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

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Date
2019-08-23
Authors
Fannjiang, Clara
Mooney, T. Aran
Cones, Seth
Mann, David
Shorter, K. Alex
Katija, Kakani
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DOI
10.1242/jeb.207654
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Keywords
Invertebrate
Accelerometry
Telemetry
Zooplankton
Jellyfish
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.
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© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Fannjiang, C., Mooney, T. A., Cones, S., Mann, D., Shorter, K. A., & Katija, K. Augmenting biologging with supervised machine learning to study in situ behavior of the medusa Chrysaora fuscescens. Journal of Experimental Biology, 222, (2019): jeb.207654, doi:10.1242/jeb.207654.
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Fannjiang, C., Mooney, T. A., Cones, S., Mann, D., Shorter, K. A., & Katija, K. (2019). Augmenting biologging with supervised machine learning to study in situ behavior of the medusa Chrysaora fuscescens. Journal of Experimental Biology, 222, jeb.207654.
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