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Kaneko H, Endo H, Henry N, Berney C, Mahé F, Poulain J, Labadie K, Beluche O, El Hourany R, Chaffron S, Wincker P, Nakamura R, Karp-Boss L, Boss E, Bowler C, de Vargas C, Tomii K, Ogata H. Predicting global distributions of eukaryotic plankton communities from satellite data. ISME COMMUNICATIONS 2023; 3:101. [PMID: 37740029 PMCID: PMC10517053 DOI: 10.1038/s43705-023-00308-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 09/03/2023] [Accepted: 09/11/2023] [Indexed: 09/24/2023]
Abstract
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.
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Affiliation(s)
- Hiroto Kaneko
- Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan
| | - Hisashi Endo
- Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan
| | - Nicolas Henry
- 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
| | - Cédric Berney
- CNRS, Sorbonne Université, FR2424, ABiMS, Station Biologique de Roscoff, 29680, Roscoff, France
- Sorbonne Université, CNRS, Station Biologique de Roscoff, UMR7144, ECOMAP, 29680, Roscoff, France
| | - Frédéric Mahé
- CIRAD, UMR PHIM, F-34398, Montpellier, France
- PHIM, Univ Montpellier, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France
| | - Julie Poulain
- Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 2 Rue Gaston Crémieux, 91057, Evry, France
| | - Karine Labadie
- Genoscope, Institut François Jacob, Commissariat à l'Energie Atomique (CEA), Université Paris-Saclay, 2 Rue Gaston Crémieux, 91057, Evry, France
| | - Odette Beluche
- Genoscope, Institut François Jacob, Commissariat à l'Energie Atomique (CEA), Université Paris-Saclay, 2 Rue Gaston Crémieux, 91057, Evry, France
| | - Roy El Hourany
- 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
| | - Samuel Chaffron
- 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
| | - Patrick Wincker
- Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 2 Rue Gaston Crémieux, 91057, Evry, France
| | - Ryosuke Nakamura
- Digital Architecture Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan
| | - Lee Karp-Boss
- School of Marine Sciences, University of Maine, Orono, 04469, ME, USA
| | - Emmanuel Boss
- School of Marine Sciences, University of Maine, Orono, 04469, ME, USA
| | - Chris Bowler
- 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
| | - Colomban de Vargas
- CNRS, Sorbonne Université, FR2424, ABiMS, Station Biologique de Roscoff, 29680, Roscoff, France
- Sorbonne Université, CNRS, Station Biologique de Roscoff, UMR7144, ECOMAP, 29680, Roscoff, France
| | - Kentaro Tomii
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan.
| | - Hiroyuki Ogata
- Institute for Chemical Research, Kyoto University, Uji, Kyoto, Japan.
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Tominaga K, Ogawa-Haruki N, Nishimura Y, Watai H, Yamamoto K, Ogata H, Yoshida T. Prevalence of Viral Frequency-Dependent Infection in Coastal Marine Prokaryotes Revealed Using Monthly Time Series Virome Analysis. mSystems 2023; 8:e0093122. [PMID: 36722950 PMCID: PMC9948707 DOI: 10.1128/msystems.00931-22] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/29/2022] [Indexed: 02/02/2023] Open
Abstract
Viruses infecting marine prokaryotes have a large impact on the diversity and dynamics of their hosts. Model systems suggest that viral infection is frequency dependent and constrained by the virus-host encounter rate. However, it is unclear whether frequency-dependent infection is pervasive among the abundant prokaryotic populations with different temporal dynamics. To address this question, we performed a comparison of prokaryotic and viral communities using 16S rRNA amplicon and virome sequencing based on samples collected monthly for 2 years at a Japanese coastal site, Osaka Bay. Concurrent seasonal shifts observed in prokaryotic and viral community dynamics indicated that the abundance of viruses correlated with that of their predicted host phyla (or classes). Cooccurrence network analysis between abundant prokaryotes and viruses revealed 6,423 cooccurring pairs, suggesting a tight coupling of host and viral abundances and their "one-to-many" correspondence. Although stable dominant species, such as SAR11, showed few cooccurring viruses, a fast succession of their viruses suggests that viruses infecting these populations changed continuously. Our results suggest that frequency-dependent viral infection prevails in coastal marine prokaryotes regardless of host taxa and temporal dynamics. IMPORTANCE There is little room for doubt that viral infection is prevalent among abundant marine prokaryotes regardless of their taxa or growth strategy. However, comprehensive evaluations of viral infections in natural prokaryotic communities are still technically difficult. In this study, we examined viral infection in abundant prokaryotes by monitoring the monthly dynamics of prokaryotic and viral communities at a eutrophic coastal site, Osaka Bay. We compared the community dynamics of viruses with those of their putative hosts based on genome-based in silico host prediction. We observed frequent cooccurrence among the predicted virus-host pairs, suggesting that viral infection is prevalent in abundant prokaryotes regardless of their taxa or temporal dynamics. This likely indicates that frequent lysis of the abundant prokaryotes via viral infection has a considerable contribution to the biogeochemical cycling and maintenance of prokaryotic community diversity.
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Affiliation(s)
- Kento Tominaga
- Graduate School of Agriculture, Kyoto University, Kyoto, Japan
- Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | | | - Yosuke Nishimura
- Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Kanagawa, Japan
| | - Hiroyasu Watai
- Graduate School of Agriculture, Kyoto University, Kyoto, Japan
| | - Keigo Yamamoto
- Research Institute of Environment, Agriculture and Fisheries, Osaka Prefecture, Osaka, Japan
| | - Hiroyuki Ogata
- Institute for Chemical Research, Kyoto University, Kyoto, Japan
| | - Takashi Yoshida
- Graduate School of Agriculture, Kyoto University, Kyoto, Japan
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