WWW.WA S .ORG • WORLD AQUACULTURE • JUNE 2022 23 (7 in diameter, 24 in long) in situ sampler rather than a sensor and can be deployed on a variety of platforms. Early Warning Systems Early warning of the timing, location and magnitude of HABs and their associated biotoxins is of great value to coastal zone managers and the aquaculture industry, informing business planning and ensuring the protection of both human and fish health (Anderson et al. 2001, Davidson et al. 2021). EWS provide a window of opportunity for users and regulatory agencies to take preventive actions against impending threats. The creation of EWS would not be possible without incorporating remotely sensed data. Although the science of forecasting HABs is dynamic, there have been recent advancements in the creation of EWS using satellite imagery, in situ data and machine-learning (ML) based approaches in tandem, a technique that has been applied to a variety of blooms (Hans et al. 2006, Fernandez-Salvador et al. 2021, Yerrapothu 2021). Current site-specific EWS using the most advanced technologies can predict the occurrence of HABs from 7-14 days in advance, allowing operators sufficient time to implement the mitigation strategies. Researchers continue to strive to create advanced EWS that can detect HABs as they form, as well as identify a variety of phytoplankton species. In addition to incorporating remotely sensed data, another vital component of EWS includes the creation and use of numeric models. Numeric modeling relies on the input of various remotely sensed data and uses computational algorithms to predict the movement of water and/or waterborne particles over time. Numeric modeling is a critical component of an EWS because it provides the means to predict the spread and transport of a bloom. Typical inputs for a numeric model include density of particles, algal cells, initial location of the bloom, water depth/bathymetry, water temperature and salinity (which affects particle buoyancy), current speed and wind direction. Numeric modeling of HABs is crucial to the development of EWS, which provides actionable information to facility operators facing impending blooms. Examples of Current and In-Development Early Warning Systems NOAA’s National Centers for Coastal Ocean Science (NCCOS) launched a new program in 2021 called the Aquaculture Phytoplankton Monitoring Network (AQPMN). NCCOS is working directly with the commercial aquaculture sector to focus on species of phytoplankton known to be harmful to common shellfish and ( C O N T I N U E D O N P A G E 2 4 ) In Situ Sensors In situ remote sensing technologies can be deployed from a variety of different platforms, including mobile autonomous underwater vehicles (AUVs) or stationary moorings. They are mainly used for monitoring algae and their associated biotoxins and directly measure various environmental parameters in real time or near-real time that can be used for detecting HABs. These parameters include temperature, nutrient levels, chlorophyll a, pH, turbidity, dissolved oxygen, toxin levels, water depth, wind speed and currents. Specialized in situ sensors have become more commercially available; however, there are very few automated systems that would allow completely hands-off remote monitoring, which would be required for in-sea monitoring of HABs and toxins (McPartlin et al. 2016). Examples of some remote systems that can be considered fully automated and handsoff include 1) AquaMeasure sensors from Innovasea, 2) the MPC buoy from LG Sonic, 3) the Algae Tracker fromAquaRealTime and 4) the FaaS (Fish-as-a-Service) fromAquaai Corporation. More advanced in situ sensors can detect a variety of organisms (Scholin et al. 2017), including individual species of phytoplankton and algal toxins in real or near real-time using a variety of imagery techniques, molecular probes and/ or environmental DNA (eDNA). Examples include 1) the Imaging Flow Cytobot and the Phytoplankton Sampler from McLane Research Laboratories, Inc., East Falmouth, MA, 2) the Environmental Sample Processor from the Monterey Bay AquariumResearch Institute and NOAA and fabricated by McLane Research Laboratories and 3) the phytO-ARM (phytoplankton observing for automated real-time management) from the Woods Hole Oceanographic Institution. These in situ sensors are commercially available and are helpful for determining the species of phytoplankton present and providing information on bloom specifics, including types and levels of toxins produced. Additionally, in situ sensors can be used to track phytoplankton growth and spread patterns, resulting in time-series data that can be used to refine future bloom predictions. Examples of various in situ sensors can be seen in Figure 4. The eDNA Sampler fromDartmouth Ocean Technologies, Inc. also has the capability to detect species of interest using eDNA from collected water samples. While the detection of fluorescence levels indicating high phytoplankton biomass is in real time, the post-processing of eDNA for species identification occurs approximately three hours after collection. This technology may be more economically feasible for smaller-scale aquaculture facilities, as compared to more advanced sensors. This is considered a small FIGURE 4. A variety of in situ sensors used to monitor HABs. Top left: A thirdgeneration ESP (shown in bottom left of photo) fromMBARI prior to its placement in a long-range autonomous underwater vehicle (LRAUV) and MBARI’s LRAUV travelling across the surface of Lake Erie during a large algal bloom (photos: Ben Yair Raanan 2019, MBARI 2018). Top right: The MAZU, an untethered robot fish from the Aquaai Corporation (photo: Liane Thompson, www.aquaai.com/). Bottom left: The MPC Buoy from LG Sonic (photo: www.lgsonic.com/nysdecwill-use-mpc-buoys-to-combat-algae/). Bottom right: The Algae Tracker from AquaRealTime (photo: www.algaetracker.com/how-it-works).
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