Evaluating putative ecological drivers of microcystin spatiotemporal dynamics using metabarcoding and environmental data

Linking Water Quality to Drinking Water Treatment Costs Using Time Series Analysis: Examining the Effect of a Treatment Plant Upgrade in Ohio

We estimate a cost function for a water treatment plant in Ohio to assess the avoided-treatment costs resulting from improved source water quality. Regulations and source water concerns motivated the treatment plant to upgrade its treatment process by adding a granular activated carbon building in 2012. The cost function uses daily observations from 2013 to 2016; this allows us to compare the results to a cost function estimated for 2007-2011 for the same plant. Both models focus on understanding the relationship between treatment costs per 1,000 gallons (per 3.79 m3) of produced drinking water and predictor variables such as turbidity, pH, total organic carbon, deviations from target pool elevation, final production, and seasonal variables. Different from the 2007-2011 model, the 2013-2016 model includes a harmful algal bloom toxin variable. We find that the new treatment process leads to a different cost model than the one that covers 2007-2011. Both total organic carbon and algal toxin are important drivers for the 2013-2016 treatment costs. This reflects a significant increase in cyanobacteria cell densities capable of producing toxins in the source water between time periods. The 2013-2016 model also reveals that positive and negative shocks to treatment costs affect volatility, the changes in the variance of costs through time, differently. Positive shocks, or increased costs, lead to higher volatility compared to negative shocks, or decreased costs, of similar magnitude. After quantifying the changes in treatment costs due to changes in source water quality, we discuss how the study results inform policy-relevant decisions.

Environmental DNA Metabarcoding for Simultaneous Monitoring and Ecological Assessment of Many Harmful Algae

Harmful algae can have profound economic, environmental, and social consequences. As the timing, frequency, and severity of harmful algal blooms (HABs) change alongside global climate, efficient tools to monitor and understand the current ecological context of these taxa are increasingly important. Here we employ environmental DNA metabarcoding to identify patterns in a wide variety of potentially harmful algae and associated ecological communities in the Hood Canal of Puget Sound in Washington State, USA. Tracking trends of occurrence in a series of water samples over a period of 19 months, we find algal sequences from genera with harmful members in a majority of samples, suggesting that these groups are routinely present in local waters. We report patterns in variants of the economically important genus Pseudo-nitzschia (of which some members produce domoic acid; family Bacillariaceae), as well as multiple potentially harmful algal taxa previously unknown or poorly documented in the region, including a cold-water variant from the genus Alexandrium (of which some members produce saxitoxin; family Gonyaulacaceae), two variants from the genus Karlodinium (of which some members produce karlotoxins; family Kareniaceae), and one variant from the parasitic genus Hematodinium (family Syndiniaceae). We then use data on environmental variables and the biological community surrounding each algal taxon to illustrate the ecological context in which they are commonly found. Environmental DNA metabarcoding thus simultaneously (1) alerts us to potential new or cryptic occurrences of algae from harmful genera, (2) expands our knowledge of the co-occurring conditions and species associated with the growth of these organisms in changing marine environments, and (3) suggests a pathway for multispecies monitoring and management moving forward.

Highlighting the promise of qPCR-based environmental monitoring: response of the ribosomal RNA:DNA ratio of calanoid copepods to toxic cyanobacteria

Calanoid copepods are integral to aquatic food webs and may drive the bioaccumulation of toxins and heavy metals, spread of infectious diseases, and occurrence of toxic cyanobacterial harmful algal blooms (HABs) in freshwater aquatic systems. However, interrelationships between copepod and cyanobacterial population dynamics and ecophysiology remain unclear. Insights into these relationships are important to aquatic resource management, as they may help guide mitigation efforts. We developed a calanoid copepod qPCR assay to investigate how copepod abundance and physiological status relate to the abundance of cyanobacteria and the concentration of total microcystin in a HAB-prone freshwater multi-use eutrophic lake. Through in silico and in vitro validation of primers and analyses of time series, we demonstrate that our assay can be used as a reliable tool for environmental monitoring. Importantly, copepod RNA:DNA ratios on and shortly after the day when microcystin concentration was at its highest within the lake were not significantly lower (or higher) than before or after this period, suggesting that copepods may have been tolerant of microcystin levels observed and capable of perpetuating bloom events by consuming competitors of toxic cyanobacteria.