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Overall, the characterizations detailed above point out that the contact between the bottom of the macroborings and the shells of the bivalves likely have represented a hotspot of microbial activity, which was not observed elsewhere at the surface of the siltstone devoid of macroborings. In the next two sections, we discuss how the association between Lignopholas fluminalis and microorganisms may have acted symbiotically to facilitate boring in siltstone.
A possible strengthening of the mechanical abrasion thanks to microbial EPS
As mentioned above, macroborings resulting from bioerosion are most often observed either in calcareous rocks, which are highly sensitive to bioweathering, or in soft substrates such as peat or clays, which are readily drilled through bioabrasion. Here, the siltstone is both chemically much more resistant than carbonates and harder than the substrates commonly subjected to bioabrasion.
Bolotov et al.8 have reported that the mean hardness of the siltstone was 62 kgf mm−2, i.e., twice as much as clayey materials. In comparison, the compilation of Yang et al.17 indicates that the hardness of Bivalvia shell is an order of magnitude lower than that of quartz, and only slightly greater than that of the siltstone, ranging between 110 and 270 kgf mm−2. In addition, both the structure and hardness of the siltstone were found to be homogeneous, such that it is unlikely that Lignopholas fluminalis took advantage of any local weakness to bore into the rock. Finally, the macroboring walls did not exhibit any marks, as opposed to the experimental results obtained by Nederlof and Muller10 using the piddock Barnea candida, which is a close relative of Lignopholas fluminalis8. However, such scrap marks resulting from the abrasion of the substrate by the denticles of the piddocks were obtained by rotating the shells in a soft materials (wax). The bioabrasion ability of the shells of Barnea candida is thought to be limited to soft substrata such as clays or peat and most likely, they cannot abrade harder substrata such as chalk10.
Similarly, we argue that the various features collected here suggest that there is no clear evidence that the direct contact between the shells of Lignopholas fluminalis and the substrate is responsible for the bioabrasion of the siltstone. Instead, single grains excavated from the borehole partly remained trapped at the surface of the shells, embedded into an organic matrix that we interpreted as a biofilm. It can reasonably be assumed that these single grains, which are essentially hard minerals such as quartz and feldspars, acted like abrasive materials that contributed to drill the siltstone through the rotation of the shells. Therefore, the presence of microorganisms in the interfacial region between the substrate and the borers possibly strengthened their boring ability, although at that point, it remains impossible to state whether this interaction is obligatory or facultative. In any case, from a mechanical standpoint, Lignopholas fluminalis bivalves likely took advantage of the biofilm attached to the surface of their shells to increase their boring ability.
Enhancing the weakening of the rocks through microbially-induced weathering
In addition to bioabrasion, some macroborers are also known for their ability to promote bioweathering5. This mechanism of bioerosion is suggested to be limited to calcareous substrates and not significant for substrates such as siltstones, whose rock-forming minerals have a dissolution rate that is between 6 and 8 orders of magnitude lower than that of calcite at circum-neutral pH conditions (according to rate data from18 for quartz19, for albite20, for chlorite and21 for calcite).
Notwithstanding, we argue that mass transfer did occur during the process of boring discussed in the present study. We detail below the reasons why we think that this mass transfer cannot result from the abiotic dissolution of the grains by the bulk fluid, and suggest that microorganisms were responsible for the dissolution of the siltstone, which ultimately facilitated the formation of borings by Lignopholas fluminalis.
The strongest evidence for mass transfer is the occurrence of secondary Mn-rich crystals found embedded in an organic matrix at the bottom of the macroborings. Because such minerals were not found elsewhere in the rock sample, this finding indicates that the contact region between the bivalves and the siltstone was not simply mechanically eroded, but also chemically weathered. The source of Mn is most likely chlorite, which represents the richest source of Mn among the rock-forming minerals (0.2 to 0.7 wt% according to quantitative EDX analyses). In addition, the location of the minerals (specifically embedded in the organic matrix) indirectly suggests that microbes were responsible for the dissolution of chlorite. This latter assertion can be further supported by comparing the residence time of a chlorite grain at the bottom of a pit to the time required to dissolve chlorite with a bulk aqueous fluid:
First, several studies estimated the lifespan of bivalve piddocks of the family Pholadidae (to which Lignopholas fluminalis belongs) to be on the order of 10 years22. The deepest macroborings that we observed, possibly corresponding to the oldest bivalves, were on the order of 1 cm, leading to a mean erosion rate of Rerosion = 1 mm yr−1.
Second, the grain size of the siltstone is comprised between 0.2 and 50 µm, with an average value around Ø = 10 µm8. The average time (t) required for a 10-µm grain to be excavated from the bottom of the pit and released to the environment can thus be estimated following:

$$t= O cdot {{R}_{erosion}}^{-1}$$
(1)

yielding t = 10–2 year. This value indicates that Mn must be efficiently released from chlorite over a time interval as short as 10–2 year (~ 3.7 days) to be incorporated into secondary minerals.
Finally, the radial retreat (∆h) of a hypothetical spherical grain of chlorite dissolved over a time interval of 3.7 days, can be calculated using:

$$Delta h= frac{M}{rho }{R}_{chlorite} cdot t$$
(2)

where M , ρ, and Rchlorite stand for the molar mass, the density and the dissolution rate of chlorite, respectively. Considering the rate data from Lowson et al.20, the far-from-equilibrium dissolution rate of chlorite at room temperature and circum-neutral pH conditions can be estimated to be on the order of 10–17 mol cm−2 s−1. Considering a typical value of ρ = 3.0 g cm−3 for chlorite and a molar mass of M = 697 g mol−1, ∆h is on the order of 0.1 Å, i.e., much less than an atomic monolayer at the chlorite surface. These crude calculations illustrate that Mn mobilization through the dissolution of chlorite with a circum-neutral pH fluid is highly unlikely. Therefore, an alternative mechanism to explain this mass transfer requires the existence of a microenvironment with greater weathering properties, such as that provided by microbial biofilm.
Several studies have demonstrated that microenvironments can be generated at the silicate-microbe contact23, where the local conditions in terms of pH and saturation state strongly differ from the bulk conditions24,25, with the development of surface biofilms further intensifying this effect through hydraulic decoupling26. Although the large-scale impact of chemical compounds secreted by microbes on silicate weathering rates remains an open and controversial question (e.g.27,28,29,30), several studies showed that chemically aggressive conditions (low pH, high concentration of organic acids) can result in a significant increase of silicate weathering rates, at least locally25,31. Here, an increase of the dissolution rate of chlorite by up to two orders of magnitude would have been required to get an appreciable release of Mn. According to the dissolution rate law developed by Lowson et al.20, such an increase can be reached if the local pH conditions in the vicinity of chlorite are on the order of 3, a value that is fully compatible with pH measured in some microbial biofilms in previous studies24.
The microorganisms are the major catalysts of manganese cycling in the natural environment32 and manganese is a micronutrient essential for the development of microbial communities, for which rocks represent the main source33. As such, it might have been targeted by microbes for several reasons, which include Mn oxidation by chemolithoautotrophs32,33,34 or incorporation as enzyme cofactor35.
One can wonder whether (i) the borers specifically targeted areas where microbes were already thriving at the surface of the siltstone and actively dissolving the crystals, or (ii) whether attachment of macroborers was a prerequisite to the establishment of microbial communities dissolving the siltstone. Supporting the first assertion, a few studies have proposed that microborings supposedly attributed to microbial weathering (e.g.,36) might weaken rocky substrates, eventually facilitating the subsequent drilling of microborings by bivalves14. However, all occurrence of silicate microborings that we are aware of dealt with volcanic rocks and more specifically, pre-fissured basalt glass15,36,37. As a matter of fact, our multiscale investigation of the rock substrate did not reveal the presence of any tubular microchannels, and biofilms were not observed anywhere other than in macroborings. As a consequence, we speculate that a nascent bioabrasion of the substrate by the bivalves was required to allow for the establishment of microbial communities and trigger the onset of microbial weathering. Supporting this assertion, freshwater mussels are known to concentrate limiting nutrients such as C, N and P in the benthos and stimulate biofilm growth (38 and references therein). In turn, microbially-induced rock weathering likely contributed to a greater dissolution along grain boundaries, ultimately facilitating grain detachment and rock-boring by Lignopholas fluminalis. Of note, this mechanism would be the biotic equivalent of the abiotic erosion and weathering of limestone39.
To conclude, our study sheds new light on the possible mechanisms of silicate bioerosion by macroborers. On the one hand, we suggest that microorganisms likely benefited from the early stages of siltstone drilling by macroborers to thrive at the bottom of macroborings. On the other hand, we provide evidence that microbes contributed to bioerosion by actively dissolving minerals, while hard minerals (quartz and feldspars) trapped in biofilms at the surface of the shells further facilitated the development of macroborings via mechanical abrasion. Therefore, the association between Lignopholas fluminalis and microbes has the main characteristics of what is commonly defined as a symbiotic action. Finally, this finding also raises three main concluding remarks:
(i)
In addition to the increase in macrofaunal diversity previously reported7, the development of macroborings also likely contributed to an unexpected increase of microbial diversity that remains largely unexplored;

(ii)
Our study underlines that preventive strategies to mitigate bioerosion might have to target on suppression of bacterial biofilm development in order to achieve effective solutions;

(iii)
Finally, although the contribution of microbes to silicate weathering at large space and time scales remains unknown and debated, the present study suggests that this impact is far from negligible when coupled to macroborers in what appears as a symbiotic relation. As suggested here, such microbial communities may contain specific microorganisms with efficient weathering-ability, which would be worth investigating to possibly identify efficient bioinspired strategies of silicate weathering, of prime importance for a range of industrial and societal concerns including CO2 sequestration. More

Experimental design
To enable simultaneous assessment of depth-resolved gross rates of light-driven sulfide consumption and O2 production, as well as the fate of freshly produced dissolved organic carbon (DOC), we sampled a cyanobacterial mat without the underlying sediment from the Frasassi sulfidic springs in September 2012 (Fig. S2). The mat was placed in a flow chamber that accommodated sufficient area for microsensor measurements and sub-sampling of the mat during defined conditions (Fig. S3) that are detailed in the following sections. The incubation started with exposure to darkness for 8 h. 13C-bicarbonate solution was added to the water column and to a spring water reservoir underneath the mat after ~5.5 h. During the following stepwise increase of light intensity (7, 19, 89, and 315 µmol photons m−2 s−1), net and gross rates of AP and OP were continuously monitored using microsensors in three replicate spots of the mat. Light intensity was only increased after a steady state had established for at least 30 min (determined from concentration depth profiles). Triplicate subsamples (1 cm2) of the mat were taken in regular intervals over the course of the experiment to (1) determine bulk rates of inorganic carbon assimilation, (2) identify the functional groups involved in this 13C assimilation based on fatty acids (FA), (3) follow the flow of assimilated carbon into the 13C-DOC pool, and (4) monitor changes in the active community based on 16S rRNA sequencing. To be able to differentiate between the effect of light intensity and photosynthetic O2 production, after exposure to 315 µmol photons m−2 s−1, DCMU (3-(3,4-dichlorophenyl)-1,1-dimethylurea; dissolved in ethanol), an inhibitor of OP [24], was added to the water column in the dark to a final concentration of ~10 µM. The mat was then again exposed to 315 µmol photons m−2 s−1 for 8 h. In a second incubation run with fresh mat material DCMU was added in the beginning, before addition of 13C-bicarbonate.
Sampling and setup
The cyanobacterial mat forms along the flow path of “Main Spring” that emerges from the Frasassi cave system (Fig. S2, 43°24′4″N, 12°57′56″E, [23]). The day before first mat sampling, water column samples for total sulfide determination were collected and conserved in 2% zinc acetate solution. Concentration was assessed on the same day according to Cline [25]. O2 concentration and pH were determined using microsensors (see below). Temperature at the mat surface was measured with a PT1000 mini-sensor (Umweltsensortechnik, Geschwenda, Germany). Spring water was collected from the outflow of main spring and transported to the laboratory facilities of the Osservatorio Geologico di Coldigioco (~45 min driving time) and immediately prepared for use in the flow chamber.
The flow chamber was a larger version of what is described in [26] (Fig. S3). Briefly, the upper part of the flow chamber was separated from a bottom chamber using fibrous web and GF/F filters. The bottom chamber was filled with HEPES-buffered (pH 7.2) spring water that was then purged with N2 using needles penetrating the rubber stoppers on the wall of the chamber. The upper flow chamber was connected with tubing via five inlets to a water pump in a thermostated 20 L recycle of freshly sampled N2-bubbled spring water.
The following day, a 30 × 40 cm piece of mat was carefully lifted off the sediment, transferred into a plastic container, and transported cooled and in the dark to the laboratory. A small subsection of the mat was flash-frozen for 16S rRNA analysis on site. Upon arrival in Coldigioco, the mat was immediately placed onto the GF/F filters in the flow chamber. Neutralized Na2S was slowly added to the 20 L recycle of the flow cell. After ~6 h of dark incubation, 12C- and 13C-sodium bicarbonate (13C-DIC final atom fraction of ≈6%) were injected into the bottom chamber and briefly stirred. Subsequently, 12C- and 13C-sodium bicarbonate (13C-DIC final atom fraction of ≈6%) was added to the recycle. To allow for homogeneous distribution of the label, the pumping speed was increased for 5–10 min. To minimize outgassing of H2S and exchange of 13CO2 with the atmosphere, the spring water in the 20 L recycle was covered with paraffin oil and the water column in the flow cell was covered with transparent plastic wrap. Small holes were kept in the wrap to allow microsensor measurements. Immediately after bicarbonate addition, the first mat and water column samples were taken. Homogenous illumination was achieved by using two large cold-white lamps (Envirolite), the distance of which to the mat was adjusted to change light conditions. Incident irradiance at the mat surface was determined using a cosine‐corrected quantum sensor connected to a LI‐250A light meter (both LI‐COR Biosciences GmbH, Germany).
Microsensors
O2, H2S, and pH microsensors with a tip diameter of 10, 20, and 50 µm, respectively, and response time of More

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Study site
Our study site is the species-rich sub-alpine meadows located in the eastern part of the Qinghai-Tibetan plateau, Hezuo, China (34°55′N, 102°53′E) with mean elevation approximately 3000 m above sea level. Although the Tibetan Plateau Monsoon and Asian Monsoon28 brings rain, the study region has cold and dry climate, with mean annual temperature of 2.4° C and mean annual precipitation of just 530 mm23. The vegetation is dominated by herbaceous species such as Elymus nutans Griseb (Poaceae), Kobresia humilis (C.A. Mey.) Serg. (Cyperaceae) and Thermopsis lanceolata R. Br. (Fabaceae)23. Human impacts include agricultural exploitation and pastoralism are the primary current land use, which in places have caused serious land degradation. In response, local governments have stopped further agricultural exploitation and constructed fences to restrict livestock grazing. These efforts gave rise to successional chronosequences, such as the ones we use in our study.
We identified a chronosequence of fields that had been undisturbed for 4-, 6-, 10-, 13-, and 40-years (the control)19,23. All our sample sites, except for the control meadows, had been used for agriculture to grow highland barley in the recent past, with cessation of cultivation within the last 4–13 years. The time since last agricultural use was determined by interviews with local farmers. There are 1–10 km apart among the five meadows and all meadows possessed comparable topographic characteristics (e.g., orientation and slope), soil types and climate (Fig. 1A). This chronosequence is one of the same chronosequence in our previous work23 and we have observed that species richness increased from 61 to 82 species during succession, with 50 species sharing among all five successional meadows. Species composition was similar between 4-year and 6-year meadows, with 60 species sharing between these two meadows. Similar patterns were found in late successional meadows, with 70 species shared among 10-year, 13-year and undisturbed meadows.
Figure 1

Location map of our study sites and our quadrat sampling design. (A) locations of five sites representing each of the five successional ages (4-, 6-, 10-, 13-year and undisturbed grassland), (B) the 30 0.5 × 0.5 m2 quadrats sampling design in each of the five successional meadows. The map of Fig. 1A was obtained from Google Earth online version (https://earth.google.com/, access on 12/10/2018). Figure labels on the map were added using Google Earth online toolkit and text labels using Windows image processing software Paint.

Full size image

Field sampling
The vegetation in each field was sampled in August 2013. An area of 120 × 120 m2 was randomly selected in each meadow. Within this area, thirty 0.5 × 0.5 m2 quadrats were regularly arranged in six parallel transects, with 20 m intervals between each two adjacent quadrats (detail please see Fig. 1B). To determine species richness and abundances, in each quadrat we recorded all the aboveground ramets and identified them to species.
To determine aboveground biomass, we removed all the ramets in each quadrat and took them to the laboratory, where they were oven-dried at 100℃ for 2 days and then weighed. Productivity is typically the amount of carbon fixed per unit time, not standing biomass. Here we follow methods of previous diversity–productivity studies in grasslands29,30, which have used aboveground biomass as proxy for productivity.
Functional trait data collection
We quantified the carbon economy of leaves by measuring specific leaf area (SLA, cm2 g−1). We quantified light capture strategy via photosynthesis rate (A, u mol−1). We estimated resistance to abiotic stress via leaf proline content (Pro, mg/kg), seed mass (SM, g) and seed germination rate (SG, %). Importantly, the functional traits for the same species at each successional age separately if they occurred in multiple meadows were measured to ensure that successional age-related intraspecific variation was appropriately incorporated into our analyses. All functional traits were determined as described in our previous work19,22,23 and the detailed procedures were given in the Supplementary Material.
Statistical methods
First, we compared variation during successional change in the proportion of total biomass for the three main functional groups of plants: forbs (dominant in early succession), legumes, and graminoids (both dominant in later succession) to check whether there are significant turnovers in the dominant plant taxa from early to late succession. Then, we used Spearman correlation analysis to quantify whether significantly positive correlations between empirical species diversity (S, numbers of species richness per square meters) and productivity (aboveground biomass per square meters, P) can be observed in each successional meadow.
For each of the five functional traits (SLA, A, Pro, Sm, and SG), we calculated two functional diversity indices: the community-weighted mean (CWM) and functional diversity (FD) represented by Rao’s quadratic entropy (RaoQ).
The two indices were calculated as follows:

$$ {CWM} = sumlimits_{i = 1}^{n} {p_{ij} times t_{ij} } $$
(1)

where pij is the relative abundance of the species i in each 0.5 × 0.5 m2 quadrat j, and tijis the mean trait value of the species i in each successional meadow j.

$$ RaoQ_{i} = sumlimits_{i = 1}^{n} {sumlimits_{i = 1}^{n} {p_{i} times p_{k} times d_{ik} } } $$
(2)

where pi and pkare the relative abundance of species i and k in each 0.5 × 0.5m2 quadrat j respectively and dik is the dissimilarity coefficient based on Euclidean distance between two species i and k in the multivariate trait space of each successional meadow j.
Then, a variance partitioning analysis was used to test the relative contributions of species richness, the CWM and FD represented by RaoQ of these five traits to productivity in each successional meadow. We also used variance partitioning to allocate changes in productivity in each successional meadow arising from four complementary components: (a) variation explained by species richness, (b) variation explained CWM of each of the five traits, (c) variation explained by FD of each of the five traits only, and (d) “unexplained variation”31. Across all successional meadows, species richness, and aboveground biomass, CWM and FD of all five traits (SLA, A, Pro, SM, and SG) were strongly right-skewed, so we log-transformed species richness, and aboveground biomass, CWM and FD of all five traits to meet the assumption of normality required by variance partitioning. At each successional meadow, variance partitioning was done using the function of “varpart” in “vegan” package in R32. All analyses above were performed in R (R Core Team 2019). More

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