Abstract
The prevalence and increasing global distribution of cyanobacteria-dominated harmful algal blooms is strongly associated with changing climatic patterns and local biogeochemical and hydrological processes. Changes to precipitation frequency and intensity, as predicted by current climate models, are likely to alter bloom development and composition due changes in nutrient fluxes and water column mixing on short time scales during the open water season. However, few studies have directly documented the effects of precipitation events on cyanobacterial composition, biomass, and toxin production. In this study, we describe an early-initiated cyanobacterial bloom in Conestogo Lake, a eutrophic flood control reservoir located in southwestern Ontario, following heavy rainfall and subsequent flooding within the catchment. An increase in bioavailable phosphorus by more than 25-fold in surface waters resulted in biomass increases of Aphanizomenon flos-aquae throughout the reservoir approximately 2 weeks post-flooding. Anabaenopeptin-A and three microcystin congeners (microcystin-LR, -YR, and - RR) were detected at varying levels across sites during the bloom period, which lasted between 3 to 5 weeks. Together, these findings indicate that water column mixing and elevated phosphorus concentrations induced by increased water flow were the key drivers for the early cyanobacterial bloom in Conestogo Lake. Mitigation strategies for bloom-related water quality impairment must be both responsive and adaptive to the complexity of drivers affecting reservoir mixing dynamics, nutrient loads, and blooms. However, bloom mitigation efforts can be severely constrained by the large watersheds and broad suite of operational goals of many reservoirs. that are most often focussed on water quantity (flood water control and flow augmentation) rather than water quality or cyanobacterial bloom management.
Introduction
Cyanobacteria are critical to the structure and function of aquatic communities (Reynolds 1984, Wetzel 2001). However, prolific cyanobacterial growth often negatively impacts ecosystems by reducing water quality and driving disruptions to aquatic food chains (Pearl 1988). Several bloom-forming species also synthesize an array of bioactive compounds that pose chronic and acute health risks to humans and animals through dermal contact, inhalation, and/or ingestion of contaminated waters (Chorus et al. 2000, Codd 2000, Carmichael 2001). As a result, blooms and their associated bioactive metabolites have economic consequences through impacts on tourism and recreation, and via costs for drinking water treatment (Dodds et al. 2009, Bullerjahn et al. 2016).
The increased prevalence of cyanobacterial blooms has been historically attributed to increased anthropogenic eutrophication, with emphasis on the relative abundances, contributions, and impacts of nitrogen and phosphorus (Winter et al. 2012, Paerl and Otten 2013). Changing climatic conditions are also now strongly considered as significant drivers in bloom intensity and distribution (Paerl and Huisman 2009, O’Neil et al. 2012, Sukenik et al. 2015, Paerl et al. 2016). Current climate models predict increasing regional temperatures likely resulting in warmer surface waters, increased thermal stratification, and water column stability that promote the growth of certain bloom-forming species (Jöhnk et al. 2008, Paerl and Huisman 2009). Precipitation frequency and intensity are also predicted to change, but less attention has been given to how these factors may influence bloom development and cyanobacterial biomass (Reichwaldt and Ghadouani 2012).
Increasing precipitation variability may impact external nutrient and sediment delivery to waterbodies, alter residence time and flushing, and reduce vertical stratification, which, in turn, may affect bloom development (Jacobsen and Simonsen 1993, Mitrovic et al. 2003, Wood et al. 2017). For example, Wood et al. (2017) reported decreased cyanobacterial biomass following an extreme rainfall event that lead to water column cooling and destratification in a shallow, eutrophic New Zealand lake. Intense precipitation, preceded or followed by extensive drought may create episodic or pulsed nutrient loads potentially further favoring cyanobacterial development (Bouvy et al. 2003, Reichwaldt and Ghadouani 2012). In addition to climatic factors, bloom development is also dependent on a suite of site-specific characteristics including hydrology, lake geomorphology, catchment size, and nutrient loading from internal and external sources. Thus, the sensitivity of each lake to these drivers, including precipitation, will vary.
Cyanobacterial blooms are of particular concern for lake and reservoir managers due to the potential for toxin production and impacts on water quality that may affect ecosystem processes and the recreational uses of the water body (Chorus and Bartram 1999, Chorus et al. 2000). Cyanobacterial metabolites are both chemically variable and bio-actively diverse (Carmichael 1997, Welker and Von Döhren 2006) with various modes of cellular action that may cause superficial skin irritation at low exposures and sickness or even death if ingested at high enough concentrations (Pouria et al. 1998, Chorus et al. 2000, Carmichael et al. 2001, Carmichael 2001). Often, the recommended course of action for recreational waterbodies is to limit activity in and exposure to bloom-affected waters due to the potential for accidental ingestion and/or inhalation during recreational activities (Backer et al. 2010). Here, we explore the effects of an extreme precipitation event on cyanobacterial blooms within Conestogo Lake (Ontario, Canada; Figure 1), a eutrophic flood-control and river augmentation reservoir, which has experienced recurring, late-fall cyanobacterial blooms dominated by the nitrogen-fixing species Aphanizomenon flos-aquae. On 23 June 2017, the Upper Conestogo watershed received a substantial amount of rainfall (daily avg. 78 mm measured at Conestogo Dam) that resulted in severe flooding. According to the GRCA, nearly 80% of the volume of Conestogo Lake was flushed downstream during this 2-day event (Grand River Conservation Authority 2018). In this study, we were specifically interested in how significant rainfall from the catchment altered water quality and nutrient concentrations and if so, how these changes impacted cyanobacterial bloom development and toxin production.
Materials and methods
Study Site
Conestogo Lake is located in southwestern Ontario, Canada. It is a bottom-draw reservoir (7.35 km2) operated by the Grand River Conservation Authority (GRCA; Mapleton Township, ON). Built in 1958, it primarily serves as a flood-control and down-stream flow augmentation system while providing ancillary recreational benefits for fishing, boating, and ∼ 400 summer cottages. Higher flows in spring from the Upper Conestogo River basin (566 km2), a predominately agricultural catchment (> 80 %, Figure 1, left), are captured in the reservoir through the end of April and used during the summer drawdown (i.e. augmentation) period from 01 May to 30 September to provide consistent downstream flow of the Conestogo and Grand Rivers. As a result of the reservoir drawdown, mean lake depth varies throughout the season with the deepest point always near the dam. Reservoir storage volume during the summer augmentation period has historically ranged between 12.7 and 56.3 Mm3 (59.3 Mm3 max cap.) with an average drop between 5 and 7 m in stage elevation.
Sample collection and analysis
Sampling at Conestogo Lake began on 21 Jun 2017 with physical and biological samples collected from the center (CLC) site closest to the dam. Two additional sites in the east (CLE) and west (CLW) arms of the reservoir were added on 05 Jul and 11 Jul, respectively, following the flooding event to assess the potential differences between catchment inputs into the reservoir (Figure 1). From each location we collected a suite of physical, chemical, and biological data throughout the water column. Physical water column profiles were collected at 0.5 m increments using an EXO2 sonde (YSI, Yellow Springs, OH) equipped with chlorophyll-a, pH, temperature, and dissolved oxygen sensors. The Secchi disk transparency at each site was used to collect samples for cyanobacterial toxin analysis described in further detail below. In addition, we collected discrete samples from 2 m for water chemistry and phytoplankton taxonomy and enumeration approximately once per week from 21 June 2017 to 17 August 2017.
Whole water samples were collected for total phosphorus (TP) and phytoplankton enumeration. Subsamples were field-filtered using a 0.45 µm syringe filter (Whatman) for soluble reactive phosphorus (SRP), total dissolved phosphorus (TDP), ammonia (NH4-N), nitrate (NO3-), total dissolved nitrogen (TDN), anions, and cations. All samples were transported on ice, stored at −20 °C, and analyzed at the Environmental Geochemistry Laboratory at the University of Waterloo (Waterloo, ON) as per AHPA (1998; see Table S1 for instrument listing and detection limits).
Phytoplankton identification and enumeration were completed by D. Findlay (Plankton-R-Us, Winnipeg, MB, http://www.plankton-r-us.ca) as per Findlay & Kling (2003). Briefly, phytoplankton samples were collected in a dark bottle, preserved with Lugol’s solution, and stored at 4 °C until analysis. Ten-mL aliquots of Lugol’s preserved sample were gravity settled for 24 h and counted using a modified Ütermohl technique (Nauwerk 1963) on an inverted microscope with phase contrast illumination. Cell counts were converted to wet weight biomass by approximating cell volume, which were obtained by measurements of up to 50 cells of an individual species and applying the geometric formula best fitted to the shape of the cell (Vollenweider 1968, Rott 1981). A specific gravity of 1 was assumed for cellular mass. All biomass estimates are expressed as mg/m3 (D. Findlay, pers. communication).
Total (i.e., intracellular and extracellular) cyanobacterial metabolite concentrations were determined from a 115 mL whole water sample calculated from the site-specific Secchi depth (2× Secchi depth) at each site. Each sample was collected in an amber NalgeneTM polyethylene terephthalate glycol (PETG) bottle to limit adsorption and overflow during freezing (Fisher reference: 322021-0125). All samples were stored at −20 °C until analysis at the University of Montreal (Montréal, QC). Samples were prepared and screened for each of seventeen cyanobacterial compounds (Table S2) as per Fayad et al. (2015) via on-line solid phase extraction ultra-high performance liquid chromatography high resolution mass spectrometry (SPE-UHPLC– HRMS) using standards purchased from Enzo Life Science, Abraxis, or Cyano Biotech GmbH. The limit of detection (LOD) and limit of quantification (LOQ) were calculated for every batch of samples. Only results that exceeded the LOQ were included in our analysis. Therefore, some samples may have had detectable, but unquantifiable toxin concentrations.
Environmental and dam-related data
Meteorological and other reservoir-related data for this study were obtained from the GRCA’s online data portal (https://data.grandriver.ca/). Because GRCA data are provided under a provisional status, all GRCA data were passed through quality assurance and quality checking metrics for outliers to ensure sound data structure before proceeding with analysis. Spatial data were obtained from open source portals at the GRCA (bathymetry), the United States Geological Society, Statistics Canada, and the Ontario Ministry of Natural Resources (land-use).
Data analysis and statistics
All data analysis was completed in R version 3.5.1 (R Core Team 2018). Water column profile data derived from sonde measurements as well as the discrete chemical profiles were constructed using a multilevel B-spline interpolation from the MBA package (Finley et al. 2017) and were adjusted to reflect the reservoir stage elevation at the time of sampling. One sampling in the east arm contained a high chlorophyll-a concentration that skewed the profiles for all other sites. Therefore, we log transformed (base 10) the data to allow for better visual comparison. Comparisons of chemical and physical data across sites were completed using principal coordinates analysis (PCoA) with a Euclidean matrix on log-normalized data to account for scaling differences in the dataset and tested for difference through time and by site using permutational analysis of variance (PERMANOVA) with adonis in the vegan package (Okasanen et al. 2016). Similarly, we tested for differences in the phytoplankton community composition by biomass using PCoA with a Bray-Curtis dissimilarity matrix on log-transformed data to account for rare species biomass in the community and tested for difference through time and by site using PERMANOVA. Biomass and toxin trends were analyzed using repeated measures ANOVA (RM-ANOVA) with a linear mixed effects model (lme) from the nlme package (Pinheiro et al. 2018) where time was a fixed effect, sampling location as a random effect, and the correlation matrix selected based on Akaike Information Criterion (AIC). Relationships between cyanobacterial biomass and environmental drivers including surface water temperature and phosphorus concentrations were also completed using RM-ANOVA. In addition, we tested for the correlation of biomass with toxin concentrations using Kendall’s rho. All code used for this project are available on github at https://github.com/biogeochem/formbloom_conestogo_2017HAB.
Results
Flood event
The Upper Conestogo watershed received an intense rainfall event on 23-24 June 2017. This two-day event increased mean daily flow from 4.56 m3/s to 219 m3/s (max 521 m3/s), increased the total reservoir volume by 3.5 Mm3 (million cubic meters), and is estimated to have replaced ∼80% of the reservoir volume (Grand River Conservation Authority 2018). Within several days, the reservoir storage and drawdown returned to engineered levels (Figure 1b-e). The sizable influx of water from the catchment during the flood event increased measurable phosphorus between 1.9 to 33× within the water column at the center site by the next sampling on 05 July 2017 but did not affect any measured nitrogen species (Figures 2 and 3).
Hydrology and Descriptive Limnological Characteristics
All measured forms of phosphorus increased in the water column following the severe rainfall event (Figures 2a and 3a, Tables 1 and S1). Epilimnetic concentrations declined throughout the sampling period while hypolimnetic concentrations, notably TDP and TP, increased between 12 July (DOY 193) and 26 July (207) across all three sites. All nitrogen species (Figure 3b and S1, Tables 1 and S1) were well mixed throughout the water column and gradually decreased throughout the sampling campaign.
Conestogo Lake was initially and only briefly thermally stratified at the center site on 21 June 2017. By 05 July (DOY 186), the lake was isothermal across the lake and remained isothermal throughout the rest of the summer augmentation period (Figure S2a). Mean surface water temperatures (0 - 2 m) ranged from 21.2 to 25.5 °C. Subsequent flow from the catchment following the 23 July rainfall reduced the surface water temperatures in the east by approximately 2 °C from the previous sampling but did not affect the surface temperatures in the west arm or center sites. Dissolved oxygen (Figure S2b, Tables 1 and S1) at the surface was highest during peak biomass. Hypoxia developed in the hypolimnion at all three sampling locations just as bloom biomass started to increase. Epilimnetic (0 - 2 m) chemical (TDN, NO3-, DOC, SRP, TDP, TP, SO42-, Mn, Ca2+, K+, Na+, Mg2+, F,-, Cl-) and physical (surface temperature, dissolved oxygen, residence time) properties were significantly different between sites (PERMANOVA, P = 0.013) and changed over the course of the sampling period (PERMANOVA, P = 0.001; Figure S2).
Flooding induced cyanobacterial bloom
GRCA managers have visibly recorded cyanobacterial blooms in Conestogo Lake in late August through September (S. Cooke, pers. communication), similar to other eutrophic lake systems in this region (Winter et al. 2012). Within two weeks of the flooding event measurable increases in biomass of Aphanizomenon flos-aquae, a filamentous nitrogen-fixer, were detected in each of the sampling locations (Figure 4). Epilimnetic phytoplankton composition significantly changed over the course of the sampling campaign with assemblages in the west and center more similar than those in the east (Figure S3, PERMANOVA, site: F = 2.22, P = 0.001, time: F = 2.69, P = 0.003). While other phytoplankton groups were represented (Figure S4), cyanobacteria exceeded 50% of the phytoplankton community in the east and west arms near 12 July and approximately a week later in the center. Total cyanobacterial biomass varied across sites and was significantly affected by eplimnetic SRP, mean daily flow rates, and surface water temperatures (RMANOVA, F1,462 = 778.79, P < 0.0001). The east arm contained the highest measured biomass on 19 July (DOY 200, 10,900 mg/m3, 4,500 cells/mL), which declined following another, smaller, rainfall event on 23 Jul 2017. The center and west arm biomass peaked near 25 Jul (DOY 206; center: 4,710 mg/m3, west: 5,230 mg/m3). Bloom duration, measured as the period during which cyanobacterial biomass composed > 50% of the fractional biomass, persisted between 26 and 34 days and occurred 4 to 6 weeks earlier than has been previously documented by the GRCA (S. Cooke, pers. communication). The cyanobacterial fraction across all sampling sites also included Woronichinia compacta, Anabaena (Dolichospermum) flos-aquae, Microcystis aeruginosa, Anabaena (Dolichospermum) crassa, Planktolyngbya limnetica, and Pseudoanabaena sp. (Figure 4a). W. compacta exhibited similar biomass trends to A. flos-aquae, though at much smaller biomass levels; by 17 Aug, its biomass increased to 8% (193.8 mg/m3), 13% (108.9 mg/m3), and 18% (585.9 mg/m3) of the cyanobacterial biomass in the west, center, and east, respectively.
Cyanotoxins were detectable at low concentrations
Detectable anabaenopeptin-A (AP-A), microcystin-LR (MC-LR), -YR, and -RR were present in 90 % of the samples (n = 19) but were only quantifiable in 81 % of samples (n = 17; Table 2). Total microcystin concentration did not exceed 1.0 µg/L throughout the sampling season and were comparable to previously measured concentrations during the summer months (Yakobowski 2008). Metabolite type and concentration varied through time, but not by site (RMANOVA, toxin × time, F3,60 = 9.90, P < 0.0001; site P > 0.05). Quantifiable concentrations of all three microcystins were present in the east arm, MC-LR and MC-YR in the center site, and only MC-LR in the west. MC-LR was the dominant microcystin variant across all sites with the highest quantified values in the east arm at peak A. flos-aquae, M. aeruginosa, and P. limnetica biomass (Figure 4b, Table 2). Further, MC-YR at the center did not correspond with the MC-LR peak suggesting its possible origin in the east arm. AP-A was present at all sampling locations throughout the bloom period, was the dominant metabolite at all sites by 25 July (DOY 206) and had replaced all microcystin variants by the end of the sampling campaign. Toxin concentrations were not significantly correlated with the biomass of any particular cyanobacterial species (Kendall rho, P > 0.05).
Discussion
Conestogo Lake has experienced annual cyanobacterial blooms over the last decade, some of which have resulted in the issuance of health advisories for recreational users and cottagers (S. Cooke, GRCA, pers. communication). Consistent with other eutrophic lake and reservoir systems in the region (Winter et al. 2012), cyanobacterial blooms typically develop in mid- to late-August and persist through September or early October (S. Cooke, GRCA, pers. Communication, Larsen et al. in prep). However, the marked increase in soluble P as a result of rainfall combined with rapid physical change from disruption of water column structure during flooding to reestablishment of a stable water column made 2017 an atypical season with an unusually early bloom. This highlights the potential for changing windows of bloom-related risk and associated management considerations.
Global increases in extreme rainfall events are predicted to outpace changes in total precipitation under various climate models (Allen and Ingram 2002, IPCC 2007) suggesting that heavy rain and flooding events will likely become more common in the near future. The impact of changes to precipitation intensity and frequency on cyanobacterial bloom development and dynamics is inherently complex and may lead to either promotion or disruption of bloom dynamics (Reichwaldt and Ghadouani 2012, Wood et al. 2017). Interactions between physical, chemical, and biological lake parameters are influenced by other factors such as catchment size, land use, soil type, etc. and ultimately, the overall effect of the rainfall event is regulated by the rainfall intensity, water inflow, and seasonal timing (reviewed in Reichwaldt and Ghadouani 2012). Limited studies investigating such events on cyanobacterial bloom development have identified changes to flushing rates, water column mixing, and nutrient inputs from rainfall events as the main factors affecting cyanobacteria and phytoplankton communities (Bouvy et al. 2003, Reichwaldt and Ghadouani 2012) – all factors that appeared to impact the bloom dynamics in Conestogo Lake. Regional climate models suggest we will continue to see impacts of extreme events that can affect bloom timing, and risk (Bouvy et al. 2003, IPCC 2007, McDermid et al. 2015).
Sizable influxes of dissolved and particulate nutrient pools are common following heavy rainfall and erosion, particularly if they are preceded by a warm, dry periods (Jones and Poplawski 1998, Chiew et al 1995). For example, in Australian reservoirs, a 440 mm rainfall in a 3-day period resulted in an input equivalent of 80% and 400% of the average annual in-lake N and P, respectively (Jones and Poplawski 1998). Here we observed a 27x increase in SRP levels in surficial waters, which was likely key to the proliferation and dominance of cyanobacteria.
Emerging cyanotoxins
Many lake monitoring programs focus on the measurement of microcystins because of their vast distribution (Loftin et al. 2016), ecotoxicity (Chorus and Bartram 1999), and relatively inexpensive and rapid detection in whole water samples (e.g. ELISA test kits). However, several studies using cyanobacterial extracts have reported harmful/toxic effects that could not be explained solely by microcystin concentration or presence, suggesting the possibility of other toxic compounds (Keil et al. 2002, Teneva et al. 2005, Baumann and Jüttner 2008, Smutná et al. 2014, Lenz et al. 2019). Improved analytical techniques have identified numerous bioactive compounds such as cyanopeptolins and anabaenopeptins that are readily detectable in freshwaters and often produced simultaneously with microcystin variants (Harada et al. 1995, Welker and Von Döhren 2006, Gkelis et al. 2015, Beversdorf et al. 2017, 2018).
In some cases, and consistent with our results, anabaenopeptins are reported in equal or higher concentrations than microcystins (Janssen 2019). For example, Beversdorf et al. (2017) reported an average of 0.65 µg/L total microcystin in Lake Koshkonong, Wisconsin, while anabaenopeptin-B and -F were measured at 6.56 μg/L combined. Though there are no case studies of human toxicity caused by anabaenopeptins, the compound inhibits carboxypeptidase A, and like microcystin, also inhibits protein phosphatases with slightly overlapping inhibitory concentration ranges (Honkanen et al. 1990, Sano et al. 2001, Spoof et al. 2016). At present, the concentration to which anabaenopeptins and other metabolites would have to reach to affect human health is currently unknown resulting in a lack of regulations or advisories for recreational or drinking water. However, ecological effects may be observed at relatively low concentrations. A recent study by Lenz et al. (2019) reported induced toxicity by low concentrations (10 µg/L) of anabaenopeptins, including AP-A, on the nematode Caenorhabditis elegans resulting in reduced reproduction, reduced lifespan, and delayed hatching. Though compounds like AP-A have been previously considered non-toxic, they may now represent a new class of emerging toxins, whose potential impacts to human health and toxicity to aquatic organisms require immediate attention and therefore, inclusion in risk assessment for lake mitigation and monitoring programs
Recommendations for management
Watershed managers have various options to mitigate cyanobacterial blooms by disrupting conditions that favor growth. These strategies, including increasing flushing rates, reducing internal/external nutrient loads, destratisfying the water column through mixing, application of chemicals, and/ or biological manipulations, each come with varying economic costs and have been extensively reviewed elsewhere (Paerl 2018). Different short-term and long-term targets for water management regimes and/or nutrient loads will likely be needed to mitigate cyanobacterial bloom formation in Conestogo Lake. However, manipulating water flow to reduce residence time is not likely a feasible option since the reservoir operates with base-flow augmentation as a primary deliverable. Therefore, management goals will likely need to incorporate strategies to mitigate internal and external nutrient loads. TDN nor NO3 concentrations increased in response to the flood event and steadily decreased throughout the campaign. This suggests that N is not likely the regulating nutrient for biomass in this system though it may play a role in selecting for cyanobacterial species, bloom timing, or contributing to other biogeochemical cycles. Concurrent with other work on Conestogo Lake (Guildford 2006), our results suggest that P management strategies should be the main given the increased SRP concentrations in the water column following the flood and hypolimnetic P release from anoxic sediments. Further work will be needed to determine whether, if in a typical year, internal P release is the main driver for the late August or early September cyanobacterial blooms that have been previously documented anecdotally.
Short-term strategies such as chemical binding (e.g. Phoslock or alum) will precipitate suspended P to the sediment and could therefore reduce the size and/or severity of the bloom (Robb et al. 2003, Lürling and Faassen 2012, Cooke et al. 2013). However, keeping P immobilized for long periods is difficult in systems with strong anoxic bottom waters like Conestogo Lake. Longer-term strategies could include reduced P catchment loads via changes to the release of P-rich wastewater from upstream water treatment facilities, the incorporation of an increased riparian zone and/or dredging (Lürling and Faassen 2012, Aguiar et al. 2015). Increased riparian zones have been effective in reducing particulate P in various other systems (Aguiar et al. 2015) and may be a possible strategy since the predominant Tavistill soil type in the Upper Conestogo River catchment is highly susceptible to erosion (Macrae et al. 2007, Grand River Conservation Authority 2018). At present, only a small strip of forested land surrounds the main body of the reservoir, hence reduction of localized inputs may be achievable, even if riverine inputs are more difficult to control.
This task of identifying solutions to mitigate cyanobacterial blooms is challenging – yet urgent – given widespread reports of worsening bloom risk (Pick 2016). Development of appropriate mitigation strategies must incorporate an understanding of watershed nutrient sources, physiochemical lake structure, biological conditions, and climatic predictions into an adaptive framework considering what is possible, what are the costs (financial costs, and trade-offs to ecosystem services), and what are the goals of ecosystem and watershed management. Within Conestogo Lake we demonstrate that extreme rainfall events can trigger early bloom development. Management strategies and risk reduction activities, which currently target the low flow period in late summer may need to be altered in light of predictions of more frequent extreme rainfall events which may trigger an earlier bloom season in some ecosystems.
Data availability statement
Data and code that support the analysis and findings in this study are available in figshare at http://doi/10.6084/m9.figshare.7811963.
Acknowledgments
Funding for this work was provided by the Canada First Research Excellence Fund program. This was a part of the Global Water Futures Initiative FORMBLOOM: FORecasting tools and Mitigation options for diverse BLOOM-affected lakes as well as the ATRAPP project (Algal Blooms, Treatment, Risk Assessment, Prediction and Prevention through Genomics) with from Genome Canada and Genome Quebec. We thank S. Cooke, D. McFadden, and the Grand River Conservation Authority for their cooperation and knowledgeable insight, R. Elgood, E. McQuay, T. Cornell, B. Gruber, S. Sine, and M. Soares-Paquin for assistance with sample collection and analysis, J. Atkins for her GIS assistance, and the members of the Venkiteswaran and Schiff lab groups for their reviews of manuscript drafts and friendly discussion.