GALCIT Colloquium

The deep waters of the ocean (below the euphotic zone) is one of the largest ecosystems on our planet, yet remains one of the least explored. Little-known marine organisms that inhabit these waters have developed life strategies that contribute to their evolutionary success, and may inspire engineering solutions for societally relevant challenges. Although significant advances in underwater vehicle technologies have improved access to this region, small-scale, behavioral observations of individual animals continue to be challenging. Here we present a number of developments from MBARI's Bioinspiration Lab that can be used to quantify biodiversity, biomechanics, and behavior of animals in the deep sea. These developments include: two 4000 m-rated, ROV-based imaging systems DeepPIV and EyeRIS used to quantify time-resolved particle fields and structures in 2D and 3D, a 1500 m-rated AUV-based plenoptic imaging system PyRIS, an underwater labeled image database called FathomNet that can be used to fuel machine learning algorithm development, and machine learning-integrated vehicle control algorithms called ML-Tracking that enable long-duration observations of individual animals. These instruments and approaches can be applied to a wide range of science use cases, both in the water column and on the benthos. We demonstrate how use of these tools can lead to surprising findings about the structure and function of giant larvacean (genus Bathochordaeus) mucus houses, and how they may serve as models for filtration and human health.