Contributed Session.
Lead Organizer: Remington Poulin, University of North Carolina Wilmington (poulinr@uncw.edu)
Co-organizers:
Bianca Rodríguez-Cardona, International Institute of Tropical Forestry (rodriguez.cardona.bm@gmail.com)
Presentations
04:30 PM
FIELD EVALUATION OF ULTRASOUND ON ALGAL BLOOMS FOR MITIGATION (8768)
Primary Presenter: Kaiden Murphy, The Ohio State University (murphy.2399@buckeyemail.osu.edu)
Algal blooms are common in freshwater bodies such as the Great Lakes and other freshwater reservoirs and have negative impacts on water quality both recreationally and for drinking water. Ultrasound has been proposed as a long-term solution to mitigate algal blooms by reducing algal growth. A field study was conducted in the reservoirs of Del-Co Water Company in Delaware, Ohio from May to October of 2023 and 2024, to observe the effects of ultrasound on algal blooms. The purpose of this study was to compare algae growth over time and between reservoirs in ultrasound treated and traditionally treated reservoirs with the same environmental factors to determine if ultrasound treatment had an impact on algae growth and populations. Sound pressure and frequency, algae type and relative abundance, and chlorophyll-a, and other water quality parameters were measured and used to monitor algae growth and impact of ultrasound across reservoirs. No significant differences were observed in algae abundance over time in ultrasound treated reservoirs compared to chemically treated reservoirs. Within the ultrasound treated reservoir, no clear impacts of ultrasound on algae growth by distance from the ultrasound units were observed. This study aimed to better understand the impacts of ultrasound on algae growth in the environment by analyzing differences in water quality parameters, algae populations, and abundance of algae over time.
04:45 PM
DO SUDDEN CHANGES IN SALINITY TRIGGER BLOOM FORMATION AND TOXIN PRODUCTION IN THE HARMFUL ALGA PRYMNESIUM PARVUM? (9020)
Primary Presenter: Karla Münzner, Leibnitz Institute of Freshwater Ecology and Inland Fisheries (karla.muenzner@igb-berlin.de)
The harmful alga Prymnesium parvum causes massive fish kills in saline rivers around the world. It is believed that such blooms are caused by an increase in salinity due to high evaporation and low discharge during droughts, natural sources of salt, or anthropogenic salt inputs. Dense blooms of P. parvum developed in the Oder River (Poland and Germany) in August 2022 and in June 2024, possibly after growing and being released from a retention basin for saline wastewater. The 2022 bloom was highly toxic, killing large amounts of fish and molluscs, whereas in 2024, P. parvum did not produce detectable amounts of algal toxins (=prymnesins). We aim to identify the causes of bloom formation and to investigate whether sudden changes in salinity trigger toxin production of P. parvum. For that, we compared the environmental conditions preceding both the toxic and non-toxic blooms using monitoring data. We also conducted microcosm experiments on P. parvum cultures (isolated from the Oder River) to investigate the effect of rapid salinity changes on P. parvum toxicity by measuring cell toxin concentrations and gene expression of polyketide synthases genes via qPCR, which have been associated with prymnesin production. This would, for the first time, confirm the role of environmental stressors in the synthesis of prymnesins, representing an experimental model to study environmental triggers of toxin production during harmful algal blooms. This will help us understand the processes causing harmful algal blooms, and to adapt bloom management efforts in rivers with high salt loading.
05:00 PM
SCIENTIFIC MACHINE LEARNING PREDICTS NUTRIENT DYNAMICS IN EUTROPHYING LAKES (9315)
Primary Presenter: Kunal Rathore, Oregon State University (rathorek@oregonstate.edu)
Forecasting ecosystem change is a critical task for effective management of natural resources. In lake systems, predicting nutrient concentration changes is challenging due to unobserved sources of nutrients like sewage infiltration, the complexity of interacting components, and feedback mechanisms, especially understanding the drivers of eutrophication. These factors limit the predictive power of many parametric models. Our aim is to efficiently predict unobserved nutrient inflows and provide accurate forecasts of change in algal concentrations using Scientific Machine Learning. Scientific Machine Learning combines parametric modeling (i.e., differential equations are used to describe known processes) with machine learning (i.e., data-driven predictive techniques like neural networks), to predict the quantities of interest. Specifically, we use Scientific Machine Learning to predict nutrient inputs from an unknown source, allowing us to forecast changes in the eutrophic state of a lake. This approach accurately captures the effects of environmental variability, nonlinear dynamics, system states (e.g., dissolved oxygen, nutrients, zooplankton, algal concentration), and complex interactions. Evaluating our approach with a mechanistic lake dynamics model revealed it outperforms standard parametric models in forecasting and predicting unobserved nutrient inflows. Integrating mathematical representation of known processes with machine learning enhances our ability to forecast lake ecosystem dynamics and predict Harmful Algal Blooms.
05:15 PM
Development of a HAB vulnerability index for Alaska drinking water systems (9439)
Primary Presenter: Kateri Salk, Tetra Tech (kateri.salk@gmail.com)
Harmful algal blooms (HABs) pose a threat to public drinking water systems through cyanotoxin production and interference with particulate removal processes. Currently, little is known about the potential vulnerability of public water systems to HABs in Alaska compared to the contiguous United States, while at the same time this region could experience the greatest change in HAB risk due to disproportionate climate change impacts. Our goal was to define which public water systems in Alaska are most vulnerable to HABs. First, we gathered geographically explicit datasets on drinking water systems (SDWIS), satellite-based HAB detection (CyAN), water quality monitoring, public and private wastewater treatment locations, watershed land cover, weather, snow and permafrost cover, and climate projections. These datasets were linked spatially via the public water system identifier, waterbody, and/or watershed (HUC12). Then, we analyzed data coverage across the 78 public water systems and developed a HAB vulnerability index. Alaska has similarities to other data-poor regions in that many geospatial datasets (e.g., high-resolution NHD, PRISM, LakeCat) are not yet accessible or require manual compilation. Further, many systems were not satellite-resolvable (10/62 lakes), and water quality monitoring data had poor coverage (2/62 lakes). Thus, we focused our vulnerability index not on a predictive HAB model but rather on the factors influencing HAB risk: drinking water system characteristics, watershed nutrient sources, current and projected climate, and potential for permafrost thaw.
05:30 PM
Persistence of low levels of Pseudo-nitzschia australis on the coast of Maine eight years after its first appearance (9332)
Primary Presenter: Sydney Greenlee, University of Maine (sydney.greenlee@maine.edu)
About half of the described species in the diatom genus Pseudo-nitzschia are capable of producing the potent neurotoxin domoic acid responsible for amnesic shellfish poisoning. Pn. australis has the highest reported cellular domoic acid concentrations in the genus, making it a species of interest for resource managers; however, Pseudo-nitzschia spp. cannot be easily distinguished by microscopy and blooms typically contain multiple species. We designed a real-time quantitative PCR assay for rapid and accurate detection and quantification of Pn. australis in environmental DNA samples. Estimated cell counts were experimentally derived for this assay. We applied this novel assay to eDNA water samples collected in the western Gulf of Maine to detect and quantify Pn. australis from 2021-2024. Pn. australis abundance via qPCR and particulate domoic acid concentration ([pDA]) peaked in magnitude in the fall when water temperatures were between 14° and 16°C. The magnitude of the signal, length of peak abundance, and [pDA] declined each year from 2021 to 2023. In 2024, no Pn. australis signal was detected. DNA metabarcoding of associated bacterial and eukaryotic communities and macronutrient analysis of select samples were also performed to identify biogeochemical drivers of Pn. australis phenology throughout this time series. This study demonstrates the use of targeted and non-targeted molecular approaches to detect, quantify, and evaluate harmful algal bloom species and their ecology in aquatic systems.
05:45 PM
What's Unclear About Clear Lake? A Case Study of Measuring a HAB in Real Time (9687)
Primary Presenter: Noel Corolla, Xylem Inc. (noel.corolla@xylem.com)
With Harmful Algal Blooms (HABs) becoming more frequent and intense every year, negatively impacting drinking water, recreation, and more, it’s important to know if spot sampling procedures are effective representations of our water resources. Researchers from the University of Southern California, with help from YSI, embarked to collect high resolution water quality data on one of the most heavily impacted lakes in California, Clear Lake. A three-day data collection effort was conducted using the YSI i3XO EcoMapper autonomous underwater vehicle (AUV), covering 36.6 km of water. This presentation will focus on the spatial water quality data collected from the lake-wide and bay-scale surveys. The results of the study outline the criticality of spatial data collection to both inform discrete grab sample collection and understand water quality heterogeneity for HAB monitoring programs in not only Clear Lake but other essential water resources.
CS09B - Harmful Blooms
Description
Time: 4:30 PM
Date: 28/3/2025
Room: W208