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J Exp Biol. 2019 Aug 23;222(Pt 16). pii: jeb207654. doi: 10.1242/jeb.207654.

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

Author information

Research and Development, Monterey Bay Aquarium Research Institute, Moss Landing, CA 95039, USA
Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA.
Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA.
Loggerhead Instruments, FL 34238, USA.
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
Research and Development, Monterey Bay Aquarium Research Institute, Moss Landing, CA 95039, USA.


Zooplankton play critical roles in marine ecosystems, yet their fine-scale behavior remains poorly understood because of the difficulty in studying individuals in situ Here, we combine biologging with supervised machine learning (ML) to propose a pipeline for studying in situ 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 Chrysaora fuscescens 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 in situ rather than laboratory data.


Accelerometry; Invertebrate; Jellyfish; Telemetry; Zooplankton

Conflict of interest statement

Competing interestsDavid Mann is owner of Loggerhead Instruments which manufactures the ITAG.

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