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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

1
Research and Development, Monterey Bay Aquarium Research Institute, Moss Landing, CA 95039, USA clarafy@berkeley.edu.
2
Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA 94720, USA.
3
Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543, USA.
4
Loggerhead Instruments, FL 34238, USA.
5
Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
6
Research and Development, Monterey Bay Aquarium Research Institute, Moss Landing, CA 95039, USA.

Abstract

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.

KEYWORDS:

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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