The western basin of Lake Erie has a history of recurrent cyanobacterial harmful algal blooms (HABs) despite decades of efforts by the United States and Canada to limit phosphorus loading, a major driver of the blooms. In response, the National Oceanic and Atmospheric Administration (NOAA) Great Lakes Environmental Research Laboratory (GLERL) and the Cooperative Institute for Great Lakes Research (CIGLR) created an annual sampling program to detect, monitor, assess, and predict HABs in western Lake Erie (WLE). Here we describe the data collected from this monitoring program from 2012 to 2021. This dataset includes observations on physicochemical properties, major nutrient fractions, phytoplankton pigments, microcystins, and optical properties for western Lake Erie. This dataset is particularly relevant for creating models, verifying and calibrating remote sensing algorithms, and informing experimental research to further understand the water quality dynamics that influence HABs in this internationally significant body of freshwater. The dataset can be freely accessed from NOAA National Centers for Environmental Information (NCEI) at https://doi.org/10.25921/11da-3x54 (Cooperative Institute for Great Lakes Research, University of Michigan and NOAA Great Lakes Environmental Research Laboratory, 2019).

Freshwater cyanobacterial harmful algal blooms (CHABs) are a well‐known global public health threat. Monitoring and early detection of CHAB toxins are currently accomplished using labor‐intensive sampling techniques and subsequent shore‐based analyses, with results typically reported 24–48 h after sample collection. We have developed and implemented an uncrewed, autonomous mobile sampler‐analytical system capable of conducting targeted in situ toxin measurements in < 2 h. A surface plasmon resonance (SPR) instrument was combined with the environmental sample processor (ESP) to fully automate detection and quantification of particle‐associated cyanobacterial microcystins (pMC). This sensor‐sampler system was integrated with a long‐range autonomous underwater vehicle (LRAUV) and deployed in western Lake Erie for field trials in the summer of 2021. The LRAUV was remotely piloted to acquire samples at selected locations within and adjacent to a CHAB. Sixteen pMC measurements ranging from 0.09 to 0.55 μg/L lake water were obtained over a 14‐day period without recovery of the LRAUV. The SPR/ESP/LRAUV system complements existing satellite, aerial, and manual sampling CHAB survey techniques, and could be used to enhance predictive models that underpin bloom and toxicity forecasts. This system is also extensible to detection of other algal toxins in freshwater and marine environments, with its near real‐time assessment of bloom toxin levels potentially offering additional socioeconomic benefits and public health protection in a variety of settings.

Cyanobacterial harmful algal blooms (CyanoHABs) in the Great Lakes pose risks to residential drinking water use, fisheries, and recreation. Active mitigation of these risks requires rapid detection of CyanoHABs and quantification of the toxins they produce. Here, we present a method of using a long‐range autonomous underwater vehicle (LRAUV) equipped with a 3^rd‐generation Environmental Sample Processor (3G‐ESP) to search for and adaptively sample areas of high chlorophyll potentially representative of CyanoHAB biomass. In August 2021, this method was used in western Lake Erie. The experiment highlighted the effectiveness of the LRAUV autonomous search‐and‐sample methodology, and demonstrated how an interdisciplinary team located in different states virtually coordinated LRAUV operations and directed sampling activities via Internet connectivity using shared, web‐based situational awareness tools. The advancements made provide a foundation for future work to increase LRAUV autonomy and adaptiveness for CyanoHAB studies and monitoring in both freshwater and marine settings.

Harmful algal blooms (HABs) are increasing globally in frequency, persistence, and geographic extent, posing a threat to ecosystem and human health. To date, no occurrences of marine phycotoxins have been recorded in Mozambique, which may be due to absence of a monitoring program and general awareness of potential threats. This study is the first documentation of neurotoxin, domoic acid (DA), produced by the diatom Pseudo-nitzschia along the east coast of Africa. Coastal Inhambane Province is a biodiversity hotspot where year-round Rhincodon typus (whale shark) sightings are among the highest globally and support an emerging ecotourism industry. Links between primary productivity and biodiversity in this area have not previously been considered or reported. During a pilot study, from January 2017 to April 2018, DA was identified year-round, peaking during Austral winter. During an intense study between May and August 2018, our research focused on identifying environmental factors influencing coastal productivity and DA concentration. Phytoplankton assemblage was diatom-dominated, with high abundances of Pseudo-nitzschia spp. Data suggest the system was influenced by nutrient pulses resulting from coastal upwelling. Continued and comprehensive monitoring along southern Mozambique would provide critical information to assess ecosystem and human health threats from marine toxins under challenges posed by global change.

The Bayesian hierarchical model (BHM) is a framework that improves parameter estimation by leveraging information from different sources. In an environmental monitoring program, we often measure important chemical concentrations using calibration-based methods. These methods require fitting a calibration curve repeatedly each time with a small number of standard solutions of known concentrations. This approach is often associated with large estimation uncertainty in the measured concentrations. BHM is a perfect method for reducing calibration curve uncertainty, thereby enhancing the accuracy and stability of the resulting concentration measurements. We demonstrate the effectiveness of a BHM approach by estimating microcystin concentrations from the Lake Erie harmful algal bloom (HAB) monitoring program operated by the Great Lakes Environmental Research Laboratory of the National Oceanic and Atmospheric Administration. We introduced a sequential updating algorithm to implement the BHM framework so that the BHM model can be fit and updated one test at a time. By comparing estimated quality control sample concentrations to their known values, we show that the BHM method yields the best accuracy compared to the currently used methods. Due to the sequential updating approach, the BHM can be readily incorporated into a lab without requiring additional changes to lab procedures, thus offering a key advantage over traditional calibration methods. This advancement could reduce health risks and false-positive shutdowns during HAB events.