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dc.contributor.authorFannjiang, Clara  Concept link
dc.contributor.authorMooney, T. Aran  Concept link
dc.contributor.authorCones, Seth  Concept link
dc.contributor.authorMann, David  Concept link
dc.contributor.authorShorter, K. Alex  Concept link
dc.contributor.authorKatija, Kakani  Concept link
dc.date.accessioned2019-09-10T17:00:18Z
dc.date.available2019-09-10T17:00:18Z
dc.date.issued2019-08-23
dc.identifier.citationFannjiang, 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.en_US
dc.identifier.urihttps://hdl.handle.net/1912/24529
dc.description© 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.en_US
dc.description.abstractZooplankton 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.en_US
dc.description.sponsorshipThis work was supported by the David and Lucile Packard Foundation (to K.K.), the Woods Hole Oceanographic Institution (WHOI) Green Innovation Award (to T.A.M., K.K. and K.A.S.) and National Science Foundation (NSF) DBI collaborative awards (1455593 to T.A.M. and K.A.S.; 1455501 to K.K.). Deposited in PMC for immediate release.en_US
dc.publisherCompany of Biologistsen_US
dc.relation.urihttps://doi.org/10.1242/jeb.207654
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectInvertebrateen_US
dc.subjectAccelerometryen_US
dc.subjectTelemetryen_US
dc.subjectZooplanktonen_US
dc.subjectJellyfishen_US
dc.titleAugmenting biologging with supervised machine learning to study in situ behavior of the medusa Chrysaora fuscescensen_US
dc.typeArticleen_US
dc.identifier.doi10.1242/jeb.207654


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International