NF-POGO CofE-AWI Regional Training Programme in the Philippines Engaging students in Harmful Algal Bloom monitoring and prediction Prof. Raphael Kudela University of California Santa Cruz, California, United States of America
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he majority of coastal regions and many freshwater systems across the globe are increasingly nega vely affected by Harmful Algal Blooms (HABs). These events impact human and environmental health, water quality, food security, and contribute to the global trend of a degrading oceanic environment. Global HAB-related economic losses across marine and freshwater systems can be es mated at ±US$10 billion annually. Using a typical Value Of Informa on (VOI) es mate of 1% of the “resource” (in this case HAB-related losses), a comprehensive HAB observing and forecas ng informa on system would represent a value of ± US$100 million annually. The majority of HABs occur in dynamic, rapidly-changing coastal and freshwater systems, and effec ve HAB observa on and modeling requires apprecia on of a wide range of scales, trophic and allelopathic interac ons, and complex species-level algal physiology and behavioural characteris cs. This in turn requires training and capacity building to ensure that the current and next genera on of scien sts and resource managers are well versed in the complexi es leading to prolifera on of HAB events. One area where progress is being made rapidly is in building a global community using satellite data/products, developing new low-cost in situ observa on programs, and developing regional modelling capabili es. These ac vi es have the poten al to rapidly increase our knowledge and understanding of HAB events in both developed and developing regions. I have had the good fortune to par cipate in several of these training ac vi es, including the NASA Student Airborne Research Program (SARP; 2010-2014), the IOC/ NOWPAP/PICES/WESTPAC Training Course on Remote
Sensing and Data Analysis (Vladivostok, 2011), and the NF-POGO AWICofE Regional Training Programme on Detec on of HABs in Southeast Asia by Remote Sensing, held in Bolinao Marine Laboratory, Philippines, in March 2014. A common theme in these training sessions has been the enthusias c par cipa on of students from around the globe who are interested in not only learning to use remote sensing and monitoring data to predict HAB events, but perhaps more importantly, developing a global network of collaborators willing to share informa on and methods. Many of these students come into the programs without preconceived no ons of what can or cannot be done and o en surpass the experts by developing novel methods for analyzing the data. One such example comes from the NASA SARP program. Building on student projects over a few years, a new method was developed for iden fying poten ally harmful freshwater cyanobacterial blooms. In California (USA), a common feature of these blooms is that the generally non-toxic alga Aphanizomenon will precede blooms of the highly toxic species MicrocysƟs aeruginosa. Using a method of analysis that relies on spectral shapes, the students developed a two-step algorithm that can not only detect cyanobacterial blooms form airborne and satellite data, but can then separate generally non-toxic from toxic blooms (Figure 1). Exci ngly, the methods work well with airborne and satellite data that are not rigorously corrected for atmospheric effects, making it possible to rapidly and rou nely generate maps for use by resource and management agencies. These methods, developed as part of student training programs, are currently being tested across the state of California to evaluate whether remote sensing could be used to cost-effec vely replace laborious hand-sampling within the many lakes and reservoirs poten ally contaminated by cyanobacterial blooms. This provides an ideal case study highligh ng the poten al for remote sensing to improve HAB monitoring, and also highlights the importance of student training sessions both to educate the next genera on of scien sts and to learn from these enthusias c young researchers. Figure 1 - Sca er plot showing MicrocysƟn concentra on (x-axis) versus a cyanobacterial index (y-axis) derived from remote sensing data. The color shading indicates the AMI value, with ~2 indica ve of a pure MicrocysƟs bloom and ~5 indica ve of a pure Aphanizomenon bloom. The ovals represent grouping of data verified from direct sampling.
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Contact us: Suriyan.S@chula.ac.th, info@nf-pogo-alumni.org, lilian.krug@nf-pogo-alumni.org NANO website: www.nf-pogo-alumni.org
