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Sinking enhances the degradation of organic particles by marine bacteria

Abstract

The sinking of organic particles in the ocean and their degradation by marine microorganisms is one of the main drivers of the biological pump. Yet, the mechanisms determining the magnitude of the pump remain poorly understood, limiting our ability to predict this carbon flux in future ocean scenarios. Current ocean models assume that the biological pump is governed by the competition between sinking speed and degradation rate, with the two processes independent from one another. Contrary to this paradigm, we show that sinking itself is a primary determinant of the rate at which bacteria degrade particles. Heterotrophic bacterial degradation rates were obtained from a laboratory study on model surface-colonized particles at atmospheric pressure under a range of flow speeds to mimic different sinking velocities. We find that even modest sinking speeds of 8 m day−1 enhance degradation rates more than 10-fold compared with degradation rates of non-sinking particles. We discovered that the molecular mechanism underlying this sinking-enhanced degradation is the flow-induced removal from the particles of the oligomeric breakdown products, which otherwise compete for enzymatic activity. This mechanism applies across several substrates and bacterial strains, suggesting its potentially broad occurrence under natural marine conditions. Integrating our findings into a mathematical model of particulate carbon flux, we propose that the coupling of sinking and degradation may contribute, in conjunction with other processes, to determining the magnitude of the vertical carbon flux in the ocean.

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Fig. 1: Marine particle degradation is flow dependent.
Fig. 2: Flow plays a key role in determining bacterial growth on, and degradation of, model marine particles.
Fig. 3: High concentrations of oligo-alginate inhibit the degradation of alginate particles.
Fig. 4: The coupling of sinking and degradation can have a major effect on vertical flux of POC in the ocean.

Data availability

The datasets and representative videos generated and analysed during the study are available in the Supplementary Information of the paper. The raw videos generated and analysed during the study are available for download from: https://doi.org/10.3929/ethz-b-000488179. Source data are provided with this paper.

Code availability

The computer codes used during the study are available for download from Zenodo (COMSOL transport model, https://doi.org/10.5281/zenodo.4818505; model of sinking-enhanced degradation, https://doi.org/10.5281/zenodo.5233234).

References

  1. 1.

    Boyd, P. W., Claustre, H., Levy, M., Siegel, D. A. & Weber, T. Multi-faceted particle pumps drive carbon sequestration in the ocean. Nature 568, 327–335 (2019).

    Google Scholar 

  2. 2.

    Buesseler, K. O. & Boyd, P. W. Shedding light on processes that control particle export and flux attenuation in the twilight zone of the open ocean. Limnol. Oceanogr. 54, 1210–1232 (2009).

    Google Scholar 

  3. 3.

    Eppley, R. W. & Peterson, B. J. Particulate organic matter flux and planktonic new production in the deep ocean. Nature 282, 677–680 (1979).

    Google Scholar 

  4. 4.

    Jiao, N. et al. Microbial production of recalcitrant dissolved organic matter: long-term carbon storage in the global ocean. Nat. Rev. Microbiol. 8, 593–599 (2010).

    Google Scholar 

  5. 5.

    Volk, T., Hoffert, M. I. in The Carbon Cycle and Atmospheric CO2: Natural Variations Archean to Present Vol. 32 (eds Sundquist, E. T. & Broecker, W. S.) 99–110 (American Geophysical Union, 1985).

  6. 6.

    Smith, D. C., Simon, M., Alldredge, A. L. & Azam, F. Intense hydrolytic enzyme activity on marine aggregates and implications for rapid particle dissolution. Nature 359, 139–142 (1992).

    Google Scholar 

  7. 7.

    Silver, M. W., Shanks, A. L. & Trent, J. D. Marine snow: microplankton habitat and source of small-scale patchiness in pelagic populations. Science 201, 371–373 (1978).

    Google Scholar 

  8. 8.

    Lampitt, R. S., Wishner, K. F., Turley, C. M. & Angel, M. V. Marine snow studies in the Northeast Atlantic Ocean: distribution, composition and role as a food source for migrating plankton. Mar. Biol. 116, 689–702 (1993).

    Google Scholar 

  9. 9.

    Briggs, N., Dall’Olmo, G. & Claustre, H. Major role of particle fragmentation in regulating biological sequestration of CO2 by the oceans. Science 367, 791–793 (2020).

    Google Scholar 

  10. 10.

    Giering, S. L. C. et al. Reconciliation of the carbon budget in the ocean’s twilight zone. Nature 507, 480–483 (2014).

    Google Scholar 

  11. 11.

    Smriga, S., Fernandez, V. I., Mitchell, J. G. & Stocker, R. Chemotaxis toward phytoplankton drives organic matter partitioning among marine bacteria. Proc. Natl Acad. Sci. USA 113, 1576–1581 (2016).

    Google Scholar 

  12. 12.

    Biddanda, B. Microbial aggregation and degradation of phytoplankton-derived detritus in seawater. II. Microbial metabolism. Mar. Ecol. Prog. Ser. 42, 89–95 (1988).

    Google Scholar 

  13. 13.

    Kiørboe, T., Grossart, H. P., Ploug, H. & Tang, K. Mechanisms and rates of colonisation of sinking aggregates. Appl. Environ. Microbiol. 68, 3996–4006 (2002).

    Google Scholar 

  14. 14.

    Yawata, Y. et al. Competition-dispersal tradeoff ecologically differentiates recently speciated marine bacterioplankton populations. Proc. Natl Acad. Sci. USA 111, 5622–5627 (2014).

    Google Scholar 

  15. 15.

    Singh, P. K. et al. Vibrio cholerae combines individual and collective sensing to trigger biofilm dispersal. Curr. Biol. 27, 3359–3366.e7 (2017).

    Google Scholar 

  16. 16.

    Buchan, A., LeCleir, G. R., Gulvik, C. A., González, J. M. & Gonzalez, J. M. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).

    Google Scholar 

  17. 17.

    Ebersbach, F. & Trull, T. W. Sinking particle properties from polyacrylamide gels during the Kerguelen ocean and plateau compared study (KEOPS): zooplankton control of carbon export in an area of persistent natural iron inputs in the Southern Ocean. Limnol. Oceanogr. 53, 212–224 (2008).

    Google Scholar 

  18. 18.

    Martin, J. H., Knauer, G. A., Karl, D. M. & Broenkow, W. W. VERTEX: carbon cycling in the Northeast Pacific. Deep Sea Res. A 34, 267–285 (1987).

    Google Scholar 

  19. 19.

    Ducklow, H. W., Steinberg, D. K. & Buesseler, K. O. Upper ocean carbon export and the biological pump. Oceanography 14, 50–58 (2001).

    Google Scholar 

  20. 20.

    Stukel, M. R., Song, H., Goericke, R. & Miller, A. J. The role of subduction and gravitational sinking in particle export, carbon sequestration, and the remineralization length scale in the California Current ecosystem. Limnol. Oceanogr. 63, 363–383 (2018).

    Google Scholar 

  21. 21.

    Siegel, D. A. et al. Prediction of the export and fate of global ocean net primary production: the exports science plan. Front. Mar. Sci. 3, 22 (2016).

    Google Scholar 

  22. 22.

    Dunne, J. P., Sarmiento, J. L. & Gnanadesikan, A. A synthesis of global particle export from the surface ocean and cycling through the ocean interior and on the seafloor. Global Biogeochem. Cycles 21, GB4006 (2007).

    Google Scholar 

  23. 23.

    Armstrong, R. A., Lee, C., Hedges, J. I., Honjo, S. & Wakeham, S. G. A new, mechanistic model for organic carbon fluxes in the ocean based on the quantitative association of POC with ballast minerals. Deep Sea Res. II 49, 219–236 (2001).

    Google Scholar 

  24. 24.

    Lutz, M., Dunbar, R. & Caldeira, K. Regional variability in the vertical flux of particulate organic carbon in the ocean interior. Global Biogeochem. Cycles 16, 11-1–11-18 (2002).

    Google Scholar 

  25. 25.

    Williams, R. G. & Follows, M. J. Ocean Dynamics and the Carbon Cycle: Principles and Mechanisms (Cambridge Univ. Press, 2011).

  26. 26.

    Omand, M. M., Govindarajan, R., He, J. & Mahadevan, A. Sinking flux of particulate organic matter in the oceans: sensitivity to particle characteristics. Sci. Rep. 10, 5582 (2020).

    Google Scholar 

  27. 27.

    Henson, S. A., Sanders, R. & Madsen, E. Global patterns in efficiency of particulate organic carbon export and transfer to the deep ocean. Global Biogeochem. Cycles 26, GB1028 (2012).

    Google Scholar 

  28. 28.

    Krause-Jensen, D. & Duarte, C. M. Substantial role of macroalgae in marine carbon sequestration. Nat. Geosci. 9, 737–742 (2016).

    Google Scholar 

  29. 29.

    Hehemann, J. H. et al. Adaptive radiation by waves of gene transfer leads to fine-scale resource partitioning in marine microbes. Nat. Commun. 7, 12860 (2016).

    Google Scholar 

  30. 30.

    Hunt, D. E. et al. Resource partitioning and sympatric differentiation among closely related bacterioplankton. Science 320, 1081–1085 (2008).

    Google Scholar 

  31. 31.

    Kiørboe, T. & Jackson, G. A. Marine snow, organic solute plumes, and optimal chemosensory behavior of bacteria. Limnol. Oceanogr. 46, 1309–1318 (2001).

    Google Scholar 

  32. 32.

    Laurenceau-Cornec, E. C., Trull, T. W., Davies, D. M., De La Rocha, C. L. & Blain, S. Phytoplankton morphology controls on marine snow sinking velocity. Mar. Ecol. Prog. Ser. 520, 35–56 (2015).

    Google Scholar 

  33. 33.

    Ploug, H. & Grossart, H. P. Bacterial production and respiration in suspended aggregates—a matter of the incubation method. Aquat. Microb. Ecol. 20, 321–329 (1999).

    Google Scholar 

  34. 34.

    Karp-Boss, L., Boss, E. & Jumars, P. A. Nutrient fluxes to planktonic osmotrophs in the presence of fluid motion. Oceanogr. Mar. Biol. 34, 71–107 (1996).

    Google Scholar 

  35. 35.

    Guidi, L. et al. Plankton networks driving carbon export in the oligotrophic ocean. Nature 532, 465–470 (2016).

    Google Scholar 

  36. 36.

    Alldredge, A. L. & Gotschalk, C. In situ settling behavior of marine snow. Limnol. Ocean. 33, 339–35 (1988).

    Google Scholar 

  37. 37.

    Buesseler, K. O., Boyd, P. W., Black, E. E. & Siegel, D. A. Metrics that matter for assessing the ocean biological carbon pump. Proc. Natl Acad. Sci. USA 117, 9679–9687 (2020).

    Google Scholar 

  38. 38.

    Guidi, L. et al. Effects of phytoplankton community on production, size and export of large aggregates: a world-ocean analysis. Limnol. Oceanogr. 54, 1951–1963 (2009).

    Google Scholar 

  39. 39.

    Stemmann, L. & Boss, E. Plankton and particle size and packaging: from determining optical properties to driving the biological pump. Ann. Rev. Mar. Sci. 4, 263–290 (2012).

    Google Scholar 

  40. 40.

    Trull, T. W. et al. In situ measurement of mesopelagic particle sinking rates and the control of carbon transfer to the ocean interior during the Vertical Flux in the Global Ocean (VERTIGO) voyages in the North Pacific. Deep Sea Res. II 55, 1684–1695 (2008).

    Google Scholar 

  41. 41.

    Alonso-Gonzalez, I. J. et al. Role of slowly settling particles in the ocean carbon cycle. Geophys. Res. Lett. 37, L13608 (2010).

    Google Scholar 

  42. 42.

    Jokulsdottir, T. & Archer, D. A stochastic, Lagrangian model of sinking biogenic aggregates in the ocean (SLAMS 1.0): model formulation, validation and sensitivity. Geosci. Model Dev. 9, 1455–1476 (2016).

    Google Scholar 

  43. 43.

    Herndl, G. J. & Reinthaler, T. Microbial control of the dark end of the biological pump. Nat. Geosci. 6, 718–724 (2013).

    Google Scholar 

  44. 44.

    Marsay, C. M. et al. Attenuation of sinking particulate organic carbon flux through the mesopelagic ocean. Proc. Natl Acad. Sci. USA 112, 1089–1094 (2015).

    Google Scholar 

  45. 45.

    Kim, B. K. et al. Vertical distributions of macromolecular composition of particulate organic matter in the water column of the Amundsen Sea polynya during the summer in 2014. J. Geophys. Res. Oceans 123, 1393–1405 (2018).

    Google Scholar 

  46. 46.

    Becker, S. et al. Laminarin is a major molecule in the marine carbon cycle. Proc. Natl Acad. Sci. USA 117, 6599–6607 (2020).

    Google Scholar 

  47. 47.

    Alldredge, A. L., Passow, U. & Logan, B. E. The abundance and significance of a class of large, transparent organic particles in the ocean. Deep Sea Res. I 40, 1131–1140 (1993).

    Google Scholar 

  48. 48.

    Drescher, K., Nadell, C. D., Stone, H. A., Wingreen, N. S. & Bassler, B. L. Solutions to the public goods dilemma in bacterial biofilms. Curr. Biol. 24, 50–55 (2014).

    Google Scholar 

  49. 49.

    Datta, M. S. et al. Successions on model marine particles. Nat. Commun. 7, 11965 (2016).

    Google Scholar 

  50. 50.

    Muhammad Yusof, N. L. B., Lim, L. Y. & Khor, E. Preparation and characterization of chitin beads as a wound dressing precursor. J. Biomed. Mater. Res. 54, 59–68 (2001).

    Google Scholar 

  51. 51.

    McDonnell, A. M. P. & Buesseler, K. O. Variability in the average sinking velocity of marine particles. Limnol. Oceanogr. 55, 2085–2096 (2010).

    Google Scholar 

  52. 52.

    Enke, T. N., Leventhal, G. E., Metzger, M., Saavedra, J. T. & Cordero, O. X. Microscale ecology regulates particulate organic matter turnover in model marine microbial communities. Nat. Commun. 9, 2743 (2018).

    Google Scholar 

  53. 53.

    Ebrahimi, A., Schwartzman, J. & Cordero, O. X. Cooperation and spatial self-organization determine rate and efficiency of particulate organic matter degradation in marine bacteria. Proc. Natl Acad. Sci. USA 116, 23309–23316 (2019).

    Google Scholar 

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Acknowledgements

We thank R. Naisbit for help with editing and members of the Simons Foundation PriME collaboration for fruitful discussions. We gratefully acknowledge funding from the European Molecular Biology Organization (EMBO; ALTF 1109-2016) and the Human Frontier Science Program (HFSP; LT001209/2017) to U.A.; the European Union’s Horizon 2020 research and innovation programme under a Marie Skłodowska-Curie grant agreement (No. 798411) to F.J.P.; the Science for Life Laboratory (SciLifeLab Fellowship grant, SLL 2019/2) to L.B.; and from a Gordon and Betty Moore Foundation Symbiosis in Aquatic Systems Investigator Award (GBMF9197; https://doi.org/10.37807/GBMF9197), the Simons Foundation through the Principles of Microbial Ecosystems (PriME) collaboration (grant 542395) and the Swiss National Science Foundation, National Centre of Competence in Research (NCCR) Microbiomes (No. 51NF40_180575) to R.S.

Author information

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Authors

Contributions

U.A., F.J.P., V.I.F. and R.S. designed the research. U.A., K.S.L. and L.B. conducted the experiments. U.A. analysed the data. F.J.P., V.I.F. and U.A. developed the theoretical models. F.J.P. performed the numerical simulations. U.A., F.J.P., V.I.F. and R.S. wrote the paper.

Corresponding authors

Correspondence to Uria Alcolombri or Roman Stocker.

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The authors declare no competing interests.

Additional information

Peer review information Nature Geoscience thanks Philip Boyd and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary handling editor: James Super.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Growth, respiration and localisation of alginate lyases of Vibrio cyclitrophicus ZF270 grown on alginate as a sole carbon source.

(a) Growth of Vibrio cyclitrophicus ZF270 on alginate particles under different flow rates. Bacterial growth was quantified as the average GFP fluorescence intensity at the interface between the alginate particle and the glass slide. All data normalized to initial values, error bars are standard deviations of three independent replicates. An expanded view of the exponential phase (0–20 h) is presented in Fig. 1c. A.U. = arbitrary units. (b) Respiration rate of Vibrio cyclitrophicus ZF270 grown on alginate particles. Respiration was measured in 2 ml vials each containing 18 alginate particles (diameter = 0.80 ± 0.05 mm) pre-colonized with Vibrio cyclitrophicus ZF270. O2 concentration was measured using O2-sensitive optode sensors and plotted as a function of time for three replicates (see Methods). Values were used to estimate O2 consumption per particle (8.9 ± 0.7 pmol min−1 particle−1; at peak growth, 40 h post-inoculation, n = 3) and per bacterium (0.07 ± 0.02 fmol min−1 cell−1, based on counts of 1.3 ± 0.3 × 106 cells per particle, at peak growth, 40 h post-inoculation, n = 3). The background consumption rate obtained from the no-bacteria control (blue curve) was subtracted from the bacterial respiration rate. (c) Vibrio cyclitrophicus ZF270 uses membrane-bound/periplasmic alginate lyases for growth on alginate. Images of Vibrio colonies grown on a 0.5% alginate-containing agar plate. Alginate lyase secretion by Vibrio splendidus FF6 (lower panel) leads to the formation of a transparent ring (marked with arrows) on the opaque plate due to degradation of alginate. This ring is absent around the Vibrio cyclitrophicus ZF270 colony (upper panel), indicating that this strain does not secrete alginate lyase into the surrounding medium.

Extended Data Fig. 2 Raman microscopy of alginate particles during degradation by Vibrio cyclitrophicus ZF270 suggests an accumulation of oligo-alginate on the surface of the particle even at intermediate flow rates during bacterial degradation, consumption and growth.

(a, b) Stitched bright-field microscopy images (22 × 30 = 660 and 29 × 35 = 1,015 images per picture) of a degraded particle (a; 42 h post-inoculation with bacteria) and a control particle (b; sterile particle). A flow rate of 7.25 m day-1 was imposed in both experiments. (c, d) Raman images of the spatial distribution of the carboxyl group (COOH) at the mid plane of the particle. Carboxyl groups are found on the mannuronate and guluronate subunits of alginate. The Raman map of the carboxyl group (I1390–1440; integrated intensity in the spectral region 1390–1440 cm−1) was normalized by the background intensity from the glass slide (I520–570). Soluble oligo-alginate, the product of bacterial degradation, diffuses into and out of the particle and results in a higher Raman signal (c). The Raman signal of the carboxyl ion, indicating the presence of dissolved oligo-alginate, was highest around the periphery of the particle, and decreased towards the particle centre. In contrast, the spatial distribution of the carboxyl ion in the absence of bacterial degradation was homogeneous (d). The white circle represents the boundary of the alginate particle. Panels (c) and (d) are stitched images with 20-µm step size in x- and y-directions. Scale bars: 100 µm.

Extended Data Fig. 3 Vibrio cyclitrophicus ZF270 biofilms on particles 30 h post-inoculation in flow of different concentrations of oligo-alginate or in 1% marine broth 2216.

Bacterial degradation was monitored under a flow of 7.25 m day-1 of medium supplemented with different concentrations of oligo-alginate or with 1% marine broth 2216 (complementary control; see main text). White arrows indicate bacterial biofilms, which form in oligo-alginate concentrations ≥0.04 mg ml-1 and in 1% marine broth 2216. Although more cells reside on particles at higher oligo-alginate concentrations, the degradation rate is lower than at lower oligo-alginate concentrations. For example, upon addition of 0.04 mg ml-1 oligo-alginate, the biofilm thickness was 33 ± 11 µm and the degradation rate was (7.5 ± 0.1) × 10−3 mm3 h-1, whereas for zero addition of oligo-alginate, the biofilm thickness was 8 ± 3 µm and the degradation rate was (20.5 ± 0.8) × 10−3 mm3 h-1. Scale bar: 400 µm.

Extended Data Fig. 4 High concentrations of oligo-alginate inhibit the degradation of alginate particles but do not affect alginate lyase activity.

(a) Particles exposed to alginate lyase enzyme under a flow rate of 7.25 m day-1 for 120 min in the presence or absence of 1 mg ml-1 oligo-alginate <10 kDa (see also Supplementary Movie 4). An experiment with 1 mg ml-1 oligo-alginate but with no alginate lyase enzyme is presented as a negative control. Degradation of the particle is only detected in the presence of alginate lyase but the absence of oligo-alginate. The volume of the particle relative to time zero (Vr) is indicated within each panel. Scale bar: 400 µm. (b) Addition of oligo-alginate does not inhibit the activity of alginate lyase. Alginate lyase activity was measured by monitoring the increase in absorption at 235 nm using a plate reader. Reactions were performed in 10 mm MOPS buffer containing 20 µg ml-1 alginate lyase, 0.1% (w/v) sodium alginate, 2 mM CaCl2 and the concentrations of oligo-alginate <10 kDa given on the x axis (see Methods). No differences in enzyme activity were observed across the range of concentrations tested (n = 3 for each value of concentration).

Extended Data Fig. 5 The coupling of sinking speed and degradation rate in models that consider a fixed minimum particle size.

Shown is the vertical flux of POC as a function of depth, F(Z), normalized by its value F(Z0) at the euphotic layer depth, Z0 = 100 m, predicted by our model when accounting for the observed coupling of sinking and degradation (continuous curves) or when excluding this coupling (reference case, dashed curves). When assuming a fixed lower bound, Rmin = 20 µm, for the radius of particles contributing to the flux (orange curves), our model shows a similar trend to the results presented in Fig. 4 (here shown with black curves), for which the range of radii varies with depth as particles are degraded. The minimum size was chosen by considering the experimental lower bound of the size of particles that can be detected by vision profilers with a fixed lower resolution35,51, to represent the lower bound on particles that are considered in empirical estimates of the POC flux. In both of the coupled approaches (continuous curves), the coupling of sinking speed with degradation results in a faster decrease of the vertical flux with depth compared to the reference case that neglects the coupling. All curves were derived from the following initial conditions: total concentration 200 particles l-1 at initial depth Z0 = 100 m, with a PSD slope of -4 over particles with initial radii in the range 125–750 µm for the varying range assumption (black) and initial radii 20–750 µm for the fixed lower bound assumption (orange).

Extended Data Fig. 6 Comparison of results from models incorporating the coupling of sinking and degradation with a reference case imposing a fixed high degradation rate for all particles.

Shown is the vertical flux of POC as a function of depth, F(Z), normalized by its value F(Z0) at the euphotic layer depth, Z0 = 100 m, predicted by our model when accounting for the observed coupling of sinking and degradation (blue curve) or when excluding this coupling (reference case, red curve). Also shown are results from a variant of the reference case that considers a uniform high rate of shrinking of the radius over the entire range of particle sizes (dash-dotted curve), corresponding to the initial shrinking rate of the largest particle considered at the initial depth Z0 (R0 = 750 µm) in the coupled case. All curves were derived from the following initial conditions: total concentration 200 particles l-1 at initial depth Z0 = 100 m and a PSD slope of -4 over particles of radii ranging between 125 and 750 µm. Note that the uncoupled high degradation case (dash-dotted curve) assumes a constant degradation rate that was measured under high flow, thus it overestimates the degradation compared to the coupled dynamics (blue curve).

Extended Data Fig. 7 Sensitivity of sinking-enhanced degradation to the characteristics of the particle size distribution (PSD).

Using our model, we computed the transfer efficiency of the vertical flux of POC to a depth of 100 m below the euphotic zone, denoted T100, that is T100 = F(Z0 + 100)/F(Z0) with Z0 = 100 m the depth of the euphotic zone, for different PSDs covering the range of existing oceanic regimes. By varying the PSD slope from -5 to -2, we move from typical oligotrophic PSDs dominated by small particles to typical eutrophic PSDs where larger particles are more prominent and can dominate in volume. For each PSD slope, we also considered a narrow range of initial particle size (R0 = 125 to 750 µm, circles), and a wider range representative of extended capabilities of Underwater Video Profilers to detect particles51 (R0 = 22.5 to 2500 µm, crosses). For each set of initial conditions, we ran our model including the sinking-enhanced degradation mechanism (coupled case, in blue) and without the coupling as a null model (uncoupled, in red). These results demonstrate that as the PSD skews towards small particles with slope closer to -5, typical of oligotrophic regions, the difference in flux between the uncoupled and coupled models becomes smaller. In contrast, a PSD with a greater contribution from large particles, with slope close to -2 like that in eutrophic regions, results in a greater difference between the coupled and uncoupled models. This is intuitive given the stronger enhancement of degradation for larger particles resulting from their faster sinking. In the same manner, we observe that including a wider range of particle sizes in the distribution for oligotrophic regions (with slopes -5 to -4) results in a weaker effect of the coupling of sinking and degradation on the POC flux, through the dominant contribution of very small particles. In contrast, the same wider range of particle sizes reinforces the effect of coupling in eutrophic regions (with PSD slopes of -2), where larger particles make a dominant contribution to the flux.

Extended Data Fig. 8 Modelling the coupling of sinking and degradation while including the effect of the water column temperature gradient on degradation rate.

Shown is the vertical flux of POC as a function of depth, F(Z), normalized by its value F(Z0) at the euphotic layer depth, Z0 = 100 m. Continuous curves account for the observed coupling of sinking and degradation (blue continuous curve) or exclude this coupling (reference case, red continuous curve), as in Fig. 4, for estimates of degradation rate at temperature T = 25 °C. The degradation rates of both coupled and uncoupled cases were also modelled while considering the reduction of degradation rate with decreasing temperature, assuming a reduction by a factor 2.5 for each 10 °C decrease in temperature, (i.e. temperature coefficient Q10 = 2.5). The results are presented for two alternative assumptions: 1) the degradation rate is uniform over the water column but corresponds to a temperature T = 15 °C (dashed curves); 2) a fitted temperature profile T(Z) for subtropical waters (see equation (17) Supplementary Information) modulates degradation over the water column (dash-dotted curves). For both assumptions, red curves correspond to the uncoupled reference case while blue curves correspond to the sinking–degradation coupled case. All curves were derived from the following initial conditions: total concentration 200 particles l-1 at initial depth Z0 = 100 m and a PSD slope of -4 over particles of radii ranging between 125 and 750 µm. As our experimental degradation rate was measured at T = 25 °C, the reduction of degradation rate imposed by considering T = 15 °C or a complex profile T(Z) both result in a weaker attenuation of the flux with depth. However, note that regardless of this effect of temperature, there remains a strong attenuation of POC flux with sinking–degradation coupling with respect to the reference uncoupled case.

Extended Data Fig. 9 Varying viscosity with seawater temperature has only a minor effect on the modelled POC flux.

Shown is the vertical flux of POC as a function of depth, F(Z), normalized by its value F(Z0) at the euphotic layer depth, Z0 = 100 m, predicted by our model when accounting for the observed coupling of sinking and degradation (blue continuous curve) or when excluding this coupling (reference case, red continuous curve), as in Fig. 4. Also shown are results from models in which seawater viscosity varies with temperature, both in the coupled (dark blue dashed curve) and uncoupled case (dark red dashed curve). The temperature profile corresponds to subtropical waters with a sharp decrease with depth (see equation (17) Supplementary Information). All curves were derived from the following initial conditions: total concentration 200 particles l-1 at initial depth Z0 = 100 m and a PSD slope of -4 over particles of radii ranging between 125 and 750 µm.

Extended Data Fig. 10 Flow plays a key role in determining bacterial degradation of chitin and chitosan particles.

(a,c) Time series of particle volume during bacterial degradation of chitin (a) and short-chain chitosan (c) particles under flow of 7.25 m day-1 and under no-flow conditions (shown relative to initial volume; mean and SD; n = 3 replicate experiments, except for flow in (a) for which n = 4). (b,d) Maximum degradation rate of chitin (b) and chitosan (d) particles. The apparent degradation rate is ~4-fold faster (chitin) or ~2.5-fold faster (chitosan) in flow compared to no-flow conditions. Note that the degradation rate estimated from volume loss in the case of chitin and chitosan is only a proxy for the mass loss, since Vibrio splendidus (1A01) and Psychromonas (6C06) degrade the particle using membrane-bound enzymes but possibly also by using secreted enzymes52,53. Thus, unlike the case for alginate, degradation may occur from within the particle as well as on the surface, so that measurements of volume reduction may underestimate degradation rate. Flow speed in all panels is represented by colour; blue – no flow, orange – flow speed of 7.25 m day-1. Movies showing the degradation dynamics can be provided by the authors upon request.

Supplementary information

Supplementary Information

Supplementary model description, text, discussion, captions for Supplementary Videos 1–5 and references.

Supplementary Table 1 Description of V. cyclitrophicus ZF270.

Supplementary Table 2 Alginate lyases of V. cyclitrophicus ZF270 (NCBI Taxon ID - 1136176).

a

Supplementary Video 1 Alginate degradation dynamics are flow dependent. Degradation dynamics of alginate particles, observed in a microfluidic channel, under different rates of flow. Images of the mid plane of each particle were acquired in phase and fluorescence microscopy every 20 min for 70 h (Methods). Brightness and contrast in the green channel (bacterial fluorescence) were adjusted in each panel to better visualize bacterial growth. , No flow. b, 1.1 m day−1. c, 4.4 m day−1. d, 21.8 m day−1. Presented are representative movies of the degradation dynamics.

a

Supplementary Video 2 Growth dynamics on alginate particles are flow dependent. Representative movie of bacterial growth on an alginate particle. Images acquired as in Supplementary Video 1, but at the interface between the glass slide and the alginate particle. ,b, Growth dynamics without flow (a) and with a slow flow rate of 1.1 m day−1 (b).

41561_2021_817_MOESM6_ESM.mp4

Supplementary Video 3 High concentrations of oligo-alginate promote bacterial biofilm formation and inhibit the degradation of alginate particles. Representative movie of the degradation dynamics of particles exposed to different concentrations of oligo-alginate (<10 kDa) or marine broth 2216 medium (1%). Images acquired as in Supplementary Video 1. Brightness and contrast are comparable between panels. Flow of oligo-alginate or 1% 2216 medium in the channel was started 17 h after the beginning of the experiment (Methods). All experiments were performed at a flow rate of 7.25 m day−1.

a

Supplementary Video 4 High concentrations of oligo-alginate inhibit the enzymatic degradation of alginate particles. Enzymatic degradation of alginate particles in the presence of oligo-alginate. Images were acquired every minute for 127 min for the control and for 180 min for the experiments (Methods). , Control (oligo-alginate only, 1 mg ml−1). b, Alginate lyase (10 µg ml−1). c, Oligo-alginate (1 mg ml−1) and alginate lyase (10 µg ml−1). All experiments were performed at a flow rate of 7.25 m day−1.

a

Supplementary Video 5 Degradation dynamics of chitin particles is flow dependent. Degradation dynamics of chitin particles by V. splendidus (1A01) and Psychromonas (6CO6), observed in a microfluidic channel, under a flow rate of 7.25 m day−1 or with no flow. Images of the mid plane of each particle were acquired in phase microscopy every 20 min for 240 h (Methods). a, No flow. b, 7.25 m day−1. Presented are representative movies of the degradation dynamics.

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Alcolombri, U., Peaudecerf, F.J., Fernandez, V.I. et al. Sinking enhances the degradation of organic particles by marine bacteria. Nat. Geosci. 14, 775–780 (2021). https://doi.org/10.1038/s41561-021-00817-x

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