Halmos Faculty Publishes Deep Sea Computer Algorithm

During May, members of the DEEPEND Consortium published the article, “An empirically validated method for characterizing pelagic habitats in the Gulf of Mexico using ocean model data” in the journal Limnology and Oceanography: Methods.

Halmos faculty members from both the Department of Biological Sciences and Department of Marine and Environmental Sciences contributed to this paper. Matthew Johnston, Ph.D. is the lead author with Rosanna Milligan, Ph.D. and Tracey Sutton, Ph.D. as two of the co-authors.

In this study, the researchers make an important contribution to better understand the dynamics of the deep ocean in the Gulf of Mexico. They developed a computer algorithm to classify the deep off-shore waters of the Gulf of Mexico (GOM) into three distinct habitat types. The algorithm uses HYCOM ocean model data (sea surface height and water temperature at depth) and was validated by satellite chlorophyll measurements, water temperature, and microbial field samples collected by the DEEPEND Consortium. The study delivers one of the first methods to categorize the open-ocean environment of the GOM using only computer model data, without the need for field sampling. Going forward, the method will be an important tool for biological oceanographers operating in the GOM to help understand how deep sea animals use their environment.

For more information: http://deependconsortium.org/