Global Distribution of Zooplankton Biomass Estimated by In Situ Imaging and Machine Learning.

Drago, Laetitia, Panaïotis, Thelma, Irisson, Jean-Olivier, Babin, Marcel, Biard, Tristan, Carlotti, François, Coppola, Laurent, Guidi, Lionel, Hauss, Helena , Karp-Boss, Lee, Lombard, Fabien, McDonnell, Andrew M. P., Picheral, Marc, Rogge, Andreas, Waite, Anya M., Stemmann, Lars and Kiko, Rainer (2022) Global Distribution of Zooplankton Biomass Estimated by In Situ Imaging and Machine Learning. Open Access Frontiers in Marine Science, 9 . Art.Nr. 894372. DOI 10.3389/fmars.2022.894372.

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Abstract

Zooplankton plays a major role in ocean food webs and biogeochemical cycles, and provides major ecosystem services as a main driver of the biological carbon pump and in sustaining fish communities. Zooplankton is also sensitive to its environment and reacts to its changes. To better understand the importance of zooplankton, and to inform prognostic models that try to represent them, spatially-resolved biomass estimates of key plankton taxa are desirable. In this study we predict, for the first time, the global biomass distribution of 19 zooplankton taxa (1-50 mm Equivalent Spherical Diameter) using observations with the Underwater Vision Profiler 5, a quantitative in situ imaging instrument. After classification of 466,872 organisms from more than 3,549 profiles (0-500 m) obtained between 2008 and 2019 throughout the globe, we estimated their individual biovolumes and converted them to biomass using taxa-specific conversion factors. We then associated these biomass estimates with climatologies of environmental variables (temperature, salinity, oxygen, etc.), to build habitat models using boosted regression trees. The results reveal maximal zooplankton biomass values around 60 degrees N and 55 degrees S as well as minimal values around the oceanic gyres. An increased zooplankton biomass is also predicted for the equator. Global integrated biomass (0-500 m) was estimated at 0.403 PgC. It was largely dominated by Copepoda (35.7%, mostly in polar regions), followed by Eumalacostraca (26.6%) Rhizaria (16.4%, mostly in the intertropical convergence zone). The machine learning approach used here is sensitive to the size of the training set and generates reliable predictions for abundant groups such as Copepoda (R2 approximate to 20-66%) but not for rare ones (Ctenophora, Cnidaria, R2 < 5%). Still, this study offers a first protocol to estimate global, spatially resolved zooplankton biomass and community composition from in situ imaging observations of individual organisms. The underlying dataset covers a period of 10 years while approaches that rely on net samples utilized datasets gathered since the 1960s. Increased use of digital imaging approaches should enable us to obtain zooplankton biomass distribution estimates at basin to global scales in shorter time frames in the future.

Document Type: Article
Keywords: global zooplankton; in situ imaging; biomass; machine learning; underwater vision profiler (UVP); spatial distribution; boosted regression trees (BRT); habitat modeling
Research affiliation: OceanRep > GEOMAR > FB3 Marine Ecology > FB3-OEB Ökosystembiologie des Ozeans
HGF-AWI
Main POF Topic: PT6: Marine Life
Refereed: Yes
Open Access Journal?: Yes
Publisher: Frontiers
Related URLs:
Projects: TRIATLAS, Make our Planet Great Again, PACES-II, INSPIRE
Date Deposited: 16 Sep 2022 12:36
Last Modified: 16 Sep 2022 12:36
URI: https://oceanrep.geomar.de/id/eprint/57041

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