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

As oceans are altered by rising temperatures, acidification and other consequences of anthropogenic activity, understanding the behavioral patterns and responses of marine animals is required for effective stewardship. Researchers have made great strides in investigating marine megafauna behavior related to long-distance migrations (Block et al., 2011; Rasmussen et al., 2007; Sequeira et al., 2018) and foraging strategies (Sims et al., 2008; Weise et al., 2010). However, the behavior of more numerous, higher total-biomass, lower trophic-level animals such as zooplankton is much less well understood. Early attempts to investigate in situ behavior of zooplankton such as jellyfish relied on scuba divers following animals with hand-held video cameras (Colin and Costello, 2002; Costello et al., 1998) and later with remotely operated vehicles (ROVs; Kaartvedt et al., 2015; Purcell, 2009; Rife and Rock, 2003). Acoustic methods have also been used to describe large-scale movement patterns of jellyfish (Båmstedt et al., 2003; Kaartvedt et al., 2007; Klevjer et al., 2009), although these methods can be resolution-limited.