Microbiome manipulation and enhanced weathering stimulate CO2 removal in reforestation

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Socioeconomic and biophysical limits on the efficacy of nature-based carbon dioxide removals (such as reforestation) mean that the natural carbon sequestration capacity of forests should be maximized, wherever reforestation is implemented. Here we report on a large-scale (11.5 ha) field trial testing co-deployment of two strategies to increase forest carbon capture: modification of the soil microbiome, and enhanced rock weathering (ERW) via addition of crushed silicate rock. Individual monitoring of 6,400 trees over three years revealed that individual saplings grew 7% larger, on average, when inoculated with soils from nearby mature forest. Meanwhile, the ERW treatment augmented aboveground carbon stocks by 27% and elevated plant tissue nutrients. We conclude that co-deploying early-stage reforestation with microbial enrichment or ERW can increase forest carbon sequestration by 69–159 kg C ha − 1 in the first three years post-planting. Earth and environmental sciences/Ecology/Restoration ecology Earth and environmental sciences/Biogeochemistry/Carbon cycle Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation afforestation carbon sequestration enhanced rock weathering mycorrhiza soil microbiome reforestation restoration rewilding trees Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Global mean temperature will likely breach the internationally agreed 1.5°C warming limit sometime in the next decade. 1 Limiting future warming requires both steep CO 2 emissions reductions, and removal of past emissions. 2 Atmospheric carbon dioxide removal (CDR) via carbon sequestration in plants and soils (i.e., ‘natural climate solutions’) represents a low cost, scalable suite of CDR strategies that can address the climate and biodiversity crises in tandem. 3–6 However, realising the full potential of natural climate solutions is challenging, in part due to ecological and practical constraints. 7 Across the broad portfolio of natural climate solutions for CDR, reforestation can achieve the highest rates of carbon removal. 8 In practice, however, forest restoration projects frequently fail to sequester significant amounts of carbon due to operational challenges at multiple scales, as well as the increasing severity of disturbances that re-release fixed CO 2 , including droughts, fires and pest outbreaks. 9 Moreover, planted trees often have extremely high mortality, leading to restoration failure. 10 Due to limited monitoring of restoration outcomes on the ground, we have scant information on the drivers of seedling mortality 11 but these are likely to include climatic stress, 12 lack of appropriate microbial symbionts, 13 herbivory, and competition with resident plants. 14,15 High mortality and poor tree growth reduce the capacity of forests to reach their full carbon sequestration potential, and this together with albedo feedbacks 16 may strongly limit the climate impact of reforestation programmes. 2,15 Restoring the soil microbiome alongside the tree community may represent a cost-effective way to reduce tree sapling mortality and augment ecosystem carbon sequestration. 17,18 Trees depend on a diverse community of root-associating soil fungi and bacteria, which promote their growth directly (as in the case of mycorrhizal fungi or rhizobia) or indirectly (e.g., by suppressing pathogens). Yet when trees are planted on former agricultural land which has been repurposed to forestry, active restoration of the soil community is largely neglected, leading to delayed and incomplete recovery of soil microbiota. 19 The majority (97%) of restoration projects aimed at ‘rewilding’ an ecosystem focus on macroscopic organisms, and few have monitored or manipulated microbial communities. 20 This suggests that there are important, unleveraged opportunities for improving tree seedling/sapling survival via the soil microbiome. For example, planted tree saplings grow larger in association with the mycorrhizal fungi characteristic of mature forests, rather than those fungal communities of early successional or scrub habitat they are planted into. 21 Yet colonisation of saplings by mycorrhizal fungi from nearby remnant forest patches is inefficient, suggesting direct inoculation with the desired microbial species may be necessary to accelerate tree growth and recovery. 22 Indeed, past restoration efforts in heathlands, tall grass prairies, and degraded mining lands have all shown significant acceleration of ecosystem recovery when an active microbial restoration approach is employed. 17,23,24 Given the limited land available for reforestation 25 , and the climatic and biotic limitations on the survival and growth of planted trees, co-deployment of reforestation and another land-based CDR technology, enhanced rock weathering (ERW), may offer opportunities for increasing rates of carbon capture for a given footprint of land. 26,27 ERW involves amending soils with crushed silicate rocks which, in the process of weathering, generate dissolved inorganic carbon in the form of bicarbonate. This inorganic carbon may ultimately be transported via rivers and groundwaters to the coastal ocean, with a residence time of 1,000 years or more. 28 Conveniently, large amounts of crushed rock already exist as a byproduct of quarrying operations. 29 Enhanced rock weathering scenarios typically assume cropland soils are amended with crushed silicate rock (e.g. basalt), because the infrastructure for large-scale application already exists in agricultural settings, and because of the potential for ERW-related yield increases. 30 However, to date, the potential for co-deployment of ERW projects in newly planted forests (where similar infrastructure is available for ground preparation over large areas) has not been investigated. Moreover, given the direct involvement of tree mycorrhizas in soil mineral weathering processes, ERW may be accelerated in forests compared to croplands. 31 Finally, depending upon their mineral composition, crushed silicate rocks could act as a tree fertiliser, stimulating organic carbon sequestration in wood as well as inorganic carbon storage in the oceans. Consistent with this hypothesis, a catchment-scale temperate forest field trial in New Hampshire, USA, showed a single application (3.4 t ha -1 ) of the silicate wollastonite enhanced tree carbon uptake by 2.5 t C ha -1 over 10 years 32 . Here, we report on a large-scale field trial to rigorously quantify the potential for soil microbial enrichment and ERW to enhance CDR rates in reforestation projects, focusing specifically on carbon capture in organic pools. We planted over 25,000 trees in a 11.5 ha former sheep pasture in Wales, UK, and monitored 6,400 of them individually for three years (2021 – 2023) in teams of researchers and citizen scientists. Microbial enrichment was implemented by adding a small amount of soil (hereafter, ‘inoculum’) from local, mature forests of similar tree composition to the roots of each planted tree. ERW was implemented by amending the soil surface with crushed basaltic andesite, a byproduct from operations at a nearby quarry. Soil and rock grains were applied to monospecific stands of Sitka spruce ( Picea sitchensis, a common exotic commercial timber species in the UK) and to multi-species stands of the native broadleaf species alder ( Alnus glutinosa ), aspen ( Populus tremula ), downy birch ( Betula pubescens ) , wild cherry ( Prunus avium ), sessile oak ( Quercus petraea ), and rowan ( Sorbus aucuparia ) . Forest type, soil inoculum, and ERW treatments were implemented in a fully factorial design ( Fig 1 ), with each treatment combination replicated across eight experimental blocks. To our knowledge, this study represents the largest and most highly replicated field study examining the impacts of ERW and whole-soil inoculation on near-term reforestation outcomes. Results and Discussion Here we show significant positive impacts of microbiome enrichment and ERW on tree growth at the individual scale, and on aboveground carbon storage at the plot scale. Our results highlight the critical role of soil microbial communities in governing restoration outcomes. They also suggest that combining two CDR technologies (reforestation and ERW) holds promise for enhancing ecosystem carbon uptake, further bolstering the case for forest restoration as a natural climate solution. Enriching the soil microbiome enhances the growth of some tree species We found that the soil inoculum treatment significantly enhanced tree growth (Table 1 , Extended Data Table S1 ), increasing sapling biomass by 6.6% on average (Fig. 2 a), but with substantial variation among species and through time (Fig. 3 a, c). All species benefited from soil inoculum addition, but observed growth enhancement ranged from 1.2% in alder to 13.1% in oak (Fig. 3 c). Temporal variation in the inoculum effect was even more dramatic: on average, inoculated saplings grew 28.6% more than uninoculated controls in 2021, but only 3.4% more by 2023 (Fig. 3 a). The magnitude of tree growth response to soil inoculum addition is remarkable, especially since we did not optimise the source of the inoculum to maximise positive outcomes (e.g., by performing preliminary experiments in a glasshouse). The impact of the single soil inoculum addition on tree growth diminished through time, possibly due to changes in the composition of microbial communities (which we only measured once), or perhaps due to reduced competition from weeds in the second and third years of the experiment (glyphosate was not applied in the first year after planting, but was added in subsequent years). However, it is likely that the early growth boost provided by the microbiome enrichment treatment will have long-lasting impacts on forest dynamics. One way to quantify these time-lagged impacts is to explore treatment impacts on tree mortality. In general, mortality rates were low. Of the 6,400 trees surveyed, 295 died in 2021, 66 died in 2022, and 40 died in 2023, for a total three-year mortality rate of 6.3% - and this rate varied significantly by species but not by treatment ( Table S2, S3 ). Yet after controlling for species identity, individual tree size at the 2021 census was a significant predictor of three-year mortality (hazard ratio = 1.023, z = -8.08, p 84 g in 2021) were 87% less likely to die by 2023, when compared to trees in the first and second quartiles (< 55 g in 2021) ( Table S4 , Fig. 4 ). Therefore, the growth boost provided by inoculation – 30.5 g in the first year – is sufficient to buffer the trees from this excess mortality. For this reason, microbiome enrichment impacts in the first 12 months, when newly planted trees are most vulnerable, probably had disproportionate impacts on survival and growth in the longer term. When we summed biomasses of all trees within each plot, and assumed 50% of tree biomass is carbon, we found that inoculated plots stored an extra 95 kg C ha − 1 , although this was not significant due to lower statistical power at the plot scale (Table 2 , Fig. 2 b). Moreover, the species that tended to benefit most from the inoculum treatment tended to be more slow-growing in general, and therefore made a lesser contribution to plot-level biomass stocks. While the magnitude of these plot-level carbon sequestration rates is relatively modest when considered in isolation, they are substantial considering these stands are in the earliest stages of forest recovery, and the trees themselves are less than 5 years old. Ongoing monitoring will determine if this early stimulation of tree growth translates to meaningful changes in ecosystem carbon removal rates as the forest matures. Microbial mechanisms underlying ecosystem responses to microbiome enrichment There was significant heterogeneity in the way different species responded to microbiome enrichment. Understanding this variation is key to predicting how best to deploy microbial inoculum as a management tool, both to modify the composition of restored plant communities and to maximize forest carbon capture. There was strong interspecific variation in tree growth responses to inoculation, ranging from no response to > 10% growth boosts, raising the possibility that microbiome enrichment could affect plant community composition – and therefore, stand-level carbon uptake – in the longer term. For example, pot-based studies generally show that arbuscular mycorrhizal tree species benefit less from the presence of microbiomes cultivated by conspecifics, whereas ectomycorrhizal species show strong positive growth responses to such microbes. 33 Consistent with this pattern, in our experiment, tree species forming arbuscular mycorrhizae (cherry, rowan) had a less positive response to inoculation than the ectomycorrhizal species (aspen, birch, oak) (Fig. 3 c). The ectomycorrhizal and N-fixing alder also showed a less positive response. These findings could result from ectomycorrhizal trees being more likely to benefit from inoculation with forest soils, or because source soils were depauperate in arbuscular mycorrhizae and/or nitrogen-fixing species. Soil inoculation significantly altered the composition of soil fungal communities ( Table S5 ), with 39 saprotrophic, 15 ectomycorrhizal, and 45 taxa associated with other/unknown guilds identified as indicator species for microbiome enrichment ( Figure S1 ). However, the microbiome enrichment treatment did not alter the proportion of ectomycorrhizal fungal taxa detected in the soil ( Table S6, Figure S2 ). It may be that shifts in ectomycorrhizal fungal species identity, rather than abundance, drove the growth gains in aspen, birch, and oak trees. Moreover, the soil eDNA analysis we performed cannot distinguish active vs. dormant microbes and does not necessarily provide insight on root colonisation by fungi. ERW stimulates carbon uptake at the ecosystem (whole-plot) scale We found that the two additions of crushed rock enhanced individual tree growth by 8.5% (Table 1 , Table S1 , Fig. 2 a), although this effect was suppressed when rock grains was applied together with soil inoculum (Fig. 2 a). Most species responded to the crushed silicate rock amendment similarly, with a tendency towards more positive responses by N-fixing species (Fig. 3 d). The effect size was stable over time (Fig. 3 a), translating to an extra 10 g of biomass per sapling in the first year, and 74 g per sapling by the third year. As a result, by 2023, aboveground carbon stocks in each plot (calculated by summing individual tree biomasses) were significantly and markedly (26.5%) greater in ERW plots (Table 2 ) : the ERW amendment generated an extra 159 kg ha − 1 of carbon capture in wood by three years post-planting (Fig. 2 b). This response shows no sign of diminishing through time, suggesting the absolute amount of carbon sequestered into tree biomass due to ERW may increase as the forest ages. In fact, a positive wood growth response to ERW was observed in a mature forest amended with wollastonite, 32 suggesting that ERW could increase organic carbon sequestration into tree biomass of older forest stands. Biogeochemical mechanisms underlying ecosystem responses to ERW We hypothesized that accelerated tree growth in the ERW plots was due to improved mineral nutrition. Consistent with this expectation, plant tissue stocks of Ca and Cu were elevated in ERW broadleaf plots by 33 and 44%, respectively, whereas tissue stocks of Mn and Cd were depleted ( Table S7 ). These patterns largely reflect changes in foliar concentrations of the corresponding elements in most broadleaf tree species ( Table S8 ). Ecosystem (i.e., plot-level) stocks of plant N and P tended to increase in all plots that received crushed basaltic andesite ( Table S7 ), despite no change in foliar concentrations of these elements ( Table S8 ). Thus, ERW plots sustained higher tree productivity without diluting concentrations of N or P in their biomass, suggesting greater plot-scale nutrient uptake, but conserved tissue stoichiometry at the individual tree level. ERW could stimulate tree nutrient uptake in several ways: by increasing soil pH to improve bioavailability of the existing soil nutrient capital, and/or by acting as a slow-release mineral fertiliser. We found that application of crushed rock increased soil pH (F 1,172 = 16.24, p < 0.001). A year after deployment, soil pH in control plots was 5.30 ± 0.04 versus 5.65 ± 0.05 in rock-grain amended plots, although pH values did not differ between the plots prior to crushed rock addition (Fig. 3 b). At pH < 6, bioavailability of N, P, K, Ca, and Mg is reduced, although the effect is strongest for phosphorus. 34 Therefore, the bulk soil pH increase could have increased plant bioavailability of these nutrients. As N and P were not present in the crushed basalt, the increases in plant tissue N and P we observed likely arose from increased uptake. Weathering of crushed basaltic andesite likely contributed to the observed increase in the tree biomass Ca pool. It contained up to 14% rapidly weathering calcite (CaCO 3 ) that readily dissolves in the acidic soil environment to release calcium ions; Ca released via weathering of feldspar is far slower. Although albite was the chief feldspar detected in semi-quantitative analyses, XRD data suggest the presence of Ca-bearing labradorite and/or anorthite. Declines in foliar Cd and Mn, meanwhile, may reflect immobilization of these elements in the clays. We therefore conclude that ERW improved tree nutrition through direct release of Ca as the basaltic minerals weathered, and through increases in soil pH. Our findings suggest that the effects of ERW on organic carbon sequestration in other forested sites will depend strongly on soil properties. Implications for forest restoration efforts We have demonstrated that straightforward restoration interventions can have positive consequences for forests in the earliest stages of woodland restoration. Inoculation of trees with whole forest soils significantly increased tree growth in the first year. Interspecific variation in the magnitude of benefits points to potentially long-lasting effects of microbial inoculum on the species composition and carbon uptake capacity of regenerating woodlands. Application of crushed silicate rock to drive inorganic CDR via chemical weathering also stimulates carbon uptake in tree biomass. This suggests additional opportunities for the deployment of ERW technologies, which at present are mainly focused on croplands. 29 , 35 , 36 Although the benefits of one soil addition and two crushed rock additions were not additive, employing either treatment stimulated carbon stocks by 69–159 kg C ha − 1 in the first three years post-planting. Moreover, we have yet to quantify how ERW affects the full suite of organic and inorganic carbon cycle processes that could further impact ecosystem-level greenhouse gas removal, e.g. changes in soil respiration, organo-mineral carbon stabilisation, and alkalinity production in drainage waters. Our results suggest that applying both treatments together does not accelerate tree growth; by contrast, the benefits of ERW were negated when amended together with soil inoculum. This occurred in direct opposition to our prediction that a larger and more active mycorrhizal biomass in the microbiome enrichment treatment should accelerate ERW rates. 31 The antagonistic effects of the two treatments could arise because ERW and microbial inoculum provide redundant functions – once plant nutrient limitation is relieved via direct fertilisation, the presence of beneficial microbiota may not provide additional benefit (and vice versa). Indeed, there is a large body of work to suggest that fertilization with inorganic nutrients suppresses plant investment in mycorrhizae. 37 , 38 It also possible that ERW (or its associated biogeochemical effects, like soil alkalization) suppressed the growth of beneficial microbes present in the soil inoculum. Interactions between microbiome enrichment and ERW influenced the composition of fungal communities after 1 year ( Table S5 ), suggesting this might be the case. Because ERW and microbiome enrichment effects were antagonistic, our data suggest that afforestation practitioners will likely choose one intervention or the other to maximise ecosystem carbon capture in organic pools. By the third year post-planting, ERW and microbiome enrichment provided similar gains in terms of aboveground biomass. However, the treatments differ in several important respects. The microbiome enrichment treatment had the largest impact in the first-year post-planting. It may therefore be the most appropriate choice in restoration contexts where early-stage survival rates are low, e.g. on highly degraded soils. It was also more effective for ectomycorrhizal tree species, and further work is required to identify whether this pattern is generalisable across sites. By contrast, the ERW treatment accelerated tree nutrient uptake, and its effectiveness could depend on soil nutrient status and pH, as well as the weatherability of the basaltic andesite feedstock being used. The feasibility of re-application is also a concern for both interventions; we do not know if other methods of applying inoculum (e.g., in a slurry or spray) would be as effective as applying whole soils directly to tree roots, which can only be done during planting. Similarly, re-applying rock grains to a mature forest plantation requires resolving technical challenges that are not insurmountable. 39 Both microbiome enrichment and ERW are associated with their own carbon footprints and with some degree of risk, especially if they are to be deployed over large scales. Sourcing large quantities of whole-soil inoculum may damage donor habitats and thwarts the goal of ecological restoration. 20 , 40 Donor sites must also be carefully chosen to avoid introduction of pathogens, and also of non-native microbial species or genotypes, which could be invasive. 41 However, the development of soil inoculation methods is still in the earliest stages, and there are many potential paths to develop scalable technology. For example, ‘bioengineering’ inoculum could minimise the requirement for raw material (i.e. donor soil) – this is a priority area for future research. 42 Similarly, application of crushed silicate rock also poses poorly quantified ecological risks (e.g., those associated with altered pH and chemistry of soil and surface water) 43 and has complex effects on net ecosystem carbon sequestration. 50 ,5152, 53 A full characterisation of the carbon cycle responses to ERW is ongoing at Glandwr. Conclusions We urgently require ecologically responsible strategies to maximise carbon sequestration on the land that is available for reforestation. This is the first study to test two promising interventions for enhancing nature-based CDR - soil microbial ‘rewilding,’ and forest-based ERW - at a large spatial scale and with a high degree of replication. We report significant increases in ecosystem carbon storage across a large site with a significant degree of environmental and soil heterogeneity, highlighting the intrinsic potential of these treatments. Our data further suggest that both soil properties and microbial communities will dictate how well trees respond to soil inoculum addition and ERW. Therefore, there is a pressing need for comparable field trials to be conducted at scale across multiple soil types and climates, building an evidence base practitioners can use to optimise their results. Methods Study site and experimental design The experiment was established in 2020 in conjunction with The Carbon Community, an environmental charity, at its Glandwr Forest research site near Cynghordy, Wales (52.0636, -3.7449). The site has a 30-year mean annual temperature of 9.6°C, a mean annual precipitation of 1,566 mm (Met Office), and nitrogen deposition of 14 kg ha -1 y -1 (Air Pollution Information System). Soils at the site mainly belong to the Manod soil series: well-drained loams and silts overlying rock, in this case sandstones, siltstones and mudstones of the Ashgill slope-apron succession, deposited 445.2 – 443.4 MYa. Prior to 2020, the land had been managed for at least 100 years as upland sheep pasture. Historically, the land was improved by reseeding with commercial grass seed and adding fertilizer. In the 20 years prior to planting in 2021, no inorganic fertilizer was applied, but farmyard manure was applied on fields within the site. The experimental site comprises 11.52 hectares, divided into eight experimental blocks (1.44 ha apiece), each of which is further parcelled into nine plots measuring approximately 40 x 40 m. To accommodate landscape features such as paths, small streams and patches of peaty soil (which could not be planted), the plots are irregularly shaped, with an average area of 1,598 m 2 (range: 1,145 to 1,649 m 2 ). All treatments described below were applied to the full area of each individual plot. However, all measurements were restricted to the central area of each plot, which measures exactly 20 x 20 m (0.04 ha). The experiment includes three treatments, each with two levels, combined in a factorial design to create eight unique treatment combinations. The first treatment, ‘forest type,’ is intended to contrast broadleaf woodland restoration with commercial spruce forestry. The native broadleaf plots were planted with a mixture of alder ( Alnus glutinosa , 22% of saplings planted), aspen ( Populus tremula , 5%), downy birch ( Betula pubescens, 35%), wild cherry ( Prunus avium , 19%), sessile oak ( Quercus petraea , 13%), and rowan ( Sorbus aucuparia, 6%), whereas the commercial forestry plots contain only non-native Sitka spruce ( Picea sitchensis ), a common fast-growing commercial softwood timber tree in the United Kingdom. The second treatment, ‘microbiome enrichment’ compares the addition of soil (described further below) with a no-addition control. The third and final treatment, ‘ERW,’ contrasts application of crushed basaltic rock with a no-application control. In addition to the eight unique treatment groups defined by these combined interventions, we also included a ‘null-intervention’ control treatment. Here, the land was not prepared, planted or amended with any external inputs. This treatment is intended to capture natural rates of forest regeneration, in the absence of any active restoration approach. These nine treatment combinations are each replicated once within each of the eight experimental blocks, for a total of 72 plots across the entire experiment ( Fig. 1 ). Ground preparation and tree planting From 10th-16th November 2020, crushed silicate rock (pulverized basaltic andesite) was applied to the appropriate plots (N = 32). This rock was sourced from Builth Wells Quarry, approximately 35km away, and was a by-product of exist mining activities. The particle size of the crushed rock was 4mm or less, and it was spread at a rate of 40 t ha -1 using a tractor and a modified lime spreader. The rock grains contained 7.2% Mg, 6.0% Ca, 0.4% K and 0.3% P by mass ( Supplementary Information ). Semi-quantitative data from XRD analysis, conducted within the infrastructure materials laboratory at Imperial College London ( Supplementary Information ), revealed that this basaltic andesite contained between 1-14% calcite, 60-80% plagioclase (mostly albite with minor contributions of labradorite and anorthite) and up to 20% clay (vermiculite and chlorite). Subsequently, the ground was prepared for tree planting in April 2021 using a continuous mounder, allowing saplings to be planted 1.8 metres apart in each row, with the rows spaced 2.0 metres apart. Trees were planted into the mounds by hand between 7th May - 11 th June 2021. Trees in the soil addition plots (N = 32) were amended with 200ml per tree of fresh whole soil, applied directly to the roots, at the time of planting. Whole-soil inoculum for the broadleaf trees was sourced from a nearby native deciduous woodland in Rhandirmwyn, approximately 7 km from the Glandwr Forest experimental site. Inoculum for Sitka spruce was obtained from a mature spruce plantation in Carmarthen, approximately 48 km away. At both source sites, soils to be used as inoculum were sampled to a depth of 10-15 cm below the leaf litter layer, collected in two batches and stored in covered 1 tonne aggregate bags until deployment between 3-120 hours after harvesting. We elected to inoculate with whole soil, rather than a soil slurry or suspension, because we did not have a priori hypotheses about which microorganisms might exert the strongest effects on tree survival or growth. Therefore, we avoided any inoculum preparation methods that might influence its microbiological composition. Soil microbial inoculum was applied only once; however, in June of 2023, plots were amended with a second application of crushed rock. This rock was sourced from the same quarry, but was this time crushed to a particle size with a maximum of 2 mm and amended at an effective rate of 24 t ha -1 . Additionally, the 331 trees that died at the first census were replanted in February 2022. However, these replanted trees were not included in subsequent analyses. Annual tree growth census Tree growth was measured in all experimental plots in 2021 (June 21 – October 28), 2022 (October 5 - 17), and 2023 (October 4 - 14) using a customized smartphone app. In 2023, a revised app was hosted on Handheld Nautiz X41 devices with a dedicated barcode scanner. The 100 trees in the 0.04 ha core of each plot were each labelled with a unique barcode (N = 6,400 in total). A team of researchers and citizen science volunteers, working in pairs, scanned each barcode using the app, and recorded the corresponding data: tree status (dead or alive), tree diameter at the base of each stem (using digital callipers) and tree height (using a ruler or telescoping measuring rod). When trees surpassed 1.3 m in height, diameter at breast height (i.e., at 1.3 m) was recorded separately. The app automatically paired each measurement with the appropriate metadata (tree species identity, date and time of measurement, and the plot and block in which the tree was located). Data were quality-checked manually at the time of collection in 2021 and 2022, to ensure all trees had been recorded and outliers were remeasured to validate and correct erroneous data. In 2023, with the move to Nautiz X41 data capture devices, data input masks were used to validate data at the point of input. In addition, all trees with a basal diameter measurement of +/-4 standard deviation from the mean were remeasured, as were height measurements with +/-5 standard deviation from the mean. Mechanisms underlying tree responses to treatments We sequenced DNA from the soil fungal community to detect differences due to the experimental treatments, including soil inoculum addition. We also assessed changes in soil pH, soil inorganic nutrient pools, and tissue nutrients to determine the response of soil biogeochemistry to ERW, and its potential to influence affect tree growth. In July 2022, soils were sampled from each plot to characterize fungal communities. Cores were sampled from the rooting zone of six individuals of each tree species present in the plot, then homogenized within species. This approach yielded six unique samples per broadleaf plot (N = 192), and one unique sample per Sitka spruce plot (N=32), for 224 samples in total. Total DNA was extracted from these soil samples (as well as soil samples archived from the source sites for broadleaf and spruce inoculum), and the internal transcribed spacer (ITS) region of the fungal ribosomal DNA was amplified following protocols described elsewhere 44 . The amplicons were sequenced using circular consensus sequencing (CCS) on the PacBio platform. There were 4.3 million CCS sequences, barcoded with 32 unique forward and 12 unique reverse primers. Cutadapt (version 4.5) was used to match forward and reverse primer sequences to the hifi reads. A total of 3,798,451 sequences, or 88% of the total, were demultiplexed using a 10% allowed error rate (per-sample range: 1,798-55,943, men: 12,293, median: 8,794). Demultiplexed sequences were filtered using dada2 (version 1.29, implemented in R version 4.3) according to the following criteria: minimum length=600, max length=3,000, maxN=0, minQ=3. After filtering out 74 bimeras, this error filtering resulted in 15,730 clean, unique sequence variants, which were matched to taxonomy against the most recent release of the dynamic qiime fasta (for all fungi) 45 . Functional guild classification was performed by manually searching genus or family identifications against the FunGuild database. 46 Prior to initiation of all treatments in 2020, then again in 2021, 2023, and 2024, soil pH was measured at the plot level. Two soil cores were obtained per plot (15 cm depth), homogenised, and mixed with deionised water in a 1:2.5 ratio to create a slurry for measurement of soil pH. Changes in soil pH may have a large impact on the processes of (de-)nitrification. Therefore, in June 2021 and 2022, soil inorganic N pools (NO 3 and NH 4 ) were quantified. Three soil cores per plot were sampled along a 10 m transect, homogenised, and transported to the laboratory on ice, where inorganic nutrients were extracted with 2 M KCl. 47 Nitrate and ammonium concentrations in the extracts were determined colorimetrically. 48,49 Finally, during the 2024 growing season, leaves were sampled from all tree species and analysed for elemental composition via ICP-OES at the Forest Research Chemical Analysis Laboratory. This analysis focused on the ERW treatment, so we sampled the 32 plots in the ‘uninoculated’ (control) soil addition treatment across the eight experimental blocks. We sampled leaves from ten individual Sitka spruce trees in each commercial forestry plot, trimming the newest needle cohort from the tip of a branch. We sampled leaves from five individuals of each of the six tree species in each of the broadleaf plots. Samples were homogenised within species and plot, yielding 16 unique Sitka spruce samples (from eight crushed-rock-amended and eight control Sitka spruce plots) and 16 unique alder, aspen, birch, cherry, oak, and rowan samples (from eight crushed-rock-amended and eight control broadleaf plots). We then multiplied tissue nutrient concentrations by the biomass of the relevant tree species in each plot to obtain an estimate of nutrient stocks held in the aboveground biomass at the plot scale. Statistical analyses Tree survival in relation to treatments To identify the best predictors of tree mortality, we conducted Cox proportional hazards models using the R package survival . 50 Because a preliminary analysis showed that individual tree survival rates varied strongly among species (see Results ), all of these models included species identity as strata. To identify possible treatment effects on tree mortality, we modelled survival through time as a function of microbiome enrichment, ERW, and block identity. Then, to assess whether tree size was linked to mortality, we modelled survival in relation to tree biomass at the first census (2021) using a Cox proportional hazards semi-parametric regression. Tree biomass accumulation in relation to treatments To determine how our treatments affected patterns of tree growth, we analysed individual tree biomass among plots and over time. These analyses excluded trees that had died prior to the 2023 census (400 individuals), and the 479 trees which showed negative growth - i.e. exhibited a decrease in height or basal area over time. (Most of these apparent growth reversals were relatively minor – for trees that failed to grow, the median basal diameter shrinkage was 1.4 mm in 2022 and 2.0 mm in 2023, respectively. Moreover, results described below were unchanged if the shrunken trees were included in the analyses.) This left a total of n = 5,558 individual trees for analysis. We used allometric equations 51 to calculate tree biomass from basal diameter measurements, using the allometric equations developed for trees <7 cm dbh. The dataset has individual trees nested within plots nested within blocks. Models also included fixed effects for the forest type, soil addition and ERW treatments, and random effects for block identity, tree species identity and year. Therefore, we explored a variety of linear mixed model formulations with different interactions between fixed and random effects ( Table S1 ) in package lme4 . 52 Because variance attributable to block identity is likely linked to spatial variation in measured and unmeasured environmental variables, we also ran a spatial simultaneous autoregressive lag model to control for spatial autocorrelation in tree growth. Regardless of the specific model formulation used, estimated coefficients were very similar, and we therefore report results from the best-fit model, as determined via AIC ( Table S1 ). To assess treatment effects on aboveground carbon stocks at the final census (2023), we calculated the amount of tree biomass carbon that accumulated in each plot, in kg C ha -1 . This was accomplished by summing individual tree biomasses of living individuals within each plot, and assuming that carbon accounted for 50% of plant tissue mass. Plot-level carbon stock data were analysed with a mixed effects model, including forest type, microbiome enrichment and ERW treatments as fixed effects, and block identity as a random effect. Soil biogeochemical data and aboveground plant nutrient pools were analysed with three-way ANOVAs, with plot as the unit of replication. Variation in soil fungal community structure across treatments Samples were rarefied to an even sequencing depth of 1,207 DNA sequences per sample. Permutational analysis of variance (PERMANOVA; package vegan 53 ) was conducted on a Bray-Curtis dissimilarity matrix to assess how treatments affected fungal community composition. The randomForest algorithm 54 was used to identify the sequence variants that served as the best classifiers of inoculation status, providing insights into the specific taxa associated with successful microbiome enrichment and enhanced tree performance. We also conducted a standard analysis of variance on the proportion of sequences in each sample that were assigned to the ectomycorrhizal fungal guild, using tree species identity and soil addition treatment as predictors. Declarations Author contribution statement The following are the contributions made by each of the authors to this work: Conceptualization and study design: C. Nicholls, H. Allen, B.G. Waring, C. Averill, T.W. Crowther, D.J. Beerling Data collection, including site logistics: B.G. Waring, C. Nicholls, H. Allen, M. Bidartondo, L.M. Suz, L. Lancastle, K. Clayton, L. Gobelius, G. Jones, O. Lindsay, B. Steidinger Data analysis: B.G. Waring, with input from C. Averill and the data collectors listed above Figures: B.G. Waring, C. Averill Writing: the manuscript was written by B.G. Waring with input and edits from C. Averill, M. Bidartondo, L.M. Suz, D.J. Beerling, H. Allen, and C. Nicholls Acknowledgements The authors wish to thank Professor Pete Smith of The Carbon Community’s Scientific Advisory Board for his guidance, steering of this project and their review and critique of this paper. The authors wish to thank the volunteer supervisors and the over 200 volunteers from The Carbon Community’s community science program who have contributed their time, care and dedication to the data collection process during the annual Big Tree Measure as well as maintaining the integrity of the field site throughout the year. Thank you to Kweku Ofosu Addae, Jess Bodé, Deanne Brettle, A Casale, Oliver Casale, Renzo Casale, Ric Casale, Geoff Cooper, Ross Fear, Fliss Fleck, David French, Paul Gibson, Shane Golaup, Owain Grant, Richard Hall, Julia Hallam, Phil Hallam, Clement Harvey, Gus Hellier, Mia Hollsing, Paul Horsman, I Ivanov, Mike Jones, Heather Kay, Nigel Koch, Nigel Little, E Luzby, Isabel Macho, Amanda Maguire, Petranka Malcheva, Andrew Martin, David Milne, Neli Miteva, Laura Moss, Jane Nicholls, O Nicholls, Helen Owers, Miriam Payne, Michele Pfeifer, C Philpin, Jacqmar Pick, Doug Pickup, James Pickup, Heshani Nimeshi Dharmathilaka Rankoth Diwela Sekara Mudiyanselage, Iain Robertson, Pierre Roy, Mariella Scott, Stefania Smul, Philip Stoyle, Chloe T, Alun Thomas, Diana Thomas, Tanya Trott, Kim Turtle, Mike Turtle, Elizabeth Wakely, Conor Walsh, Darren West, Paul Westmacott, Stella Westmacott, Peter Wheeler, Graham Woodward, Jim Wright, Kathryn Wykes, Jason Hui Hong Yapp, T Yontem, and many more community science volunteers. Thank you to the following organisations who made it possible for people to participate in The Carbon Community’s Big Tree Measure in 2021, 2022 and 2023: Partneriaeth Natur Sir Gaerfyrddin | Carmarthenshire Nature Partnership Heidelberg Materials UK Cyfoeth Naturiol Cymru | Natural Resources Wales Comisiynydd Cenedlaethau’r Dyfodol Cymru | Office of the Future Generations Commissioner for Wales Nature-based Solutions Initiative, University of Oxford Pure Good Foundation SAP (UK) Ltd. and the SAP Green Team Department of Geography, Swansea University Thank you to Sonia Winder from Tilhill Forestry UK, the planting teams and loyal contractors who continue to take such care in the implementation of this research. Thank you to Jenna Luecke for figure graphic design. Imperial College London’s Civil and Environmental Engineering Material Laboratory, where XRD analyses were performed, receives EPSRC funding under grant No. EP/R010161/1 and from support from the UKCRIC Coordination Node, EPSRC grant number EP/R017727/1, which funds UKCRIC’s ongoing coordination. The Carbon Community received funding to support the woodland creation and maintenance for this research from Llywodraeth Cymru’s Creu Coetir Glastir (cyfnod ymgeisio 10) | Welsh Government’s Glastir Woodland Creation Grant (window 10). The Glastir Woodland Creation scheme was part funded by the EU under the European Agricultural Fund for Rural Development (EAFRD). The Carbon Community received funding to support the Big Tree Measure program in 2023 through the Forestry Commission’s Woods into Management Forestry Innovation Funds (RWR_37). The Carbon Community received funding from The Woodland Investment Grant (TWIG) for access improvements and educational signs that support public access and The Carbon Community's community science program. TWIG is jointly funded by Welsh Government and The National Lottery Heritage Fund. The Waring Lab at Imperial College London received funding from The Grantham Foundation to support analyses related to enhanced rock weathering. The Waring Lab at Imperial College London also gratefully acknowledges funding received from The Carbon Community in support of biogeochemical measurements. T Crowther received research funds from Oath Incorporated. Competing Interests Colin Averill is a co-founder of the Society for the Protection of Underground Networks, an organization that advocates for the protection of belowground network forming fungi. Colin Averill is the founder of Funga, an organization that facilitates the restoration of belowground fungal biodiversity. 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FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol 20 , 241–248 (2016). Keeney, D. R. & Nelson, D. W. Nitrogen—Inorganic Forms. in 643–698 (1982). doi:10.2134/agronmonogr9.2.2ed.c33. Doane, T. A. & Horwáth, W. R. Spectrophotometric Determination of Nitrate with a Single Reagent. Anal Lett 36 , 2713–2722 (2003). Sims, G. K., Ellsworth, T. R. & Mulvaney, R. L. Microscale determination of inorganic nitrogen in water and soil extracts. Commun Soil Sci Plant Anal 26 , 303–316 (1995). Therneau, T. M. L. T. A. E. C. C. survival: Survival Analysis. Preprint at (2024). Randle, T., Matthews, R. & Jenkins, T. Technical Specification for the Biomass Equations Developed for the 2011 Forecast . Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J Stat Softw 67 , (2015). Oksanen, J. et al. vegan: Community Ecology Package. Preprint at https://cran.r-project.org/package=vegan (2022). Liaw, A. & Weiner, M. Classification and Regression by randomForest. Preprint at (2002). Tables Table 1. Type III analysis of variance (using Satterthwaite’s approximation) on a linear mixed model exploring treatment effects on tree growth. See Table S1 (Extended Data) for model coefficients. Treatment Mean squares F statistic ( p value) ERW 595532 3.36 (0.112) Soil inoculum 1046016 5.90 (0.032) ERW ´ soil inoculum 2259394 12.73 (<0.001) Table 2. Results of a linear mixed effects model examining treatment effects on aboveground carbon stocks (kg C ha -1 ) in each plot. Model term Coefficient (SE) t p value Intercept 1242 (102) 12.19 <0.001 Forest type (F) – spruce -691 (126) -5.47 <0.001 + EWR (E) 326 (126) 2.58 0.013 + soil inoculum (I) 166 (126) 1.32 0.195 F x E -333 (179) -1.86 0.068 F x I -143 (178) -0.80 0.428 E x I -344 (179) -1.93 0.060 F x E x I 320 (253) 1.27 0.211 Additional Declarations Yes there is potential Competing Interest. Colin Averill is a co-founder of the Society for the Protection of Underground Networks, an organization that advocates for the protection of belowground network forming fungi. Colin Averill is the founder of Funga, an organization that facilitates the restoration of belowground fungal biodiversity. Tom Crowther is the founder of Restor, a non-governmental organization that facilitates the global restoration movement. Supplementary Files GlandwrTreeGrowthSuppInfo5Feb2025.docx Supplementary Information: Microbiome manipulation and enhanced weathering stimulate CO2 removal in reforestation ExtendedData.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5982308","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":420800130,"identity":"3c5b56b6-b9bb-4fdb-a24d-6ad16a2ddac5","order_by":0,"name":"Bonnie 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15:10:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5982308/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5982308/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77305893,"identity":"d91fcfc2-8fad-405a-a841-ed3cd202f91e","added_by":"auto","created_at":"2025-02-27 09:06:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":654664,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study site, including all 72 plots and their associated treatments.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5982308/v1/d4260ac34d96a7702aafd785.png"},{"id":77305896,"identity":"5bd79990-e763-4a7d-953d-3d9b2bbc4572","added_by":"auto","created_at":"2025-02-27 09:06:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":172791,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea. \u003c/strong\u003eAverage effects of soil inoculum and ERW (crushed basaltic rock) treatments on individual tree growth, expressed as a percentage increase in biomass (relative to controls) across the three year experiment. Error bars represent ± 1 SE, and N = 5,558 individual trees. Effect sizes are derived from model coefficients in \u003cstrong\u003eTable S1. b. \u003c/strong\u003eAboveground carbon stocks as a function of soil inoculum and ERW treatments. Error bars represent ± 1 SE, and N = 64 plots.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5982308/v1/5f4ffc1310d52e4ca7276990.png"},{"id":77308889,"identity":"8935b455-e10d-4dfd-b19d-557cebaf1a96","added_by":"auto","created_at":"2025-02-27 09:38:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":156380,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea. \u003c/strong\u003eVariation in soil inoculum and ERW (crushed basaltic rock) effect sizes over time, estimated from linear mixed models of the growth of 5,558 individual trees. \u003cstrong\u003eb. \u003c/strong\u003eChange in soil pH between the ERW and control plots – note that data from 2020 reflect pre-treatment values. \u003cstrong\u003ec. \u003c/strong\u003eTree species-specific effects of the soil inoculum treatment. \u003cstrong\u003ed. \u003c/strong\u003eTree species-specific effects of the ERW treatment.\u003cstrong\u003e \u003c/strong\u003eEffect sizes are derived from random effects coefficients of the model presented in \u003cstrong\u003eTable S1. \u003c/strong\u003eThe overall mean treatment effect sizes in panels \u003cstrong\u003ec. \u003c/strong\u003eand \u003cstrong\u003ed.\u003c/strong\u003e are indicated by the dashed lines. Note that because effects plotted in panels \u003cstrong\u003ea, c, \u003c/strong\u003eand \u003cstrong\u003ed \u003c/strong\u003eare random effects, they therefore do not have associated confidence intervals.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5982308/v1/c574ebc435e468933e0f3a55.png"},{"id":77307049,"identity":"fca0e248-3b48-4b7e-905d-af12c490d0e4","added_by":"auto","created_at":"2025-02-27 09:14:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":123363,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves for trees (N = 5,558) in the four quartiles of the tree sapling biomass distribution (biomass data are from the 2021 census). Error bars represent ± 1 SE.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5982308/v1/8b8dc5c429731d137ac23c7b.png"},{"id":105564228,"identity":"05a8aed3-33e9-4633-8e25-d00227be732e","added_by":"auto","created_at":"2026-03-27 12:49:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2195491,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5982308/v1/c5069dce-4cba-43b1-839f-851d1a83a457.pdf"},{"id":77305892,"identity":"09edb9d9-1328-4cb5-9100-c0b40532328c","added_by":"auto","created_at":"2025-02-27 09:06:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":28675,"visible":true,"origin":"","legend":"Supplementary Information: Microbiome manipulation and enhanced weathering stimulate CO2 removal in reforestation","description":"","filename":"GlandwrTreeGrowthSuppInfo5Feb2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-5982308/v1/0ccf639b97200bfe684ca8e6.docx"},{"id":77305916,"identity":"fd09fcd3-e2a7-41af-b14b-df3cb47973a1","added_by":"auto","created_at":"2025-02-27 09:06:09","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":489237,"visible":true,"origin":"","legend":"","description":"","filename":"ExtendedData.docx","url":"https://assets-eu.researchsquare.com/files/rs-5982308/v1/720fc16a342c1ac2b6d7d9bb.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nColin Averill is a co-founder of the Society for the Protection of Underground Networks, an organization that advocates for the protection of belowground network forming fungi. Colin Averill is the founder of Funga, an organization that facilitates the restoration of belowground fungal biodiversity. Tom Crowther is the founder of Restor, a non-governmental organization that facilitates the global restoration movement.","formattedTitle":"Microbiome manipulation and enhanced weathering stimulate CO2 removal in reforestation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobal mean temperature will likely breach the internationally agreed 1.5°C warming limit sometime in the next decade.\u003csup\u003e1\u003c/sup\u003eLimiting future warming requires both steep CO\u003csub\u003e2\u003c/sub\u003e emissions reductions, and removal of past emissions.\u003csup\u003e2\u003c/sup\u003e Atmospheric carbon dioxide removal (CDR) via carbon sequestration in plants and soils (i.e., ‘natural climate solutions’) represents a low cost, scalable suite of CDR strategies that can address the climate and biodiversity crises in tandem.\u003csup\u003e3–6\u003c/sup\u003e However, realising the full potential of natural climate solutions is challenging, in part due to ecological and practical constraints.\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAcross the broad portfolio of natural climate solutions for CDR, reforestation can achieve the highest rates of carbon removal.\u003csup\u003e8\u003c/sup\u003eIn practice, however, forest restoration projects frequently fail to sequester significant amounts of carbon due to operational challenges at multiple scales, as well as the increasing severity of disturbances that re-release fixed CO\u003csub\u003e2\u003c/sub\u003e, including droughts, fires and pest outbreaks.\u003csup\u003e9\u003c/sup\u003e Moreover, planted trees often have extremely high mortality, leading to restoration failure.\u003csup\u003e10\u003c/sup\u003e Due to limited monitoring of restoration outcomes on the ground, we have scant information on the drivers of seedling mortality\u003csup\u003e11\u003c/sup\u003ebut these are likely to include climatic stress,\u003csup\u003e12\u003c/sup\u003e lack of appropriate microbial symbionts,\u003csup\u003e13\u003c/sup\u003e herbivory, and competition with resident plants.\u003csup\u003e14,15\u003c/sup\u003eHigh mortality and poor tree growth reduce the capacity of forests to reach their full carbon sequestration potential, and this together with albedo feedbacks\u003csup\u003e16\u003c/sup\u003e may strongly limit the climate impact of reforestation programmes.\u003csup\u003e2,15\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eRestoring the soil microbiome alongside the tree community may represent a cost-effective way to reduce tree sapling mortality and augment ecosystem carbon sequestration.\u003csup\u003e17,18\u003c/sup\u003e Trees depend on a diverse community of root-associating soil fungi and bacteria, which promote their growth directly (as in the case of mycorrhizal fungi or rhizobia) or indirectly (e.g., by suppressing pathogens). Yet when trees are planted on former agricultural land which has been repurposed to forestry, active restoration of the soil community is largely neglected, leading to delayed and incomplete recovery of soil microbiota.\u003csup\u003e19\u003c/sup\u003eThe majority (97%) of restoration projects aimed at ‘rewilding’ an ecosystem focus on macroscopic organisms, and few have monitored or manipulated microbial communities.\u003csup\u003e20\u003c/sup\u003eThis suggests that there are important, unleveraged opportunities for improving tree seedling/sapling survival \u003cem\u003evia\u003c/em\u003e the soil microbiome. For example, planted tree saplings grow larger in association with the mycorrhizal fungi characteristic of mature forests, rather than those fungal communities of early successional or scrub habitat they are planted into.\u003csup\u003e21\u003c/sup\u003e Yet colonisation of saplings by mycorrhizal fungi from nearby remnant forest patches is inefficient, suggesting direct inoculation with the desired microbial species may be necessary to accelerate tree growth and recovery.\u003csup\u003e22\u003c/sup\u003e Indeed, past restoration efforts in heathlands, tall grass prairies, and degraded mining lands have all shown significant acceleration of ecosystem recovery when an active microbial restoration approach is employed.\u003csup\u003e17,23,24\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eGiven the limited land available for reforestation\u003csup\u003e25\u003c/sup\u003e, and the climatic and biotic limitations on the survival and growth of planted trees, co-deployment of reforestation and another land-based CDR technology, enhanced rock weathering (ERW), may offer opportunities for increasing rates of carbon capture for a given footprint of land.\u003csup\u003e26,27\u003c/sup\u003e ERW involves amending soils with crushed silicate rocks which, in the process of weathering, generate dissolved inorganic carbon in the form of bicarbonate. This inorganic carbon may ultimately be transported via rivers and groundwaters to the coastal ocean, with a residence time of 1,000 years or more.\u003csup\u003e28\u003c/sup\u003eConveniently, large amounts of crushed rock already exist as a byproduct of quarrying operations.\u003csup\u003e29\u003c/sup\u003e Enhanced rock weathering scenarios typically assume cropland soils are amended with crushed silicate rock (e.g. basalt), because the infrastructure for large-scale application already exists in agricultural settings, and because of the potential for ERW-related yield increases.\u003csup\u003e30\u003c/sup\u003e However, to date, the potential for co-deployment of ERW projects in newly planted forests (where similar infrastructure is available for ground preparation over large areas) has not been investigated. Moreover, given the direct involvement of tree mycorrhizas in soil mineral weathering processes, ERW may be accelerated in forests compared to croplands.\u003csup\u003e31\u003c/sup\u003e Finally, depending upon their mineral composition, crushed silicate rocks could act as a tree fertiliser, stimulating organic carbon sequestration in wood as well as inorganic carbon storage in the oceans. Consistent with this hypothesis, a catchment-scale temperate forest field trial in New Hampshire, USA, showed a single application (3.4 t ha\u003csup\u003e-1\u003c/sup\u003e) of the silicate wollastonite enhanced tree carbon uptake by 2.5 t C ha\u003csup\u003e-1\u003c/sup\u003e over 10 years\u0026nbsp;\u003csup\u003e32\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere, we report on a large-scale field trial to rigorously quantify the potential for soil microbial enrichment and ERW to enhance CDR rates in reforestation projects, focusing specifically on carbon capture in organic pools. We planted over 25,000 trees in a 11.5 ha former sheep pasture in Wales, UK, and monitored 6,400 of them individually for three years (2021 – 2023) in teams of researchers and citizen scientists. \u0026nbsp;Microbial enrichment was implemented by adding a small amount of soil (hereafter, ‘inoculum’) from local, mature forests of similar tree composition to the roots of each planted tree. ERW was implemented by amending the soil surface with crushed basaltic andesite, a byproduct from operations at a nearby quarry. Soil and rock grains were applied to monospecific stands of Sitka spruce (\u003cem\u003ePicea sitchensis,\u0026nbsp;\u003c/em\u003ea common exotic commercial timber species in the UK) and to multi-species stands of the native broadleaf species alder (\u003cem\u003eAlnus glutinosa\u003c/em\u003e), aspen (\u003cem\u003ePopulus tremula\u003c/em\u003e), downy birch (\u003cem\u003eBetula pubescens\u003c/em\u003e)\u003cem\u003e,\u003c/em\u003e wild cherry (\u003cem\u003ePrunus avium\u003c/em\u003e), sessile oak (\u003cem\u003eQuercus petraea\u003c/em\u003e), and rowan (\u003cem\u003eSorbus aucuparia\u003c/em\u003e)\u003cem\u003e.\u0026nbsp;\u003c/em\u003eForest type, soil inoculum, and ERW treatments were implemented in a fully factorial design (\u003cstrong\u003eFig 1\u003c/strong\u003e), with each treatment combination replicated across eight experimental blocks. To our knowledge, this study represents the largest and most highly replicated field study examining the impacts of ERW and whole-soil inoculation on near-term reforestation outcomes.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eHere we show significant positive impacts of microbiome enrichment and ERW on tree growth at the individual scale, and on aboveground carbon storage at the plot scale. Our results highlight the critical role of soil microbial communities in governing restoration outcomes. They also suggest that combining two CDR technologies (reforestation and ERW) holds promise for enhancing ecosystem carbon uptake, further bolstering the case for forest restoration as a natural climate solution.\u003c/p\u003e\n\u003ch3\u003eEnriching the soil microbiome enhances the growth of some tree species\u003c/h3\u003e\n\u003cp\u003eWe found that the soil inoculum treatment significantly enhanced tree growth (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003eExtended Data Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e), increasing sapling biomass by 6.6% on average (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), but with substantial variation among species and through time (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, c). All species benefited from soil inoculum addition, but observed growth enhancement ranged from 1.2% in alder to 13.1% in oak (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Temporal variation in the inoculum effect was even more dramatic: on average, inoculated saplings grew 28.6% more than uninoculated controls in 2021, but only 3.4% more by 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The magnitude of tree growth response to soil inoculum addition is remarkable, especially since we did not optimise the source of the inoculum to maximise positive outcomes (e.g., by performing preliminary experiments in a glasshouse).\u003c/p\u003e \u003cp\u003eThe impact of the single soil inoculum addition on tree growth diminished through time, possibly due to changes in the composition of microbial communities (which we only measured once), or perhaps due to reduced competition from weeds in the second and third years of the experiment (glyphosate was not applied in the first year after planting, but was added in subsequent years). However, it is likely that the early growth boost provided by the microbiome enrichment treatment will have long-lasting impacts on forest dynamics. One way to quantify these time-lagged impacts is to explore treatment impacts on tree mortality. In general, mortality rates were low. Of the 6,400 trees surveyed, 295 died in 2021, 66 died in 2022, and 40 died in 2023, for a total three-year mortality rate of 6.3% - and this rate varied significantly by species but not by treatment (\u003cb\u003eTable S2, S3\u003c/b\u003e). Yet after controlling for species identity, individual tree size at the 2021 census was a significant predictor of three-year mortality (hazard ratio\u0026thinsp;=\u0026thinsp;1.023, \u003cem\u003ez\u003c/em\u003e = -8.08, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Trees in the fourth quartile of the size distribution (i.e., those\u0026thinsp;\u0026gt;\u0026thinsp;84 g in 2021) were 87% less likely to die by 2023, when compared to trees in the first and second quartiles (\u0026lt;\u0026thinsp;55 g in 2021) (\u003cb\u003eTable S4\u003c/b\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Therefore, the growth boost provided by inoculation \u0026ndash; 30.5 g in the first year \u0026ndash; is sufficient to buffer the trees from this excess mortality. For this reason, microbiome enrichment impacts in the first 12 months, when newly planted trees are most vulnerable, probably had disproportionate impacts on survival and growth in the longer term.\u003c/p\u003e \u003cp\u003eWhen we summed biomasses of all trees within each plot, and assumed 50% of tree biomass is carbon, we found that inoculated plots stored an extra 95 kg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, although this was not significant due to lower statistical power at the plot scale (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Moreover, the species that tended to benefit most from the inoculum treatment tended to be more slow-growing in general, and therefore made a lesser contribution to plot-level biomass stocks. While the magnitude of these plot-level carbon sequestration rates is relatively modest when considered in isolation, they are substantial considering these stands are in the earliest stages of forest recovery, and the trees themselves are less than 5 years old. Ongoing monitoring will determine if this early stimulation of tree growth translates to meaningful changes in ecosystem carbon removal rates as the forest matures.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMicrobial mechanisms underlying ecosystem responses to microbiome enrichment\u003c/h2\u003e \u003cp\u003eThere was significant heterogeneity in the way different species responded to microbiome enrichment. Understanding this variation is key to predicting how best to deploy microbial inoculum as a management tool, both to modify the composition of restored plant communities and to maximize forest carbon capture. There was strong interspecific variation in tree growth responses to inoculation, ranging from no response to \u0026gt;\u0026thinsp;10% growth boosts, raising the possibility that microbiome enrichment could affect plant community composition \u0026ndash; and therefore, stand-level carbon uptake \u0026ndash; in the longer term. For example, pot-based studies generally show that arbuscular mycorrhizal tree species benefit less from the presence of microbiomes cultivated by conspecifics, whereas ectomycorrhizal species show strong positive growth responses to such microbes.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Consistent with this pattern, in our experiment, tree species forming arbuscular mycorrhizae (cherry, rowan) had a less positive response to inoculation than the ectomycorrhizal species (aspen, birch, oak) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). The ectomycorrhizal and N-fixing alder also showed a less positive response. These findings could result from ectomycorrhizal trees being more likely to benefit from inoculation with forest soils, or because source soils were depauperate in arbuscular mycorrhizae and/or nitrogen-fixing species. Soil inoculation significantly altered the composition of soil fungal communities (\u003cb\u003eTable S5\u003c/b\u003e), with 39 saprotrophic, 15 ectomycorrhizal, and 45 taxa associated with other/unknown guilds identified as indicator species for microbiome enrichment (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). However, the microbiome enrichment treatment did not alter the proportion of ectomycorrhizal fungal taxa detected in the soil (\u003cb\u003eTable S6, Figure S2\u003c/b\u003e). It may be that shifts in ectomycorrhizal fungal species identity, rather than abundance, drove the growth gains in aspen, birch, and oak trees. Moreover, the soil eDNA analysis we performed cannot distinguish active vs. dormant microbes and does not necessarily provide insight on root colonisation by fungi.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eERW stimulates carbon uptake at the ecosystem (whole-plot) scale\u003c/h3\u003e\n\u003cp\u003eWe found that the two additions of crushed rock enhanced individual tree growth by 8.5% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), although this effect was suppressed when rock grains was applied together with soil inoculum (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Most species responded to the crushed silicate rock amendment similarly, with a tendency towards more positive responses by N-fixing species (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). The effect size was stable over time (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), translating to an extra 10 g of biomass per sapling in the first year, and 74 g per sapling by the third year. As a result, by 2023, aboveground carbon stocks in each plot (calculated by summing individual tree biomasses) were significantly and markedly (26.5%) greater in ERW plots (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e: the ERW amendment generated an extra 159 kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of carbon capture in wood by three years post-planting (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). This response shows no sign of diminishing through time, suggesting the absolute amount of carbon sequestered into tree biomass due to ERW may increase as the forest ages. In fact, a positive wood growth response to ERW was observed in a mature forest amended with wollastonite,\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e suggesting that ERW could increase organic carbon sequestration into tree biomass of older forest stands.\u003c/p\u003e\n\u003ch3\u003eBiogeochemical mechanisms underlying ecosystem responses to ERW\u003c/h3\u003e\n\u003cp\u003eWe hypothesized that accelerated tree growth in the ERW plots was due to improved mineral nutrition. Consistent with this expectation, plant tissue stocks of Ca and Cu were elevated in ERW broadleaf plots by 33 and 44%, respectively, whereas tissue stocks of Mn and Cd were depleted (\u003cb\u003eTable S7\u003c/b\u003e). These patterns largely reflect changes in foliar concentrations of the corresponding elements in most broadleaf tree species (\u003cb\u003eTable S8\u003c/b\u003e). Ecosystem (i.e., plot-level) stocks of plant N and P tended to increase in all plots that received crushed basaltic andesite (\u003cb\u003eTable S7\u003c/b\u003e), despite no change in foliar concentrations of these elements (\u003cb\u003eTable S8\u003c/b\u003e). Thus, ERW plots sustained higher tree productivity without diluting concentrations of N or P in their biomass, suggesting greater plot-scale nutrient uptake, but conserved tissue stoichiometry at the individual tree level.\u003c/p\u003e \u003cp\u003eERW could stimulate tree nutrient uptake in several ways: by increasing soil pH to improve bioavailability of the existing soil nutrient capital, and/or by acting as a slow-release mineral fertiliser. We found that application of crushed rock increased soil pH (F\u003csub\u003e1,172\u003c/sub\u003e = 16.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A year after deployment, soil pH in control plots was 5.30 \u0026plusmn; 0.04 \u003cem\u003eversus\u003c/em\u003e 5.65 \u0026plusmn; 0.05 in rock-grain amended plots, although pH values did not differ between the plots prior to crushed rock addition (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). At pH\u0026thinsp;\u0026lt;\u0026thinsp;6, bioavailability of N, P, K, Ca, and Mg is reduced, although the effect is strongest for phosphorus.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Therefore, the bulk soil pH increase could have increased plant bioavailability of these nutrients. As N and P were not present in the crushed basalt, the increases in plant tissue N and P we observed likely arose from increased uptake.\u003c/p\u003e \u003cp\u003eWeathering of crushed basaltic andesite likely contributed to the observed increase in the tree biomass Ca pool. It contained up to 14% rapidly weathering calcite (CaCO\u003csub\u003e3\u003c/sub\u003e) that readily dissolves in the acidic soil environment to release calcium ions; Ca released via weathering of feldspar is far slower. Although albite was the chief feldspar detected in semi-quantitative analyses, XRD data suggest the presence of Ca-bearing labradorite and/or anorthite. Declines in foliar Cd and Mn, meanwhile, may reflect immobilization of these elements in the clays. We therefore conclude that ERW improved tree nutrition through direct release of Ca as the basaltic minerals weathered, and through increases in soil pH. Our findings suggest that the effects of ERW on organic carbon sequestration in other forested sites will depend strongly on soil properties.\u003c/p\u003e\n\u003ch3\u003eImplications for forest restoration efforts\u003c/h3\u003e\n\u003cp\u003eWe have demonstrated that straightforward restoration interventions can have positive consequences for forests in the earliest stages of woodland restoration. Inoculation of trees with whole forest soils significantly increased tree growth in the first year. Interspecific variation in the magnitude of benefits points to potentially long-lasting effects of microbial inoculum on the species composition and carbon uptake capacity of regenerating woodlands. Application of crushed silicate rock to drive inorganic CDR via chemical weathering also stimulates carbon uptake in tree biomass. This suggests additional opportunities for the deployment of ERW technologies, which at present are mainly focused on croplands.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAlthough the benefits of one soil addition and two crushed rock additions were not additive, employing either treatment stimulated carbon stocks by 69\u0026ndash;159 kg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the first three years post-planting. Moreover, we have yet to quantify how ERW affects the full suite of organic and inorganic carbon cycle processes that could further impact ecosystem-level greenhouse gas removal, e.g. changes in soil respiration, organo-mineral carbon stabilisation, and alkalinity production in drainage waters. Our results suggest that applying both treatments together does not accelerate tree growth; by contrast, the benefits of ERW were negated when amended together with soil inoculum. This occurred in direct opposition to our prediction that a larger and more active mycorrhizal biomass in the microbiome enrichment treatment should accelerate ERW rates.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e The antagonistic effects of the two treatments could arise because ERW and microbial inoculum provide redundant functions \u0026ndash; once plant nutrient limitation is relieved \u003cem\u003evia\u003c/em\u003e direct fertilisation, the presence of beneficial microbiota may not provide additional benefit (and vice versa). Indeed, there is a large body of work to suggest that fertilization with inorganic nutrients suppresses plant investment in mycorrhizae.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e It also possible that ERW (or its associated biogeochemical effects, like soil alkalization) suppressed the growth of beneficial microbes present in the soil inoculum. Interactions between microbiome enrichment and ERW influenced the composition of fungal communities after 1 year (\u003cb\u003eTable S5\u003c/b\u003e), suggesting this might be the case.\u003c/p\u003e \u003cp\u003eBecause ERW and microbiome enrichment effects were antagonistic, our data suggest that afforestation practitioners will likely choose one intervention or the other to maximise ecosystem carbon capture in organic pools. By the third year post-planting, ERW and microbiome enrichment provided similar gains in terms of aboveground biomass. However, the treatments differ in several important respects. The microbiome enrichment treatment had the largest impact in the first-year post-planting. It may therefore be the most appropriate choice in restoration contexts where early-stage survival rates are low, e.g. on highly degraded soils. It was also more effective for ectomycorrhizal tree species, and further work is required to identify whether this pattern is generalisable across sites. By contrast, the ERW treatment accelerated tree nutrient uptake, and its effectiveness could depend on soil nutrient status and pH, as well as the weatherability of the basaltic andesite feedstock being used. The feasibility of re-application is also a concern for both interventions; we do not know if other methods of applying inoculum (e.g., in a slurry or spray) would be as effective as applying whole soils directly to tree roots, which can only be done during planting. Similarly, re-applying rock grains to a mature forest plantation requires resolving technical challenges that are not insurmountable.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBoth microbiome enrichment and ERW are associated with their own carbon footprints and with some degree of risk, especially if they are to be deployed over large scales. Sourcing large quantities of whole-soil inoculum may damage donor habitats and thwarts the goal of ecological restoration.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e Donor sites must also be carefully chosen to avoid introduction of pathogens, and also of non-native microbial species or genotypes, which could be invasive.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e However, the development of soil inoculation methods is still in the earliest stages, and there are many potential paths to develop scalable technology. For example, \u0026lsquo;bioengineering\u0026rsquo; inoculum could minimise the requirement for raw material (i.e. donor soil) \u0026ndash; this is a priority area for future research.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Similarly, application of crushed silicate rock also poses poorly quantified ecological risks (e.g., those associated with altered pH and chemistry of soil and surface water)\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and has complex effects on net ecosystem carbon sequestration.\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,5152,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e A full characterisation of the carbon cycle responses to ERW is ongoing at Glandwr.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe urgently require ecologically responsible strategies to maximise carbon sequestration on the land that is available for reforestation. This is the first study to test two promising interventions for enhancing nature-based CDR - soil microbial \u0026lsquo;rewilding,\u0026rsquo; and forest-based ERW - at a large spatial scale and with a high degree of replication. We report significant increases in ecosystem carbon storage across a large site with a significant degree of environmental and soil heterogeneity, highlighting the intrinsic potential of these treatments. Our data further suggest that both soil properties and microbial communities will dictate how well trees respond to soil inoculum addition and ERW. Therefore, there is a pressing need for comparable field trials to be conducted at scale across multiple soil types and climates, building an evidence base practitioners can use to optimise their results.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eStudy site and experimental design\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe experiment was established in 2020 in conjunction with The Carbon Community, an environmental charity, at its Glandwr Forest research site near Cynghordy, Wales (52.0636, -3.7449). The site has a 30-year mean annual temperature of 9.6\u0026deg;C, a mean annual precipitation of 1,566 mm (Met Office), and nitrogen deposition of 14 kg ha\u003csup\u003e-1\u003c/sup\u003e y\u003csup\u003e-1\u003c/sup\u003e (Air Pollution Information System). Soils at the site mainly belong to the Manod soil series: well-drained loams and silts overlying rock, in this case sandstones, siltstones and mudstones of the Ashgill slope-apron succession, deposited 445.2 \u0026ndash; 443.4 MYa. Prior to 2020, the land had been managed for at least 100 years as upland sheep pasture. Historically, the land was improved by reseeding with commercial grass seed and adding fertilizer. In the 20 years prior to planting in 2021, no inorganic fertilizer was applied, but farmyard manure was applied on fields within the site.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe experimental site comprises 11.52 hectares, divided into eight experimental blocks (1.44 ha apiece), each of which is further parcelled into nine plots measuring approximately 40 x 40 m. To accommodate landscape features such as paths, small streams and patches of peaty soil (which could not be planted), the plots are irregularly shaped, with an average area of 1,598 m\u003csup\u003e2\u003c/sup\u003e (range: 1,145 to 1,649 m\u003csup\u003e2\u003c/sup\u003e). All treatments described below were applied to the full area of each individual plot. However, all measurements were restricted to the central area of each plot, which measures exactly 20 x 20 m (0.04 ha).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe experiment includes three treatments, each with two levels, combined in a factorial design to create eight unique treatment combinations. The first treatment, \u0026lsquo;forest type,\u0026rsquo; is intended to contrast broadleaf woodland restoration with commercial spruce forestry. The native broadleaf plots were planted with a mixture of alder (\u003cem\u003eAlnus glutinosa\u003c/em\u003e, 22% of saplings planted), aspen (\u003cem\u003ePopulus tremula\u003c/em\u003e, 5%), downy birch (\u003cem\u003eBetula pubescens,\u003c/em\u003e 35%), wild cherry (\u003cem\u003ePrunus avium\u003c/em\u003e, 19%), sessile oak (\u003cem\u003eQuercus petraea\u003c/em\u003e, 13%), and rowan (\u003cem\u003eSorbus aucuparia,\u0026nbsp;\u003c/em\u003e6%), whereas the commercial forestry plots contain only non-native Sitka spruce (\u003cem\u003ePicea sitchensis\u003c/em\u003e), a common fast-growing commercial softwood timber tree in the United Kingdom. The second treatment, \u0026lsquo;microbiome enrichment\u0026rsquo; compares the addition of soil (described further below) with a no-addition control. The third and final treatment, \u0026lsquo;ERW,\u0026rsquo; contrasts application of crushed basaltic rock with a no-application control. In addition to the eight unique treatment groups defined by these combined interventions, we also included a \u0026lsquo;null-intervention\u0026rsquo; control treatment. Here, the land was not prepared, planted or amended with any external inputs. This treatment is intended to capture natural rates of forest regeneration, in the absence of any active restoration approach. These nine treatment combinations are each replicated once within each of the eight experimental blocks, for a total of 72 plots across the entire experiment (\u003cstrong\u003eFig. 1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eGround preparation and tree planting\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFrom 10th-16th November 2020, crushed silicate rock (pulverized basaltic andesite) was applied to the appropriate plots (N = 32). This rock was sourced from Builth Wells Quarry, approximately 35km away, and was a by-product of exist mining activities. The particle size of the crushed rock was 4mm or less, and it was spread at a rate of 40 t ha\u003csup\u003e-1\u003c/sup\u003e using a tractor and a modified lime spreader. The rock grains contained 7.2% Mg, 6.0% Ca, 0.4% K and 0.3% P by mass (\u003cstrong\u003eSupplementary Information\u003c/strong\u003e). Semi-quantitative data from XRD analysis, conducted within the infrastructure materials laboratory at Imperial College London (\u003cstrong\u003eSupplementary Information\u003c/strong\u003e), revealed that this basaltic andesite contained between 1-14% calcite, 60-80% plagioclase (mostly albite with minor contributions of labradorite and anorthite) and up to 20% clay (vermiculite and chlorite).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSubsequently, the ground was prepared for tree planting in April 2021 using a continuous mounder, allowing saplings to be planted 1.8 metres apart in each row, with the rows spaced 2.0 metres apart. Trees were planted into the mounds by hand between 7th May - 11\u003csup\u003eth\u003c/sup\u003e June 2021. Trees in the soil addition plots (N = 32) were amended with 200ml per tree of fresh whole soil, applied directly to the roots, at the time of planting. Whole-soil inoculum for the broadleaf trees was sourced from a nearby native deciduous woodland in Rhandirmwyn, approximately 7 km from the Glandwr Forest experimental site. Inoculum for Sitka spruce was obtained from a mature spruce plantation in Carmarthen, approximately 48 km away. At both source sites, soils to be used as inoculum were sampled to a depth of\u0026nbsp;10-15 cm below the leaf litter layer,\u0026nbsp;collected in two batches and stored in covered 1 tonne aggregate bags until deployment between 3-120 hours after harvesting.\u0026nbsp;We elected to inoculate with whole soil, rather than a soil slurry or suspension, because we did not have \u003cem\u003ea priori\u0026nbsp;\u003c/em\u003ehypotheses about which microorganisms might exert the strongest effects on tree survival or growth. Therefore, we avoided any inoculum preparation methods that might influence its microbiological composition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSoil microbial inoculum was applied only once; however, in June of 2023, plots were amended with a second application of crushed rock. This rock was sourced from the same quarry, but was this time crushed to a particle size with a maximum of 2 mm and amended at an effective rate of 24 t ha\u003csup\u003e-1\u003c/sup\u003e. Additionally, the 331 trees that died at the first census were replanted in February 2022. However, these replanted trees were not included in subsequent analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAnnual tree growth census\u0026nbsp;\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTree growth was measured in all experimental plots in 2021 (June 21 \u0026ndash; October 28), 2022 (October 5 - 17), and 2023 (October 4 - 14) using a customized smartphone app. In 2023, a revised app was hosted on Handheld Nautiz X41 devices with a dedicated barcode scanner. The 100 trees in the 0.04 ha core of each plot were each labelled with a unique barcode (N = 6,400 in total). A team of researchers and citizen science volunteers, working in pairs, scanned each barcode using the app, and recorded the corresponding data: tree status (dead or alive), tree diameter at the base of each stem (using digital callipers) and tree height (using a ruler or telescoping measuring rod). When trees surpassed 1.3 m in height, diameter at breast height (i.e., at 1.3 m) was recorded separately. The app automatically paired each measurement with the appropriate metadata (tree species identity, date and time of measurement, and the plot and block in which the tree was located). Data were quality-checked manually at the time of collection in 2021 and 2022, to ensure all trees had been recorded and outliers were remeasured to validate and correct erroneous data. In 2023, with the move to Nautiz X41 data capture devices, data input masks were used to validate data at the point of input. In addition, all trees with a basal diameter measurement of +/-4 standard deviation from the mean were remeasured, as were height measurements with +/-5 standard deviation from the mean.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMechanisms underlying tree responses to treatments\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe sequenced DNA from the soil fungal community to detect differences due to the experimental treatments, including soil inoculum addition. We also assessed changes in soil pH, soil inorganic nutrient pools, and tissue nutrients to determine the response of soil biogeochemistry to ERW, and its potential to influence affect tree growth.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn July 2022, soils were sampled from each plot to characterize fungal communities. Cores were sampled from the rooting zone of six individuals of each tree species present in the plot, then homogenized within species. This approach yielded six unique samples per broadleaf plot (N = 192), and one unique sample per Sitka spruce plot (N=32), for 224 samples in total. Total DNA was extracted from these soil samples (as well as soil samples archived from the source sites for broadleaf and spruce inoculum), and the internal transcribed spacer (ITS) region of the fungal ribosomal DNA was amplified following protocols described elsewhere\u003csup\u003e44\u003c/sup\u003e. The amplicons were sequenced using circular consensus sequencing (CCS) on the PacBio platform. There were 4.3 million CCS sequences, barcoded with 32 unique forward and 12 unique reverse primers. \u0026nbsp;Cutadapt (version 4.5) was used to match forward and reverse primer sequences to the hifi reads. A total of 3,798,451 sequences, or 88% of the total, were demultiplexed using a 10% allowed error rate (per-sample range: 1,798-55,943, men: 12,293, median: 8,794). Demultiplexed sequences were filtered using dada2 (version 1.29, implemented in R version 4.3) according to the following criteria: minimum length=600, max length=3,000, maxN=0, minQ=3. \u0026nbsp;After filtering out 74 bimeras, this error filtering resulted in 15,730 clean, unique sequence variants, which were matched to taxonomy against the most recent release of the dynamic \u003cem\u003eqiime fasta\u003c/em\u003e (for all fungi)\u003csup\u003e45\u003c/sup\u003e.\u0026nbsp;Functional guild classification was performed by manually searching genus or family identifications against the FunGuild database.\u003csup\u003e46\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003ePrior to initiation of all treatments in 2020, then again in 2021, 2023, and 2024, soil pH was measured at the plot level. Two soil cores were obtained per plot (15 cm depth), homogenised, and mixed with deionised water in a 1:2.5 ratio to create a slurry for measurement of soil pH. Changes in soil pH may have a large impact on the processes of (de-)nitrification. Therefore, in June 2021 and 2022, soil inorganic N pools (NO\u003csub\u003e3\u003c/sub\u003e and NH\u003csub\u003e4\u003c/sub\u003e) were quantified. Three soil cores per plot were sampled along a 10 m transect, homogenised, and transported to the laboratory on ice, where inorganic nutrients were extracted with 2 M KCl.\u003csup\u003e47\u003c/sup\u003e Nitrate and ammonium concentrations in the extracts were determined colorimetrically.\u003csup\u003e48,49\u003c/sup\u003e\u0026nbsp; Finally, during the 2024 growing season, leaves were sampled from all tree species and analysed for elemental composition via ICP-OES at the Forest Research Chemical Analysis Laboratory. This analysis focused on the ERW treatment, so we sampled the 32 plots in the \u0026lsquo;uninoculated\u0026rsquo; (control) soil addition treatment across the eight experimental blocks. We sampled leaves from ten individual Sitka spruce trees in each commercial forestry plot, trimming the newest needle cohort from the tip of a branch. We sampled leaves from five individuals of each of the six tree species in each of the broadleaf plots. Samples were homogenised within species and plot, yielding 16 unique Sitka spruce samples (from eight crushed-rock-amended and eight control Sitka spruce plots) and 16 unique alder, aspen, birch, cherry, oak, and rowan samples (from eight crushed-rock-amended and eight control broadleaf plots). We then multiplied tissue nutrient concentrations by the biomass of the relevant tree species in each plot to obtain an estimate of nutrient stocks held in the aboveground biomass at the plot scale.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTree survival in relation to treatments\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo identify the best predictors of tree mortality, we conducted Cox proportional hazards models using the R package \u003cem\u003esurvival\u003c/em\u003e.\u003csup\u003e50\u003c/sup\u003e Because a preliminary analysis showed that individual tree survival rates varied strongly among species (see \u003cem\u003eResults\u003c/em\u003e), all of these models included species identity as strata. To identify possible treatment effects on tree mortality, we modelled survival through time as a function of microbiome enrichment, ERW, and block identity. Then, to assess whether tree size was linked to mortality, we modelled survival in relation to tree biomass at the first census (2021) using a Cox proportional hazards semi-parametric regression.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTree biomass accumulation in relation to treatments\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo determine how our treatments affected patterns of tree growth, we analysed individual tree biomass among plots and over time. These analyses excluded trees that had died prior to the 2023 census (400 individuals), and the 479 trees which showed negative growth - i.e. exhibited a decrease in height or basal area over time. (Most of these apparent growth reversals were relatively minor \u0026ndash; for trees that failed to grow, the median basal diameter shrinkage was 1.4 mm in 2022 and 2.0 mm in 2023, respectively. Moreover, results described below were unchanged if the shrunken trees were included in the analyses.) This left a total of \u003cem\u003en\u003c/em\u003e = 5,558 individual trees for analysis. We used allometric equations\u003csup\u003e51\u003c/sup\u003e to calculate tree biomass from basal diameter measurements, using the allometric equations developed for trees \u0026lt;7 cm dbh.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe dataset has individual trees nested within plots nested within blocks. Models also included fixed effects for the forest type, soil addition and ERW treatments, and random effects for block identity, tree species identity and year. Therefore, we explored a variety of linear mixed model formulations with different interactions between fixed and random effects (\u003cstrong\u003eTable S1\u003c/strong\u003e) in package \u003cem\u003elme4\u003c/em\u003e.\u003csup\u003e52\u003c/sup\u003e Because variance attributable to block identity is likely linked to spatial variation in measured and unmeasured environmental variables, we also ran a spatial simultaneous autoregressive lag model to control for spatial autocorrelation in tree growth. Regardless of the specific model formulation used, estimated coefficients were very similar, and we therefore report results from the best-fit model, as determined via AIC (\u003cstrong\u003eTable S1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo assess treatment effects on aboveground carbon stocks at the final census (2023), we calculated the amount of tree biomass carbon that accumulated in each plot, in kg C ha\u003csup\u003e-1\u003c/sup\u003e. This was accomplished by summing individual tree biomasses of living individuals within each plot, and assuming that carbon accounted for 50% of plant tissue mass. Plot-level carbon stock data were analysed with a mixed effects model, including forest type, microbiome enrichment and ERW treatments as fixed effects, and block identity as a random effect. Soil biogeochemical data and aboveground plant nutrient pools were analysed with three-way ANOVAs, with plot as the unit of replication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eVariation in soil fungal community structure across treatments\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSamples were rarefied to an even sequencing depth of 1,207 DNA sequences per sample. Permutational analysis of variance (PERMANOVA; package \u003cem\u003evegan\u003c/em\u003e\u003csup\u003e53\u003c/sup\u003e) was conducted on a Bray-Curtis dissimilarity matrix to assess how treatments affected fungal community composition. The randomForest algorithm\u003csup\u003e54\u003c/sup\u003e was used to identify the sequence variants that served as the best classifiers of inoculation status, providing insights into the specific taxa associated with successful microbiome enrichment and enhanced tree performance. We also conducted a standard analysis of variance on the proportion of sequences in each sample that were assigned to the ectomycorrhizal fungal guild, using tree species identity and soil addition treatment as predictors. \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following are the contributions made by each of the authors to this work:\u003c/p\u003e\n\u003cp\u003eConceptualization and study design: C. Nicholls, H. Allen, B.G. Waring, C. Averill, T.W. Crowther, D.J. Beerling\u003c/p\u003e\n\u003cp\u003eData collection, including site logistics: B.G. Waring, C. Nicholls, H. Allen, M. Bidartondo, L.M. Suz, L. Lancastle, K. Clayton, L. Gobelius, G. Jones, O. Lindsay, B. Steidinger\u003c/p\u003e\n\u003cp\u003eData analysis: B.G. Waring, with input from C. Averill and the data collectors listed above\u003c/p\u003e\n\u003cp\u003eFigures: B.G. Waring, C. Averill\u003c/p\u003e\n\u003cp\u003eWriting: the manuscript was written by B.G. Waring with input and edits from C. Averill, M. Bidartondo, L.M. Suz, D.J. Beerling, H. Allen, and C. Nicholls\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank Professor Pete Smith of The Carbon Community\u0026rsquo;s Scientific Advisory Board for his guidance, steering of this project and their review and critique of this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank the volunteer supervisors and the over 200 volunteers from The Carbon Community\u0026rsquo;s community science program who have contributed their time, care and dedication to the data collection process during the annual Big Tree Measure as well as maintaining the integrity of the field site throughout the year.\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003cbr\u003eThank you to Kweku Ofosu Addae, Jess Bod\u0026eacute;, Deanne Brettle, A Casale, Oliver Casale, Renzo Casale, Ric Casale, Geoff Cooper, Ross Fear, Fliss Fleck, David French, Paul Gibson, Shane Golaup, Owain Grant, Richard Hall, Julia Hallam, Phil Hallam, Clement Harvey, Gus Hellier, Mia Hollsing, Paul Horsman, I Ivanov, Mike Jones, Heather Kay, Nigel Koch, Nigel Little, E Luzby, Isabel Macho, Amanda Maguire, Petranka Malcheva, Andrew Martin, David Milne, Neli Miteva, Laura Moss, Jane Nicholls, O Nicholls, Helen Owers, Miriam Payne, Michele Pfeifer, C Philpin, Jacqmar Pick, Doug Pickup, James Pickup, Heshani Nimeshi Dharmathilaka Rankoth Diwela Sekara Mudiyanselage, Iain Robertson, Pierre Roy, Mariella Scott, Stefania Smul, Philip Stoyle, Chloe T, Alun Thomas, Diana Thomas, Tanya Trott, Kim Turtle, Mike Turtle, Elizabeth Wakely, Conor Walsh, Darren West, Paul Westmacott, Stella Westmacott, Peter Wheeler, Graham Woodward, Jim Wright, Kathryn Wykes, Jason Hui Hong Yapp, T Yontem, and many more community science volunteers.\u003c/p\u003e\n\u003cp\u003eThank you to the following organisations who made it possible for people to participate in The Carbon Community\u0026rsquo;s Big Tree Measure in 2021, 2022 and 2023:\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003ePartneriaeth Natur Sir Gaerfyrddin | Carmarthenshire Nature Partnership\u003c/li\u003e\n \u003cli\u003eHeidelberg Materials UK\u003c/li\u003e\n \u003cli\u003eCyfoeth Naturiol Cymru | Natural Resources Wales\u003c/li\u003e\n \u003cli\u003eComisiynydd Cenedlaethau\u0026rsquo;r Dyfodol Cymru | Office of the Future Generations Commissioner for Wales\u003c/li\u003e\n \u003cli\u003eNature-based Solutions Initiative, University of Oxford\u003c/li\u003e\n \u003cli\u003ePure Good Foundation\u003c/li\u003e\n \u003cli\u003eSAP (UK) Ltd. and the SAP Green Team\u003c/li\u003e\n \u003cli\u003eDepartment of Geography, Swansea University\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThank you to Sonia Winder from Tilhill Forestry UK, the planting teams and loyal contractors who continue to take such care in the implementation of this research.\u003c/p\u003e\n\u003cp\u003eThank you to Jenna Luecke for figure graphic design.\u003c/p\u003e\n\u003cp\u003eImperial College London\u0026rsquo;s Civil and Environmental Engineering Material Laboratory, where XRD analyses were performed, receives EPSRC funding under grant No. EP/R010161/1 and from support from the UKCRIC Coordination Node, EPSRC grant number EP/R017727/1, which funds UKCRIC\u0026rsquo;s ongoing coordination.\u003c/p\u003e\n\u003cp\u003eThe Carbon Community received funding to support the woodland creation and maintenance for this research from Llywodraeth Cymru\u0026rsquo;s Creu Coetir Glastir (cyfnod ymgeisio 10) | Welsh Government\u0026rsquo;s Glastir Woodland Creation Grant (window 10). The Glastir Woodland Creation scheme was part funded by the EU under the European Agricultural Fund for Rural Development (EAFRD). The Carbon Community received funding to support the Big Tree Measure program in 2023 through the Forestry Commission\u0026rsquo;s Woods into Management Forestry Innovation Funds (RWR_37). The Carbon Community received funding from The Woodland Investment Grant (TWIG) for access improvements and educational signs that support public access and The Carbon Community\u0026apos;s community science program. TWIG is jointly funded by Welsh Government and The National Lottery Heritage Fund.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Waring Lab at Imperial College London received funding from The Grantham Foundation to support analyses related to enhanced rock weathering. The Waring Lab at Imperial College London also gratefully acknowledges funding received from The Carbon Community in support of biogeochemical measurements. T Crowther received research funds from Oath Incorporated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eColin Averill is a co-founder of the Society for the Protection of Underground Networks, an organization that advocates for the protection of belowground network forming fungi. Colin Averill is the founder of Funga, an organization that facilitates the restoration of belowground fungal biodiversity. Tom Crowther is the founder of Restor, a non-governmental organization that facilitates the global restoration movement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatasets will be uploaded to datadryad.org upon acceptance of the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJones, N. 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Spectrophotometric Determination of Nitrate with a Single Reagent. \u003cem\u003eAnal Lett\u003c/em\u003e \u003cstrong\u003e36\u003c/strong\u003e, 2713\u0026ndash;2722 (2003).\u003c/li\u003e\n\u003cli\u003eSims, G. K., Ellsworth, T. R. \u0026amp; Mulvaney, R. L. Microscale determination of inorganic nitrogen in water and soil extracts. \u003cem\u003eCommun Soil Sci Plant Anal\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 303\u0026ndash;316 (1995).\u003c/li\u003e\n\u003cli\u003eTherneau, T. M. L. T. A. E. C. C. survival: Survival Analysis. Preprint at (2024).\u003c/li\u003e\n\u003cli\u003eRandle, T., Matthews, R. \u0026amp; Jenkins, T. \u003cem\u003eTechnical Specification for the Biomass Equations Developed for the 2011 Forecast\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eBates, D., M\u0026auml;chler, M., Bolker, B. M. \u0026amp; Walker, S. C. Fitting linear mixed-effects models using lme4. \u003cem\u003eJ Stat Softw\u003c/em\u003e \u003cstrong\u003e67\u003c/strong\u003e, (2015).\u003c/li\u003e\n\u003cli\u003eOksanen, J. \u003cem\u003eet al.\u003c/em\u003e vegan: Community Ecology Package. Preprint at https://cran.r-project.org/package=vegan (2022).\u003c/li\u003e\n\u003cli\u003eLiaw, A. \u0026amp; Weiner, M. Classification and Regression by randomForest. Preprint at (2002).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eType III analysis of variance (using Satterthwaite\u0026rsquo;s approximation) on a linear mixed model exploring treatment effects on tree growth. See \u003cstrong\u003eTable S1 (Extended Data)\u0026nbsp;\u003c/strong\u003efor model coefficients.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 45.5907%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1314%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean squares\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2779%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eF statistic (\u003cem\u003ep\u003c/em\u003e value)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 45.5907%;\"\u003e\n \u003cp\u003eERW\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1314%;\"\u003e\n \u003cp\u003e595532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2779%;\"\u003e\n \u003cp\u003e3.36 (0.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 45.5907%;\"\u003e\n \u003cp\u003eSoil inoculum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1314%;\"\u003e\n \u003cp\u003e1046016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2779%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.90 (0.032)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 45.5907%;\"\u003e\n \u003cp\u003eERW\u0026nbsp;\u0026acute;\u0026nbsp;soil inoculum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.1314%;\"\u003e\n \u003cp\u003e2259394\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.2779%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12.73 (\u0026lt;0.001)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eResults of a linear mixed effects model examining treatment effects on aboveground carbon stocks (kg C ha\u003csup\u003e-1\u003c/sup\u003e) in each plot.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.7852%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel term\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6376%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient (SE)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9396%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6376%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.7852%;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6376%;\"\u003e\n \u003cp\u003e1242 (102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9396%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12.19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6376%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.7852%;\"\u003e\n \u003cp\u003eForest type (F) \u0026ndash; spruce\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6376%;\"\u003e\n \u003cp\u003e-691 (126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9396%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-5.47\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6376%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.7852%;\"\u003e\n \u003cp\u003e+ EWR (E)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6376%;\"\u003e\n \u003cp\u003e326 (126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9396%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.58\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6376%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.7852%;\"\u003e\n \u003cp\u003e+ soil inoculum (I)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6376%;\"\u003e\n \u003cp\u003e166 (126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9396%;\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6376%;\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.7852%;\"\u003e\n \u003cp\u003eF x E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6376%;\"\u003e\n \u003cp\u003e-333 (179)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9396%;\"\u003e\n \u003cp\u003e-1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6376%;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.7852%;\"\u003e\n \u003cp\u003eF x I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6376%;\"\u003e\n \u003cp\u003e-143 (178)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9396%;\"\u003e\n \u003cp\u003e-0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6376%;\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.7852%;\"\u003e\n \u003cp\u003eE x I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6376%;\"\u003e\n \u003cp\u003e-344 (179)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9396%;\"\u003e\n \u003cp\u003e-1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6376%;\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.7852%;\"\u003e\n \u003cp\u003eF x E x I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6376%;\"\u003e\n \u003cp\u003e320 (253)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.9396%;\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6376%;\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"afforestation, carbon sequestration, enhanced rock weathering, mycorrhiza, soil microbiome, reforestation, restoration, rewilding, trees","lastPublishedDoi":"10.21203/rs.3.rs-5982308/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5982308/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLimiting future warming requires both drastic reductions in carbon emissions, and removal of past emissions from the atmosphere. Socioeconomic and biophysical limits on the efficacy of nature-based carbon dioxide removals (such as reforestation) mean that the natural carbon sequestration capacity of forests should be maximized, wherever reforestation is implemented. Here we report on a large-scale (11.5 ha) field trial testing co-deployment of two strategies to increase forest carbon capture: modification of the soil microbiome, and enhanced rock weathering (ERW) via addition of crushed silicate rock. Individual monitoring of 6,400 trees over three years revealed that individual saplings grew 7% larger, on average, when inoculated with soils from nearby mature forest. Meanwhile, the ERW treatment augmented aboveground carbon stocks by 27% and elevated plant tissue nutrients. We conclude that co-deploying early-stage reforestation with microbial enrichment or ERW can increase forest carbon sequestration by 69\u0026ndash;159 kg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the first three years post-planting.\u003c/p\u003e","manuscriptTitle":"Microbiome manipulation and enhanced weathering stimulate CO2 removal in reforestation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-27 09:06:00","doi":"10.21203/rs.3.rs-5982308/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"424060c9-9eee-4c18-8a4e-cf5505ec2912","owner":[],"postedDate":"February 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44855773,"name":"Earth and environmental sciences/Ecology/Restoration ecology"},{"id":44855774,"name":"Earth and environmental sciences/Biogeochemistry/Carbon cycle"},{"id":44855775,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation"}],"tags":[],"updatedAt":"2026-04-16T12:26:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-27 09:06:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5982308","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5982308","identity":"rs-5982308","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}