Augmenting biologging with supervised machine learning to study <i>in situ</i> behavior of the medusa <i>Chrysaora fuscescens</i>

Abstract

Zooplankton play critical roles in marine ecosystems, yet their fine-scale behavior remains poorly understood because of the difficulty in studying individuals <i>in situ</i> Here, we combine biologging with supervised machine learning (ML) to propose a pipeline for studying <i>in situ</i> behavior of larger zooplankton such as jellyfish. We deployed the ITAG, a biologging package with high-resolution motion sensors designed for soft-bodied invertebrates, on eight <i>Chrysaora fuscescens</i> in Monterey Bay, using the tether method for retrieval. By analyzing simultaneous video footage of the tagged jellyfish, we developed ML methods to: (1) identify periods of tag data corrupted by the tether method, which may have compromised prior research findings, and (2) classify jellyfish behaviors. Our tools yield characterizations of fine-scale jellyfish activity and orientation over long durations, and we conclude that it is essential to develop behavioral classifiers on <i>in situ</i> rather than laboratory data.