How oceanic properties and biological communities are distributed in time and space can have significant implications for trophic transfer, harmful algal bloom monitoring, observation strategies, and ecological forecasting. Satellites provide a synoptic view of the ocean surface, but lack the high-resolution, depth-resolved observations needed to characterize smaller-scale patchiness. Here we use an ongoing time series of autonomous underwater vehicle transects in Monterey Bay, California (2016-present, ~ monthly) that covers most of the bay in a diamond pattern. The dataset includes continuous measurements of physical (temperature, salinity), chemical (oxygen, nitrate), and biological (fluorescence, optical backscatter, bioluminescence) parameters, as well as information on biological community composition from machine-learning derived proxies and environmental DNA samples. The combination of space-time coverage, high resolution, and broad range of oceanic properties provides an unprecedented opportunity to understand what determines the spatiotemporal distribution of physical and biological variables. We use various distance metrics as a function of time and space to identify decorrelation scales and assess whether biological community primarily vary in time or space in an upwelling-influenced bay.

Primary Presenter: Monique Messié, Monterey Bay Aquarium Research Institute (monique@mbari.org)

Authors:

Monique Messié, Monterey Bay Aquarium Research Institute  ()

Grant Sanderson,   ()

Kathleen Pitz, Monterey Bay Aquarium Research Institute  ()

Kieran Claassen, Moss Landing Marine Laboratories, San Jose State University  ()

Mike McCann, Monterey Bay Aquarium Research Institute  ()

Michael Anderson, Monterey Bay Aquarium Research Institute  ()

Francisco Chavez, Monterey Bay Aquarium Research Institute  ()