Characterizing animal-fluid interactions in the deep sea benthos using combined DeepPIV and respiration measurements

Joost Daniels1, Amanda S Kahn2, Joshua Lord3, Kakani Katija1 and James Barry1, (1)Monterey Bay Aquarium Research Institute, Moss Landing, CA, United States, (2)Moss Landing Marine Laboratories, Moss Landing, CA, United States, (3)Moravian College, Bethlehem, United States
Abstract:
Many midwater and benthic organisms rely on fluid motion around and through their bodies for feeding, propulsion, gas exchange, and propagation of offspring. In order to study these animal-fluid interactions, detailed flow measurements at small spatial and temporal scales are required. While quantitative techniques have been employed in the lab, many deep-sea organisms are difficult to study ex situ due to our inability to emulate the physical and chemical conditions of their environment. Advances in underwater vehicle technologies have improved access to deep sea ecosystems, but the deployment of instrumentation to quantify detailed fluid motion in situ is limited.


To address this need, we developed DeepPIV (particle image velocimetry), a 2D flow diagnostic instrument that enables PIV measurements using a laser and high-definition camera, that can be deployed on the manipulator arm of a remotely operated vehicle. We used DeepPIV in conjunction with an optical oxygen sensor to measure osculum excurrent, background flow, and respiration rates of glass sponges (Class Hexactinellida) at Sur Ridge in the Monterey Bay National Marine Sanctuary. The combined data revealed a broad behavioral spectrum over which pumping and respiration occurred in a surprisingly dynamic, benthic boundary layer. Furthermore, excurrent flow measurements show a complex interplay of ambient current, turbulence due to sponge shape, and contributions from active pumping. Therefore, the ability to perform in situ measurements of flow is vital for studies of the dynamic properties of rigid and soft-bodied animals in benthic and pelagic environments alike.