DEEPEND: Characterizing Pelagic Habitats in the Gulf of Mexico Using Model, Empirical, and Remotely-Sensed Data
Gulf of Mexico Oil Spill & Ecosystem Science Conference, New Orleans, LA, February 6-9, 2017
Pelagic waters of the Gulf of Mexico (GOM) are dominated by mesoscale features such as cyclonic and anticyclonic eddies and the strongly flowing Loop Current. These GOM features may be important drivers of population structure and trophic linkages within the water column. It is important, therefore, to classify water bodies associated with these features to allow quantitative evaluation of community assemblages. We first used an algorithm that integrated sea surface height anomaly and water velocity gradients to classify GOM surface waters between the years 2011-2016, founded on ocean condition data from the 1/25 ° GOM HYbrid Coordinate Ocean Model (HYCOM). The water bodies were segregated into anti-cyclonic, cyclonic, anti-cyclonic boundary, cyclonic boundary, and common water units. Next we compared these classifications to empirically derived ocean conditions as measured by CTD casts within each unit that were collected during the same period on cruises by the Deep Pelagic Nekton Dynamics of the Gulf of Mexico (DEEPEND) consortium. The classification scheme was further cross-validated by comparing the identified water bodies to the depths of the 20° and 22° isotherms, microbial community assemblages within each unit, and chlorophyll concentrations derived from satellite measurements. We found good agreement of the classification scheme between model (i.e., HYCOM), empirical (i.e., CTD and microbial assemblages), and remotely sensed (i.e., chlorophyll) data. Going forward, the classification scheme will be used to characterize assemblages of pelagic fauna that were collected by DEEPEND cruises in the GOM between 2010-2017.
Johnston, Matthew; Milligan, Rosanna; Easson, Cole; DeRada, Sergio; Penta, Brad; and Sutton, Tracey, "DEEPEND: Characterizing Pelagic Habitats in the Gulf of Mexico Using Model, Empirical, and Remotely-Sensed Data" (2017). Oceanography Faculty Proceedings, Presentations, Speeches, Lectures. 457.