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

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 i...

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Vydáno v:Frontiers in Marine Science Ročník 9
Hlavní autoři: 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, Kiko, Rainer
Médium: Journal Article
Jazyk:angličtina
Vydáno: Frontiers Media SA 09.08.2022
Frontiers Media
Frontiers Media S.A
Témata:
[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
[SDE.BE]Environmental Sciences/Biodiversity and Ecology
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
Atmosphere
Biodiversity and Ecology
biomass
boosted regression trees (BRT)
Computer Science
Environmental Sciences
General. Including nature conservation, geographical distribution
global zooplankton
habitat modeling
Image Processing
in situ imaging
Machine Learning
Modeling and Simulation
Ocean, Atmosphere
Q
QH1-199.5
Science
Sciences of the Universe
spatial distribution
Statistics
underwater vision profiler (UVP)
underwater vision profiler (UVP)
biomass
habitat modeling
global zooplankton
boosted regression trees (BRT)
in situ imaging
machine learning
spatial distribution
ISSN:2296-7745, 2296-7745
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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°N and 55°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 ≈ 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.
AbstractList 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°N and 55°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 ≈ 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.
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°N and 55°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 ≈ 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.
Author Anya M. Waite
Jean-Olivier Irisson
Andrew M. P. McDonnell
François Carlotti
Helena Hauss
Thelma Panaïotis
Rainer Kiko
Marcel Babin
Tristan Biard
Lee Karp-Boss
Andreas Rogge
Laurent Coppola
Fabien Lombard
Marc Picheral
Laetitia Drago
Lars Stemmann
Lionel Guidi
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https://hal.sorbonne-universite.fr/hal-03810062$$DView record in HAL
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ContentType Journal Article
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ANR-19-MPGA-0012,TAD,Tropical Atlantic Deoxygenation: gateway dynamics, feedback mechanisms and ecosystem impacts
Helmholtz Centre for Ocean Research Kiel (GEOMAR)
Institut universitaire de France (IUF) ; Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.)
Institut méditerranéen d'océanologie (MIO)
Dalhousie University [Halifax]
Helmholtz Centre for Ocean Research [Kiel] (GEOMAR)
Laboratoire d’Océanologie et de Géosciences (LOG) - UMR 8187 (LOG) ; Institut national des sciences de l'Univers (INSU - CNRS)-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [Ile-de-France])
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ISSN 2296-7745
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Keywords underwater vision profiler (UVP)
biomass
habitat modeling
global zooplankton
boosted regression trees (BRT)
in situ imaging
machine learning
spatial distribution
Language English
License Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0
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Snippet 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...
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SubjectTerms [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
[SDE.BE]Environmental Sciences/Biodiversity and Ecology
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
Atmosphere
Biodiversity and Ecology
biomass
boosted regression trees (BRT)
Computer Science
Environmental Sciences
General. Including nature conservation, geographical distribution
global zooplankton
habitat modeling
Image Processing
in situ imaging
Machine Learning
Modeling and Simulation
Ocean, Atmosphere
Q
QH1-199.5
Science
Sciences of the Universe
spatial distribution
Statistics
underwater vision profiler (UVP)
Title Global Distribution of Zooplankton Biomass Estimated by In Situ Imaging and Machine Learning
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