# Rapid microbial activity in marine sediments significantly enhances silica cycling rates compared to abiotic processes

Panagiotis Michalopoulos, Jeffrey Krause, Rebecca Pickering, Eleni Rousselaki, Kanchan Maiti, Martial Taillefert, Christophe Rabouille, Rudolph Corvaisier

► To cite this version:

Panagiotis Michalopoulos, Jeffrey Krause, Rebecca Pickering, Eleni Rousselaki, Kanchan Maiti, et al. "Rapid microbial activity in marine sediments significantly enhances silica cycling rates compared to abiotic processes." Communications Earth & Environment, 2025, 6 (1), pp.982. 10.1038/s43247-025-02941-7. hal-05403907

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Submitted on 21 Jan 2026

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# Rapid microbial activity in marine sediments significantly enhances silica cycling rates compared to abiotic processes

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Panagiotis Michalopoulos Ⓔ<fnref n="1" />✉, Jeffrey W. Krause Ⓔ²,³✉, Rebecca A. Pickering Ⓔ³, Eleni Rousselaki<fnref n="1" />, Kanchan Maiti Ⓔ⁴, Martial Taillefert⁵, Christophe Rabouille Ⓔ⁶ & Rudolph Corvaisier⁷

Silicon, earth's second most abundant element, cycles via biogeochemical processes in terrestrial and oceanic environments. The marine sediment-water interface is thought to divide biological-dominated (water) from abiotic-dominated (sediment) silica cycling processes. Studies indicate marine extremophile bacteria induce silica precipitation, but the quantitative importance of microorganisms in broader sediment silica cycling remains unexplored. We hypothesize that microorganisms significantly influence sediment silica cycling, challenging the prevailing divide of microbial importance. Within hours, sediment microbes expedited silica sequestration through rapid coupled dissolution/reprecipitation processes in marine deltaic sediment experiments. Using radiotracer silicon-32, we reveal that microbial mediation enhanced silica uptake, surpassing abiotic uptake by 3.6-fold in dilute suspensions (silica < 100 µM) and 3.4-fold in surface sediment porewater simulations. We assert that microorganisms drive significant diagenetic silica cycling rates, facilitating authigenic silica precipitation through previously reported mechanisms from microscopy analysis. This unifies the critical role of microorganisms in Si cycling to both marine pelagic waters and surface sediments.

A silicon atom is recycled ~16 times in the water column before being buried in marine sediments<fnref n="1" />. While water column dissolved silica (DSi) uptake is dominated by organisms (e.g. diatoms, protozoa, and sponges, which all produce biogenic silica, bSiO₂'), this efficient cycling is largely facilitated by microbes that strip diatom organic matter post-mortem and indirectly promote bSiO₂ dissolution, i.e., remineralization²,³. Upon burial in sediments, bSiO₂ undergoes diagenetic reactions such as dissolution, dissolution/reprecipitation into authigenic solid phases (i.e., reverse weathering reactions) and long-term transformation into opal-CT, cristobalite, tridymite, etc.⁴,⁵ Reverse weathering reactions, which are important in early diagenetic silica cycling, represent one of the largest individual sink terms within the global marine silica budget<fnref n="1" />,⁶⁻⁸ and affect the global climate and carbon cycles⁹,¹⁰.

Current understanding suggests that early diagenetic silica cycling and oceanic reverse weathering processes are solely influenced by abiotic factors, such as temperature, pressure, particle surface area, pore water DSi concentration, and metal availability⁴,¹¹,¹². As a result, a fundamental limitation in the study of silica diagenesis rates in marine sediments in the last 25 years has been the tendency to take a reductionist approach by focusing solely on geochemical factors, thereby overlooking the potential role of microorganisms. Sediment microorganisms (Bacteria, Archaea) are the laborers of early diagenesis, influencing sediment porewater redox conditions and driving biogeochemical cycles of bioactive elements (e.g., C, N, Fe, Mn, P, S). Despite the extensive documentation of microbial clay authigenesis in terrestrial¹³⁻¹⁷ and, more recently, marine

<footnote n="1">¹Institute of Oceanography, Hellenic Centre for Marine Research, Anavyssos, Greece. ²Dauphin Island Sea Lab, Dauphin Island, AL, USA. ³Stokes School of Marine and Environmental Sciences, University of South Alabama, Mobile, AL, USA. ⁴Department of Oceanography and Coastal Studies, Louisiana State University, Baton Rouge, LA, USA. ⁵Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA. ⁶LSCE/IPSL, Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, Gif-sur-Yvette, France. ⁷LEMAR – UMR CNRS 6539, IUEM-UBO, Plouzané, France. ✉e-mail: pmichalo@hcmr.gr; jkrause@disl.edu</footnote>
<figure><img src="image_4.png" /><figcaption>**Fig. 1** | Change of dissolved silica (DSi) relative to initial conditions (DSi: DSi₀) in CDSF experiments. a Selected cores. Archived (frozen) samples were used from multiple core sites and sediment depths at 20 °C indicated in the legend. b Experiments conducted using only surface sediments at one site but with increasing SSR at 15 °C. Plots show bioactive (filled symbols) and abiotic-only (open symbols) conditions over time. The legend in panel b shows the various solid-solution ratios (SSR) tested. The average bioactive to abiotic ratio calculated from the two data sets in each experiment is shown in parenthesis. All experimental data from CDSF are shown in Supplementary Fig. 1.</figcaption></figure>

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environmental conditions¹⁸⁻²⁰, quantitative evidence for the broader biogeochemical importance of microbial-facilitated silica cycling rates is lacking. As a result, microbial mediation is typically omitted from models of early diagenetic silica cycling processes despite the known importance of establishing redox conditions in the regions where rates are high, e.g., coastal river plume systems⁴ and known mechanisms of microbially-induced precipitation¹⁸⁻²⁰ that may occur.

# Results

## Si dissolution in sediment incubations

The link between microbial activity and early diagenetic silica cycling was studied in deep-sea (Congo Deep Sea Fan, CDSF) and coastal marine sediments (Mississippi River Plume, MRP) using sediment slurry incubation experiments in the dark. In the CDSF, lobe sediments are dominantly muddy. Sand represents ca. 13% of the deposits and is restricted to the feeding channel and distributaries. The overall lobe body is composed of thin, muddy to silty turbidites. Lobe sediments are high in organic carbon (up to 3%) but amorphous Si is similar to deep-sea sediments²¹. In the MRP, bulk XRD showed total clay ranged from ~20 to 40% and quartz ranged from 40–65% of the bulk fraction; the clays were primarily montmorillonite²². Total organic carbon in surface sediments in the two samples sites varied from 0.4% (April 2017) to 1.2% (May 2017)²² and various operationally measured silica pools in the second site yielded: traditional bSiO₂ digestion (T-bSiO₂)~41.3 µmole Si g⁻¹, total reactive Si (i.e., Mild Alkaline-Leachable Si (Si-Alk) + Acid-Leachable Silica (Si-HCl))~176.1 µmole Si g⁻¹ and, following the Si-Alk leach, an additional NaOH Digestion (Si-NaOH) = 822 µmole Si g⁻¹ ²³. Slurries were prepared by mixing sediment and filtered seawater, with and without the addition of a biocide: the bioactive treatment (i.e., no biocide) includes both bioactive and abiotic processes, whereas the biocide treatment only considers abiotic processes. Initially, one set of five exploratory 12-day experiments used archived frozen surface sediments collected from four sites within the CDSF²⁴ to determine site variability. The variability could depend on factors such as the position within the Congo lobe, particle grain size and composition, amorphous silica content, and microbial diversity and abundance²¹,²⁵. A second CDSF set of 19-day experiments used sediment from the same core and depth interval as inoculum but varied the solid-solution ratio (SSR) of the slurry incubations and hence the amount of microbial inoculum (note: SSR was low as to not quickly saturate Si release in the closed system). Building on the CDSF results, two sets of six-day

experiments (varying SSR) were conducted using freshly collected sediments from the MRP in different months. Collectively, these experiments demonstrate that microbial processes facilitate quantitatively important rates of authigenic Si precipitation, on short time scales, in marine deltaic systems.

CDSF experiments showed that microbial activity reduces the DSi increase relative to initial conditions (DSi:DSi₀) compared to abiotic controls (Fig. 1a, Supplementary Fig. 1a, Supplementary Table 1). In the first set of experiments, the average bioactive to abiotic ratio (Fig. 1a, Supplementary Fig. 1a) ranged from 0.67 to 0.91. Similarly, in the second experimental set with increasing SSR, the average bioactive to abiotic ratio ranged from 0.89 to 0.96 (Fig. 1b, Supplementary Fig. 1b). These results suggest microbial processes reduce the net solubilization rate of sedimentary silica. However, the observed biological influence could not be differentiated between dissolution/reprecipitation or simple particle adsorption/desorption of DSi, e.g., associated with metal (oxyhydroxide). Furthermore, the use of archived frozen sediments and elevated temperatures (15 °C and 20 °C) raises uncertainty in the extrapolation to in-situ early diagenetic conditions in the CDSF.

## Si uptake in Mississippi incubations

To address these issues, we conducted experiments at sea with freshly collected surface sediment from the more accessible MRP during the spring freshet (April, May 2017) and used radiotracer (³²Si) to differentiate between precipitation into new solid phases and adsorption onto particles.

Precipitation is calculated as the difference between the Si uptake pools and the adsorption pools, both determined through measurement. Mass balance calculations, along with the solid-to-solution silicon ratios used in the experiments, suggest that equilibrium isotope exchange contributes minimally to the observed ³²Si uptake. The quantity of Si in solution in our incubations was initially only ~10–20 µM, compared to a reactive silica pool of ~1000 µmol g⁻¹ ²³. This yields high solid:solution ratios (20–1000), conditions under which isotopic re-equilibration of the solid phase is expected to be negligible because the small aqueous reservoir cannot drive significant isotopic change. In systems dominated by the solid phase, isotope exchange reactions proceed too slowly to affect the isotopic composition of the solid over short experimental timescales²⁶. Laboratory observations further indicate that even under high dissolved Si conditions (~400–700 µM) and extended durations ~400 days, only partial Si isotope equilibration occurs, and that the presence of organic matter can enhance
<figure><img src="image_6.png" /><figcaption>**Fig. 2** | Change of dissolved silica (DSi) relative to initial conditions (DSi: $DSi_0$) with time for May MRP experiments and change of $^{32}$Si in solution and particles with time. **a–c** Select plots of DSi: $DSi_0$ vs time for experiments with increasing SSR; Plots show bioactive (filled blue circles) and abiotic-only (open black circles). note — All DSi: $DSi_0$ experimental data from April and May are reported in Supplementary Fig. 2, absolute DSi changes (not normalized) for both experiments are shown in Supplementary Fig. 3. The average Bioactive to Abiotic ratio calculated from the two data sets in each graph is shown in parenthesis. **d–f** Composite plots of $^{32}$Si</figcaption></figure>

measurements vs time in solution (squares symbols) and particles (hexagonal symbols) for the same experiments shown in (a–c). Filled symbols represent bioactive experiments and open symbols represent abiotic-only; $^{32}$Si activity in plot corresponds to SSR in plot immediately above (e.g., panel a and d). All experimental data from April and May are reported in Supplementary Figs. 4–6 (including results from $^{32}$Si adsorption/desorption experiments). Particle $^{32}$Si activity includes both adsorption and precipitation.

dissolution and isotope exchange rates by promoting surface complexation reactions. However, our incubations were short (6 days), dilute (DSi <100 µM), and not enriched in organic matter, eliminating key factors that accelerate isotopic re-equilibration. Under these undersaturated and low-organic conditions, isotope exchange reactions are expected to be too slow to account for the observed Si uptake, indicating that the incorporation of Si reflects particle uptake rather than isotopic re-equilibration$^{26,27}$.

Relative DSi trends using fresh MRP sediments were similar to frozen CDSF sediments. The average bioactive to abiotic ratio was 0.77 to 0.99 among various SSR (total range ~0.2–11.5) between both experiments (Fig. 2a–c, Supplementary Fig. 2, Supplementary Table 1). The DSi in abiotic (~70 µM) and bioactive (~60 µM) incubations for the highest SSR treatments showed ~10 µM differences during the April experiment (Supplementary Fig. 3a). This pattern was more pronounced in the May experiment, reaching ~30 µM difference for SSR = 10.45 g l$^{-1}$ (~100 µM in abiotic, ~70 µM in bioactive) (Supplementary Fig. 3b). Overall, these results point to quantitatively important microbial control in the net release of DSi rate to solution even at relatively low porewater DSi concentrations (<100 µM).

The radioisotope tracer revealed significant dynamics that were not resolvable in the net DSi release rate measurements in either system. Despite accumulation of DSi over time (Supplementary Fig. 3), consistent with prior studies which did not use $^{32}$Si tracer$^{11,28}$, the solution $^{32}$Si activity gradually decreased with time and concurrently increased in the corresponding particulate material (Fig. 2d–f, Supplementary Fig. 4a, b, Supplementary Fig. 5a, b, Supplementary Fig. 6a, b). This trend demonstrated that a fraction

of the DSi pool was removed from solution over time by the sediments (and their associated microbes), despite the net increase in DSi, by the mixed material (living and non-living) assemblage in the slurries. Although this trend was observed in both treatments, the removal was more pronounced in the bioactive treatments. The $^{32}$Si activity trend was also proportional to the SSR, suggesting it was directly related to the increasing sediment mass (and corresponding microbial biomass) in the high SSR treatments.

To quantify the biotic effect, we examined the changes in the solution specific (radio)activity (SSA) after subtracting $^{32}$Si particle uptake for each treatment (i.e., SSA = [ $^{32}$Si added to solution — $^{32}$Si measured in particles (precipitated + adsorbed)] divided by DSi) (Supplementary Fig. 7). When the concentrations of DSi in the bioactive and abiotic treatments are plotted together against SSA, a clear difference between the treatments emerges, with the bioactive data points trending below the abiotic data points (Supplementary Fig. 8); this difference reflects both the lower $^{32}$Si activities in solution and the lower DSi concentrations in the bioactive samples. Leveraging all time-points, replicates, and different SSR measurements in each experiment allows a robust quantification of the biological effect (Fig. 3a, b). The SSA for each treatment (bioactive, abiotic) plotted against the corresponding DSi depicts cumulative changes in SSA as DSi increases over time and, by proxy, simulate the increased SSR which approaches overall down-core trends as porosity decreases at increasing depth (Fig. 3a, b). Similarly, the quotient of $^{32}$Si added to the solution divided by measured DSi in all sample replicates yields a hypothetical SSA trajectory governed by silica dissolution (red line in Fig. 3a, b), i.e. this represents the
<figure><img src="image_7.png" /><figcaption>**Fig. 3 |** Relationship between solution specific activity and solution DSi and natural logarithmic extrapolations to higher DSi concentration found in sediment pore waters. **a, b** Solution Specific Activity vs DSi composite plots for April (a) and May (b) experiments. Plots show bioactive (filled blue circles) and abiotic-only (open black circles) points. Power law regressions were fit to each experimental dataset (32 raw data points, error for each term is propagated standard error of mean (triplicate)). Symbol type denotes three trajectories: 1) the predicted isotopic dilution of specific activity based on the increased DSi over time (total ³²Si solution activity is constant) in the chamber (red lines; note this is predicted but data constrained), 2) the observed trends in the bioactive treatments which include the sum of bioactive and abiotic factors (blue symbols/lines) (R² > 0.98 for blue lines), and 3) the observed trends in the biocide treatments which reflect just abiotic factors (black symbols/</figcaption></figure>

lines) (R² > 0.99 for black dotted lines). Note that for the calculation of Solution Specific Activity for each time point, we used a calculated ³²Si solution activity (calculated ³²Si solution activity = ³²Si added to solution – ³²Si measured in particles) in order to eliminate the effect of ³²Si adsorbed on vial walls, instead of measured ³²Si activity in solution which includes ³²Si particle uptake + vial wall adsorption.
**c, d** Natural log-transformation of data in plots shown in **a** and **b** but extrapolated (dashed lines) to lower ³²Si specific activity & higher DSi corresponding to in situ pore water conditions; original data also shown (solid-colored lines). Also shown are the amorphous Si solubility (horizontal black line, 1498 µM at 15 °C³¹) and DSi zone of auto precipitation (gray area). When the projected values (red points) to the isotope dilution line (green dashed lines) fall in the gray area, we used amorphous silica solubility as the maximum value (green arrow) for Si budget calculations.

evolution of the experimental system in the absence of any silica uptake, either bioactive or abiotic, because the ³²Si activity in solution is fixed and SSA can only change due to DSi. To quantify total silica uptake at any experimental point, we project from the point along the Y axis (i.e., DSi in bioactive or abiotic treatment measured at time point t_n) to the hypothetical silica dissolution curve with the same SSA. The difference in DSi between two points with the same SSA (i.e., Si_uptake(abiot) = DSi_Hypothetical - DSi_Abiotic or Si_uptake(bio+abiot) = DSi_Hypothetical - DSi_Bioactive) represents the cumulative amount of silica uptake (µmol l⁻¹) since the start of the experiment for the specific condition (Fig. 4). In bioactive experiments, dissolved Si uptake reflects the combined effects of biological and abiotic processes. In contrast, abiotic experiments measure dissolved Si uptake resulting solely from abiotic processes. Therefore, the net dissolved Si uptake (in µM) attributable to biological processes, such as microbial mediation, is calculated as: Si_uptake(bio) = Si_uptake(bio+abiot) - Si_uptake(abiot) (Fig. 4).

The deviation of the bioactive and abiotic curves from the hypothetical dissolution curve illustrates the extent of DSi uptake in each experiment as a function of SSA. Two important trends emerge: (1) Both the bioactive and abiotic experimental curves show DSi uptake in comparison to what is

expected from simple dissolution of particulate silica, and (2) the deviation (i.e., uptake) is more pronounced in the bioactive treatments. This is interpreted as strong evidence that microbial activity plays a significant role in facilitating the consumption of pore water DSi, even at low concentrations (< 100 µM) compared to marine pore water DSi (e.g., 230–600 µM). Although this result was hypothesized from the results of the CDSF experiments, such a trend could not be captured without the application of the ³²Si tracer.

For each MRP experiment we derived silica cycling rates in the bioactive and abiotic treatments in units of µmol Si g⁻¹ h⁻¹ (Table 1). Direct measurements include the net silica dissolution rate ($R_{diss-bio}$, $R_{diss-abiot}$) (Supplementary Method 1, Supplementary Fig. 3), Si uptake rate by particulates ($R_{up-bio}$, $R_{up-abiot}$), and fraction of Si adsorbed ($f_{ads-bio}$, $f_{ads-abiot}$); the latter two are based on ³²Si measurement of particle-associated and adsorbed Si (Supplementary Method 2, Supplementary Figs. 4c, 5c). These parameters were used to calculate: Si adsorption rate ($R_{ads-bio}$, $R_{ads-abiot}$), precipitation rate ($R_{prec-bio}$, $R_{prec-abiot}$), gross dissolution rate ($R_{gross\_diss-bio}$, $R_{gross\_diss-abiot}$), and microbially mediated precipitation rate ($R_{prec-bio}$ - $R_{prec-abiot}$) (calculations in Table 1 and equations 1–7 in Supplementary Method 3). At higher SSRs, more reflective of sediment conditions,
<figure><img src="image_8.png" /><figcaption>**Fig. 4** | Analysis theory conceptually depicting the calculation of dissolved Si uptake from plots of Solution Specific Activity vs Dissolved Si. The three lines, bioactive (blue), abiotic (dotted black) and isotopic dilution (red) share a common starting point at the lowest dissolved Si concentrations (i.e., start of the incubation experiments with the lowest solid:solution ratio). The trends in each line toward higher dissolved Si concentrations and lower Solution Specific Activity represent data from experiments with higher solid:solution ratios and/or data as time has progressed during each incubation experiment. As the Solution Specific Activity decreases due to particle uptake and dissolved Si concentration increases, the bioactive (blue) and abiotic (black) lines diverge from the isotopic dilution (red) line. To quantify net DSi uptake (bioactive, abiotic) in µM at any time</figcaption></figure>

point ($t_n$), we project along the y-axis the data point (filled blue circle for bioactive, open black circle for abiotic) with a specific activity value corresponding to a DSi value (DSi<sub>bio</sub>@$t_n$ and DSi<sub>abiot</sub>@$t_n$) to the point with the same solution specific activity value on the theoretical isotopic dilution curve (red line). The difference in the Y-values (i.e., difference in DSi) between two points with the same specific activity corresponds to the amount of Si uptake (in µM). In bioactive experiments, the dissolved Si uptake (blue arrow) represents the combined Si uptake due to biological and abiotic processes. In abiotic experiments the dissolved Si uptake (black arrow) represents Si uptake only due to abiotic processes. Thus the net dissolved Si uptake (in µM) due to biological processes is calculated as Si uptake (bio) = Si uptake (bio+abiot) - Si uptake (abiot).

the adsorption of Si onto particles was less than 10% of total Si uptake (Table 1).

## Si uptake in surface sediments

To differentiate the bioactive and abiotic Si uptake in MRP surface sediments, we assumed a typical 90% porosity for MRP surface sediments²⁹, which corresponds to SSR ~ 270 $g l^{-1}$ using the average sediment density. To calculate the contribution of the different processes to the diagenetic cycling of silica, we first extrapolated the relationship between log ($R_{diss-bio}$) and log (SSR) from all bioactive experiments to SSR = 270 $g l^{-1}$ to find a representative overall rate of silica dissolution in surface sediments of $R_{diss-bio}$ ~ $5.9 \times 10^{-3}$ µM $g^{-1} h^{-1}$ (Supplementary Method 4, Supplementary Fig. 9). For reference, this rate would release ~230 µM DSi at the end of the experiments (144 h), which is within the range (100 to 600 µM) of surface MRP sediment pore waters⁷ and significantly higher than bottom water DSi concentration in the region, i.e., <40 µM³⁰. Using the natural log-transformation of experimental data (Fig. 3a, b), linear regression lines were extrapolated to include the relevant DSi concentrations encountered in MRP sediment pore waters and the solubility of amorphous silica³¹ (Fig. 3c, d). According to the hypothetical dissolution curve, the gross DSi concentration accumulated in typical MRP surface sediments is predicted to be significantly higher than the net DSi accumulation (230 µM) and to approach, but not exceeded due to thermodynamic constraints, amorphous silica saturation during the May experiment. This suggests that biotic or abiotic processes consume porewater DSi (as demonstrated experimentally) and maintain it below saturation. Furthermore, projecting the highest net

DSi concentration accumulated in MRP surface sediments (600 µM) along the hypothetical dissolution continuum indicates that the gross DSi accumulation would have approached, but not exceeded, amorphous silica saturation in both the April and May experiments. Both biotic and abiotic consumption processes likely contributed to maintaining porewater DSi levels below saturation (Fig. 3c, d).

For both experiments, these observations indicate that microbially-facilitated Si uptake can help decrease the realized DSi concentrations in pore waters to levels that fall below the saturation of amorphous silica (given the reactive silica content for regional sediments⁷,²³). While these findings reinforce the role of abiotic factors (illustrated in the biocide treatments) in maintaining the long-observed low DSi concentrations (compared to amorphous Si solubility) in marine sediment porewater, these data point to not only a role for biology in this process (consistent with prior studies describing mechanisms, e.g., see refs. 18–20), but suggest it is more significant than abiotic processes in typical sediment porewater conditions. This microbial mediation is discussed below:

## Microbe-mediated vs abiotic Si cycling

The tracer allows quantifying two-way exchange processes (e.g. dissolution instead of net dissolution) and the comparison between microbially-mediated and abiotic treatments can be made to derive a microbial effect. In seven out of eight MRP experimental units (i.e., 2 experiments, 4 SSR per experiment), microbially mediated Si precipitation surpassed abiotic precipitation ($R_{prec-mm}:R_{prec-abiot}$ ranges from 1.2 to 8.3) (Table 2). For the average SSR among all data (~4 $g l^{-1}$), the mean $R_{prec-mm}:R_{prec-abiot}$ (~3.6)
<table><caption>Table 1 | Silicon budget parameters for the April and May experiments for all SSR and treatments</caption><thead><tr><th colspan="10">EXPERIMENTALY DETERMINED PARAMETERS</th></tr><tr><th rowspan="2">Cruise</th><th rowspan="2">SSR</th><th rowspan="2">Treatment</th><th colspan="4">Net Dissolution Rate</th><th colspan="4">Uptake Rate</th><th colspan="3">Adsorption Fraction (of total uptake)</th></tr><tr><th colspan="2">R<sub>diss-bio</sub></th><th colspan="2">R<sub>up-bio</sub></th><th colspan="2">f<sub>ads-bio</sub></th><th rowspan="2">n</th><th rowspan="2">St. Error</th></tr><tr><th></th><th></th><th></th><th>Rate</th><th>St. Error</th><th>p</th><th>R<sup>2</sup></th><th>Rate</th><th>St. Error</th><th>p</th><th>R<sup>2</sup></th><th></th><th></th><th></th></tr></thead><tbody><tr><td>April</td><td>0.2</td><td>Bioactive</td><td>67.1</td><td>3.7</td><td>&lt;0.001</td><td>0.98</td><td>10.2</td><td>1.4</td><td>&lt;0.001</td><td>0.90</td><td>0.31</td><td>5</td><td>0.09</td></tr><tr><td>April</td><td>1.0</td><td>Bioactive</td><td>44.1</td><td>1.7</td><td>&lt;0.001</td><td>0.99</td><td>10.1</td><td>1.6</td><td>&lt;0.001</td><td>0.87</td><td>0.18</td><td>5</td><td>0.05</td></tr><tr><td>April</td><td>3.8</td><td>Bioactive</td><td>32.5</td><td>2.7</td><td>&lt;0.001</td><td>0.96</td><td>16.3</td><td>2.5</td><td>&lt;0.001</td><td>0.88</td><td>0.19</td><td>5</td><td>0.06</td></tr><tr><td>April</td><td>11.4</td><td>Bioactive</td><td>21.7</td><td>2.3</td><td>&lt;0.001</td><td>0.94</td><td>28.1</td><td>2.8</td><td>&lt;0.001</td><td>0.95</td><td>0.08</td><td>5</td><td>0.01</td></tr><tr><td>May</td><td>0.2</td><td>Bioactive</td><td>59.6</td><td>6.2</td><td>&lt;0.001</td><td>0.94</td><td>44.6</td><td>6.5</td><td>&lt;0.001</td><td>0.89</td><td>0.10</td><td>6</td><td>0.04</td></tr><tr><td>May</td><td>0.9</td><td>Bioactive</td><td>40.3</td><td>3.2</td><td>&lt;0.001</td><td>0.96</td><td>44.4</td><td>5.3</td><td>&lt;0.001</td><td>0.92</td><td>0.09</td><td>6</td><td>0.02</td></tr><tr><td>May</td><td>3.5</td><td>Bioactive</td><td>22.7</td><td>2.4</td><td>&lt;0.001</td><td>0.94</td><td>58.4</td><td>9.6</td><td>&lt;0.001</td><td>0.86</td><td>0.05</td><td>6</td><td>0.01</td></tr><tr><td>May</td><td>10.5</td><td>Bioactive</td><td>12.5</td><td>1.1</td><td>&lt;0.001</td><td>0.95</td><td>36.1</td><td>5.6</td><td>&lt;0.001</td><td>0.87</td><td>0.06</td><td>6</td><td>0.01</td></tr><tr><th colspan="10">CALCULATED PARAMETERS</th></tr><tr><th rowspan="2">Cruise</th><th rowspan="2">SSR</th><th rowspan="2">Treatment</th><th colspan="2">Adsorption rate</th><th colspan="2">Precipitation Rate</th><th colspan="2">Gross Dissolution Rate</th><th colspan="2">Precipitation Rate (microbially mediated)</th></tr><tr><th>R<sub>ads-bio</sub></th><th>=R<sub>up-bio</sub> * f<sub>ads-bio</sub></th><th>R<sub>prec-bio</sub></th><th>=R<sub>up-bio</sub> * (1-f<sub>ads-bio</sub>)</th><th>R<sub>gross-diss-bio</sub></th><th>=R<sub>diss-bio</sub> + R<sub>up-bio</sub></th><th>R<sub>prec-mm</sub></th><th>=R<sub>prec-bio</sub> - R<sub>prec-abiot</sub></th></tr><tr><th></th><th></th><th></th><th>Rate</th><th>St. Error</th><th>Rate</th><th>St. Error</th><th>Rate</th><th>St. Error</th><th>Rate</th><th>St. Error</th></tr><tr><td>April</td><td>0.2</td><td>Bioactive</td><td>3.1</td><td>1.0</td><td>7.1</td><td>1.3</td><td>77.3</td><td>4.3</td><td>5.0</td><td>1.4</td></tr><tr><td>April</td><td>1.0</td><td>Bioactive</td><td>1.8</td><td>0.6</td><td>8.3</td><td>1.4</td><td>54.2</td><td>4.7</td><td>2.8</td><td>1.6</td></tr><tr><td>April</td><td>3.8</td><td>Bioactive</td><td>3.2</td><td>1.0</td><td>13.2</td><td>2.2</td><td>48.8</td><td>7.2</td><td>9.4</td><td>2.2</td></tr><tr><td>April</td><td>11.4</td><td>Bioactive</td><td>2.2</td><td>0.4</td><td>25.9</td><td>2.6</td><td>49.8</td><td>6.6</td><td>17.6</td><td>2.6</td></tr><tr><td>May</td><td>0.2</td><td>Bioactive</td><td>4.5</td><td>1.8</td><td>40.0</td><td>6.1</td><td>104.2</td><td>17.0</td><td>35.7</td><td>6.3</td></tr><tr><td>May</td><td>0.9</td><td>Bioactive</td><td>4.1</td><td>1.2</td><td>40.3</td><td>4.9</td><td>84.7</td><td>11.0</td><td>34.3</td><td>5.1</td></tr><tr><td>May</td><td>3.5</td><td>Bioactive</td><td>2.8</td><td>0.6</td><td>55.6</td><td>9.1</td><td>81.1</td><td>14.0</td><td>47.4</td><td>9.1</td></tr><tr><td>May</td><td>10.5</td><td>Bioactive</td><td>2.2</td><td>0.5</td><td>33.9</td><td>5.3</td><td>48.6</td><td>8.1</td><td>18.5</td><td>5.5</td></tr><tr><th colspan="10"></th></tr><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2"></th><th colspan="2">R<sub>ads-abiot</sub></th><th colspan="2">R<sub>prec-abiot</sub></th><th colspan="2">R<sub>gross-diss-abiot</sub></th><th rowspan="2"></th><th rowspan="2"></th></tr><tr><th>=R<sub>up-abiot</sub> * f<sub>ads-abiot</sub></th><th>=R<sub>up-abiot</sub> * (1-f<sub>ads-abiot</sub>)</th><th>=R<sub>diss-abiot</sub> + R<sub>up-abiot</sub></th><th>=R<sub>diss-abiot</sub> + R<sub>up-abiot</sub></th></tr><tr><td>April</td><td>0.2</td><td>Abiotic</td><td>0.6</td><td>0.3</td><td>2.1</td><td>0.5</td><td>96.9</td><td>5.1</td><td></td><td></td></tr><tr><td>April</td><td>1.0</td><td>Abiotic</td><td>1.3</td><td>0.3</td><td>5.5</td><td>0.7</td><td>86.2</td><td>6.6</td><td></td><td></td></tr><tr><td>April</td><td>3.8</td><td>Abiotic</td><td>0.9</td><td>0.3</td><td>3.8</td><td>0.4</td><td>37.9</td><td>2.9</td><td></td><td></td></tr><tr><td>April</td><td>11.4</td><td>Abiotic</td><td>0.5</td><td>0.1</td><td>8.2</td><td>0.3</td><td>35.8</td><td>1.8</td><td></td><td></td></tr><tr><td>May</td><td>0.2</td><td>Abiotic</td><td>1.7</td><td>0.8</td><td>4.3</td><td>1.4</td><td>68.6</td><td>10.0</td><td></td><td></td></tr><tr><td>May</td><td>0.9</td><td>Abiotic</td><td>3.0</td><td>1.2</td><td>6.0</td><td>1.3</td><td>58.7</td><td>5.5</td><td></td><td></td></tr><tr><td>May</td><td>3.5</td><td>Abiotic</td><td>0.9</td><td>0.2</td><td>8.2</td><td>0.5</td><td>42.3</td><td>3.7</td><td></td><td></td></tr><tr><td>May</td><td>10.5</td><td>Abiotic</td><td>0.9</td><td>0.3</td><td>15.4</td><td>1.3</td><td>47.9</td><td>4.3</td><td></td><td></td></tr></tbody></table>

Experimentally determined parameters include the net DSI dissolution rate (Supplementary Method 1), net uptake rates (Supplementary Method 2), and the adsorbed fraction (Supplementary Method 3) of

<footnote n="32">³²Si quantified on particles at the 120 h time point for bioactive (top rows) and abiotic (bottom rows) treatments. Calculated parameters reported are described in the Supplementary Method 3 (Equations 1–7) and include the absolute adsorption rate, the total precipitation rate, gross dissolution rate (accounting for biotic and abiotic uptake), and the microbially mediated precipitation rate. The algorithm used for their calculation is shown below each term. Note: Solid Solution Ratios (SSR) in g l⁻¹. Reported rates in x10⁻³ µmol g⁻¹ h⁻¹. Rates of experimentally determined parameters (dissolution, uptake) are obtained from the slopes of the linear regression of the measured parameter vs time. R² obtained from the regression and St. Error is the error of the slope. St. Errors in calculated parameters obtained with the error propagation functionality in MAPLE³³.</footnote>
<table><caption>Table 2 | Budget calculations for sampled SSR (0.2–11.4 g l⁻¹) and extrapolations to 270 g l⁻¹ for April and May experiments</caption><thead><tr><th rowspan="3">Cruise</th><th colspan="5">Si Budget</th><th rowspan="3">Precipitation Ratio (microbially-mediated:abiotic)</th><th rowspan="3">Gross Dissolution Ratio</th></tr><tr><th rowspan="2">SSR</th><th colspan="2">Dissolution</th><th colspan="2">Precipitation (microbially mediated)</th><th rowspan="2">Adsorption</th></tr><tr><th>(% diss-bio)</th><th>(% prec-abiot)</th><th>(% prec-mm)</th><th>(% ads-bio)</th></tr></thead><tbody><tr><td>April</td><td>0.2</td><td>86.8</td><td>2.7</td><td>6.5</td><td>4.1</td><td>2.43</td><td>0.80</td></tr><tr><td>April</td><td>1.0</td><td>81.4</td><td>10.2</td><td>5.1</td><td>3.3</td><td>0.50</td><td>0.63</td></tr><tr><td>April</td><td>3.8</td><td>66.5</td><td>7.7</td><td>19.3</td><td>6.5</td><td>2.50</td><td>1.29</td></tr><tr><td>April</td><td>11.4</td><td>43.6</td><td>16.5</td><td>35.4</td><td>4.5</td><td>2.14</td><td>1.39</td></tr><tr><td>May</td><td>0.2</td><td>57.2</td><td>4.1</td><td>34.3</td><td>4.4</td><td>8.26</td><td>1.52</td></tr><tr><td>May</td><td>0.9</td><td>47.6</td><td>7.1</td><td>40.4</td><td>4.8</td><td>5.69</td><td>1.44</td></tr><tr><td>May</td><td>3.5</td><td>28.0</td><td>10.1</td><td>58.5</td><td>3.5</td><td>5.79</td><td>1.92</td></tr><tr><td>May</td><td>10.5</td><td>25.8</td><td>31.7</td><td>38.0</td><td>4.4</td><td>1.20</td><td>1.01</td></tr><tr><td colspan="8" style="text-align: center;">Average</td></tr><tr><td colspan="8" style="text-align: center;">Average</td></tr><tr><td colspan="8" style="text-align: center;">1.25</td></tr><tr><td colspan="8" style="text-align: center;">3.6</td></tr><tr><th colspan="8" style="text-align: left; text-decoration: underline;">Experimental</th></tr><tr><td>Average % (April, May)</td><td>11</td><td>34.7</td><td>24.1</td><td>36.7</td><td>4.5</td><td>1.52</td><td></td></tr><tr><td>Average Rate</td><td></td><td>17.1</td><td>11.8</td><td>18.1</td><td>2.2</td><td></td><td></td></tr><tr><th colspan="8" style="text-align: left; text-decoration: underline;">Extrapolations</th></tr><tr><td colspan="8" style="text-align: center;">%prec-mm:%prec-abiot</td></tr><tr><td>April</td><td>270</td><td>40.6</td><td>13.6</td><td>41.0</td><td>4.7</td><td>3.01</td><td></td></tr><tr><td>May</td><td>270</td><td>15.4</td><td>16.7</td><td>61.9</td><td>6.0</td><td>3.71</td><td></td></tr><tr><td>Average %</td><td></td><td>28.0</td><td>15.2</td><td>51.4</td><td>5.4</td><td>Average</td><td></td></tr><tr><td>Average Rate</td><td></td><td>7.4</td><td>4.0</td><td>13.7</td><td>1.4</td><td>3.36</td><td></td></tr></tbody></table>

For the sampled SSR (in g l⁻¹) the proportions of dissolution, precipitation (abiotic and microbial mediated) and adsorption are derived from rates in Table 1 as percentages of R<sub>gross-diss-bio</sub>, along with the ratio of precipitation and gross dissolution rates for microbially-mediated and bioactive, respectively, compared to abiotic factors. We also present separately the Si budget for the average experimental data (April, May) with the highest SSR (Average SSR = 11 g l⁻¹) used in Fig. 5 (in parentheses the average calculated rates in x10⁻³ µmol g⁻¹ h⁻¹). For the extrapolations to SSR = 270 g l⁻¹ the various Si components (dissolution, abiotic precipitation, microbially mediated precipitation, adsorption) are obtained as percentages of Si<sub>gross-diss-bio</sub> from the calculated Si values (simple dissolution, bioactive and abiotic uptake) shown in Table 1, Supplementary Method 4 and Supplementary Fig. 10. We used the biotic adsorption fraction (f<sub>ads-bio</sub>) from Table 1 at the highest SSR for the corresponding month (i.e., f<sub>ads-bio</sub> = 0.08 and 0.06 for April and May respectively) to obtain %Si<sub>ads-bio</sub> (i.e., %Si<sub>ads-bio</sub> = (Si<sub>up-bio</sub> + f<sub>ads-bio</sub>)/Si<sub>gross-diss-bio</sub>). Similarly for the calculation of %Si<sub>prec-abiot</sub> we used the abiotic adsorption fraction (f<sub>ads-abiot</sub>) at the highest SSR for the corresponding month (i.e., f<sub>ads-abiot</sub> = 0.05 and 0.06 for April and May respectively). In the case of the extrapolated May Si budget, we capped the Si<sub>gross-diss-bio</sub> = 1498 µM (i.e., amorphous silica solubility at 15 °C³¹) instead of the Si<sub>gross-diss-bio</sub> = 1966 µM obtained from the extrapolated red line in Supplementary Fig. 10b which falls in the area of spontaneous amorphous silica precipitation. For the extrapolations we present in parentheses the average rates in x10⁻³ µmol g⁻¹ h⁻¹.

demonstrates a quantitatively dominant, and directly quantified, microbial role in the precipitation of silica in marine river-plume sediment suspensions. This conclusion reflects conditions specific to these experimental setups and may not fully capture the variability present in natural systems.

A silica budget was also constructed using all MRP experimental data and for the simulated surface pore water diagenetic conditions with SSR = 270 g l⁻¹ (Table 2). For the average data from the high SSR experiments in April and May (Average SSR = 11 g l⁻¹), 24.1% of recycled silica reprecipitates into neoformed phases abiotically, 36.7% reprecipitates through microbial mediation, 4.5% is adsorbed onto particles, and 34.7% is released to solution after six days (Fig. 5b, Table 2). The average extrapolated data (SSR = 270 g l⁻¹) show that 15.2% of recycled silica reprecipitates into neoformed phases abiotically, 51.6% reprecipitates through microbial mediation, 5.4% is adsorbed onto particles, and 28% is released to solution after six days. The extrapolated budget is based on the assumption that amorphous silica solubility defines the maximum DSi concentration achievable in pore waters. (Fig. 5c, Table 2). Both the experimentally derived and extrapolated budgets consistently highlight the predominance of microbially mediated silica precipitation. We postulate that this mediation is driven by enhanced Si dissolution and reprecipitation, as well as the production of substrates during the microbial life cycle and metabolism. These substrates can serve as initial nucleation and deposition sites for Si-rich precipitates, thereby facilitating the removal of silica from the solution (Fig. 5a). In the higher SSR experiments and simulated surface sediments, the microbial effect on precipitation is estimated to be 1.5x to 3.4x greater than the abiotic component (Table 2). Furthermore, R<sub>gross-diss-bio</sub> >

R<sub>gross-diss-abiot</sub> for the May experiment (all treatments and SSR), the two highest SSR's during April, and all the extrapolated simulations (Table 2), suggest that higher microbial activity may also promote higher rates of gross silica dissolution. This is akin to the critical role microbes play in mediating bSiO₂ dissolution in the water column², which we deduce has been masked in sediments by the lack of resolution obtained with traditional analyses (e.g. net DSi analysis) as opposed to application of a sensitive tracer (e.g. ³²Si) that can resolve two-way exchange between porewater and particulates.

Discussion

These results strongly support that microbial communities exert a quantitatively significant control on silica cycling rates in dilute sediment suspensions (e.g., resuspended sediments, fluid muds) and our projections suggest this may be similar at higher SSR. Such an effect may be more significant during early diagenetic silica cycling in deltaic conditions (e.g., CDSF, MRP, Amazon plume). The neoformed phases incorporating recycled silica are suggested to include Al, Si, and K (Fig. 6, Supplementary Note 1). Given prior work, such neoformed phases may be similar to the most common authigenic phases of marine sediments⁶,⁷,³² and phases associated with microbial surfaces in terrestrial environments¹⁵,¹⁶. In Amazon deltaic sediments, authigenic aluminosilicates range from amorphous-poorly crystalline to crystalline phases with the characteristics of illite (~10.0 Å, K-rich)⁶,³². Common substrates for authigenesis could include detrital particles, biosiliceous particles (e.g., diatoms, consistent with recent Si stable isotope analyses²³ and electron microscopy⁷), external microbial cells, and other microbially-produced
<figure><img src="image_9.png" /><figcaption>**Fig. 5 | Conceptual hypothesized model of microbial effects on silica cycling and average ³²Si budget in marine sediments.** a The conceptual model (not to scale) depicts microbial effects on particle silica dissolution and reprecipitation of authigenic phases as disseminated particles and coatings on various detrital and biological substrates as a function of increasing SSR in marine sediments. Low SSR simulates sediment resuspension or fluid mud conditions, whereas high SSR simulates surface sediments with a corresponding increase in sedimentary microbial abundances and sediment mass. Dissolution and reprecipitation of authigenic phases are accompanied by the release and incorporation of Si, Al, Fe, K, Mg, etc. as</figcaption></figure>

evidenced by the transient production of dissolved Al and significant uptake of K from solution in these experiments (Fig. 6). Microbes are hypothesized to facilitate the dissolution and reprecipitation processes through organic matter degradation, formation of metabolic products and changing sediment pH or redox conditions.

b Average ³²Si budget for both MRP experiments directly measured in incubations after six days (SSR = 11 g l⁻¹). c Average ³²Si budget for both MRP experiments extrapolated to simulated surface pore water conditions after six days (SSR = 270 g l⁻¹). Si budget data and calculations shown in Table 2, Supplementary Method 4 and Supplementary Fig. 10.

components (e.g., EPS, sheaths, capsules) as illustrated in a hypothesized conceptual model (Fig. 5a)²⁰,³³. Accordingly, we propose that in recently deposited sediments (i.e., higher SSR conditions compared to fluidic muds) the enhanced sediment microbial activity promotes silica neoformation (Fig. 5a). Microbially enhanced silica neoformation, as indicated by our experimental trajectories, appears to regulate DSi concentrations originating from the dissolution of detrital and biogenic silica particles⁷,²³, preventing them from reaching amorphous silica saturation levels (Fig. 3c, d); we surmise that this may provide an additional physiological benefit by preventing cells from becoming encrusted in solid amorphous silica. It has also been suggested that microbial mediated authigenesis in terrestrial environments provides a source of exchangeable inorganic nutrients to microbes, stimulates the respiration of bacteria by maintaining growth-suitable environmental pH, and protects the living cells from toxin-producing microorganisms and predation by grazing protozoans¹⁵.

Facilitating porewater Si uptake may be an evolutionary adaptation for microbes inhabiting sediment suspension environments where detrital mineral and bSiO₂ dissolution elevates DSi (also inferred in ref. 20). Given the organisms likely involved, this adaptation could have ancient origins, as modern marine sediment pore water DSi concentrations range

at least an order of magnitude, ~100–1000 µmol l⁻¹ ³⁴, and are similar in magnitude to concentrations inferred in the pelagic ocean between 200 and 600 million years ago, i.e., ~500–1000 µmol l⁻¹ ³⁵,³⁶. This aligns with evidence of an ancient role for cyanobacteria in the marine silica cycle based on Si accumulation observed in marine *Synechococcus*³⁷⁻³⁹. We show quantitatively significant microbially enhanced silica neoformation occurs in aerobic (tested directly here) environments and suggest this needs to be further studied in anaerobic (e.g., see refs. 18,20) environments. While prior work has interpreted a microbial effect considering the microbial fossil record²⁰, we purport this process has significant implications for rate processes among biogeochemical cycles and the distribution of all major and trace elements involved in authigenic mineral formation. Authigenic reactions have been shown to be important sinks for K, Mg, Li, Ge, and alkalinity³²,⁴⁰⁻⁴⁴, and appear to promote climatic stability and regulate variations in elemental cycling over long time scales⁹,⁴⁵,⁴⁶. Furthermore, the microbially-mediated sequestration of Si may reduce the effectiveness of proposed geoengineering solutions that aim to increase atmospheric CO₂ uptake through artificial silica weathering in the marine environment⁴⁷, as microbial reactions which promote authigenic reactions would also yield increased CO₂. Authigenic Si reactions are among the most enigmatic processes
<figure><img src="image_10.png" /><figcaption>**Fig. 6 | Metal and cation cycling in the bioactive MRP May experiments.**
a Dissolved Al at three sampling times (t = 0, 72, and 144). Dissolved Al increased as a function of time until t = 72 h, then was completely removed from solution by t = 144 h. b Dissolved K relative to initial conditions (K:K₀). For SSR = 10.45 g l⁻¹,</figcaption></figure>

decreased between t = 72 and t = 144 h in the bioactive experiments. The decrease is statistically significant at 0.05 significance level (One way Anova: DF = 8, F value = 16.9, p = 0.003). Error bars: a Standard error of the mean from 3 replicates. b Propagated standard error of the mean from 3 replicates).

a The discovery that microbial mediation of silica precipitation significantly influences Si cycling rates in surface marine sediments represents a major step toward demystifying authigenic Si reactions and potentially providing insights into related reverse weathering processes.

# Materials and methods

## Incubations with Congo Deep Sea Fan sediments-varying sites

Sediment samples were collected with a multicorer from the Congo Deep Sea Fan²⁴. The studied area (Sites A, C, F) was in the active part of the terminal lobe complex (channel-levee). This terminal lobe complex is connected to the Congo canyon and extends 750 km across the continental slope, rise, and the abyssal plain. Another study area (Site E) is on an older lobe complex 45 km to the north of the recent lobe. Here the feeding channel is connected to active channel-levee but was abandoned after the avulsion of the active channel to the south (to feed the active lobe complex).

Initial exploratory incubations were conducted with near-surface sediment from multicore deployments at various sites of the Congo Deep Sea Fan. In Site A (depth 4765 m), multicores were collected from an active levee (Col-A-MTB13). In Site C (depth 4950 m) with the most recent sedimentation events, the multicore was collected from an active levee (Col-C-MTB6). Undisturbed levee sediments were samples in Site F (depth 4886 m) (Col-F-MTB5).

Cores were subsampled in a cold room (4 °C) under an inert atmosphere and sectioned at intervals of 0.5 cm (0–2 cm), 1 cm (2–10 cm), and 2 cm (below 10 cm depth). After centrifugation and extraction of pore waters, sediment samples were stored frozen at –20 °C until the incubation experiments. Sediments were thawed and wet sediment was weighed. Two parallel incubations per sediment sample were run: a) incubations in filtered seawater b) incubations in filtered sea pattern water with 5% glutaraldehyde as a biocide (acting as abiotic controls). Glutaraldehyde was used due to institutional regulations prohibiting the use of HgCl₂. Final incubation slurries had solid-solution ratios (SSR) ranging from 1 to 17 g l⁻¹ among the three sites sampled. Twelve-day incubations were conducted at room temperature (~20 °C) under aerobic conditions and constant stirring (Fig. 1a, Supplementary Fig. 1a). Stirring ensures the resupply and uniform distribution of oxygen while preventing concentration gradients in sediments and solutes. We obtained 13 subsamples from each experiment, bi-daily until day 4 and every 24–48 hours through day 12. Samples were syringe filtered (0.2 µm pore) and analyzed for DSi by colorimetry (e.g. see below).

## Incubations with Congo Deep Sea Fan sediments-increasing solid solution ratio

A second set of laboratory incubations was carried out at a different institution with archived frozen surface sediment from Col-A-MTB13 (0–10 cm). Slurries were prepared with thawed wet sediment and filtered seawater in 500 ml polypropylene bottles; SSR was varied between ~0.6 and ~4.5 g l⁻¹. Saturated HgCl₂ (2.2 ml total) was added three times during the incubations: (a) 0.2 ml of at the beginning of the incubations (b) 1 ml at day 7 (c) 1 ml at day 21. Incubations were carried with constant stirring (orbital shaker) in the dark at 15 °C in a temperature-controlled room for ~138 days (Supplementary Fig. 12); data presented are mainly for the first 18 days (Fig. 1b, Supplementary Fig. 1b). Subsamples were collected at 2, 6, 12, 24 h and then every 1–3 days. Ammonium was measured periodically to ensure active heterotrophy was taking place in the bioactive incubations (Supplementary Fig. 13). Subsamples were filtered and analyzed for DSi by colorimetry. As an estimate of uncertainty of DSi measurements we used % relative standard deviation= 4.4%. This is based on an expanded standard uncertainty calculated by multiplying the standard deviation of repeated measurements (n = 30) by 2.77⁴⁹.

## Incubations with Mississippi River Plume sediments- increasing solid solution ratio and addition of isotope tracer

All surface sediment samples in the MRP were collected using a MC-800 multicorer system (Ocean Instruments). Surface sediments (upper 1 cm) were homogenized and used fresh (within hours of collection) for experiments while at sea aboard the R/V Pelican in April and May 2017. Because of the added radioisotope, and local government regulations of mixing with other hazardous waste, ZnCl₂ solution (1% final concentration) was used as the biocide (as in prior studies⁵⁰⁻⁵⁴) instead of glutaraldehyde or HgCl₂ as in the CDSF experiments, and samples were buffered at pH 7 in 15 mmol l⁻¹ 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES, high purity grade). Experimental units were subsampled (7 ml triplicates per treatment and time point) from a 250 ml master bottle for each SSR (four levels) and treatment (bioactive, abiotic) into 15 ml plug seal polypropylene centrifuge tubes. To each 15-ml tube with a slurry, 155 Bq of ³²Si(OH)₄ (104 Bq (µg Si)⁻¹) was added. All tubes were capped, placed on a shaker table, and incubated at 15 °C for 6 days in the dark. There was 8 mL of headspace in each tube (filled with ambient air) and tubes were vented to oxygenate twice daily. Three tubes per treatment and SSR were sacrificially sampled per time point.

³²Si samples were processed with a common workflow to determine activity in solution and particles along with DSi concentration. ³²Si in solution was quantified using liquid scintillation counting⁵⁵, particulate
activity was quantified using gas-flow proportional counting, optimized for low signals⁵⁶, and DSi was quantified using a manual colorimetric molybdate reaction and spectrophotometric analysis⁵⁶. All errors among direct measurements displayed in Fig. 3 and Supplementary Figs. 2–8 represent standard error of the mean from triplicate measurements.

### ³²Si Particle adsorption and Si adsorption fraction

In our experimental approach we assume total Si uptake was the sum of particle-adsorbed Si and silica precipitated into neoformed solid phases. To quantify total Si uptake, we directly measured the total ³²Si associated with filtered particles (subsample of 7 mL volume); for a second subsample of the filtered particulates, we quantified particle-adsorbed Si by measuring the ³²Si activity liberated into solution after a 1 h leach in 0.0025 M CaCl₂ at room temperature (reviewed in ref. 57). In all experiments, particle adsorption of Si constituted a smaller part of the total particle uptake, and ranged from 30% in April live experiments (SSR = 0.2 g l⁻¹) to ~6% in May experiments (SSR = 10.5 g l⁻¹). Importantly, at higher SSRs, precipitation of Si into neoformed phases comprised >90 % of total Si uptake (Supplementary Figs. 4c, 5c). ³²Si particle adsorption varied as follows for bioactive experiments: 10%, 9%, 5% and 6% at SSR= ~0.2, ~0.9, ~3.5, ~10.5 g l⁻¹, respectively. For the same SSRs, the average ³²Si particle adsorption in abiotic treatments was: 28%, 33%, 10% and 6%, respectively (Table 1).

### Temperature of incubations vs sediment temperatures

The temperatures used in this study (15 and 20 °C) broadly align with the estimated trend of converging temperatures (13–17 °C) below 2 m in simulations of nearshore sediments⁵⁸. These simulations reveal that, while sediment temperatures in the upper 2 m experience significant seasonal fluctuations driven by variations in interconnected sea surface and sediment surface temperatures, the deeper sediment layers (down to 15 m) exhibit more stable thermal conditions, converging to approximately 15 °C.

### Statistics

All error propagation estimates in calculated parameters are obtained with the error propagation functionality in MAPLE (ScientificErrorAnalysis package)⁵⁹.

In Congo Deep Sea fan data we used % relative standard deviation =4.4% as an estimate of uncertainty for DSi measurements. This is based on an expanded standard uncertainty calculated by multiplying the standard deviation of repeated measurements (n = 30) with a coverage factor= 2.77⁴⁹. Error bars shown in Supplementary Fig. 1 represent propagated error as % Relative Standard Deviation=6.6%, derived from the initial uncertainty estimate of % RSD = 4.4%.

Congo Deep Sea fan DSi:DSi₀ data in Supplementary Fig. 1. were fitted in Origin Pro⁶⁰ with a two-phase exponential growth function with time constant parameters: y = y₀ + A1*e⁻⁰.⁰¹ + A2* e⁻⁰.⁰². The fitted datasets for each set of bioactive and abiotic incubation were compared with an F test (“compare data set” functionality in OriginPro⁶⁰) and found to be statistically different at the α = 0.01 significance level.

In Mississippi river plume, all data values represent averages of triplicate measurements. For measured parameters (DSi, ³²Si) the error bar for each point is the standard error of mean (triplicate). For all calculated parameters (e.g., DSi:DSi₀, Solution Specific Activity), the error bars were obtained from propagated standard error of the mean. In Fig. 3, regressions were fit to each experimental dataset (32 raw data points) with the Allo-metric 1 (i.e., power law, y=axᵇ) model in OriginPro⁶⁰.

Mississippi DSi:DSi₀ data in Supplementary Fig. 2 were fitted in Origin Pro⁶⁰ with a one-phase exponential growth function with time constant parameter: y = y₀ + A1* e⁻⁰.⁰¹. The fitted datasets for each set of bioactive and abiotic incubations were compared with an F test (“compare data set” functionality in OriginPro⁶⁰) and found to be statistically different at the α = 0.05 significance level.

### Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

### Data availability

CDSF - The entire dataset is deposited in ZENODO (10.5281/zenodo.15783056). MRP - Data is available at the Biological and Chemical Oceanography Data Management Office under project 712667.

Received: 15 January 2024; Accepted: 23 October 2025;

Published online: 28 November 2025

### References

1. Tréguer, P. J. et al. Reviews and syntheses: The biogeochemical cycle of silicon in the modern ocean. *Biogeosciences* **18**, 1269–1289 (2021).

2. Bidle, K. D. & Azam, F. Accelerated dissolution of diatom silica by marine bacterial assemblages. *Nature* **397**, 508–512 (1999).

3. Lehtimäki, M., Sinkko, H. & Tallberg, P. The role of oxygen conditions in the microbial dissolution of biogenic silica under brackish conditions. *Biogeochemistry* **129**, 355–371 (2016).

4. Aller, R. C. in *Treatise on Geochemistry (Second Edition)* (eds Heinrich D. Holland & Karl K. Turekian) 293–334 (Elsevier, 2014).

5. DeMaster, D. J. in *Treatise on Geochemistry (Second Edition)* (eds Heinrich D. Holland & Karl K. Turekian) 103–111 (Elsevier, 2014).

6. Michalopoulos, P. & Aller, R. C. Early diagenesis of biogenic silica in the Amazon delta: alteration, authigenic clay formation, and storage. *Geochimica et Cosmochimica Acta* **68**, 1061–1085 (2004).

7. Presti, M. & Michalopoulos, P. Estimating the contribution of the authigenic mineral component to the long-term reactive silica accumulation on the western shelf of the Mississippi River Delta. *Continental Shelf Res.* **28**, 823–838 (2008).

8. Rahman, S., Aller, R. C. & Cochran, J. K. The Missing Silica Sink: Revisiting the Marine Sedimentary Si Cycle Using Cosmogenic 32Si. *Glob. Biogeochemical Cycles* **31**, 1559–1578 (2017).

9. Isson, T. T. & Planavsky, N. J. Reverse weathering as a long-term stabilizer of marine pH and planetary climate. *Nature* **560**, 471–475 (2018).

10. Isson, T. T. et al. Evolution of the Global Carbon Cycle and Climate Regulation on Earth. *Glob. Biogeochemical Cycles* **34**, e2018GB006061 (2020).

11. Van Cappellen, P. & Qiu, L. Biogenic silica dissolution in sediments of the Southern Ocean. I. Solubility. *Deep Sea Res. Part II: Topical Stud. Oceanogr.* **44**, 1109–1128 (1997).

12. Dixit, S., Van Cappellen, P. & van Bennekom, A. J. Processes controlling solubility of biogenic silica and pore water build-up of silicic acid in marine sediments. *Mar. Chem.* **73**, 333–352 (2001).

13. Ferris, F. G., Fyfe, W. S. & Beveridge, T. J. Bacteria as nucleation sites for authigenic minerals in a metal-contaminated lake sediment. *Chem. Geol.* **63**, 225–232 (1987).

14. Konhauser, K. O., Schiffman, P. & Fisher, Q. J. Microbial mediation of authigenic clays during hydrothermal alteration of basaltic tephra, Kilauea Volcano. *Geochem., Geophysics, Geosystems* **3**, 1–13 (2002).

15. Konhauser, K. O. & Urrutia, M. M. Bacterial clay authigenesis: a common biogeochemical process. *Chem. Geol.* **161**, 399–413 (1999).

16. Urrutia, M. M. & Beveridge, T. J. Formation of fine-grained metal and silicate precipitates on a bacterial surface (Bacillus subtilis). *Chem. Geol.* **116**, 261–280 (1994).

17. Kawano, M. & Tomita, K. Microbial biomineralization in weathered volcanic ash deposit and formation of biogenic minerals by experimental incubation. *Am. Mineralogist* **86**, 400–410 (2001).

18. Chen, Y. et al. Biomineralization mediated by anaerobic methane-consuming cell consortia. *Sci. Rep.* **4**, 5696 (2014).

19. Ta, K. et al. Hydrothermal nontronite formation associated with microbes from low-temperature diffuse hydrothermal vents at the
South Mid-Atlantic Ridge. *J. Geophys. Res.: Biogeosciences* **122**, 2375–2392 (2017).

20. Osorio-Rodriguez, D. et al. Microbially induced precipitation of silica by anaerobic methane-oxidizing consortia and implications for microbial fossil preservation. *Proc. Natl. Acad. Sci.* **120**, e2302156120 (2023).

21. Rabouille, C. et al. Carbon and silica megasink in deep-sea sediments of the Congo terminal lobes. *Quat. Sci. Rev.* **222**, 105854 (2019).

22. Ghaisas, N. A., Maiti, K. & Roy, A. Iron-Mediated Organic Matter Preservation in the Mississippi River-Influenced Shelf Sediments. *J. Geophys. Res.: Biogeosciences* **126**, e2020JG006089 (2021).

23. Pickering, R. A. et al. Using Stable Isotopes to Disentangle Marine Sedimentary Signals in Reactive Silicon Pools. *Geophys. Res. Lett.* **47**, e2020GL087877 (2020).

24. Rabouille, C. et al. The Congolobe project, a multidisciplinary study of Congo deep-sea fan lobe complex: Overview of methods, strategies, observations and sampling. *Deep Sea Res. Part II: Topical Stud. Oceanogr.* **142**, 7–24 (2017).

25. Pastor, L. et al. Early diagenesis in the sediments of the Congo deep-sea fan dominated by massive terrigenous deposits: Part III – Sulfate- and methane- based microbial processes. *Deep Sea Res. Part II: Topical Stud. Oceanogr.* **142**, 139–150 (2017).

26. Gorski, C. A. & Fantle, M. S. Stable mineral recrystallization in low temperature aqueous systems: A critical review. *Geochimica et Cosmochimica Acta* **198**, 439–465 (2017).

27. Slagter, S. et al. Silicon isotope exchange in silica biomineralizers. *Geochimica et Cosmochimica Acta* **405**, 1–14 (2025).

28. Fanning, K. A. & Pilson, M. E. Q. The lack of inorganic removal of dissolved silica during river-ocean mixing. *Geochimica et Cosmochimica Acta* **37**, 2405–2415 (1973).

29. Reide Corbett, D., McKee, B. & Allison, M. Nature of decadal-scale sediment accumulation on the western shelf of the Mississippi River delta. *Continental Shelf Res.* **26**, 2125–2140 (2006).

30. Murrell, M. et al. Gulf of Mexico Hypoxia Research Program Data Report 2002–2007 (2013).

31. Kamatani, A. & Riley, J. P. Rate of dissolution of diatom silica walls in seawater. *Mar. Biol.* **55**, 29–35 (1979).

32. Michalopoulos, P. & Aller, R. C. Rapid Clay Mineral Formation in Amazon Delta Sediments: Reverse Weathering and Oceanic Elemental Cycles. *Science* **270**, 614 (1995).

33. Moore, K. R. et al. A new model for silicification of cyanobacteria in Proterozoic tidal flats. *Geobiology* **19**, 438–449 (2021).

34. Gallinari, M., Ragueneau, O., Corrin, L., DeMaster, D. J. & Tréguer, P. The importance of water column processes on the dissolution properties of biogenic silica in deep-sea sediments I. Solubility. *Geochimica et Cosmochimica Acta* **66**, 2701–2717 (2002).

35. Conley, D. J. et al. Biosilicification Drives a Decline of Dissolved Si in the Oceans through Geologic Time. *Front. Marine Sci.* **4**, 397 (2017).

36. Siever, R. in *Scientists in Gaia*. (ed S. H. Boston Schneider, P. J.) 285–295 (MIT Press).

37. Baines, S. B. et al. Significant silicon accumulation by marine picocyanobacteria. *Nat. Geosci.* **5**, 886–891 (2012).

38. Brzezinski, M. A. et al. Patterns and regulation of silicon accumulation in Synechococcus spp. *J. Phycol.* **53**, 746–761 (2017).

39. Krause, J. W. et al. Picoplankton contribution to biogenic silica stocks and production rates in the Sargasso Sea. *Glob. Biogeochemical Cycles* **31**, 762–774 (2017).

40. Mackenzie, F. T. & Garrels, R. M. Chemical mass balance between rivers and oceans. *Am. J. Sci.* **264**, 507–525 (1966).

41. Higgins, J. A. & Schrag, D. P. Constraining magnesium cycling in marine sediments using magnesium isotopes. *Geochimica et Cosmochimica Acta* **74**, 5039–5053 (2010).

42. Misra, S. & Froelich, P. N. Lithium Isotope History of Cenozoic Seawater: Changes in Silicate Weathering and Reverse Weathering. *Science* **335**, 818 (2012).

43. Baronas, J. J., Hammond, D. E., Berelson, W. M., McManus, J. & Severmann, S. Germanium–silicon fractionation in a river-influenced continental margin: The Northern Gulf of Mexico. *Geochimica et Cosmochimica Acta* **178**, 124–142 (2016).

44. Sun, X., Higgins, J. & Turchyn, A. V. Diffusive cation fluxes in deep-sea sediments and insight into the global geochemical cycles of calcium, magnesium, sodium and potassium. *Mar. Geol.* **373**, 64–77 (2016).

45. Dunlea, A. G., Murray, R. W., Santiago Ramos, D. P. & Higgins, J. A. Cenozoic global cooling and increased seawater Mg/Ca via reduced reverse weathering. *Nat. Commun.* **8**, 844 (2017).

46. Higgins, J. A. & Schrag, D. P. The Mg isotopic composition of Cenozoic seawater – evidence for a link between Mg-clays, seawater Mg/Ca, and climate. *Earth Planet. Sci. Lett.* **416**, 73–81 (2015).

47. Bach, L. T., Gill, S. J., Rickaby, R. E. M., Gore, S. & Renforth, P. CO2 Removal With Enhanced Weathering and Ocean Alkalinity Enhancement: Potential Risks and Co-benefits for Marine Pelagic Ecosystems. *Front. Clim.* **1**, 7 (2019).

48. Krissansen-Totton, J. & Catling, D. C. Constraining climate sensitivity and continental versus seafloor weathering using an inverse geological carbon cycle model. *Nat. Commun.* **8**, 15423 (2017).

49. Ellison, S. L. & Williams, A. Eurachem/CITAC guide: quantifying uncertainty in analytical measurement. *LGC, Teddington* (2012).

50. McCarthy, M., Newell, S., Carini, S. & Gardner, W. Denitrification Dominates Sediment Nitrogen Removal and Is Enhanced by Bottom-Water Hypoxia in the Northern Gulf of Mexico. *Estuarie Coasts* **38**, 2279–2294 (2015).

51. Steingruber, S., Friedrich, J., René, G. & Wehrli, B. Measurement of Denitrification in Sediments with the 15N Isotope Pairing Technique. *Appl. Environ. Microbiol.* **67**, 3771–3778 (2001).

52. Bonaglia, S. et al. Effect of reoxygenation and Marenzelleria spp. bioturbation on Baltic Sea sediment metabolism. *Mar. Ecol. Prog. Ser.* **482**, 43–55 (2013).

53. Thamdrup, B. & Dalsgaard, T. Production of N2 through Anaerobic Ammonium Oxidation Coupled to Nitrate Reduction in Marine Sediments. *Appl. Environ. Microbiol.* **68**, 1312–1318 (2002).

54. Sloth, N. P., Blackburn, H., Hansen, L. S., Risgaard-Petersen, N. & Lomstein, B. Nitrogen Cycling in Sediments with Different Organic Loading. *MAR. ECOL.-Prog. SER. - MAR ECOL-PROGR SER 116*, 163–170 (1995).

55. Krause, J. W. et al. Silicic acid limitation drives bloom termination and potential carbon sequestration in an Arctic bloom. *Sci. Rep.* **9**, 8149 (2019).

56. Krause, J. W., Brzezinski, M. A. & Jones, J. L. Application of low-level beta counting of 32Si for the measurement of silica production rates in aquatic environments. *Mar. Chem.* **127**, 40–47 (2011).

57. Sauer, D., Saccone, L., Conley, D., Herrmann, L. & Sommer, M. Review of methodologies for extracting plant-available and amorphous Si from soils and aquatic sediments. *Biogeochemistry* **80**, 89–108 (2006).

58. Yang, G.-B. et al. Development of a Sea-Sediment Coupled Model Incorporating Ocean Bottom Heat Flux. *J. Phys. Oceanogr.* **52**, 3331–3348 (2022).

59. Maplesoft (Maplesoft, Waterloo, Ont., 2019).

60. Origin Pro (Version 2022b) (OriginLab Corporation, Northampton, MA, 2022).

## Acknowledgements

P.M. benefited from initial discussions with Dr. Olivier Ragueneau on materials and methods for this study. For the MRP experiments, we thank the J. Lehrter for field support, and S. Acton, I. Marquez, D. Wiik, M. Taylor, C. Bates, and D. Cole for laboratory and logistic support. This work was funded by the European Union's Horizon 2020 grant # 739562 (to P.M.); United States National Science Foundation OCE 1558957 (to J.W.K., K.M.); United States National Science Foundation grants OCE 1438648 and OCE
1948914 (to M.T.); French National Research Agency (ANR) Blanc SIMI5-6, no.11BS56030 (to C.R.).

**Author contributions**

P.M., J.W.K. conceived, developed (with R.A.P.), interpreted the data and wrote the paper. P.M., J.W.K., R.A.P., E.R., R.C. performed the analyses. K.M. provided additional data for the analysis. R.A.P., E.R., K.M., M.T., C.R., R.C. provided conceptual and/or editorial input to improve the manuscript. All authors reviewed and approved the manuscript.

**Competing interests**

The authors declare no competing interests.

**Additional information**

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s43247-025-02941-7.

**Correspondence** and requests for materials should be addressed to Panagiotis Michalopoulos or Jeffrey W. Krause.

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