The AGU-ASM-GoMRI Colloquium will close on 10 April with a public lecture. The lecture, “Station ALOHA: A Proving Ground for Microbial Oceanography,” will be presented by David M. Karl. You can read the abstract description below the events itinerary. The lecture, including Q&A, will begin at 5:30 p.m., followed by a reception at 6:30 p.m.
Please be sure to register for this free event.
STATION ALOHA: A PROVING GROUND FOR MICROBIAL OCEANOGRAPHY
David M. Karl
Daniel K. Inouye Center for Microbial Oceanography: Research and Education, University of Hawaii at Manoa, Honolulu, HI, USA
Microbial oceanography is a relatively new discipline that integrates the principles of marine microbiology, microbial ecology and oceanography to study the role of microorganisms in the biogeochemical dynamics of natural marine ecosystems. A general goal of microbial oceanography is to observe and understand microbial life in the sea well enough to make accurate ecological predictions, for example, of the impact of climate variability on microbial processes in the global ocean. Since Oct 1988, interdisciplinary teams of scientists from the University of Hawaii and around the world have conducted research at Station ALOHA (22.75 N, 158 W), a site chosen to be representative of the expansive North Pacific Subtropical Gyre. Numerous scientific discoveries from Station ALOHA, including novel microorganisms, unprecedented metabolic pathways and complex interactions, have transformed our understanding of microbial life in the sea. The uncertain nature of future climate change and the potential impacts on the structure and function of marine ecosystems demands a comprehensive description and understanding of the sea around us. Sustained research of marine microbes is vital, so continued field observations and experimentation at Station ALOHA, and at selected locations elsewhere including the Gulf of Mexico, is both timely and important. After three decades of intensive study at Station ALOHA, we now have a new view of an old ocean, with revised paradigms built on the strength of high-quality time-series data, insights from the application of –omics techniques and observations from autonomous gliders. The pace of new discovery, and the importance of integrating this new understanding into predictive models is an enormous contemporary challenge with great scientific and societal relevance.
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