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Effect of tectonic processes on biosphere–geosphere feedbacks across a convergent margin


The subsurface is among Earth’s largest biomes, but the extent to which microbial communities vary across tectonic plate boundaries or interact with subduction-scale geological processes remains unknown. Here we compare bacterial community composition with deep-subsurface geochemistry from 21 hot springs across the Costa Rican convergent margin. We find that cation and anion compositions of the springs reflect the dip angle and position of the underlying tectonic structure and also correlate with the bacterial community. Co-occurring microbial cliques related to cultured chemolithoautotrophs that use the reverse tricarboxylic acid cycle (rTCA) as well as abundances of metagenomic rTCA genes correlate with concentrations of slab-volatilized carbon. This, combined with carbon isotope evidence, suggests that fixation of slab-derived CO2 into biomass may support a chemolithoautotrophy-based subsurface ecosystem. We calculate that this forearc subsurface biosphere could sequester 1.4 × 109 to 1.4 × 1010 mol of carbon per year, which would decrease estimates of the total carbon delivered to the mantle by 2 to 22%. Based on the observed correlations, we suggest that distribution and composition of the subsurface bacterial community are probably affected by deep tectonic processes across the Costa Rican convergent margin and that, by sequestering carbon volatilized during subduction, these chemolithoautotrophic communities could in turn impact the geosphere.

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Fig. 1: Sites span the Costa Rican convergent margin, with organic matter produced from chemolithoautotrophy of deep-slab inorganic carbon.
Fig. 2: Clustering of the sites based on microbial community diversity and geochemical characteristics.
Fig. 3: Bacterial cliques each have different relationships to subduction zone geochemistry.
Fig. 4: Metagenome-derived genes from the same carbon-fixation pathway correlate with each other and with subduction zone geochemistry.

Data availability

This Targeted Locus Study project has been deposited at DDBJ/EMBL/GenBank under the accession KEBJ00000000, with project ID PRJNA579365. The version described in this paper is the first version, KEBJ01000000. Metagenomic data are in the NCBI SRA with project ID PRJNA627197. The full environmental dataset is available at and released as a permanent version (v1.0) using Zenodo under Source data are provided with this paper.

Code availability

A complete R script containing all the steps to reproduce our analysis, including the full environmental dataset, is available at and released as a permanent version (v1.0) using Zenodo under,


  1. 1.

    Magnabosco, C. et al. The biomass and biodiversity of the continental subsurface. Nat. Geosci. 11, 707–717 (2018).

    Article  Google Scholar 

  2. 2.

    Merino, N. et al. Living at the extremes: extremophiles and the limits of life in a planetary context. Front. Microbiol. 10, 780 (2019).

    Article  Google Scholar 

  3. 3.

    Colman, D. R. et al. Geobiological feedbacks and the evolution of thermoacidophiles. ISME J. 12, 225–236 (2018).

    Article  Google Scholar 

  4. 4.

    Reveillaud, J. et al. Subseafloor microbial communities in hydrogen-rich vent fluids from hydrothermal systems along the Mid-Cayman Rise. Environ. Microbiol. 18, 1970–1987 (2016).

    Article  Google Scholar 

  5. 5.

    Lau, M. C. Y. et al. An oligotrophic deep-subsurface community dependent on syntrophy is dominated by sulfur-driven autotrophic denitrifiers. Proc. Natl Acad. Sci. USA 113, 7927–7936 (2016).

    Article  Google Scholar 

  6. 6.

    Momper, L., Jungbluth, S. P., Lee, M. D. & Amend, J. P. Energy and carbon metabolisms in a deep terrestrial subsurface fluid microbial community. ISME J. 11, 2319–2333 (2017).

    Article  Google Scholar 

  7. 7.

    Brazelton, W. J. et al. Metagenomic identification of active methanogens and methanotrophs in serpentinite springs of the Voltri Massif, Italy. PeerJ 5, e2945 (2017).

    Article  Google Scholar 

  8. 8.

    Havig, J. R., Raymond, J., Meyer-Dombard, D. R., Zolotova, N. & Shock, E. L. Merging isotopes and community genomics in a siliceous sinter-depositing hot spring. J. Geophys. Res. Biogeosci. 116, G01005 (2011).

    Article  Google Scholar 

  9. 9.

    Power, J. F. et al. Microbial biogeography of 925 geothermal springs in New Zealand. Nat. Commun. 9, 2876 (2018).

    Article  Google Scholar 

  10. 10.

    Lauber, C. L., Hamady, M., Knight, R. & Fierer, N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120 (2009).

    Article  Google Scholar 

  11. 11.

    Kelemen, P. B. & Manning, C. E. Reevaluating carbon fluxes in subduction zones, what goes down, mostly comes up. Proc. Natl Acad. Sci. USA 112, E3997–E4006 (2015).

    Article  Google Scholar 

  12. 12.

    Brovarone, A. V. et al. Subduction hides high-pressure sources of energy that may feed the deep subsurface biosphere. Nat. Commun. 11, 3880 (2020).

    Article  Google Scholar 

  13. 13.

    Plümper, O. et al. Subduction zone forearc serpentinites as incubators for deep microbial life. Proc. Natl Acad. Sci. USA 114, 4324–4329 (2017).

    Article  Google Scholar 

  14. 14.

    Syracuse, E. M. & Abers, G. A. Global compilation of variations in slab depth beneath arc volcanoes and implications. Geochem. Geophys. Geosyst. 7, Q05017 (2006).

    Article  Google Scholar 

  15. 15.

    Shaw, A. M., Hilton, D. R., Fischer, T. P., Walker, J. A. & Alvarado, G. E. Contrasting He–C relationships in Nicaragua and Costa Rica: insights into C cycling through subduction zones. Earth Planet. Sci. Lett. 214, 499–513 (2003).

    Article  Google Scholar 

  16. 16.

    Barry, P. H. et al. Forearc carbon sink reduces long-term volatile recycling into the mantle. Nature 568, 487–492 (2019).

    Article  Google Scholar 

  17. 17.

    Arce-Rodríguez, A. et al. Thermoplasmatales and sulfur-oxidizing bacteria dominate the microbial community at the surface water of a CO2-rich hydrothermal spring located in Tenorio Volcano National Park, Costa Rica. Extremophiles 23, 177–187 (2019).

    Article  Google Scholar 

  18. 18.

    Crespo-Medina, M. et al. Methane dynamics in a tropical serpentinizing environment: the Santa Elena ophiolite, Costa Rica. Front. Microbiol. 8, 916 (2017).

    Article  Google Scholar 

  19. 19.

    Probst, A. J. & Moissl-Eichinger, C. “Altiarchaeales”: uncultivated Archaea from the subsurface. Life (2015).

  20. 20.

    Giggenbach, W. F. Geothermal solute equilibria, derivation of Na-K-Mg-Ca geoindicators. Geochim. Cosmochim. Acta 52, 2749–2765 (1988).

    Article  Google Scholar 

  21. 21.

    Giggenbach, W. F. & Soto, R. C. Isotopic and chemical composition of water and steam discharges from volcanic–magmatic–hydrothermal systems of the Guanacaste Geothermal Province, Costa Rica. Appl. Geochem. 7, 309–332 (1992).

    Article  Google Scholar 

  22. 22.

    Rodríguez, A. & van Bergen, M. J. Superficial alteration mineralogy in active volcanic systems: an example of Poás volcano, Costa Rica. J. Volcanol. Geotherm. Res. 346, 54–80 (2017).

    Article  Google Scholar 

  23. 23.

    Chan, C. S., Fakra, S. C., Emerson, D., Fleming, E. J. & Edwards, K. J. Lithotrophic iron-oxidizing bacteria produce organic stalks to control mineral growth: implications for biosignature formation. ISME J. 5, 717–727 (2011).

    Article  Google Scholar 

  24. 24.

    Lücke, O. H. & Arroyo, I. G. Density structure and geometry of the Costa Rican subduction zone from 3-D gravity modeling and local earthquake data. Solid Earth 6, 1169–1183 (2015).

    Article  Google Scholar 

  25. 25.

    Protti, M., Gündel, F. & McNally, K. The geometry of the Wadati–Benioff zone under southern Central America and its tectonic significance: results from a high-resolution local seismographic network. Phys. Earth Planet. Inter. 84, 271–287 (1994).

    Article  Google Scholar 

  26. 26.

    de Moor, J. M. et al. A new sulfur and carbon degassing inventory for the Southern Central American Volcanic Arc: the importance of accurate time-series data sets and possible tectonic processes responsible for temporal variations in arc-scale volatile emissions: new volatile budget for Central America. Geochem. Geophys. Geosyst. 18, 4437–4468 (2017).

    Article  Google Scholar 

  27. 27.

    Delgado-Baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. Science 359, 320–325 (2018).

    Article  Google Scholar 

  28. 28.

    Kim, M. S., Jo, S. K., Roh, S. W. & Bae, J. W. Alishewanella agri sp. nov., isolated from landfill soil. Int. J. Syst. Evol. Microbiol. 60, 2199–2203 (2010).

    Article  Google Scholar 

  29. 29.

    Chen, W. M. et al. Aquabacterium limnoticum sp. nov., isolated from a freshwater spring. Int. J. Syst. Evol. Microbiol. 62, 698–704 (2012).

    Article  Google Scholar 

  30. 30.

    Garrity, G. M. & Bell, J. A. Bergey’s Manual of Systematics of Archaea and Bacteria (Bergey’s Manual Trust, 2015).

  31. 31.

    Hayashi, N. R., Ishida, T., Yokota, A., Kodama, T. & Igarashi, Y. Hydrogenophilus thermoluteolus gen. nov., sp. nov., a thermophilic, facultatively chemolithoautotrophic, hydrogen-oxidizing bacterium. Int. J. Syst. Evol. Microbiol. 49, 783–786 (1999).

    Article  Google Scholar 

  32. 32.

    Berg, I. A. et al. Autotrophic carbon fixation in archaea. Nat. Rev. Microbiol. 8, 447–460 (2010).

    Article  Google Scholar 

  33. 33.

    Giovannelli, D. et al. Insight into the evolution of microbial metabolism from the deep-branching bacterium, Thermovibrio ammonificans. eLife 6, e18990 (2017).

    Article  Google Scholar 

  34. 34.

    Yokochi, R. et al. Noble gas radionuclides in Yellowstone geothermal gas emissions: a reconnaissance. Chem. Geol. 339, 43–51 (2013).

    Article  Google Scholar 

  35. 35.

    Harris, R. N. & Wang, K. Thermal models of the Middle America Trench at the Nicoya Peninsula, Costa Rica. Geophys. Res. Lett. 29, 6-1–6-4 (2010).

    Google Scholar 

  36. 36.

    Jelen, B. I., Giovannelli, D. & Falkowski, P. G. The role of microbial electron transfer in the coevolution of the biosphere and geosphere. Annu. Rev. Microbiol. 70, 45–62 (2016).

    Article  Google Scholar 

  37. 37.

    Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive Earth’s biogeochemical cycles. Science 320, 1034–1039 (2008).

    Article  Google Scholar 

  38. 38.

    Tassi, F. et al. The geothermal resource in the Guanacaste region (Costa Rica): new hints from the geochemistry of naturally discharging fluids. Front. Earth Sci. 6, 69 (2018).

    Article  Google Scholar 

  39. 39.

    Tassi, F., Vaselli, O., Barboza, V., Fernandez, E. & Duarte, E. Fluid geochemistry and seismic activity in the period 1998–2002 at Turrialba Volcano (Costa Rica). Ann. Geophys. 47, 4 (2004).

    Google Scholar 

  40. 40.

    Barry, P. H. et al. Helium, inorganic and organic carbon isotopes of fluids and gases across the Costa Rica convergent margin. Sci. Data (2019).

  41. 41.

    Vetriani, C., Jannasch, H. W., MacGregor, B. J., Stahl, D. A. & Reysenbach, A.-L. Population structure and phylogenetic characterization of marine benthic archaea in deep-sea sediments. Appl. Environ. Microbiol. 65, 4375–4384 (1999).

    Article  Google Scholar 

  42. 42.

    Wright, J. J., Lee, S., Zaikova, E., Walsh, D. A. & Hallam, S. J. DNA extraction from 0.22 μm Sterivex filters and cesium chloride density gradient centrifugation. JOVE (2009).

  43. 43.

    Teare, J. M. et al. Measurement of nucleic acid concentrations using the DyNA QuantTM and the GeneQuantTM. Biotechniques 22, 1170–1174 (1997).

    Article  Google Scholar 

  44. 44.

    Simbolo, M. et al. DNA qualification workflow for next generation sequencing of histopathological samples. PLoS ONE 8, e62692 (2013).

    Article  Google Scholar 

  45. 45.

    Giovannelli, D. et al. Diversity and distribution of prokaryotes within a shallow-water pockmark field. Front. Microbiol. 7, 941 (2016).

    Article  Google Scholar 

  46. 46.

    Huse, S. M. et al. VAMPS: a website for visualization and analysis of microbial population structures. BMC Bioinformatics 15, 41 (2014).

    Article  Google Scholar 

  47. 47.

    Huse, S. M. et al. Comparison of brush and biopsy sampling methods of the ileal pouch for assessment of mucosa-associated microbiota of human subjects. Microbiome 2, 5 (2014).

    Article  Google Scholar 

  48. 48.

    Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).

    Article  Google Scholar 

  49. 49.

    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).

    Article  Google Scholar 

  50. 50.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  Google Scholar 

  51. 51.

    Zhu, C. et al. Functional sequencing read annotation for high precision microbiome analysis. Nucleic Acids Res. 46, e23 (2018).

    Article  Google Scholar 

  52. 52.

    R Core Team, R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).

  53. 53.

    McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).

    Article  Google Scholar 

  54. 54.

    vegan (CRAN, 2019).

  55. 55.

    Hamilton, N. E. & Ferry, M. ggtern: ternary diagrams using ggplot2. J. Stat. Softw. 87, 1–17 (2018).

    Article  Google Scholar 

  56. 56.

    Stekhoven, D. J. & Buhlmann, P. MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics 28, 112–118 (2012).

    Article  Google Scholar 

  57. 57.

    Genuer, R., Poggi, J.-M. & Tuleau-Malot, C. VSURF: an R package for variable selection using random forests. R J. 7, 1–19 (2015).

    Article  Google Scholar 

  58. 58.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).

  59. 59.

    Sheik, C. S. et al. Identification and removal of contaminant sequences from ribosomal gene databases: lessons from the Census of Deep Life. Front. Microbiol. 9, 840 (2018).

    Article  Google Scholar 

  60. 60.

    Sugimori, K. et al. Microbial life in the acid lake and hot springs of Poas Volcano, Costa Rica. In Proc. Colima Volcano International Meeting (2002).

  61. 61.

    Mcmurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput. Biol. 10, e1003531 (2014).

    Article  Google Scholar 

  62. 62.

    Weiss, S. et al. Normalization and microbial differential abundance strategies depend upon data characteristics. Microbiome 5, 27 (2017).

    Article  Google Scholar 

  63. 63.

    Giovannelli, D. et al. Large-scale distribution and activity of prokaryotes in deep-sea surface sediments of the Mediterranean Sea and the adjacent Atlantic Ocean. PLoS ONE 8, e72996 (2013).

  64. 64.

    Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).

    Google Scholar 

  65. 65.

    Schruben, P. G. Geology and Resource Assessment of Costa Rica DDS-19-R (USGS, 1987).

  66. 66.

    Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).

    Article  Google Scholar 

  67. 67.

    Kurtz, Z. D. et al. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput. Biol. 11, e1004226 (2015).

    Article  Google Scholar 

  68. 68.

    Schwager, E., Mallick, H., Ventz, S. & Huttenhower, C. A Bayesian method for detecting pairwise associations in compositional data. PLoS Comput. Biol. 13, e1005852 (2017).

    Article  Google Scholar 

  69. 69.

    Zar, J. H. Significance testing of the spearman rank correlation coefficient. J. Am. Stat. Assoc. 67, 578–580 (1972).

    Article  Google Scholar 

  70. 70.

    Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal 1695, 1–9 (2006).

    Google Scholar 

  71. 71.

    Braun, S. et al. Microbial turnover times in the deep seabed studied by amino acid racemization modelling. Sci. Rep. 7, 5680 (2017).

    Article  Google Scholar 

  72. 72.

    Whitman, W. B., Coleman, D. C. & Wiebe, W. J. Prokaryotes: the unseen majority. Proc. Natl Acad. Sci. USA 95, 6578–6583 (1998).

    Article  Google Scholar 

  73. 73.

    McMahon, S. & Parnell, J. Weighing the deep continental biosphere. FEMS Microbiol. Ecol. 87, 113–120 (2013).

    Article  Google Scholar 

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This work is part of the Biology Meets Subduction project, a collaboration of 46 researchers from 19 institutions from 9 nationalities. We thank P. Barcala Dominguez for assistance with figure illustrations, and T. Hoehler for advice. Principal support came from the Alfred P. Sloan Foundation and the Deep Carbon Observatory (G-2016-7206) to P.H.B., J.M.d.M, D.G. and K.G.L., with DNA sequencing from the Census of Deep Life. Additional support came from NSF OCE-1431598, NASA Exobiology NNX16AL59G and Simons Foundation 404586 to K.G.L., NSF 1144559 to P.H.B., NSF 1850699 to J.M.d.M., NSF MCB 15–17567 to D.G. and C.V., ELSI Origins Network (EON) Research Fellowship from the John Templeton Foundation to D.G., Deep Life Modeling and Visualization Fellowship from the Deep Carbon Observatory to D.G., FONDECYT Grant 11191138 (ANID Chile) to G.L.J., ENIGMA (NASA Astrobiology Institute cycle 8, 80NSSC18M0093) to D.G., S.M.M. and J.B, U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research (DE-SC0020369) to A.D.S. and K.G.L., JSPS KAKENHI grants JP17K14412, JP17H06105 and JP17H02989 to M.N. and DEKOSIM grant BAP-08-11-DPT.2012K120880, financed by the Strategy and Budget Ministry of Turkey, to M.Y. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the John Templeton Foundation.

Author information




K.M.F. and K.G.L. wrote the original draft and performed initial data analyses. Conceptualization and funding acquisition were performed by P.H.B., J.M.d.M., D.G. and K.G.L. Formal analysis and visualization were performed by K.M.F. and D.G. Investigations and data acquisition were performed by K.M.F., M.Y., E.M., G.D., D.F., M.D.C., F.R., M.N., F.S., H.M., S.M.M., T.J.R., M.B., J.B., A.D.S. and D.G. Writing and editing of the final draft was performed by M.O.S., M.Y., M.N., C.V., C.R., G.L.J., H.M., T.J.R., M.M., J.B., J.M.d.M., P.H.B., A.D.S., D.G. and K.G.L.

Corresponding authors

Correspondence to Donato Giovannelli or Karen G. Lloyd.

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Peer review information Primary Handling Editor(s): Rebecca Neely. Nature Geoscience thanks Jan Amend and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Tables 1–17 and Figures 1–12.

Source data

Source Data Fig. 1

Source data for Fig. 1.

Source Data Fig. 2

Source data for the NMDS and ternary plots appearing in Fig. 2.

Source Data Fig. 3

Source data for the network analysis and the correlation of the cliques appearing in Fig. 3.

Source Data Fig. 4

Source data for the network analysis and the correlation of the gene-cliques appearing in Fig. 4.

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Fullerton, K.M., Schrenk, M.O., Yücel, M. et al. Effect of tectonic processes on biosphere–geosphere feedbacks across a convergent margin. Nat. Geosci. 14, 301–306 (2021).

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