯级水库群联合多目标优化调度实践,应是今后进一步努力的方向。     

太湖蓝藻监测及暴发情况分析

为了及时掌握太湖蓝藻发生和暴发状况,为保障流域供水安全和太湖富营养化治理提供必要的基础信息,以1996-2008年太湖各湖区藻类监测资料为基础,结合蓝藻历史变化状况,对太湖13 a的藻类群落组成、优势种的构成、数量和季节变化,以及蓝藻暴发情况进行分析,并对太湖9个湖区分区进行蓝藻暴发特征分析,探讨治理蓝藻水华的措施.结...     

Effects of temperature and nutrients on phytoplankton biomass during bloom seasons in Taihu Lake

Long-term variations of phytoplankton chlorophyll-a (Chl-a), nutrients,and suspended solids (SS) in Taihu Lake, a large shallow freshwater lake in China, during algal bloom seasons from May to August were analyzed using the monthly investigated data from 1999 to 2007. The effective accumulated water temperature (EAWT) in months from March to June was calculated with daily monitoring data from the Taihu Laboratory for Lake Ecosystem Research (TLLER).The concentrations of Chl-a and nutrients significantly decreased from Meiliang Bay to Central Lake. Annual averages of the total nitrogen (TN), total phosphorus (TP), and Chl-a concentrations, and EAWT generally increased in the nine years. In Meiliang Bay, the concentration of Chl-a was significantly correlated with EAWT, ammonia nitrogen (NH4+-N ), TN, the soluble reactive phosphorus (SRP),TP, and SS. In Central Lake, however, the concentration of Chl-a was only correlated with EAWT, TP, and SS. Multiple stepwise linear regression revealed that EAWT, dissolved total phosphorus (DTP), and TP explained 99.2% of the variation of Chl-a in Meiliang Bay, and that EAWT, NH4+-N, and TP explained 98.7% of the variation of Chl-a in Central Lake. Thus EAWT is an important factor influencing the annual change of phytoplankton biomass. Extreme climate change, such as extremely hot springs or cold springs, could cause very different bloom intensities in different years. It is also suggested that both nutrients and EAWT played important roles in the growth of phytoplankton in Taihu Lake. The climate factors and nutrients dually controlled the risk of harmful algal blooms in Taihu Lake. Cutting down phosphorus and nitrogen loadings from catchments should be a fundamental strategy to reduce the risk of blooms in Taihu Lake.     

Phytoplankton community and hydrochemical characteristics of the Western Black Sea.

Since the early 1990s the persistent reports about the irreversibly degrading Black Sea have started to disappear. A large set of data, reflecting the temporal and spatial variability of microalgae and hydrochemical parameters in Western Black Sea waters (WBS), collected in 2001-2002 (EU 5th FP project "daNubs"), allows us to compare the present-day ecosystem functioning to previous ones and to certainly conclude on positive signs of relaxation. The years 2001 and 2002 were without ample, negative biological and hydrochemical events in the WBS. Nutrient general trend was a decreasing one since the late 1980s, and inorganic phosphorus and nitrogen concentrations were frequently below the method detection limits during summer autumn periods of 2001-2002. The shelf waters were properly oxygenated. The phytoplankton community was producing biomass in a decreasing tendency, especially obviously since 1995. Simultaneously, there was an increase in Si:P and Si:N molar ratios and concurrent increase in the diversity of mass algal species (mainly diatoms) but only a few of them generated exceptional blooms. Reduction of the ratios between phytoplankton and zooplankton biomasses became evident. The observed ecological status in 2001-2002 confirms that the Black Sea is no longer a reference point for progressive water quality deterioration.     

河湖水华预测方法研究

以水华预测研究方法的发展为主线,着重从机理生态建模和智能方法建模两方面对水华预测方法进行总结。通过对国内外常用预测模型的建立思路和应用实例进行分析和描述,比较了机理模型和智能模型的优缺点和使用范围。最后,针对目前水华预测存在的问题进行了分析,指出机理建模和智能方法的有机结合将为准确预测水华提供有效的途径。     

Dolichospermum blooms in Lake Superior: DNA-based approach provides insight to the past,present and future of blooms

Cyanobacterial blooms are increasing in frequency, duration, and severity globally in freshwater ecosystems. The Laurentian Great Lakes are prone to toxin-producing cyanobacterial blooms and have experienced annually recurring blooms. Because of its oligotrophic nature, Lake Superior has been relatively free of bloom occurrences. However, in recent years, Dolichospermum blooms have occurred with increasing frequency, especially in the western arm. During a Dolichospermum bloom in 2018, opportunistic samples were collected from the offshore bloom and investigated with shotgun metagenomics. We identified a near-complete Dolichospermum genome that is highly similar to genomes from cultures recovered in Lakes Erie and Ontario. The genomes from the Laurentian Great Lakes are typified by their putative ability to produce a suite of secondary metabolites like anabaenopeptin, but not toxins like microcystin. Additionally, we recovered a Dolichospermum lemmermannii 16S rRNA gene from the bloom and using datasets collected from the epilimnion and sediments in Lake Superior show this organism is ubiquitous and that several strains may exist. While there is much to learn about Lake Superior cyanobacterial bloom development and triggers, understanding this organism is endemic to the region, what its genome is capable of and that specific strains may have provenance within the lake provides a distinct ecological basis for understanding and working towards a predictive framework for future blooms.