Satellite monitoring of cyanobacterial harmful algal bloom frequency in recreational waters and drinking water sources

Cyanobacterial harmful algal blooms (cyanoHAB) cause extensive problems in lakes worldwide, including human and ecological health risks, anoxia and fish kills, and taste and odor problems. CyanoHABs are a particular concern in both recreational waters and drinking water sources because of their dense biomass and the risk of exposure to toxins. Successful cyanoHAB assessment using satellites may provide an indicator for human and ecological health protection. In this study, methods were developed to assess the utility of satellite technology for detecting cyanoHAB frequency of occurrence at locations of potential management interest. The European Space Agency's MEdium Resolution Imaging Spectrometer (MERIS) was evaluated to prepare for the equivalent series of Sentinel-3 Ocean and Land Colour Imagers (OLCI) launched in 2016 as part of the Copernicus program. Based on the 2012 National Lakes Assessment site evaluation guidelines and National Hydrography Dataset, the continental United States contains 275,897 lakes and reservoirs >1 ha in area. Results from this study show that 5.6% of waterbodies were resolvable by satellites with 300 m single-pixel resolution and 0.7% of waterbodies were resolvable when a three by three pixel (3 × 3-pixel) array was applied based on minimum Euclidian distance from shore. Satellite data were spatially joined to U.S. public water surface intake (PWSI) locations, where single-pixel resolution resolved 57% of the PWSI locations and a 3 × 3-pixel array resolved 33% of the PWSI locations. Recreational and drinking water sources in Florida and Ohio were ranked from 2008 through 2011 by cyanoHAB frequency above the World Health Organization’s (WHO) high threshold for risk of 100,000 cells mL⁻¹. The ranking identified waterbodies with values above the WHO high threshold, where Lake Apopka, FL (99.1%) and Grand Lake St. Marys, OH (83%) had the highest observed bloom frequencies per region. The method presented here may indicate locations with high exposure to cyanoHABs and therefore can be used to assist in prioritizing management resources and actions for recreational and drinking water sources.     

Improved monitoring of HABs using autonomous underwater vehicles (AUV)

Blooms of toxic algae are increasing in magnitude and frequency around the globe, causing extensive economic and environmental impacts. On the west coast of Florida, blooms of the toxic dinoflagellate Karenia brevis (Davis) have been documented annually for the last 30 years causing respiratory irritation in humans, fish kills, and toxin bioaccumulation in shellfish beds. As a result, methods need to be established to monitor and predict bloom formation and transport to mitigate their harmful effects on the surrounding ecosystems and local communities. In the past, monitoring and mitigation efforts have relied on visual confirmation of water discoloration, fish kills, and laborious cell counts, but recently satellite remote sensing has been used to track harmful algal blooms (HABs) along the Florida coast. Unfortunately satellite ocean color is limited by cloud cover, lack of detection below one optical depth, and revisit frequency, all of which can lead to extended periods without data. To address these shortcomings, an optical phytoplankton discriminator (OPD) was developed to detect K. brevis cells in mixed phytoplankton assemblages. The OPD was integrated into autonomous underwater vehicle (AUV) platforms to gather spatially and temporally relevant data that can be used in collaboration with satellite imagery to provide a 3D picture of bloom dynamics over time. In January 2005, a Remote Environmental Monitoring UnitS (REMUS) AUV with an OPD payload was deployed on the west coast of Florida to retrieve a similarity index (SI), which indicates when K. brevis dominates the phytoplankton community. SI was used to monitor a K. brevis bloom in relation to temperature, salinity, chlorophyll, and ocean currents. Current speed, SI, temperature, salinity, and chlorophyll a from the AUV were used to quantify a 1 km displacement of the K. brevis bloom front that was observed over the deployment period. The ability to monitor short term bloom movement will improve monitoring and predictive efforts that are used to provide warnings for local tourism and fishing industries. In addition, understanding the fine scale environmental conditions associated with bloom formation will increase our ability to predict the location and timing of K. brevis bloom formation. This study demonstrates the use of one autonomous platform and provides evidence that a nested array of AUVs and moorings equipped with new sensors, combined with remote sensing, can provide an early warning and monitoring system to reduce the impact of HABs.     

Monitoring Karenia brevis blooms in the Gulf of Mexico using satellite ocean color imagery and other data

Harmful algal blooms (HABs) of Karenia brevis are a recurrent problem in the Gulf of Mexico, with nearly annual occurrences on the Florida southwest coast, and fewer occurrences on the northwest Florida and Texas coasts. Beginning in 1999, the National Oceanic and Atmospheric Administration has issued the Gulf of Mexico HAB Bulletins to support state monitoring and management efforts. These bulletins involve analysis of satellite imagery with field and meteorological station data. The effort involves several components or models: (a) monitoring the movement of an algal bloom that has previously been identified as a HAB (type 1 forecast); (b) detecting new blooms as HAB or non-HAB (type 2); (c) predicting the movement of an identified HAB (type 3); (d) predicting conditions favorable for a HAB to occur where blooms have not yet been observed (type 4). The types 1 and 2 involve methods of bloom detection requiring routine remote sensing, especially satellite ocean color imagery and in situ data. Prediction (types 3 and 4) builds on the monitoring capability by using interpretative and numerical modeling. Successful forecasts cover more than 1000 km of coast and require routine input of remotely sensed and in situ data.The data sources used in this effort include ocean color imagery from the Sea-Viewing Wide Field-of-View Sensor/OrbView-2 satellite and processed using coastal-specific algorithms, wind data from coastal and offshore buoys, field observations of bloom location and intensity provided by state agencies, and forecasts from the National Weather Service. The HAB Bulletins began in coordination with the state of Florida in autumn of 1999 and included K. brevis bloom monitoring (type 1), with limited advisories on transport (type 3) and the detection of blooms in new areas (type 2). In autumn 2000, we improved both the transport forecasts and detection capabilities and began prediction of conditions favorable for bloom development (type 4). The HAB Bulletins have had several successes. The state of Florida was advised of the potential for a bloom to occur at the end of September 2000 (type 4), and the state was alerted to the position of blooms in January 2000 and October 2001 in areas that had not been previously sampled (type 3). These successful communications of HAB activity allowed Florida agencies responsible for shellfish management and public health to respond to a rapidly developing event in a timely, efficient manner.     

Evolution and structure of a shelf coccolithophore bloom in the Western English Channel

The physical factors associated with the development, advectionand disappearance of a shelf bloom of the coccolithophore Emilianiahuxleyi were investigated in the Western English Channel inJune 1992 from an early reflective stage to a mature and dissipativephase (3–4 weeks lifetime) in conjunction with thermaland visible satellite imagery [Advanced Very High ResolutionRadiometer (AVHRR)]. The physical processes that appeared importantin patch evolution and structure were differential stratificationin an area of weak tidal currents, initial zero resultant windconditions (allowing local bloom development), later strengtheningNE winds (driving a coastal warm surface current). entrainmentof the bloom water into the anticyclonic tidal circulation aroundthe Isles of Scilly, and finally bloom dispersal by mixing andflow divergence. Sea-truth results simultaneous with the satelliteimages on 25 and 26 June were examined in relation to the bloomintensity and vertical structure (E.huxleyi cells, detachedcoccoliths and empty coccospheres). Bloom conditions were characterizedby sections of temperature, chlorophyll a, inorganic nutrients,particulate organic and inorganic carbon, and carbon to nitrogenatom ratio. The phytoplankton composition of non-coccolithophorespecies presented significant variations inside and outsidethe bloom. Measurements of beam attenuation coefficient (660nm),diffuse attenuation coefficient [Photosynthetically active radiation(PAR)] and reflectance spectra (415–660 nm) were usedto analyse the extent to which the bloom changed the local opticalproperties. Optical measurements at sea were correlated withcoccolith concentration in order to quantify concentration estimatesderived from remote sensing reflectance measurements.     

Combined application of remote sensing and in situ measurements in monitoring environmental processes

Combined use of remote sensing in the visible, infrared and microwave spectral regions, direct in situ measurements and model numerical experiments makes it possible to study inland water bodies as elements of water body-catchment-atmosphere-systems with good spatial and temporal resolution. In this paper examples are presented of the remote sensing methods developed for detection of hydrodynamics of large water bodies (e.g. frontal and upwelling zones, internal waves, warm and cold surface layers), monitoring of chlorophyll concentration, suspended minerals and dissolved organic matter (DOM) in lakes, mapping of shallow water zones, wetlands and landscape structures, monitoring of ecological condition and changes of drainage basins, and studying the state of the atmosphere over lakes and catchment areas.     

Three-dimensional structure of a Karenia brevis bloom: Observations from gliders,satellites, and field measurements

Autonomous underwater gliders with customized sensors were deployed in October 2011 on the central West Florida Shelf to measure a Karenia brevis bloom, which was captured in satellite imagery since late September 2011. Combined with in situ taxonomy data, satellite measurements, and numerical circulation models, the glider measurements provided information on the three-dimensional structure of the bloom. Temperature, salinity, fluorescence of colored dissolved organic matter (CDOM) and chlorophyll-a, particulate backscattering coefficient, and K. brevis-specific chlorophyll-a concentrations were measured by the gliders over >250 km from the surface to about 30-m water depth on the shallow shelf. At the time of sampling the bloom was characterized by uniform vertical structures, with relatively high chlorophyll-a and CDOM fluorescence, low temperature, and high salinity. Satellite data extracted along the glider tracks demonstrated coherent spatial variations as observed by the gliders. Further, the synoptic satellite observations revealed the bloom evolution during the 7 months between late September 2011 and mid April 2012, and showed the maximum bloom size of ∼3000 km² around 23 November. The combined satellite and in situ data also confirmed that the ratio of satellite-derived fluorescence line height (FLH) to particulate backscattering coefficient at 547 nm (b_bp(547)) could be used as a better index than FLH alone to detect K. brevis blooms. Numerical circulation models further suggested that the bloom could have been initiated offshore and advected onshore via the bottom Ekman layer. The case study here demonstrates the unique value of an integrated coastal ocean observing system in studying harmful algal blooms (HABs).     

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.     

High-resolution satellite remote sensing of littoral vegetation of Lake Sevan (Armenia) as a basis for monitoring and assessment

Physics-based remote sensing in littoral environments for ecological monitoring and assessment is a challenging task that depends on adequate atmospheric conditions during data acquisition, sensor capabilities and correction of signal disturbances associated with water surface and water column. Airborne hyper-spectral scanners offer higher potential than satellite sensors for wetland monitoring and assessment. However, application in remote areas is often limited by national restrictions, time and high costs compared to satellite data. In this study, we tested the potential of the commercial, high-resolution multi-spectral satellite QuickBird for monitoring littoral zones of Lake Sevan (Armenia). We present a classification procedure that uses a physics-based image processing system (MIP) and GIS tools for calculating spatial metrics. We focused on classification of littoral sediment coverage over three consecutive years (2006–2008) to document changes in vegetation structure associated with a rise in water levels. We describe a spectral unmixing algorithm for basic classification and a supervised algorithm for mapping vegetation types. Atmospheric aerosol retrieval, lake-specific parameterisation and validation of classifications were supported by underwater spectral measurements in the respective seasons. Results revealed accurate classification of submersed aquatic vegetation and sediment structures in the littoral zone, documenting spatial vegetation dynamics induced by water level fluctuations and inter-annual variations in phytoplankton blooms. The data prove the cost-effective applicability of satellite remote-sensing approaches for high-resolution mapping in space and time of lake littoral zones playing a major role in lake ecosystem functioning. Such approaches could be used for monitoring wetlands anywhere in the world.     

遥感在生物多样性研究中的应用进展

随着人口的持续增长, 人类经济活动对自然资源的利用强度不断升级以及全球气候变暖, 全球物种正以前所未有的速度丧失, 生物多样性成为了全球关注的热点问题。传统生物多样性研究以地面调查方法为主, 重点关注物种或样地水平, 但无法满足景观尺度、区域尺度以及全球尺度的生物多样性保护和评估需求。遥感作为获取生物多样性信息的另一种手段, 近年来在生物多样性领域发展迅速, 其覆盖广、序列性以及可重复性等特点使之在大尺度生物多样性监测和制图以及评估方面具有极大优势。本文主要通过文献收集整理, 从观测手段、研究尺度、观测对象和生物多样性关注点等方面综述了遥感在生物多样性研究中的应用现状, 重点分析不同遥感平台的技术优势和局限性, 并探讨了未来遥感在生物多样性研究的应用趋势。遥感平台按观测高度可分为近地面遥感、航空遥感和卫星遥感, 能够获取样地-景观-区域-洲际-全球尺度的生物多样性信息。星载平台在生物多样性研究中应用最多, 航空遥感的应用研究偏少主要受飞行成本限制。近地面遥感作为一个新兴平台, 能够直接观测到物种的个体, 获取生物多样性关注的物种和种群信息, 是未来遥感在生物多样性应用中的发展方向。虽然遥感技术在生物多样性研究中的应用存在一定的局限性, 未来随着传感器发展和多源数据融合技术的完善, 遥感能更好地从多个尺度、全方位地服务于生物多样性保护和评估。     

Distribution and dynamics of two species of Dinophyceae producing high biomass blooms over the French Atlantic Shelf

The frequency and distribution of high biomass blooms produced by two dinoflagellate species were analysed along the French continental shelf from 1998 to 2012. Two species were specifically studied: Karenia mikimotoi and Lepidodinium chlorophorum. Based on remote-sensing reflectances at six channels (410, 430, 480, 530, 550 and 670 nm), satellite indices were created to discriminate the species forming the blooms. A comparison with observations showed that the identification was good for both species in spite of a lower specificity for L. chlorophorum. The overall analysis of the satellite indices, in association with some monitoring data and cruise observations, highlights the regularity of these events and their extent on the continental shelf. L. chlorophorum blooms may occur all along the South Coast of Brittany. All the coastal areas under the influence of river plumes and the stratified northern shelf area of the Western English Channel appear to be areas of bloom events for both species. These two species are likely to be in competitive exclusion as they share the same spatial distribution and the timing of their bloom is very close. Finally, due to the scarcity of off-shore observations, these satellite indices provide useful information regarding HABs management and the development of a warning system along the French coast.     

2002-2018年滇池外海蓝藻水华暴发时空变化特征及其驱动因子

为弄清滇池外海蓝藻水华暴发时空变化规律及其影响因素,将滇池外海分为北、中、南3个区域,基于2002—2018年期间中分辨率成像光谱仪(MODIS)反演的水华面积,分析了上述3个区域蓝藻水华的时空变化特征。基于2007—2018年水文、气象和出入流数据,构建了外海三维水动力生态模型(AEM3D),并计算了各区域的水力滞留时间。通过冗余分析(RDA)、随机森林(RF)和斯皮尔曼相关分析方法,分析了影响以上区域蓝藻水华暴发的主要驱动因子。结果表明：2002—2018年期间,整个滇池外海区域年平均水华面积比(水华面积占该区域总面积比例)呈缓慢下降趋势,空间上由北向南依次递减,整个外海水华暴发面积最大主要发生在秋季。在外海北部区域,其东部水华较西部更为严重,而在中部和南部区域,呈现西部水华较东部更为严重的空间分布模式。通过对各影响因子的统计分析发现,风速、水温和日照时长是上述各区域中蓝藻水华暴发的主要决定性因素。水华暴发期间以西南风为主导风向,且上述区域的水华面积比随风速增加呈下降趋势。在外海各区域,水力滞留时间与水华暴发面积均呈显著正相关,空间上水力滞留时间由北向南逐渐增大,风速和风向是影响蓝...     

Large-scale patterns of Nodularia spumigena blooms in Pyramid Lake,Nevada, determined from Landsat imagery: 1972–1986

Magnitude and long-term periodicity of summer-autumn blooms of the nitrogen-fixing cyanobacterium, Nodularia spumigena, were characterized for hyposaline Pyramid Lake, Nevada, from Landsat MSS band 3 film negatives. Predicted lakewide mean chlorophyll a concentrations for Landsat overpasses during the July–October Nodularia bloom season ranged from 27 to 72 mg m^–3 with an overall average concentration of 32 ± 7 mg m^–3 between 1972 and 1986. Nodularia blooms were usually annual events. Blooms were not observed on Landsat images in only three of 15 years (1973, 1980, 1982) and midsummer calcium carbonate whitings occurred in two of these years (1973, 1980). Magnitude of Nodularia blooms was highly variable among years and very large blooms, where maximum mean chlorophyll a concentration exceeded one standard deviation of the 15 year overall mean (> 39 mg m^–3) appeared in 1974, 1975, 1977, 1979, 1984, 1985 and 1986. Very large early-July blooms always occurred during or following years of above average fluvial discharge to Pyramid Lake (1984–1986) and were associated with meromixis produced by the large influx of freshwater.Several problems arise using Landsat remote sensing to estimate magnitude and periodicity of scum-forming blue-green algal blooms. These complications may reduce accuracy and precision of phytoplankton biomass estimates made from Landsat images. Nevertheless, Landsat remote sensing enabled us to quantify relative bloom magnitude with limited collection of ground-based data and at a large-scale temporal and spatial resolution not possible using alternative methodologies.     

Monitoring of harmful algal blooms in the era of diminishing resources: A case study of the U.S. West Coast

Spatial and temporal decorrelation scales in phytoplankton bloom magnitudes are reviewed with the goal of informing the design of efficient and informative observing networks for monitoring of potentially harmful algal blooms (HABs) along the U.S. West Coast. Our analysis of historic MODIS Fluorescent Line Height data shows that, unlike several previous studies, seasonal timing of phytoplankton blooms off the U.S. West Coast propagates from South to North. In situ data show that temporal decorrelation scales are shortest off Northern California (∼4 days) and longest in the Southern California Bight (∼17 days). In a cross-shore direction, we find that variability in the near-shore biomass is decoupled from biomass further offshore (∼2–4 km).Our review of the cloud cover gap statistics suggests that satellite data provide reasonably inexpensive information about bloom events, particularly at seasonal to inter-annual scales, but is insufficient to capture many event-scale blooms. Absent adequate satellite data, in situ monitoring becomes essential. Existing networks of automated monitoring sites from piers and moorings off the California coast is insufficient to describe regional variability in blooms, but is likely informative of bloom magnitudes in the immediate proximity of observation stations. We suggest that a more effective network will have a combination of shore-based stations and a few (5–10) offshore moorings. Shore-based stations should be located in proximity to sensitive HAB targets. This would provide monitoring of existing conditions and guide decision-making about beach closures and aquaculture management practices. Offshore stations can serve as proxy for regional conditions and can be used to issue early warnings of potential HAB conditions developing in a specific region.     

Toxin-producing cyanobacteria in freshwater: A review of the problems, impact on drinking water safety, and efforts for protecting public health

Cyanobacteria have adapted to survive in a variety of environments and have been found globally. Toxin-producing cyanobacterial harmful algal blooms (CHABs) have been increasing in frequency worldwide and pose a threat to drinking and recreational water. In this study, the prevalence, impact of CHABs and mitigation efforts were reviewed, focusing on the Lake Erie region and Ohio’s inland lakes that have been impacted heavily as an example so that the findings can be transferrable to other parts of the world that face the similar problems due to the CHABs in their freshwater environments. This paper provides a basic introduction to CHABs and their toxins as well as an overview of public health implications including exposure routes, health effects, and drinking water issues, algal bloom advisory practices in Ohio, toxin measurements results in Ohio public water supplies, and mitigation efforts.     

闽南三市绿地景观格局与地表温度的空间关系

深入探索绿地景观格局对热环境的影响机制,对于改善城市生态环境具有重要意义。基于厦门、漳州、泉州三市的Landsat遥感数据,应用景观生态学与空间自相关理论,探讨了绿地景观与地表温度的空间分布特征,并用双变量空间自相关与空间自回归模型分析两者之间的空间关系。结果表明:闽南三市绿地与地表温度均具有显著的空间自相关性;内陆的高海拔地区,绿地分布密集,地表温度较低,而城区、乡镇及大片的耕地,绿地分布较少,地表温度较高;漳州、泉州绿地景观格局对地表温度的影响更显著,厦门最弱;绿地的景观类型比例、最大斑块指数、聚集度指数、平均斑块面积与地表温度呈负相关,斑块密度与地表温度呈正相关;边缘密度、平均形状指数与地表温度的关系存在不确定性;空间滞后模型与空间误差模型能更好地解释绿地景观格局与地表温度的空间关系。     

A large and prolonged bloom of Karenia mikimotoi in Scottish waters in 2006

A prolonged bloom of Karenia mikimotoi was observed during 2006 in Scottish waters. This bloom is thought to be unique in the region in terms of its large spatial extent. From its first detection in the west of the country, the bloom moved clockwise around the coast eventually reaching the east coast and the Shetland Isles to the north. The bloom resulted in extensive mortalities of benthic organisms including annelids and molluscs and some species of fish. Farmed fish mortalities were absent but gill damage was reported. The availability of satellite remote sensing, phytoplankton counts from multiple sites, meteorological data and some water chemistry, as well as information on the physical characteristics of the sampling sites, provided an extensive spatial and temporal data set. Analysis of remotely sensed chlorophyll-a data from Aqua-MODIS indicated that this parameter is a useful early warning indicator of K. mikimotoi in shelf waters off the Scottish west coast, and suggested that the bloom developed in this region prior to its advection to coastal waters. An earth observation (EO) based harmful bloom classifier for K. mikimotoi recognised areas of elevated K. mikimotoi cell density but generated false positives in areas of high reflectance. Data were also used to evaluate, in Scottish waters, various hypotheses that exist to explain the formation of K. mikimotoi blooms including phototaxis, nutrient availability, cell transport and elevated water temperature. Specifically, we sought to evaluate if routinely collected environmental data (water temperature, insolation, wind strength and direction, and sea-loch aspect) could be used as a predictor of bloom magnitude near aquaculture facility locations, which typically lie within fjordic sea lochs. Path analysis was used to derive intuitive models linking environmental drivers to bloom magnitude. Once the effects of latitude such as northward water cooling were taken into account, only rainfall was a significant predictor of bloom magnitude at the sampling sites. Therefore, while the offshore development and progression of a bloom may be predicted from satellite information, it is likely that local hydrodynamic influences are crucial in determining its magnitude at coastal aquaculture sites.     

Satellite discrimination of Karenia mikimotoi and Phaeocystis harmful algal blooms in European coastal waters: Merged classification of ocean colour data

The detection of dense harmful algal blooms (HABs) by satellite remote sensing is usually based on analysis of chlorophyll-a as a proxy. However, this approach does not provide information about the potential harm of bloom, nor can it identify the dominant species. The developed HAB risk classification method employs a fully automatic data-driven approach to identify key characteristics of water leaving radiances and derived quantities, and to classify pixels into “harmful”, “non-harmful” and “no bloom” categories using Linear Discriminant Analysis (LDA). Discrimination accuracy is increased through the use of spectral ratios of water leaving radiances, absorption and backscattering. To reduce the false alarm rate the data that cannot be reliably classified are automatically labelled as “unknown”. This method can be trained on different HAB species or extended to new sensors and then applied to generate independent HAB risk maps; these can be fused with other sensors to fill gaps or improve spatial or temporal resolution. The HAB discrimination technique has obtained accurate results on MODIS and MERIS data, correctly identifying 89% of Phaeocystis globosa HABs in the southern North Sea and 88% of Karenia mikimotoi blooms in the Western English Channel. A linear transformation of the ocean colour discriminants is used to estimate harmful cell counts, demonstrating greater accuracy than if based on chlorophyll-a; this will facilitate its integration into a HAB early warning system operating in the southern North Sea.     

Differential Drawdown of Ammonium,Nitrate, and Urea by Freshwater Chlorophytes and Cyanobacteria1

The chemical form of nitrogen (N) is deemed to be decisive in shaping the composition of the primary producer community. Recently, there has been a shift in the dominant form of N delivered to agricultural landscapes. Urea-based fertilizers are a mainstay in modern agriculture, and their ubiquitous use has increased the likelihood of urea export to nearby freshwaters. The shift to urea fertilizers has coincided with the recent expansion of cyanobacteria harmful algal blooms (cyanoHABs). This study investigated N drawdown patterns between two major freshwater phytoplankton groups—chlorophytes and cyanobacteria. Experiments were designed to understand if different patterns of N drawdown occurred among taxa and the potential synergistic effects of multiple N substrates. Nitrate (NO₃⁻), ammonium (NH₄⁺), and urea were supplied in a series of paired combinations, and N concentrations were monitored to track N drawdowns. We did not find significant differences between phytoplankton classes when supplied with a single N substrate. However, we found that when N substrates were supplied in combination, significant differences in N drawdown patterns were observed. Urea was consumed more rapidly among cyanobacteria, being drawn down at significantly higher rates relative to inorganic N substrates. In contrast, inorganic N substrates were drawn down more rapidly among chlorophytes relative to urea. Our findings support the emerging urea–cyanoHAB link and the potential importance of urea in freshwater eutrophication. As society becomes increasingly dependent on urea for agricultural crops, the need to understand how urea influences phytoplankton community composition may be instrumental in predicting bloom dynamics.