Cyanobacterial blooms have substantial direct and indirect negative impacts on freshwater ecosystems including releasing toxins, blocking light needed by other organisms, and depleting oxygen. There is growing concern over the potential for climate change to promote cyanobacterial blooms, as the positive effects of increasing lake surface temperature on cyanobacterial growth are well documented in the literature; however, there is increasing evidence that cyanobacterial blooms are also being initiated and persisting in relatively cold‐water temperatures (< 15°C), including ice‐covered conditions. In this work, we provide evidence of freshwater cold‐water cyanobacterial blooms, review abiotic drivers and physiological adaptations leading to these blooms, offer a typology of these lesser‐studied cold‐water cyanobacterial blooms, and discuss their occurrence under changing climate conditions.

Carbon, nitrogen, and phosphorus are critical macroelements in freshwater systems. Historically, researchers and managers have focused on inorganic forms, based on the premise that the organic pool was not available for direct uptake by phytoplankton. We now know that phytoplankton can tap the organic nutrient pool through a number of mechanisms including direct uptake, enzymatic hydrolysis, mixotrophy, and through symbiotic relationships with microbial communities. In this review, we explore these mechanisms considering current and projected future anthropogenically-driven changes to freshwater systems. In particular, we focus on how naturally- and anthropogenically- derived organic nutrients can influence phytoplankton community structure. We also synthesize knowledge gaps regarding phytoplankton physiology and the potential challenges of nutrient management in an organically dynamic and anthropogenically modified world. Our review provides a basis for exploring these topics and suggests several avenues for future work on the relation between organic nutrients and eutrophication and their ecological implications in freshwater systems.

Recent studies have shown that the total nitrogen to total phosphorus (TN:TP) ratio and nitrogen oxidation state may have substantial effects on secondary metabolite (e.g., microcystins) production in cyanobacteria. We investigated the relationship between the water column TN:TP ratio and the cyanobacterial secondary metabolites geosmin, 2-methylisoborneol (MIB), and microcystin using multiple years of data from 4 reservoirs located in the Midwestern United States. We also examined the relationship between water column concentrations of chemically oxidized (NO3) and reduced (NH3) nitrogen, the NO3:NH3 ratio, cyanobacterial biovolume, and associated secondary metabolites. We found that the cyanobacterial secondary metabolites geosmin, MIB, and microcystin primarily occurred when the TN:TP ratio was <30:1 (by mass), likely due to higher cyanobacterial biovolumes at lower TN:TP ratios. We also found that relative cyanobacterial biovolume was inversely related to the NO3:NH3 ratio. Both N2- and non-N2...

Harris TD, Graham JL. 2017. Predicting cyanobacterial abundance, microcystin, and geosmin in a eutrophic drinking-water reservoir using a 14-year dataset. Lake Reserve Manage. 33:32-48. Cyanobacterial blooms degrade water quality in drinking water supply reservoirs by producing toxic and taste-and-odor causing secondary metabolites, which ultimately cause public health concerns and lead to increased treatment costs for water utilities. There have been numerous attempts to create models that predict cyanobacteria and their secondary metabolites, most using linear models; however, linear models are limited by assumptions about the data and have had limited success as predictive tools. Thus, lake and reservoir managers need improved modeling techniques that can accurately predict large bloom events that have the highest impact on recreational activities and drinking-water treatment processes. In this study, we compared 12 unique linear and nonlinear regression modeling techniques to predict cyanobacterial abundance and the cyanobacterial secondary metabolites microcystin and geosmin using 14 years of physiochemical water quality data collected from Cheney Reservoir, Kansas. Support vector machine (SVM), random forest (RF), boosted tree (BT), and Cubist modeling techniques were the most predictive of the compared modeling approaches. SVM, RF, and BT modeling techniques were able to successfully predict cyanobacterial abundance, microcystin, and geosmin concentrations <60,000 cells/mL, 2.5 µg/L, and 20 ng/L, respectively. Only Cubist modeling predicted maxima concentrations of cyanobacteria and geosmin; no modeling technique was able to predict maxima microcystin concentrations. Because maxima concentrations are a primary concern for lake and reservoir managers, Cubist modeling may help predict the largest and most noxious concentrations of cyanobacteria and their secondary metabolites.

Cyanobacterial blooms present substantial challenges to managers and threaten ecological and public health. Although the majority of cyanobacterial bloom research and management focuses on factors that control bloom initiation, duration, toxicity, and geographical extent, relatively little research focuses on the role of loss processes in blooms and how these processes are regulated. Here, we define a loss process in terms of population dynamics as any process that removes cells from a population, thereby decelerating or reducing the development and extent of blooms. We review abiotic (e.g., hydraulic flushing and oxidative stress/UV light) and biotic factors (e.g., allelopathic compounds, infections, grazing, and resting cells/programmed cell death) known to govern bloom loss. We found that the dominant loss processes depend on several system specific factors including cyanobacterial genera-specific traits, in situ physicochemical conditions, and the microbial, phytoplankton, and consumer community composition. We also address loss processes in the context of bloom management and discuss perspectives and challenges in predicting how a changing climate may directly and indirectly affect loss processes on blooms. A deeper understanding of bloom loss processes and their underlying mechanisms may help to mitigate the negative consequences of cyanobacterial blooms and improve current management strategies.