Marie-Ève Garneau

USC CINAPS Builds Bridges

More than 70% of our earth is covered by water, yet we have explored less than 5% of the aquatic environment. Aquatic robots, such as autonomous underwater vehicles (AUVs), and their supporting infrastructure play a major role in the collection of oceanographic data. To make new discoveries and improve our overall understanding of the ocean, scientists must make use of these platforms by implementing effective monitoring and sampling techniques to study ocean upwelling, tidal mixing, and other ocean processes. Effective observation and continual monitoring of a dynamic system as complex as the ocean cannot be done with one instrument in a fixed location. A more practical approach is to deploy a collection of static and mobile sensors, where the information gleaned from the acquired data is distributed across the network. Additionally, orchestrating a multisensor, long-term deployment with a high volume of distributed data involves a robust, rapid, and cost-effective communication network. Connecting all of these components, which form an aquatic robotic system, in synchronous operation can greatly assist the scientists in improving our overall understanding of the complex ocean environment.

Autonomous Inland Water Monitoring: Design and Application of a Surface Vessel

This article presents a novel autonomous surface vessel (ASV) that was designed and manufactured specifically for the monitoring of water resources, resources that are not only constantly drained but also face the growing threat of mass proliferation (bloom) of noxious cyanobacteria. On one hand, the distribution of these blooms in a given water body requires a surveillance of biological data at high spatial resolution on both vertical and horizontal axes, whereas on the other hand, the understanding of the temporal evolution of the cyanobacteria necessitates repeated sampling at the same location. Therefore, our ASV was designed to combine the ability to take measurements within a range of depths, with its custom-made winch, and accurate localization provided by the global positioning system (GPS), without the need for static installations. This article first describes the ASV conception, and then the results of extended field tests on the waypoint navigation mode are discussed. Finally, the first results of a sampling campaign for monitoring algal blooms in Lake Zurich are presented. This work constitutes advances in the deployment of mobile measurement platforms for environmental monitoring in lacustrine environments. Furthermore, it investigates the application of a single ASV to capture both spatial and temporal dynamics of harmful cyanobacterial blooms in lakes. Combining surface mobility with depth measurements within a single robot allows fast deployments in remote location, which is cost efficient for lake sampling. This reduces the need for fixed installations, which can be impossible in recreational areas. The high-resolution sampling of lakes will contribute to understand and predict the occurrence of harmful cyanobacterial blooms for a better management of water resources.

Examination of the Seasonal Dynamics of the Toxic Dinoflagellate Alexandrium catenella at Redondo Beach, California, by Quantitative PCR

ABSTRACT The presence of neurotoxic species within the genus Alexandrium along the U.S. coastline has raised concern of potential poisoning through the consumption of contaminated seafood. Paralytic shellfish toxins (PSTs) detected in shellfish provide evidence that these harmful events have increased in frequency and severity along the California coast during the past 25 years, but the timing and location of these occurrences have been highly variable. We conducted a 4-year survey in King Harbor, CA, to investigate the seasonal dynamics of Alexandrium catenella and the presence of a particulate saxitoxin (STX), the parent compound of the PSTs. A quantitative PCR (qPCR) assay was developed for quantifying A. catenella in environmental microbial assemblages. This approach allowed for the detection of abundances as low as 12 cells liter−1, 2 orders of magnitude below threshold abundances that can impact food webs. A. catenella was found repeatedly during the study, particularly in spring, when cells were detected in 38% of the samples (27 to 5,680 cells liter−1). This peak in cell abundances was observed in 2006 and corresponded to a particulate STX concentration of 12 ng liter−1, whereas the maximum STX concentration of 26 ng liter−1 occurred in April 2008. Total cell abundances and toxin levels varied strongly throughout each year, but A. catenella was less abundant during summer, fall, and winter, when only 2 to 11% of the samples yielded positive qPCR results. The qPCR method developed here provides a useful tool for investigating the ecology of A. catenella at subbloom and bloom abundances.