Animal Borne Ocean Sensors – AniBOS – an essential component of the Global Ocean Observing System

Clive R. McMahon*, Fabien Roquet, Sophie Baudel, Mathieu Belbeoch, Sophie Bestley, Clint Blight, Lars Boehme, Fiona Carse, Daniel P. Costa, Michael A. Fedak, Christophe Guinet, Robert Harcourt, Emma Heslop, Mark A. Hindell, Xavier Hoenner, Kim Holland, Mellinda Holland, Fabrice R. A. Jaine, Tiphaine Jeanniard du Dot, Ian JonsenTheresa R. Keates, Kit M. Kovacs, Sara Labrousse, Philip Lovell, Christian Lydersen, David March, Matthew Mazloff, Megan K. McKinzie, Mônica M. C. Muelbert, Kevin O’Brien, Lachlan Phillips, Esther Portela, Jonathan Pye, Stephen Rintoul, Katsufumi Sato, Ana M. M. Sequeira, Samantha E. Simmons, Vardis M. Tsontos, Victor Turpin, Esmee van Wijk, Danny Vo, Mia Wege, Frederick Gilbert Whoriskey, Kenady Wilson, Bill Woodward

*Corresponding author for this work

    Research output: Contribution to journalReview articlepeer-review

    45 Citations (Scopus)
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    Abstract

    Marine animals equipped with biological and physical electronic sensors have produced long-term data streams on key marine environmental variables, hydrography, animal behavior and ecology. These data are an essential component of the Global Ocean Observing System (GOOS). The Animal Borne Ocean Sensors (AniBOS) network aims to coordinate the long-term collection and delivery of marine data streams, providing a complementary capability to other GOOS networks that monitor Essential Ocean Variables (EOVs), essential climate variables (ECVs) and essential biodiversity variables (EBVs). AniBOS augments observations of temperature and salinity within the upper ocean, in areas that are under-sampled, providing information that is urgently needed for an improved understanding of climate and ocean variability and for forecasting. Additionally, measurements of chlorophyll fluorescence and dissolved oxygen concentrations are emerging. The observations AniBOS provides are used widely across the research, modeling and operational oceanographic communities. High latitude, shallow coastal shelves and tropical seas have historically been sampled poorly with traditional observing platforms for many reasons including sea ice presence, limited satellite coverage and logistical costs. Animal-borne sensors are helping to fill that gap by collecting and transmitting in near real time an average of 500 temperature-salinity-depth profiles per animal annually and, when instruments are recovered (∼30% of instruments deployed annually, n = 103 ± 34), up to 1,000 profiles per month in these regions. Increased observations from under-sampled regions greatly improve the accuracy and confidence in estimates of ocean state and improve studies of climate variability by delivering data that refine climate prediction estimates at regional and global scales. The GOOS Observations Coordination Group (OCG) reviews, advises on and coordinates activities across the global ocean observing networks to strengthen the effective implementation of the system. AniBOS was formally recognized in 2020 as a GOOS network. This improves our ability to observe the ocean’s structure and animals that live in them more comprehensively, concomitantly improving our understanding of global ocean and climate processes for societal benefit consistent with the UN Sustainability Goals 13 and 14: Climate and Life below Water. Working within the GOOS OCG framework ensures that AniBOS is an essential component of an integrated Global Ocean Observing System.

    Original languageEnglish
    Article number751840
    Pages (from-to)1-21
    Number of pages21
    JournalFrontiers in Marine Science
    Volume8
    DOIs
    Publication statusPublished - Nov 2021

    Bibliographical note

    Copyright the Author(s) 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

    Keywords

    • animal behavior
    • climate change
    • Essential Ocean Variables (EOVs)
    • marine animals
    • physical oceanography

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