دورية أكاديمية
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Predicting global distributions of eukaryotic plankton communities from satellite data.

التفاصيل البيبلوغرافية
العنوان: Predicting global distributions of eukaryotic plankton communities from satellite data.
المؤلفون: Kaneko H; Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan., Endo H; Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan., Henry N; CNRS, Sorbonne Université, FR2424, ABiMS, Station Biologique de Roscoff, 29680, Roscoff, France.; Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/Tara GOSEE, 75016, Paris, France., Berney C; CNRS, Sorbonne Université, FR2424, ABiMS, Station Biologique de Roscoff, 29680, Roscoff, France.; Sorbonne Université, CNRS, Station Biologique de Roscoff, UMR7144, ECOMAP, 29680, Roscoff, France., Mahé F; CIRAD, UMR PHIM, F-34398, Montpellier, France.; PHIM, Univ Montpellier, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France., Poulain J; Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 2 Rue Gaston Crémieux, 91057, Evry, France., Labadie K; Genoscope, Institut François Jacob, Commissariat à l'Energie Atomique (CEA), Université Paris-Saclay, 2 Rue Gaston Crémieux, 91057, Evry, France., Beluche O; Genoscope, Institut François Jacob, Commissariat à l'Energie Atomique (CEA), Université Paris-Saclay, 2 Rue Gaston Crémieux, 91057, Evry, France., El Hourany R; Univ. Littoral Côte d'Opale, Univ. Lille, CNRS, IRD, UMR 8187, LOG, Laboratoire d'Océanologie et de Géosciences, F 62930, Wimereux, France.; Institut de Biologie de l'Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, CNRS, INSERM, Université PSL, 75005, Paris, France., Chaffron S; Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/Tara GOSEE, 75016, Paris, France.; Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000, Nantes, France., Wincker P; Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 2 Rue Gaston Crémieux, 91057, Evry, France., Nakamura R; Digital Architecture Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan., Karp-Boss L; School of Marine Sciences, University of Maine, Orono, 04469, ME, USA., Boss E; School of Marine Sciences, University of Maine, Orono, 04469, ME, USA., Bowler C; Research Federation for the study of Global Ocean Systems Ecology and Evolution, FR2022/Tara GOSEE, 75016, Paris, France.; Institut de Biologie de l'Ecole Normale Supérieure (IBENS), Ecole Normale Supérieure, CNRS, INSERM, Université PSL, 75005, Paris, France., de Vargas C; CNRS, Sorbonne Université, FR2424, ABiMS, Station Biologique de Roscoff, 29680, Roscoff, France.; Sorbonne Université, CNRS, Station Biologique de Roscoff, UMR7144, ECOMAP, 29680, Roscoff, France., Tomii K; Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan. k-tomii@aist.go.jp., Ogata H; Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan. ogata@kuicr.kyoto-u.ac.jp.
مؤلفون مشاركون: Tara Oceans Coordinators
المصدر: ISME communications [ISME Commun] 2023 Sep 22; Vol. 3 (1), pp. 101. Date of Electronic Publication: 2023 Sep 22.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Oxford University Press Country of Publication: England NLM ID: 9918205372406676 Publication Model: Electronic Cited Medium: Internet ISSN: 2730-6151 (Electronic) Linking ISSN: 27306151 NLM ISO Abbreviation: ISME Commun Subsets: PubMed not MEDLINE
أسماء مطبوعة: Publication: 3 2024- : Oxford : Oxford University Press
Original Publication: [London] : Springer Nature on behalf of the International Society for Microbial Ecology, [2021]-
مستخلص: Satellite remote sensing is a powerful tool to monitor the global dynamics of marine plankton. Previous research has focused on developing models to predict the size or taxonomic groups of phytoplankton. Here, we present an approach to identify community types from a global plankton network that includes phytoplankton and heterotrophic protists and to predict their biogeography using global satellite observations. Six plankton community types were identified from a co-occurrence network inferred using a novel rDNA 18 S V4 planetary-scale eukaryotic metabarcoding dataset. Machine learning techniques were then applied to construct a model that predicted these community types from satellite data. The model showed an overall 67% accuracy in the prediction of the community types. The prediction using 17 satellite-derived parameters showed better performance than that using only temperature and/or the concentration of chlorophyll a. The constructed model predicted the global spatiotemporal distribution of community types over 19 years. The predicted distributions exhibited strong seasonal changes in community types in the subarctic-subtropical boundary regions, which were consistent with previous field observations. The model also identified the long-term trends in the distribution of community types, which suggested responses to ocean warming.
(© 2023. ISME Publications B.V.)
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معلومات مُعتمدة: 18H02279 MEXT | Japan Society for the Promotion of Science (JSPS); 19H05667 MEXT | Japan Society for the Promotion of Science (JSPS); JPMJSP2110 MEXT | Japan Science and Technology Agency (JST); ANR-10-INBS-09 Agence Nationale de la Recherche (French National Research Agency); 101082021 EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020); 835067 EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
فهرسة مساهمة: Investigator: SG Acinas; M Babin; P Bork; C Bowler; G Cochrane; C de Vargas; G Gorsky; L Guidi; N Grimsley; P Hingamp; D Iudicone; O Jaillon; S Kandels; E Karsenti; F Not; N Poulton; S Pesant; C Sardet; S Speich; L Stemmann; MB Sullivan; S Sunagawa
تواريخ الأحداث: Date Created: 20230922 Latest Revision: 20231123
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC10517053
DOI: 10.1038/s43705-023-00308-7
PMID: 37740029
قاعدة البيانات: MEDLINE
الوصف
تدمد:2730-6151
DOI:10.1038/s43705-023-00308-7