Abstract
Improved monitoring and prediction of harmful algal blooms (HABs) have become necessary in coastal and freshwaters worldwide for protection of human health in terms of seafood safety and drinking water supplies, protection of aquaculture facilities, and understanding of mass mammal strandings or die-off events, as well as measuring progress toward water quality targets. There have been rapidly advancing tools and technologies for measuring, monitoring, and predicting HABs over the past nearly two decades since the Global Ecology and Oceanography of Harmful Algal Blooms (GEOHAB) program was conceived. These advances in technologies are leading to new data and new types of data that are being incorporated into models allowing for nowcasts and forecasts. New instruments and techniques are furthering automation and identification of cell detection on moorings or autonomous underwater vehicles. These approaches take advantage of either unique genetic signatures (DNA, RNA) targeted by molecular probes, imaging technology that can take pictures of cells in rapid succession, allowing cell identification through automated image analysis software, or use of other cell signatures, such as fluorescent parameters. Satellite remote sensing, specifically ocean color radiometry, can provide the systematic spatial coverage that mooring instruments and ship surveys are unable to achieve—and are subsequently often widely used both for operational bloom monitoring and analysis of bloom phenology. Improved algorithms are helping to advance these applications in high-biomass, Case 1, waters. Coupled with these technologies are advances in capabilities for monitoring environmental conditions, including antecedent conditions, such as nutrient concentrations. Many new approaches have also been developed to detect their toxins. One of the most important advances is the application of passive samplers that adsorb toxins in water and that can be deployed inexpensively for varying lengths of time. The revolution in techniques is leading to new data for modeling, as well as new challenges for model constructs. Investments in infrastructure (equipment and tools), in conceptual understanding of the antecedent conditions promoting each different type of HAB, as well as improved data management and modeling at all levels, will continue to be required.
Keywords
- Harmful Algal Blooms (HABs)
- Automated Image Analysis Software
- Autonomous Underwater Vehicles (AUV)
- Unique Genetic Signature
- Bloom Phenology
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Acknowledgments
This is a contribution of the GEOHAB Core Research Project on HABs in Eutrophic Systems and HABs in Upwelling Systems. We thank V. Kelly of Greens Eyes, Inc., Easton, MD, USA, for supplying unpublished nutrient data and for helpful comments. Support for the preparation of this paper was provided in part by NOAA award NA17NOS4780180 to ML and PMG. This is contribution number 5408 from the University of Maryland Center for Environmental Science and NOAA ECOHAB contribution number 919.
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Glibert, P.M., Pitcher, G.C., Bernard, S., Li, M. (2018). Advancements and Continuing Challenges of Emerging Technologies and Tools for Detecting Harmful Algal Blooms, Their Antecedent Conditions and Toxins, and Applications in Predictive Models. In: Glibert, P., Berdalet, E., Burford, M., Pitcher, G., Zhou, M. (eds) Global Ecology and Oceanography of Harmful Algal Blooms . Ecological Studies, vol 232. Springer, Cham. https://doi.org/10.1007/978-3-319-70069-4_18
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