The soil microbiome as an indicator of ecosystem multifunctionality in European soils

Domain: earth_science
Published: 2025-12-14
Editorial outcome: major_revision

Abstract
The role of soil microorganisms in supporting multiple ecosystem functions (multifunctionality) remains poorly understood across diverse environmental conditions. Here, we investigate 484 soils from 27 European countries spanning a range of climatic and edaphic contexts. We assess the contribution of climate, soil properties, and soil microbiome traits (i.e., the relative abundance of co-occurring taxa) to explain six key functional proxies related to soil structure, biochemical activity, and productivity. We find the highest multifunctionality values in grasslands, woodlands, loamy and acidic soils, and temperate humid regions, and the lowest in croplands, alkaline soils, and drier regions. Soil properties explain 12–31% of variation in multifunctionality, with microbial biomass and nitrogen content emerging as the strongest predictors. The soil microbiome accounts for 2–14% of unique variance in multifunctionality but explains more than 25% of variation in enzymatic activities and primary productivity in clay-rich soils and soils originating from temperate dry regions. Specific taxa, particularly within Actinobacteria, Acidobacteria, and the fungal genus Mortierella consistently emerge as strong predictors of ecosystem multifunctionality. Our findings highlight that ecosystem multifunctionality is jointly shaped by soil properties and microbial communities. We argue that specific taxa hold potential as context-dependent indicators for multifunctionality monitoring across environmental gradients.

Introduction
Soils are the foundation of terrestrial ecosystems and represent an important habitat for organisms across the tree of life1. Soils also contribute to a wide range of ecosystem services including food production, climate regulation, as well as cultural and educational services2. These ecosystem services are directly supported by ecosystem functions, such as nutrient cycling, water holding capacity, and primary production3–5. In recent years, research has increasingly focused on ecosystem multifunctionality, recognizing that ecosystem services depend on multiple functions simultaneously, often assessed through measurable functional proxies (e.g., enzymatic activities, soil respiration)6–8. Previous research has shown that greater soil biodiversity is associated with higher levels of multiple functions, such as nutrient cycling and primary production9–11.

Microorganisms are sensitive, ubiquitous, and react quickly to environmental stress, reflecting cumulative impacts12, and potentially constituting an important indicator of changes in ecosystem multifunctionality13. This is especially important as sequencing is becoming a cheaper and highly standardized technology allowing direct comparison of data across different studies and national surveys14. However, assessments on the capacity of the soil microbiome in explaining ecosystem multifunctionality at larger scale are urgently needed to support the monitoring and conservation of soils.

Unfortunately, there are still important uncertainties about the capacity of the soil microbiome to predict multifunctionality across large spatial scales. First, despite the enormous diversity of soil biota (particularly microbial diversity), the focus is still often placed on a narrow subset of organisms. For instance, mycorrhizal fungi and nitrogen-fixing bacteria are among the few microbial functional groups currently assessed in Europe as potential indicators of ecosystem multifunctionality15,16. Second, despite growing interest in microbial indicators of ecosystem multifunctionality, current studies do not specifically address how multiple environmental contexts, such as land use, soil texture, and climate, influence the relationship between the taxonomic identity of soil organisms and their capacity to predict multifunctionality17. Finally, although overall microbial diversity (i.e., taxa richness) has been shown to positively influence multifunctionality in global and national contexts, similar large-scale analyses linking microbial community structure to multifunctionality are lacking for Europe11,18,19. This gap is especially relevant considering that less than 40% of European soils are currently categorized as healthy, despite the continent’s long history of land management and its significant role in global food production and biodiversity20. These knowledge gaps hinder our ability to evaluate how the soil microbiome relate to specific ecosystem functions across diverse contexts (i.e., soil types, climatic regions, or land uses).

Here, we use a harmonized pan-European observational field survey (named LUCAS Soil) to quantify the contribution of the soil microbiome in explaining ecosystem multifunctionality. We analyze 484 fresh soil samples collected across diverse environmental conditions, using categorical groupings to test the effects of land use, climatic region, soil texture, and soil pH, as these factors have previously been shown to structure soil microbial communities and ecosystem multifunctionality21. Using metabarcoding, we obtain site-level information on the relative abundance of bacteria and fungi. We assess ecosystem multifunctionality by integrating several functional proxies that capture key functions linked to broader ecosystem services. Specifically, we measure soil aggregate stability (flood regulation, habitat for organisms), net primary productivity (food and fiber/timber provision), and basal respiration (climate regulation). In addition, we quantify enzymatic activities including N-acetyl-glucosaminidase (nitrogen cycling), phosphatase (phosphorus cycling), and xylosidase (carbon cycling), all of which are involved in organic matter decomposition. Together, these indicators provide a multidimensional picture of ecosystem functioning, which we summarize as ecosystem multifunctionality.

To quantify the contribution of the soil microbiome to explain multifunctionality, we apply Random Forest models to identify the most important predictors of multifunctionality, considering a wide set of abiotic (climate and soil properties) and biotic (microbial community composition) variables. To quantify the relative contributions of these predictor groups, we perform variance partitioning analysis, allowing us to attribute the unique and shared variance in multifunctionality to climate, soil properties, and the soil microbiome. We finally employ structural equation models (SEMs) to test for the direct and indirect effects of climate, soil properties, and the soil microbiome on ecosystem multifunctionality. We hypothesize that soils from less-disturbed environments (e.g., woodlands) would support higher multifunctionality due to more favorable conditions for microbial activity. We also hypothesize that the contribution of microbial taxa to multifunctionality would be context-dependent, varying across land use, climatic region, pH, and soil texture.

Results
We analyzed 484 soils across Europe to investigate how ecosystem multifunctionality varies with land use, climatic region, soil texture, and pH, and we also investigated the relative contribution of the soil microbiome compared to soil properties and climate as indicators of multifunctionality. For each soil, we measured six functional proxies: soil basal respiration, xylosidase, N-acetylglucosaminidase, phosphatase activities, soil aggregation, and primary productivity (Table S1). Consistent with our first hypothesis, our study found the highest multifunctionality values in less-disturbed grassland and woodland soils (average multifunctionality value=0.33±0.09, n=92, and 0.31±0.10, n=165, respectively), loam soils (0.31±0.09, n=179), acidic soils (0.30±0.09, n=290), and soils originating from temperate humid locations (0.31±0.08, n=249). On the contrary, cropland soils (0.25±0.07, n=227), alkaline soils (0.25±0.09, n=131), and soils originating from drier regions (0.25±0.10, n=122) showed the lowest multifunctionality values (Fig. 1, Table S3). Multifunctionality in cropland soils was 24.2% lower than in grasslands, and 19.4% lower than in woodlands. Our study also found that a combination of soil properties (i.e., total nitrogen, organic carbon, and microbial biomass), together with the relative abundance of a few taxa, significantly associate with multifunctionality (Fig. 2). More importantly, the soil microbiome explained a portion of variation of multifunctionality across soils. Specifically, the community composition of bacterial and fungal modules (i.e., co-occurring OTUs) explained between 2.27% and 14.08% of unique variance (Fig. 3, Table S4). Soil properties accounted for 12.2–31.4% of the unique variance in multifunctionality, whereas climate explained very little (0–1%), suggesting that climate influences multifunctionality mainly through indirect effects. At the level of individual functional proxies, the soil microbiome explained more unique variance than soil properties or climate for primary productivity and enzymatic activities (Figures S4–S7), while soil properties dominated to explain variation in soil aggregation and basal respiration (Figures S8–S9).

Context-dependency of soil microbiome contributions to multifunctionality
Our findings indicate that the potential of the soil microbiome to act as indicator of ecosystem multifunctionality is strongly context-dependent, varying with soil characteristics and environmental conditions. We observed that in croplands, the unique variance in multifunctionality explained by the soil microbiome (13.94%) was comparable to that explained by soil properties (13.92%), suggesting that microbial composition in these intensively managed systems is an important factor explaining ecosystem performance (Fig. 3). A similar pattern was evident in soils from temperate dry climates, in neutral pH conditions (pH between 6.5 and 7.3), and in clay-rich soils, where the predictive power of the microbiome closely matched that of soil properties (Fig. 3, Table S4). These results suggest that under certain soil and environmental configurations, the microbiome may serve as a reliable indicator for assessing ecosystem functioning. In contrast, in soils from other contexts, such as those originating from woodlands and continental climatic regions, the unique contribution of the microbiome to explaining multifunctionality was noticeably lower compared to that of soil properties (Fig. 3). Our results contrast with previous studies showing that multifunctionality in intensively managed ecosystems (e.g., croplands) depends primarily on external inputs (e.g., fertilizers), as we demonstrate that these systems also rely on their soil microbiome (Gossner et al., 2016). We also suggest that the utility of the soil microbiome as indicator of multifunctionality is not universal but depends on the interaction between microbial communities and their surrounding environment, which is in line with previous research showing that soil microbial diversity depends on the interplay between soil properties and climate21,22.

At the level of individual functions, the soil microbiome explained substantial unique variance (>25%) for xylosidase activity in dry and clay-rich soils, phosphatase activity in neutral and clay-rich soils, and N-acetylglucosaminidase activity in neutral soils (Figures S4–S6). The soil microbiome was also the main predictor of primary productivity, particularly in alkaline and sandy soils (Figure S7). On the other hand, soil properties (e.g., organic carbon) were the main predictors of basal respiration. Future studies should address whether the freeze–thaw of soil samples might have disrupted microbial community signals and make respiration more strongly dependent on substrate availability than on community composition.

We hypothesized that microbial indicator taxa of multifunctionality vary across soil types, and our results confirmed the hypothesis. Random forest analyses revealed that the contribution of microbial groups to ecosystem multifunctionality is context-dependent and varies across land uses and environmental gradients (Fig. 2). In croplands, multiple microbial modules (particularly bacterial modules 3, 5, 7, and 9, and fungal modules 13 and 1) emerged as strong predictors of multifunctionality. Taxonomically, most bacterial modules were dominated by Proteobacteria, representing 30–35% of OTUs in nearly all modules (Figure S10, Supplementary dataset 1). Module 7, however, was distinct in being dominated by Actinobacteria, especially the genus Gaiella, a pattern also observed to a lesser extent in module 3. Both modules 3 and 7 also contained numerous OTUs affiliated with Sphingomonas, Nocardioides, Solirubrobacter, and Streptomyces (Actinobacteria). Module 1, together with modules 2 and 5, included a high proportion of Acidobacteria. Module 5 also contained many OTUs within Bacteroidetes, particularly taxa from the genus Flavobacteria (Figure S10). Fungal modules were largely dominated by Ascomycota, which accounted for 48–61% of OTUs across modules (Figure S11). Modules 1, 3, and 5 contained many OTUs associated with the genus Mortierella, while modules 5 and 13 included members of Glomeromycota (arbuscular mycorrhizal fungi). In woodlands, fungal module 5, enriched in Mortierella and symbiotic fungi such as Archaeorhizomyces and members of Glomeromycota, emerged among the top predictors of multifunctionality for certain soil categories (Fig. 2). Notably, Glomeromycota (arbuscular mycorrhizal fungi) have repeatedly been shown to support plant growth and contribute to ecosystem multifunctionality23,24, although they represented as small proportion of all fungal sequences in our dataset (≈1.00%). Similarly, previous research at the regional scale have identified Actinobacteria and members of Mortierella as hubs in microbial co-occurrence networks25,26. We finally acknowledge that all taxa identified here as potential indicators of multifunctionality are strict aerobes, which is consistent with sequencing bulk soil from the top 20 cm where oxygen is abundant. Future studies should extend to deeper layers, where community composition may shift and alternative microbial indicators could emerge27.

Taken together, our results open the path towards the development of biomarkers where the abundance of these taxa could be monitored through qPCR assays, integrated into soil health indices, or used as features in predictive models28. Given their sensitivity to land-use and soil conditions, these taxa may serve as early-warning markers for shifts in ecosystem multifunctionality.

To explore how environmental context shapes the relationships between microbial communities, soil properties, climate, and ecosystem multifunctionality, we employed multi-group structural equation models (SEMs). These models tested direct contributions of climate, soil properties, and the microbiome to multifunctionality, as well as indirect effects of climate and soil mediated through the microbiome (Fig. 4, Figure S3, Supplementary dataset 2). For each environmental classification (land use, climatic region, soil texture, and pH class), we compared unconstrained models (allowing regression paths to vary among groups) with constrained models that assumed equal relationships across groups. Unconstrained models consistently outperformed constrained ones based on likelihood ratio tests, supporting the idea that the effects of microbial, soil, and climatic factors on multifunctionality are context dependent (Table S5). Among the classification schemes tested, grouping by land use yielded the best model fit, as indicated by the lowest AIC in chi-squared comparisons (Fig. 4). This, together with previous research, suggests that land use is a primary organizing factor in determining how environmental drivers influence multifunctionality29.

SEMs also confirmed that while the direct effects of climate, soil properties, and the microbiome were relatively balanced in croplands and grasslands, woodlands exhibited a high influence of soil properties on multifunctionality (Fig. 4). This may reflect the greater influence of accumulated organic matter and nutrient pools in woodland soils, together with microclimatic buffering by tree cover30. These factors strengthen the role of soil properties in regulating ecosystem multifunctionality in woodlands, while reducing the relative contribution of climate and microbial community composition.

SEMs showed that the direct effect of climate (air temperature and precipitation) on ecosystem multifunctionality was strongest in soils from dry regions, where water limitation potentially amplifies both direct and microbiome-mediated climate effects (Fig. 4). In soil originating from continental regions, soil properties also played a dominant direct role, exceeding their influence in other regions. Analyses by soil texture showed that in loam soils, the microbiome had a greater direct influence on multifunctionality than soil properties, a pattern not observed in clay or sandy soils, where soil properties were more dominant. Loamy soils occupy the middle of the soil texture spectrum, reflecting a balanced composition of sand, silt, and clay31. Such conditions likely support diverse and active microbial communities, enabling them to directly regulate multiple ecosystem processes. Finally, pH classification revealed that in alkaline (pH > 7.3) and neutral (pH 6.5–7.3) soils, the microbiome and soil properties contributed equally to multifunctionality (Fig. 4). In contrast, in acidic soils (pH < 6.5), multifunctionality was driven primarily by soil properties, with the microbiome playing a minor role. We argue that acidic soils impose stronger physiological constraints on microorganisms, enhancing the relative importance of soil chemical properties such as pH and nutrient availability in regulating ecosystem multifunctionality32. Taken together, these findings highlight the highly context-specific nature of soil functioning and underscore the need to consider environmental background when interpreting the role of the soil microbiome. To confirm the mechanisms proposed (regarding the influence of loam texture, woodland organic matter, or acidic soils on the relationship between the soil microbiome and multifunctionality), controlled manipulative experiments are needed. Laboratory or field studies altering soil texture, pH, or organic inputs could directly test microbial contributions to multifunctionality.

European-level assessment of the link between microbiomes and functions
We here assess ecosystem multifunctionality across Europe by simultaneously comparing multiple environmental factors, including land use (croplands, grasslands, and woodlands), climatic regions (continental, temperate dry, and temperate humid), soil texture (clay, loam, and sand), and soil pH classes (acidic, neutral, and alkaline soils). Previous works have modeled multifunctionality in grasslands and croplands, showing that agricultural practices can pose risks to ecosystem functions by creating trade-offs between biodiversity and functions such as productivity and nutrient cycling, with pesticide use and fertilization emerging as key drivers33,34. Similarly, a European-scale study covering 94 soils in 13 countries found that interactions between land use and climatic zones drive ecosystem functions in croplands and grasslands35. More recently, Sünnemann and collaborators (2023) found a Europe-wide decline in ecosystem multifunctionality under rising temperatures and dry conditions, worsened by fertilizer and pesticide application. Our study adds primary productivity as an additional ecosystem function, there by moving beyond soil-focused analyses toward a broader ecosystem perspective of multifunctionality. We argue that future studies should incorporate direct measurements of actual biomass, such as kilograms of dry weight per hectare, because these are likely to provide more accurate estimates than satellite-derived proxies36. Remote sensing has been used in similar studies and captures vegetation greenness or productivity indices at coarse resolution, while field-based biomass measurements directly reflect the material available for carbon storage, reducing uncertainty and improving the precision of multifunctionality assessments37,38. Finally, ecosystem multifunctionality relies on the number of functions integrated7. In this study, six ecosystem functions were measured across all the 484 sites, including primary productivity, which is a key function. Future studies should also incorporate direct measurements of soil gas emissions (e.g., CO₂, CH₄, N₂O), as these are critical processes linking microbial activity to climate regulation39,40.

Considering that habitat conversion to croplands will likely increase worldwide for food production and to satisfy needs of a growing population, our results stress that the capacity of ecosystems to supply multiple functions simultaneously could be compromised if conversion to croplands is preferred. In line with this, Jeanneret and collaborators (2021) estimated that conversion to arable lands negatively impacts biodiversity of vascular plants and arthropods across Europe. Similarly, conversion to cropland causes homogenization of microbial communities41,42. A similar study found that European croplands were particularly abundant in potentially pathogenic fungi compared to grasslands and woodlands, but the richness of beneficial taxa (e.g., mycorrhizal fungi) decreased43. Our results add to these that the conversion of natural ecosystems to croplands not only has an impact on soil biodiversity but also on multifunctionality. Moreover, we show that the contribution of the soil microbiome to multifunctionality is context-dependent. We acknowledge that comparing multifunctionality across highly disturbed ecosystems (croplands) and natural or semi-natural ecosystems (woodlands or grasslands) is challenging, since these are very different ecosystem types with different disturbance regimes and the dynamics of each ecosystem might differentially impact multifunctionality. For example, woodlands are stable ecosystems dominated by trees where primary productivity is rather constant over time, whereas croplands are subjected to crop rotations, whereas grasslands are often dominated by annual herbs and forbs, with consequences for organic carbon stocks, and productivity44. Taken together, we argue that despite being part of the same landscape, soils from different land uses, textures, climatic regions, and pH classes respond to distinct microbial predictors of multifunctionality. This indicates that monitoring should adopt context-specific microbial indicators rather than a uniform approach.

Our continental analysis provides evidence that ecosystem multifunctionality is shaped by both environmental context and biotic composition, with land use, soil properties, climate, and the soil microbiome each contributing to ecosystem multifunctionality. Multifunctionality was highest in grasslands and woodlands, in loam-textured and acidic soils (pH < 6.5), and in temperate humid regions. The best predictors of ecosystem multifunctionality overall were microbial biomass and nitrogen content. Random forest and variance partitioning analyses further revealed that the soil microbiome (i.e., co-occurring microbial OTUs) explains a substantial and context-dependent share of multifunctionality. The soil microbiome was the main factor contributing to variation in enzymatic activities and primary productivity, while soil basal respiration and soil aggregation were mostly explained by soil properties. We also found that modules dominated by Actinobacteria (e.g., Gaiella, Sphingomonas), Acidobacteria, and Mortierella frequently emerged as strong predictors of ecosystem multifunctionality. Our use of multi-group structural equation models further highlights that the strength and nature of relationships among climate, soil properties, the microbiome, and multifunctionality differ across environmental classifications. Among all groupings, land use emerged as the most influential structuring variable. Collectively, these findings underscore the need for context-aware approaches in using microbial indicators for soil monitoring. As land-use change intensifies globally, understanding how local conditions modulate microbiome-function relationships is essential to designing effective, scalable soil health strategies and sustaining ecosystem services under environmental change.

Methods
Field survey and soil sampling
Our study was built upon the EU Statistical Office’s Land Use and Coverage Area Frame Survey (LUCAS) Soil, the largest pan-European scheme for assessing soil characteristics in relation to land cover and land use45,46. The sampling approach used a composite strategy, where the final sample at each location consisted of five combined topsoil (0–20 cm) subsamples. The initial subsample was obtained precisely at the coordinates of the designated LUCAS point, while the four additional subsamples were collected 2 m from the central point, aligning with the cardinal directions (North, East, South, and West). To minimize the impact of seasonality and temporal variation, all samples were collected over the shortest possible timeframe during spring/summer 2018. For this study, a total of 484 soil samples were collected, comprising 165 from woodlands, 92 from grasslands, and 227 from croplands. Based on the Köppen–Geiger climate classification, 113 samples originated from sites in a continental climate, 122 from temperate dry regions, and 249 from temperate humid regions (Figure S1).

Soil edaphic factors and climatic variables
Soil samples were used to determine a range of edaphic factors following standard procedures46. Total phosphorus content (mg kg-1) was measured by the ISO 11263:1994 protocol following phosphorus solubilization in a sodium hydrogencarbonate solution. Total nitrogen content (g kg−1) was measured following the ISO 11261:1995 protocol. Extractable potassium content (mg kg−1) was determined with atomic absorption spectrophotometry47. Soil pH was measured according to the ISO 10390:1994 standard using 0.01M CaCl₂ as the extractant, while the proportions of silt, clay, and sand were determined using laser diffraction particle size analysis following ISO 13320:2009. Based on these measurements, samples were further grouped by pH and texture. For soil texture classification, we used the USDA system integrated within the soil texture wizard48. We classified 290 soils as acidic (pH < 6.5), 63 as neutral (pH 6.5–7.3), and 131 as alkaline (pH > 7.3). Regarding texture, 129 soils were identified as clay, 179 as loam, and 176 as sand.

Soil organic carbon was measured following the ISO 10694:1995 protocol. Briefly, the total carbon content in each sample was determined using an elemental analyzer following dry combustion and corrected for carbonate content (ISO 10693:1994), resulting in soil organic carbon (g kg−1). Soil bulk density was measured following the ISO 11272:2017 procedure49. Soil bulk density was expressed as the dry weight of soil divided by its volume (g cm−3), which includes the volume of soil particles and the pores among them50. Soil microbial biomass (expressed as μg C g−¹ soil dry weight) was estimated as a proxy using substrate-induced respiration, based on the respiration response to glucose addition measured directly after potential basal respiration on the same soil samples. Climatic variables, including monthly mean air temperature and precipitation (averaged values over the 1970–2000 period) were obtained for each sampling location from the WorldClim database (worldclim.org).

Soil microbial community composition
Soil samples (n=484) were analyzed for bacterial and fungal biodiversity using DNA metabarcoding. DNA was extracted using the Qiagen DNeasy PowerSoil HTP 96 Kit, with three 0.2g aliquots per sample pooled post-extraction51. DNA quality and quantity were assessed using the Qubit™ 1X dsDNA HS Assay Kit. PCR amplification was performed in triplicate using 5× HOT FIREPol® Blend Master Mix (Solis BioDyne, Tartu, Estonia) in 25 μl volume. The PCR conditions for bacterial amplification included 55°C annealing temperature, 26 cycles, and 1.5ng of DNA template in 1µl, following an optimized protocol derived from the Earth Microbiome Project (https://earthmicrobiome.org/protocols-and-standards/16s) to minimize PCR bias while ensuring sufficient yield for sequencing. For fungal amplification, the PCR conditions included an annealing temperature of 55°C, 30 cycles, and 1.5ng of DNA template in 1µl. Amplified DNA was tagged with multiplex identifier tags, pooled, and verified on a TBE 1% agarose gel. Primer sets for barcode amplification of 16S rRNA gene were 515F (GTGYCAGCMGCCGCGGTAA) and 926R (GGCCGYCAATTYMTTTRAGTTT), targeting the bacterial V4-V5 hypervariable region52,53 and ITS9mun (GTACACACCGCCCGTCG) and ITS4ngsUni (CGCCTS CCTTANTDATATGC) for the fungal ITS region54. Sequencing was performed using Illumina MiSeq platform with 2×300 paired-end mode for bacterial amplicons and PacBio Sequel II platform for fungal amplicons.

The Illumina and PacBio amplicon data (for bacteria and fungi, respectively) were demultiplexed using LotuS2 and paired-end reads were assembled using FLASH 1.2.1055,56. For bacteria, zero-radius operational taxonomic units (zOTUs) were generated using the UPARSE algorithm (usearch version 10.0.02457). The process involved merging paired-end reads, trimming off 16S primer sequences, quality filtering, and denoising. For fungi, 98%-OTUs were obtained following the VSEARCH algorithm58. Our datasets, consisting of 484 soils, included 79,593 zOTUs (bacteria) and 35,152 98%-OTUs (fungi). The bacterial dataset was rarefied at 40,000 sequences per sample, and the fungal dataset was rarefied at 1000 sequences per sample. Taxonomy was assigned to the bacterial dataset using the Ribosomal Database Project v1659 and to the fungal dataset using MegaBLAST searches against the UNITE 9.1 database60.

To identify groups of co-occurring microbial taxa, we used the WGCNA (Weighted Gene Co-expression Network Analysis) R package61. OTU tables containing relative abundances were first formatted with samples as rows and OTUs as columns. We assessed the data for missingness using the goodSamplesGenes function, followed by hierarchical clustering (hclust) to detect potential sample outliers. A soft-thresholding power was selected based on scale-free topology and mean connectivity criteria (pickSoftThreshold, with blockSize=1000). An adjacency matrix was then constructed using this threshold and transformed into a Topological Overlap Matrix (TOM) to compute dissimilarity between OTUs. Taxa were clustered using hierarchical clustering, and modules were defined via the cutreeDynamic function, with a minimum module size of 15 for fungi and 100 for bacteria. Module eigengenes were calculated (moduleEigengenes function) and used as indicators of microbial community composition. Finally, OTUs were taxonomically annotated and assigned to their corresponding modules for interpretation. This network-based approach allowed us to reduce community data dimensionality and link modules to environmental variables and multifunctionality.

Ecosystem multifunctionality
We assessed six functional proxies across 484 soils (Table S1): soil basal respiration, three enzymatic activities (N-acetylglucosaminidase, phosphatase, and xylosidase), net primary productivity, and soil aggregate stability. These proxies capture different ecosystem processes and associated services. Soil basal respiration reflects microbial activity and relates to climate regulation. N-acetylglucosaminidase breaks down chitin and complex carbohydrates, informing nitrogen cycling and organic matter decomposition. Phosphatase releases inorganic phosphate from organic compounds, a key process in phosphorus cycling. Xylosidase cleaves xylose from hemicellulose, contributing to plant litter decomposition and carbon cycling. Net primary productivity reflects primary production and food provision, while soil aggregate stability represents soil structure, linked to erosion regulation, flood regulation, and habitat provision. Our selection of functions was based on biotic or abiotic processes that can be measured as a rate or directly contribute to ecosystem services7.

Potential soil basal respiration (μL O2 g−1 soil dry weight h−1) was measured using an O2-microcompensation apparatus: respiration was determined at 20°C using the pressure difference between a sealed chamber containing the sample and a control chamber at atmospheric pressure. For this, 5–7g of thawed soil, which had been previously acclimated for five days at 4°C and sieved at 2 mm, was used. Measurements of potential basal respiration lasted from 22 to 42 hours, depending on the time taken for respiration rates to reach detectable levels. A timeframe of 5–7 consecutive measurement hours, in which respiration was stable, was used to calculate the average potential basal respiration of the sample62,63. The activities of the soil enzymes N-acetylglucosaminidase, xylosidase, and phosphatase were measured based on 4-methylumbelliferone (MUF)-coupled substrates63. Briefly, fresh soil samples (250mg each) were suspended in 50μM acetate buffer (pH 5), sonicated to disrupt soil aggregates, and incubated at 25°C for 60 min in 96-well microplates containing substrates, MUF dilutions for quenching and extinction coefficients, and controls for substrate and soil suspensions. Reactions were stopped with 2M NaOH, and fluorescence was measured in eight technical replicates using an Infinite 200 PRO instrument (Tecan Group, Männedorf, Switzerland). Enzymatic activities were quantified as substrate turnover rates, expressed as nmol of MUF per gram of dry soil per hour. Net Primary Productivity (NPP) was estimated at each sampling location using MODerate resolution Imaging Spectroradiometers (MODIS) imagery from NASA’s Terra and Aqua satellites, specifically through the MOD17 PSN/NPP algorithm. NPP was calculated as the sum of eight-day net photosynthesis (PSN) products based on the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). FAPAR, which indicates the solar radiation absorbed by plants, is derived using variables such as the maximum radiation conversion efficiency, ground temperature, and vapor pressure64. The NPP data for 2018, aligning with LUCAS sampling locations, were averaged across ±6 months surrounding the sampling times at a 500 m resolution, and expressed as g C m−2 yr−1. Soil aggregation was determined from the percentage of water-stable aggregates (WSA) following the wet-sieving method63,65. Briefly, 4g of fresh soil (FM) were placed into 0.25-mm sieves and allowed to rewet by capillarity for 5 min. Samples were then wet-sieved for 3 min and dried overnight at 70°C, after which dry matter was measured. Coarse Matter (CM) was measured after another night at 70°C. Finally, the percentage of water-stable aggregates was calculated as follows:

%WSA = (WSA - CM) / (FM - CM) * 100 (1)

To quantify ecosystem multifunctionality, we calculated four distinct indices based on the six measured functions. The first index was a simple average, where all functions were standardized to a 0–1 scale and then averaged, assuming equal contribution of each function. The second index applied a z-score transformation, standardizing each function using the formula z = (X−µ)/σ, where X is the function value, µ is the mean, and σ is the standard deviation6,66. This approach facilitates direct comparability among functions by expressing values in terms of standard deviations from the mean. The third index was a weighted average, which accounted for the fact that three of the six functions were enzymatic activities representing similar processes. In this index, each enzymatic function was assigned a relative weight of 0.33, while non-redundant functions retained full weight. In addition, we calculated a multiple-threshold index by assessing the number of functions (0–6) performing above predefined thresholds (25%, 50%, and 75% of the maximum observed value). We also analyzed each function individually to provide detailed insights into how predictors influenced both single functions and overall multifunctionality. Because the calculated indices were highly correlated (Figure S2), we selected the weighted index for downstream analyses (see next section). This index best reflects ecosystem multifunctionality, as it corrects for the overrepresentation of enzymatic activities (3 out of the 6 measured proxies).

Statistical analyses
All statistical analyses were conducted in R version 4.2.167. We explored the relative contribution of the soil microbiome, soil properties, and climate in explaining patterns on multifunctionality by means of random forest analyses, variance partitioning, and structural equation models (SEMs). We first investigated how multifunctionality and individual soil functions varied across different environmental groupings. Specifically, we categorized our 484 soil samples based on land use (cropland=227, grassland=92, woodland=165), climatic region (continental=113, temperate dry=122, temperate humid=249), soil texture (clay=129, loam=179, sand=176), and pH class (acidic=290, neutral=63, alkaline=131). We then used Kruskal-Wallis tests followed by pairwise Dunn’s tests (Bonferroni correction) to assess group differences. For each test, we reported the chi-squared statistic, p value, and eta-squared as a measure of effect size. Since overlaps among soil groupings could confound interpretations, we quantified the percentage of overlap between categories. With few exceptions, overlap was limited. However, woodland, continental, and sandy soils largely coincided with acidic soils (Table S2). This indicates that patterns attributed to these groupings may partly reflect their strong association with soil pH.

To identify key predictors of multifunctionality (weighted index), we employed Random Forest (RF) regression using the rfPermute package with 1000 trees68. Models were trained on standardized data and evaluated using out-of-bag (OOB) R² and 10-fold cross-validation (repeated 3 times). Variable importance was assessed based on the increase in mean square error when each predictor (microbial modules, soil properties, climate) was permuted.

Next, we partitioned the variance in multifunctionality using adjusted R² to determine the unique and shared contributions of three predictor groups: climate (precipitation, temperature), edaphic factors (excluding total nitrogen due to collinearity with SOC), and microbial composition (bacterial and fungal modules). Unique and shared contributions were calculated through a nested linear models.

Finally, to disentangle direct and indirect effects, we used multi-group Structural Equation Models (SEMs) with the lavaan package69. Predictors were grouped into climate, soil properties, and microbial variables (see Figure S3 for a priori model). We fit SEMs and estimated standardized path coefficients. A constrained model was also fit to test whether relationships differed significantly across groups. Direct effects on multifunctionality and indirect effects on the microbiome were summarized by group and category.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Dear authors, dear Ferran Romero,\nYour manuscript provides an insight into the soil microbiome as indicator of function across Europe. It is an actual topic that\nis important for the future of sustainable food systems. The work is novel and impactful by looking at a large number of sites\nacross Europe and by combining amplicon sequencing data (16S rRNA gene & ITS sequences) with indicators of microbial\nabundance (such as microbial biomass) and activity (enzymes). The large dataset on 500 soils across Europe is of interest\nfor the scientific community. The paper is overall well written and I enjoyed reading the work.\nHaving said this, I have some larger points of feedback, mainly focusing on the set-up of the study and the claimed impact on\nsoil multifunctionality and agriculture.\nMultifunctionality is a central point of the paper. Overall “multifunctionality” would benefit from a better definition at the start.\nWhat is multifunctionality within the context of your work, which main processes do you target and how do the parameters\nlink to that? It is important to stay close to your own work to avoid speculating too much.\nRegarding the conclusions and claims, I miss a section on the limitations and perspectives of this work. In line with the more\nspecific comments given below, it would be very useful to provide an overview of the current limitations and challenges of\nthis work. This would, in my opinion, better embed it within the current research field, and it would better highlight the added\nvalue of this work without overinterpretation of the work / the risk of speculation.\nThe figures are overall clear, and they provide good visual support for the written text. I can see that this has been a lot of\nwork and I appreciate the efforts. The supplementary figures further support the work and they facilitate future research on\nthe topic.\nI have added my comments, linking to the points mentioned above under a major comments section in the attachment. The\nminor comments section, also in the attachment, contains mainly minor textual and structural suggestions. I hope these\ncomments support you in further improving the manuscript.", "reviewer_2": "This study investigates the relationship between soil microbiome composition and ecosystem multifunctionality across\nEuropean croplands, grasslands, and woodlands. While the manuscript is well-structured and employs robust statistical\nmethods (Random Forest and SEM), the novelty and analytical depth of the study are limited compared to existing literature.\nThe link between microbial diversity and multifunctionality has been extensively documented, including the roles of soil\norganisms (Fan et al., 2023. Soil biodiversity supports the delivery of multiple ecosystem functions in urban greenspaces),\ndiversity (Delgado-Baquerizo et al., 2016. Microbial diversity drives multifunctionality in terrestrial ecosystems), and land-use\neffects (Delgado-Baquerizo et al., 2020. Multiple elements of soil biodiversity drive ecosystem functions across biomes). The\nfindings largely confirm prior knowledge rather than offering new mechanistic insights. Additionally, the methodological\napproach to assessing the relationship of multifunctionality and soil microbiome lacks sophistication compared to threshold-\nbased or averaging methods used in previous studies, which may limit the interpretability of the results. Conclusions lack\nsufficient support from the current results. The current stage of this manuscript is not suitable to publish in Nature\nCommunications\nMajor concerns:\n-1. The manuscript does not appear to include a dedicated figure explicitly supporting the central claim that \"the soil\nmicrobiome explains ecosystem multifunctionality across Europe after accounting for climate and soil properties. (lines 259-\n261)\"\n-2. The authors claim that \"the bacteriome predicts function in croplands while mycobiomes support woodlands,\" but this\nconclusion appears overstated given the currently evidence. First, the reliance on Random Forest (RF) variable importance\nmetrics (%IncMSE) alone is insufficient to establish causality or even robust predictive relationships, especially since the\nactual performance metrics of the RF models (e.g., out-of-bag error, cross-validated R²) are not reported, leaving the strength\nand reliability of these predictions unclear. Figure 2 shows that while Acidobacteria and Nitrospira are identified as important\npredictors in croplands, their relative importance (%IncMSE) is notably lower than abiotic factors like total nitrogen and\norganic carbon, which raises questions about the justification for emphasizing bacteriomes as primary drivers. Similarly, for\nwoodlands, fungal taxa like ectomycorrhiza and Agaricomycetes are highlighted, yet their importance values are\ncomparable to or lower than edaphic factors (e.g., organic carbon), undermining the assertion that mycobiomes \"support\"\nwoodlands multifunctionality more significantly than other variables. Furthermore, the selection of specific microbial taxa\n(e.g., Acidobacteria Gp5, Nitrospira, ectomycorrhiza) lacks transparency—was this based on ecological relevance? The\nauthors state: \"Random Forest results aligned with linear regressions linking predictor variables to cropland\nmultifunctionality, with the strongest correlations observed for total nitrogen (R² = 0.29, p < 0.001), soil organic carbon (R² =\n0.14, p < 0.001), and Acidobacteria Gp5 abundance (R² = 0.13, p < 0.001).\" However, judging from the correlation\ncoefficients, the strongest associations between predictor variables and multifunctionality are actually total nitrogen, soil\norganic carbon, and monthly precipitation in croplands.\n-3. In \"Ecosystem-level understanding of the microbiome-function relationship\" part:\nThe text lacks systematic description of Figure 3 (SEM analysis) results. Examination shows fungi do not significantly\ninfluence multifunctionality in any ecosystem, contradicting the \"fungi strongly support woodlands multifunctionality \" claim.\nThe statement that \"…as our results show that certain classes within this phylum (i.e., Acidobacteria Gp22) can indeed be\nused as predictors of multifunctionality (particularly in croplands) …\" is unsupported by Figure 3A, which shows only\nphototrophic bacteria (PHO) significantly directly affect multifunctionality.\nThe claim that \"…In grasslands, however, basal respiration was related to Acidobacteria Gp22 and Actinobacteria...\" (line\n308) lacks support, which shows no significant effect of these taxa on basal respiration.\nThe conclusion that \" ectomycorrhizal fungi and Mortierellomycetes emerged as key drivers of soil aggregation and plant\nproductivity in woodlands \" (lines 320-321) need be further supported.\n-4. The manuscript includes multiple speculative recommendations for future studies “We argue that future studies need to\naddress the impacts of Acidobacteria on ecosystem functions at (line 298); Future research should explore the role of\nphototrophic bacteria as drivers of ecosystem (line314); These extensive discussion of future research directions appears\nunjustified since these proposed studies could and should have been addressed within the current experimental framework\nusing the existing data.\nMinor concerns:\n-1. The reliance on z-score and PCA-based indices is less informative than threshold-based or averaging approaches,\nwhich better capture trade-offs and synergies among functions. Why were more advanced multifunctionality metrics (e.g.,\nmultiple threshold analysis) not considered?\n-2. The RF analysis lacks critical details:\nHow were hyperparameters (e.g., number of trees, mtry) optimized?\nWas out-of-bag (OOB) error or cross-validation used to assess model performance?\nWere variable interactions examined, given that microbiome-environment relationships are often non-linear?\n-3. The authors did not explain the difference between the black dashed lines and gray dashed lines in Figure 3."}, "author_response": "Point-by-point response to reviewers\nIn this file, the line numbers associated with the reviewers’ comments (shown in black,\nreproduced verbatim) correspond to the previously submitted version of the manuscript,\nwhereas the line numbers in our responses (shown in blue) refer to the current revised\nversion (track-changes, simple markup).\nReviewer #1 – General comment (R1GC)\nDear authors, dear Ferran Romero,\nYour manuscript provides an insight into the soil microbiome as indicator of function\nacross Europe. It is an actual topic that is important for the future of sustainable food\nsystems. The work is novel and impactful by looking at a large number of sites across\nEurope and by combining amplicon sequencing data (16S rRNA gene & ITS sequences)\nwith indicators of microbial abundance (such as microbial biomass) and activity\n(enzymes). The large dataset on 500 soils across Europe is of interest for the scientific\ncommunity. The paper is overall well written, and I enjoyed reading the work.\nHaving said this, I have some larger points of feedback, mainly focusing on the set-up of\nthe study and the claimed impact on soil multifunctionality and agriculture.\nMultifunctionality is a central point of the paper. Overall “multifunctionality” would\nbenefit from a better definition at the start. What is multifunctionality within the context\nof your work, which main processes do you target and how do the parameters link to that?\nIt is important to stay close to your own work to avoid speculating too much.\nRegarding the conclusions and claims, I miss a section on the limitations and\nperspectives of this work. In line with the more specific comments given below, it would\nbe very useful to provide an overview of the current limitations and challenges of this\nwork. This would, in my opinion, better embed it within the current research field, and it\nwould better highlight the added value of this work without overinterpretation of the work\n/ the risk of speculation.\nThe figures are overall clear, and they provide good visual support for the written text. I\ncan see that this has been a lot of work, and I appreciate the e]orts. The supplementary\nfigures further support the work, and they facilitate future research on the topic.\nI have added my comments, linking to the points mentioned above under a major\ncomments section in the attachment. The minor comments section, also in the\nattachment, contains mainly minor textual and structural suggestions. I hope these\ncomments support you in further improving the manuscript.\nResponse to R1GC\nWe thank the reviewer for taking the time to carefully review our manuscript. We\nacknowledge that the definition of ecosystem multifunctionality is still debated within\nthe scientific community. This is why we stick to the definition and recommendations\ngiven in Garland et al 2021 (https://doi.org/10.1111/1365-2745.13511). To better align\nwith this, we have now re-named our functions to “functional proxies”, (see Garland et al\ntable 2). To increase clarity and help the reader understand the logic between our\nfunctional indicators and associated functions/services, we have now included a\nsupplementary table (Table S1, see also table below) linking our measured proxies to\nfunctions and broader service categories. We have also clarified and improved the\ndefinition of multifunctionality within the context of our article in the introduction (lines\n55-66). Note that, out of six, three of our indicators are enzymatic activities. We\nacknowledge this (lines 266-268) and argue that even if our multifunctionality index was\n“biased” towards organic matter processing, we selected 3 enzymatic activities that\nrepresent di]erent indicators of degradation targeting contrasting components of\norganic matter: N-acetyl-glucosaminidase, phosphatase, xylosidase. N-acetyl-\nglucosaminidase breaks down chitin and other complex carbohydrates into simple\nsugars, aiding in the degradation of fungal cell walls and invertebrate exoskeletons.\nPhosphatase releases inorganic phosphate from organic compounds, playing a key role\nin phosphorus cycling. Xylosidase breaks down hemicellulose by cleaving xylose units,\ncontributing to the decomposition of plant material (lines 103-107). In addition, we\nperformed an additional analysis and down-weighted the importance of the enzymes for\nmultifunctionality by giving them the same weight as the remaining three variables (e.g.\n25% of the three enzymes and 25% for the three other variables measures, see “weighted\nmultifunctionality index” (line 258). In line with the reviewer comments, we also expand\non the limitations and future research directions, including, among others, the fact that\nour analyses were restricted to the top 0–20 cm of soil (line 380), soils were frozen prior\nto analysis (line 352), and primary productivity was estimated from satellite imagery\nrather than direct biomass measurements (line 447).\nFunctional Indicator Ecosystem function/process Service category\nSoil aggregate stability Soil structure Erosion regulation/ Flood regulation/\nHabitat for organisms\nNet primary productivity Primary production Food and fiber/timber provision\nSoil basal respiration Microbial activity Climate regulation\nN-acetyl-glucosaminidase Nitrogen cycling Org. matter decomposition/ Nutrient\nactivity cycling\nPhosphatase activity Phosphorus cycling Org. matter decomposition/ Nutrient\ncycling\nXylosidase activity Carbon cycling Org. matter decomposition/ Nutrient\ncycling\nTable 1. Functional proxies measured in this study, their associated ecosystem functions or\nprocesses, and the broader ecosystem service categories to which they contribute. These indicators\nwere used to calculate ecosystem multifunctionality.\nReviewer #1 – Specific comment 1 (R1SC1)\nOverall, the pooling of all sampled soils in three major categories seems rather rough, as\nyou showed earlier that there are quite big di]erences in microbial composition between\ndi]erent woodland soils: Coniferous wood and broadleaved wood was always\nsignificantly di]erent in fungal and bacterial richness and Shannon diversity (Labouyrie\n2023). In addition, it is known that you find major di]erences in microbial communities\nof clay soils and sandy soils. This is not addressed in the paper, but it does need some\nattention. In addition, it is known that the pH has a strong e]ect on microbial\ncommunities. How does the relatively low pH of e.g. pine forests a]ect the results?\nThe consequence of zooming out to these three major categories is that you obtain very\ngeneral microbiomes.\nResponse to R1SC1\nWe sincerely thank Reviewer #1 for this thoughtful and important comment. Indeed, we\nfully agree that pooling all soils into broad land-use categories (cropland, grassland,\nwoodland) can obscure important ecological variation, particularly when known drivers\nsuch as soil texture or pH strongly influence microbial communities. In the initial version\nof the study, our rationale for using broad land-use categories was to identify microbial\nindicators that might be applicable across wide environmental gradients. However, as\nrightly pointed out, this aggregation resulted in a rather generalized view of soil microbial\ncommunities, limiting the resolution and ultimately the predictive power of the\nmicrobiome for multifunctionality. This limitation was also emphasized by both\nreviewers.\nIn response, we have now substantially revised the manuscript. Specifically, we have\nrestructured the analysis to include several relevant environmental groupings, including\nclimatic region (comparing continental, temperate dry and temperate humid regions),\nsoil texture (comparing clay, loam, sand), and soil pH (comparing acidic, neutral,\nalkaline), in addition to land use (lines 95-96, 146-147, 274 and Figure 1). This approach\nallowed us to explicitly assess the context-dependency of the microbiome’s contribution\nto multifunctionality. Our revised results show that the predictive capacity of the soil\nmicrobiome varies considerably across categories, being stronger in croplands, alkaline\nsoils, loams, and soils in temperate dry regions, while more limited in acidic soils (such\nas pine forest soils), and soils originating from continental climates (see e.g.., lines 415,\n420, 470, Figure 2-3). We now also perform variance partition analyses for individual\nfunctional proxies and show that the soil microbiome is the main predictor of soil\nenzymatic activities and primary productivity (Figures S4-S9).\nTo our knowledge, the novelty of our manuscript relies on the systematic evaluation of\nthe ability of soil microbial community composition to explain ecosystem\nmultifunctionality across such environmental contrasts, especially at the continental\nscale. We believe, in line with the reviewer, this additional analysis strengthens the\nmanuscript, enhances the resolution and relevance of our findings, and increases the\nnovelty and utility of the study for informing soil monitoring and ecosystem management\nstrategies.\nReviewer #1 – Specific comment 2 (R1SC2)\nThe z-score needs to be better embedded/introduced. A range of functions is mentioned\nin the introduction, but not all are represented in the z-score. If I see it correctly, the non-\nweighted calculation of z-score is based for 50% on enzymatic OM decomposing activity,\nand the rest on net primary productivity, microbial biomass and aggregation is used to\ndraw the conclusion that multifunctionality is driven by microbial factors. Individual\ngraphs per cover type do not support the claim that the z-score is driven by microbial\nfactors (Supplementary figure 2), neither do the SEM, and rather point at edaphic\nparameters to be driving the z-score I miss the reasoning on why you think these three\nenzymes can define (half of) multifunctionality.\nResponse to R1SC2\nWe thank the reviewer for this important point regarding the construction and\ninterpretation of the multifunctionality index based on the z-score transformation. We\nagree that in the initial version of the manuscript, the index did not su]iciently account\nfor the potential overrepresentation of enzymatic activities, which all relate to organic\nmatter decomposition. To address this concern, we have now implemented a weighted\nmultifunctionality index, where each function is given a proportionate weight.\nSpecifically, enzymatic activities (xylosidase, N-acetylglucosaminidase, and\nphosphatase) together contribute 25% of the final index (each with a weight of 0.25/3),\nwhile soil aggregation, net primary productivity, and basal respiration each contribute\n25% individually (see line 258). This adjustment ensures that decomposition-related\nprocesses do not disproportionately drive the multifunctionality metric. For downstream\nanalyses (variance partitioning, random forest, and SEMs), we now use the weighted\nindex instead of the z-score index applied in the first version of the manuscript. To provide\ntransparency, we also present individual values of all calculated indices (Table S3), and\nthe correlations among all calculated indices (average, z-score, weighted, and multiple-\nthresholds), showing that they are highly correlated with each other (see Figure S2).\nWe also expanded the explanation of the enzymatic functions to clarify their ecological\nrelevance. Although all relate to organic matter cycling, they target di]erent biochemical\npathways: phosphatase relates to phosphorus availability, N-acetylglucosaminidase\nreflects chitin decomposition, and xylosidase tracks plant litter breakdown via\nhemicellulose degradation. Thus, these enzymes provide complementary insights into\nbelowground nutrient cycling dynamics (see lines 100-107).\nMoreover, in the revised manuscript, we now use multiple multifunctionality indices,\nincluding (i) the weighted index described above, (ii) the unweighted z-score index,\n(iii) a simple average based on 0–1 standardization, and (iv) a multiple-threshold index.\nImportantly, we now also analyze the relationship between microbial composition and\nindividual functions, not just the multifunctionality index (see lines 252-268).\nIn this new version of the analysis, we show that the soil microbiome explains a\nsubstantial share of the unique variance for certain functions such as enzymatic\nactivities and primary production. These findings are now explicitly discussed and\nvisualized, and they support our conclusion that microbial indicators can be informative,\nbut context-dependent, predictors of soil multifunctionality (see e.g., lines 326, 336,\n357, 388, 397).\nReviewer #1 – Specific comment 3 (R1SC3)\nThe sampling time in spring makes it challenging to compare (mainly) the cropland to the\ngrassland and woodland sites. Please also discuss this in the paper.\nResponse to R1SC3\nThank you for this comment. We agree that the timing of sampling can influence\ncomparisons across land-use types. LUCAS 2018 soil samples were collected between\nMay and September, during the main vegetation period. For croplands, the protocol\naimed to sample during the growing season, avoiding post-harvest periods when\npossible. However, the vegetation cover in croplands is highly dependent on the local\ncrop calendar, meaning some samples may have been taken before or after harvest,\nunlike grasslands and woodlands, which generally have more stable cover during this\nperiod. This variability could influence certain ecological functions, particularly primary\nproduction, which is part of our multifunctionality index. To account for this, primary\nproduction was not estimated from the precise month of sampling but rather inferred\nfrom satellite imagery averaged across a ±6-month window around the sampling date\n(see line 243). This approach reduces the potential bias introduced by vegetation cover\ndi]erences across land uses and sampling times. We now briefly discuss limitations\nrelated to the use of satellite data (line 447). Note, however, that this concern is less\ncritical in the new (revised) version of the manuscript, as we now also classify samples\nusing parameters that are more stable over time, such as soil texture. These provide a\nmore consistent basis for comparison than vegetation cover alone (lines 94-98).\nReviewer #1 – Specific comment 3 (R1SC3)\nThe paper stresses a lot on taxa as valuable multifunctionality indicators. The focus on\nthree major areas however has several limitations, which should be better discussed. At\nthe end the need for conservation strategies that preserve microbial-driven functions in\ndiverse land uses are highlighted. However, by focusing on three major land use types\nand using DNA-based taxonomy at higher taxonomic level this feels like a bridge too far.\nIt is very speculative. The link to the z-score is also relevant here: The z-score cannot tell\nmultifunctionality from grasslands and forests apart, how useful is it then in practice?\nResponse to R1SC3\nWe thank the reviewer for this constructive comment. We acknowledge that our initial\nfocus on three major land-use types, along with the use of higher-level taxonomic data,\nmay have limited the depth and applicability of our conclusions regarding microbial\nindicators of multifunctionality. To address these concerns, we have made several\nsubstantial changes in the revised manuscript. First, we now go beyond the initial land-\nuse categories and classify soils according to key abiotic variables: climatic region\n(continental, temperate dry, temperate humid), pH (acidic, neutral, alkaline), and soil\ntexture (clay, loam, sand) (see lines 128-133, 144-147). These classifications o]er greater\nstability over time and allow us to test the predictive power of the soil microbiome across\na broader range of environmental contexts. This approach also responds directly to the\nconcern that a land-use–only focus is overly simplistic and potentially obscures\nmeaningful variation.\nSecond, we agree that higher-level taxonomic data (e.g., phylum or class) may not be the\nmost suitable for identifying reliable bioindicators. We have now implemented a network-\nbased analysis using Weighted Gene Co-expression Network Analysis (WGCNA) to group\nOTUs into modules based on co-occurrence patterns (see lines 190-203). These\nmodules better capture microbial community structure and were used as predictors in\nall downstream analyses, including Random Forest and structural equation models (line\n269).\nThird, we have substantially revised our multifunctionality assessment. In addition to the\nz-score index, we now include a simple average of 0–1 standardized functions, a weighted\nindex that accounts for the redundancy of enzymatic activities, and a multiple-threshold\nindex assessing the number of functions above defined percentiles (see lines 252-268).\nThis approach provides a more robust and nuanced view of ecosystem multifunctionality\nand addresses the reviewer’s concern regarding the limited sensitivity of the z-score\nindex alone.\nFinally, we have revised the language in the discussion and conclusions to avoid\noverextending our findings. We now emphasize that microbial indicators show context-\ndependent potential and that our aim is to identify patterns across environmental\ngradients, rather than propose universally applicable taxa for conservation strategies\n(line 326). We hope these revisions adequately address the reviewer’s concerns and\nimprove the rigor and applicability of our study.\nReviewer #1 – Specific comment 5 (R1SC5)\nIs there a specific reason/motivation to not include AMF in the work?\nAMF were e.g.\nincluded in the work of Labouyrie (2023), but not in this work. It is good to, at least, shortly\nmention the motivation for not including this here.\nResponse to R1SC5\nIn the current version of our manuscript, we no longer use taxonomy-based functional\npredictions, as these have been extensively addressed in previous studies. Instead, we\nfocus on groups of co-occurring OTUs defined as modules. We note, however, that ≈1%\nof the fungal OTUs were mycorrhizal (phylum Glomeromycota), and these were present\nin several modules (e.g., fungal module 5, see line 371). We also identified other\nsymbiotic fungi (ectomycorrhiza, e.g., Archaeorhizomyces) in module 5, which\nconsistently emerged among the top predictors of multifunctionality in specific contexts\n(woodland soils, sandy soils, neutral soils, and soils of continental origin). These points\nare now clarified in the revised manuscript (see lines 355-382), and full taxonomic details\nfor each fungal and bacterial module are provided in Supplementary Dataset 1 and\nFigure S10 and S11.\nReviewer #1 – Specific comment 6 (R1SC6)\nI could not find a link to the sequencing data (online archive). This needs to be included.\nResponse to R1SC6\nThank you for pointing this out. This study used DNA sequences generated during the\n2018 campaign of the LUCAS soil survey, coordinated by the European Commission.\nAccordingly, DNA sequences are accessible upon request via the European Soil Data\nCentre (ESDAC). Researchers can request access to the sequences by filling in the form\navailable here: https://esdac.jrc.ec.europa.eu/content/soil-biodiversity-dna-bacteria-\nand-fungi#tabs-0-description=1. This has been included in the Data availability\nstatement.\nReviewer #1 – Specific comment 7 (R1SC7)\nLine 42-43: microbial biomass as driver of microbial-driven functionality – circular\nreasoning? Please rephrase.\nResponse to R1SC7\nIn the new version of the manuscript, we do no longer use the concept of microbial-driven\nfunction, instead we refer to functional proxies, their associated ecosystem functions,\nand the ecosystem service categories they represent (see e.g., lines 40, 63, 100, 205),\nsee also Table S1).\nReviewer #1 – Specific comment 8 (R1SC8)\nLine 52-53: you focus on functions at di]erent levels/magnitudes. It is useful to have a\nlook at Creamer et al. (2020): The life of soils: Integrating the who and how of\nmultifunctionality. E.g. nutrient cycling is a main function, but water holding capacity is a\nsmaller part of “water regulation and purification”\nResponse to R1SC8\nThank you for this pointing out. We’ve aligned our framing with Creamer et al. (2020) by\nexplicitly mapping each functional proxy to an ecosystem function/process, and a\nbroader service category (Table S1, see also lines 60-66). For example, soil aggregate\nstability (soil structure) links to erosion/flood regulation and habitat provision; enzyme\nactivities (N, P, C cycling) link to nutrient cycling and organic matter decomposition; NPP\n(primary production) to food/fiber provision; and basal respiration (microbial activity) to\nclimate regulation. This hierarchy is now summarized in Table S1 and used consistently\nthroughout the manuscript. We have also added the reference suggested by the reviewer\n(line 63).\nReviewer #1 – Specific comment 9 (R1SC9)\nLine 58: “soil biodiversity tends to support higher multifunctionality” rather, this is higher\nlevels of functioning within these specific functions (that you consider for the study).\nResponse to R1SC9\nInstead of stating that soil biodiversity supports higher multifunctionality, we now clarify\nthat it is associated with higher levels of functioning within the specific functions\nassessed in this study (line 64-66).\nReviewer #1 – Specific comment 10 (R1SC10)\nLine 61-61: it is not clear to me what you mean by “critical aspects of function”. Could\nyou clarify this?\nResponse to R1SC10\nSince this is now addressed in lines 64-66 (see R1SC9), we have removed this and the\nparagraph now starts directly like this “Microorganisms are sensitive, ubiquitous, and\nreact quickly to environmental stress, reflecting cumulative impacts” (line 67).\nReviewer #1 – Specific comment 11 (R1SC11)\nLine 61-69: this needs some nuance regarding studies on microbial activity vs studies on\npresence (e.g.DNA-based studies)\nResponse to R1SC11\nWe have re-phrased the introduction and the sentence referred to in this comment does\nno longer appear.\nReviewer #1 – Specific comment 12 (R1SC12)\nLine 71: “First, focus is often on their activity” → First, the focus is often on their general\nactivity\nResponse to R1SC12\nWe have re-phrased the introduction and the sentence referred to in this comment does\nno longer appear.\nReviewer #1 – Specific comment 13 (R1SC13)\nLine 71-72: you emphasize the focus on general activity rather than taxonomy. However,\nthe activity is also a large part of the z-score you use. How do you look at this in your work?\nResponse to R1SC13\nIn the introduction, our intention was to highlight that many studies use microbial activity\n(e.g., enzymatic activity) rather than taxonomy (e.g., OTU relative abundance) as\npredictors of ecosystem functions and services. In our case, while we do measure\nmicrobial activity, we treat this as a functional endpoint rather than as a predictor. We\nagree with the reviewer that our original wording may be misleading, and we have\ntherefore rephrased the introduction to clarify that our predictors of multifunctionality\nare based on soil properties, climate, and the soil microbiome (lines 93-119).\nReviewer #1 – Specific comment 14 (R1SC14)\nLine 81: Here you mention the “valuable multifunctionality indicators”. I would like this to\nbe mentioned at the start, to make it immediately clear that this is your focus.\nResponse to R1SC14\nIn the revised version of the manuscript, we now introduce the concept of functional\nproxies earlier in the introduction (lines 55-66). This makes it immediately clear that the\nmanuscript focuses on evaluating ecosystem multifunctionality by means of six\nfunctional proxies (three enzymes, productivity, respiration, and soil aggregation, see\ndetails in lines 101-107).\nReviewer #1 – Specific comment 15 (R1SC15)\nLine 82-83: please elaborate on these other projects and highlight the novelty.\nResponse to R1SC15\nIn the revised version of the manuscript, we no longer focus on comparisons between\nmanaged vs. unmanaged ecosystems. Instead, we expand our analyses to include\nmultiple environmental dimensions, grouping soils not only by land use but also by\nclimatic region, soil texture, and soil pH (lines 130-133, 144-147). This broader\nperspective allows us to highlight the novelty of our work, as it reveals how the predictive\ncapacity of the soil microbiome for ecosystem multifunctionality depends strongly on the\nenvironmental context (line 326).\nReviewer #1 – Specific comment 16 (R1SC16)\nLine 85: this sentence needs references to studies in Australia, USA, China.\nResponse to R1SC16\nIn the revised version of the manuscript, we no longer make specific reference to\nAustralia, USA, or China at this point of the text. Instead, we have reformulated the\nsentence and included relevant references to support the statement in a broader and\nmore balanced way (lines 83-92).\nReviewer #1 – Specific comment 17 (R1SC17)\nLine 85-86: The claim that Europe is one of the most important economic and ecological\nregions with one of the longest histories in land management is quite Eurocentric, and\nnot necessarily accurate. Formulate this in a more neutral way.\nResponse to R1SC17\nWe have rephrased the sentence (lines 88-89).\nReviewer #1 – Specific comment 18 (R1SC18)\nLine 87: this needs a reference.\nResponse to R1SC18\nWe have rephrased this sentence and added relevant references (lines 89-90).\nReviewer #1 – Specific comment 19 (R1SC19)\nLine 88-90: this is very general and not yet clear. What does it mean in the context of your\nstudy? Is this what you (want to) show?\nResponse to R1SC19\nThank you for the suggestion. We have rephrased the sentence to align with what we\nmeasure in the manuscript: soil texture, pH, land use, and climatic region. The revised\nwording now focuses on how the soil microbiome relates to ecosystem functions across\nthese specific contexts, providing a clear lead-in to the following paragraph where we\ndescribe our study design and analyses (lines 90-92).\nReviewer #1 – Specific comment 20 (R1SC20)\nLine 113-114: also shortly mention the motivation to go for 0-20 cm for all samples.\nSeveral studies perform di]erent sample strategies for arable, grassland and forest soils\ndue to expected di]erences in soil profiles / rooting depth. What was your motivation to\ngo for a general sampling depth? In addition, multifunctionality at greater depth might be\nimportant (e.g. Xu et al., 2023 https://doi.org/10.1016/j.scitotenv.2023.162972)\nResponse to R1SC20\nWe thank the reviewer for this important comment. Our study relied on the European\nCommission–coordinated LUCAS sampling campaign (Orgiazzi et al., 2018;\nhttps://doi.org/10.1111/ejss.12499), which is a large-scale e]ort where several thousand\nsoil samples are collected across Europe, of which almost 500 were used for\nmetabarcoding in our study. Given the scale of this campaign, only one soil horizon per\nsite can be sampled, as including multiple depths would dramatically increase the total\nnumber of samples. The LUCAS protocol therefore focuses on the 0–20 cm layer, as this\nis the most practical to sample consistently across thousands of sites, and the soil layer\nwhere most microbial metabolic activity is expected, particularly aerobic degradation of\norganic matter.\nWe fully agree with the reviewer that exploring the potential of the soil microbiome as a\npredictor of ecosystem multifunctionality at greater depths is a promising research\ndirection. Such work could provide valuable insights into specific ecosystem functions,\nfor example greenhouse gas dynamics, since deeper layers are more likely to host\nmethanogens and other relevant microbial groups. We now acknowledge this as an\navenue for future research in the manuscript (lines 378-382).\nReviewer #1 – Specific comment 21 (R1SC21)\nLine 127: this is the old soil pH protocol. New one: 10390:2021. In addition, also mention\nwhich extraction liquids were used: water, CaCl2 and/or KCl?\nResponse to R1SC21\nWe thank the reviewer for this observation. The soil pH values used in our study come\nfrom the LUCAS 2018 sampling campaign (see\nhttps://esdac.jrc.ec.europa.eu/content/lucas-2018-topsoil-data), where pH was\ndetermined according to ISO 10390:1994 using 0.01 M CaCl₂ as the extractant. We have\nclarified this in the manuscript (lines 41-42). We are aware that the most recent standard\nis ISO 10390:2021 (which replaced the one from 2005), but since our data originate from\nLUCAS, which followed the 1994 version, we report the original protocol here for\ntransparency.\nReviewer #1 – Specific comment 22 (R1SC22)\nLine 137: please also mention here that this is a proxy for microbial biomass.\nResponse to R1SC22\nDone, we have re-phrased this sentence to clarify that (see line 155).\nReviewer #1 – Specific comment 23 (R1SC23)\nLine 144: as mentioned before, it is good to highlight that AMF were not taken into\naccount, since ITS data does not capture them. In the paper by Labouryie it was\nmentioned that 18S rRNA gene analysis was performed, it is good to refer to this work and\nalso shortly mention why this data is not shown here.\nResponse to R1SC23\nWe thank the reviewer for this helpful comment. As correctly noted, we did not include\nAMF in our analyses because the sequencing region we used (ITS) is not optimal for\ncapturing AMF. In addition, the LUCAS sampling campaign works with bulk soil samples,\nwhereas AMF are primarily associated with the rhizosphere due to their symbiotic\nlifestyle. More importantly, we also decided not to include AMF or other functional groups\n(e.g., nitrogen-fixing bacteria), since there are already several studies focusing on\nfunctional groups (see for example our recent publication on soil health, Romero et al.,\nhttps://www.nature.com/articles/s41559-024-02511-8). Instead, in the new version of\nthe present manuscript we focus on groups of co-occurring OTUs (networks), which does\nnot rely on database-dependent functional annotations. Note however, that some\nmycorrhizal OTUs were found within our co-occurring modules, and that ≈1.00% of\nfungal OTUs were annotated as Glomeromycota. We now acknowledge this and its\nimplications through the manuscript (lines 190-203, 355, 373-376).\nReviewer #1 – Specific comment 24 (R1SC24)\nLine 147: also, shortly mention that this deviates from the standard 0.25 g.\nResponse to R1SC24\nIn our study, we used 0.20 g of soil for DNA extraction instead of extraction instead of 0.25 g. This choice is\nsupported by previous work showing that extractions from 0.2 g and 1 g soil samples yield\nstable numbers of taxa and comparable α- and β-diversity indices across taxonomic\nranks, with 0.2 g often preferable due to improved DNA shearing e]iciency and reduced\nreagent use (see this reference https://doi.org/10.1371/journal.pone.0260121, now cited\nin methods, line 163). To ensure su]icient DNA yield and capture microbial diversity, we\nperformed extractions in triplicate and pooled the DNA prior to sequencing (indicated in\nlines 162-163).\nReviewer #1 – Specific comment 25 (R1SC25)\nLine 150: is the relatively low number of cycles based on an optimized protocol? Good to\nshortly mention.\nResponse to R1SC25\nWe thank the reviewer for pointing this out. We have rephrased the sentence to indicate\nthat the relatively low number of PCR cycles follows an optimized protocol derived from\nthe Earth Microbiome Project, which aims to minimize PCR bias while still providing\nsu]icient yield for sequencing (lines 168-169).\nReviewer #1 – Specific comment 26 (R1SC26)\nLine 154-157: please also mention the 16S rRNA gene regions that were targeted by your\napproach.\nResponse to R1SC26\nThese primers target the V4-V5 hypervariable region of the gene; we have now added this\nto the text (lines 173-174).\nReviewer #1 – Specific comment 27 (R1SC27)\nLine 165: it is good to mention/stress that default parameters / standard settings were\nused, if this was the case. Otherwise the specific modifications need to be mentioned\nhere.\nResponse to R1SC27\nWe now stress that default parameters were used (line 184). Note we also cite the\nrelevant reference (Tedersoo et al., line 185).\nReviewer #1 – Specific comment 28 (R1SC28)\nLine 167-168: not clear if this is for the total dataset, or per sample?\nResponse to R1SC28\nRarefaction values refer to number of sequences per sample, we have now added this\ninformation to the text (line 187).\nReviewer #1 – Specific comment 29 (R1SC29)\nLine 169: what were the considerations to use this older reference dataset? I would\nexpect the newer Silva database (v138) to be a better reference, since it is 4 years newer.\nResponse to R1SC29\nWe thank the reviewer for this suggestion. We used the RDP v16 database in this study to\nensure consistency with other works based on the LUCAS 2018 dataset, which all\nemployed the same reference for taxonomic annotation. Since a key aim of our study is\ncomparability within this body of work, we preferred to retain the same database here.\nWe fully agree that newer databases such as SILVA v138 o]er improved resolution. In\nfact, for the ongoing analyses of the LUCAS 2022 dataset we are adopting the updated\ntaxonomy annotation, but for the present study we opted for RDP to preserve\ncomparability.\nReviewer #1 – Specific comment 30 (R1SC30)\nLine 184: here it becomes clear that the samples were frozen, which will have an impact\non the potential soil basal respiration rate. Please emphasize this and let this come back\nin the results and discussion section. This is not necessarily a major issue, but it needs\nto be clearly indicated for correct interpretation.\nResponse to R1SC30\nWe thank the reviewer for this important comment. We agree that freezing can a]ect soil\nrespiration rates, particularly in the first days after thawing. However, our previous\nexperience in the lab has shown that acclimating soils for ~4 days results in consistent\nand comparable respiration measurements across replicates. For this reason, as\nindicated in the Methods, soils were acclimated in the lab for 5 days prior to measuring\nrespiration. To illustrate this, we attach to this response (see below) a figure showing CO₂\nemissions measured during the first 250 hours after thawing in nine replicate samples\n(from a nearby grassland). As can be seen, respiration rates stabilize after ~3–4 days,\nwhile the higher variation observed immediately after thawing reflects a short-lived peak\nof organic matter consumption from dead cells that did not survive freezing. In line with\nthe reviewer’s suggestion, we now also briefly mention this as a limitation in our study\n(lines 352-354).\nFigure 1. Soil CO₂ emissions during the first 250 hours after thawing (nine replicates from a nearby\ngrassland). These measurements were taken using an Aeris Ultra gas analyzer model 100880,\navailable at the École Polytechnique Fédérale de Lausanne (EPFL, Sion, Switzerland). Unpublished\ndata.\nReviewer #1 – Specific comment 31 (R1SC31)\nLine 231: Is an R2 of 0.83 su]icient to continue with the weighted z-score? It would be\ngood to shortly mention this.\nResponse to R1SC31\nWe thank the reviewer for this comment. We think that an R2 of 0.83 indicates a good level\nof agreement, and this supported our decision to proceed with the weighted index. More\nimportantly, we chose the weighted index because it best reflects the concept of\necosystem multifunctionality in our study. Three out of the six functional indicators we\nmeasured are enzymatic activities; although they target di]erent processes (carbon,\nphosphorus, and nitrogen cycling), they are all related to organic matter decomposition.\nWithout weighting, the multifunctionality index would be biased towards enzymatic\nactivity and would resemble an “enzymatic activity index.” The weighted index ensures\nthat all functions are given equal weight (lines 266-268). For transparency, we now\nprovide a supplementary figure (Figure S2) showing correlations among all calculated\nindices.\nReviewer #1 – Specific comment 32 (R1SC32)\nLine 260-261: “even after accounting for critical ecosystem aspects such as climate and\nsoil properties” Here I am missing a short discussion on the impact of soil texture. Could\nyou elaborate on that?\nResponse to R1SC32\nWe thank the reviewer for this comment. In the revised version of the manuscript, we\nhave removed the sentence in question. Following the suggestions from both reviewers 1\nand 2, we now explicitly group the samples according to land use (cropland, grassland,\nwoodland), climatic region (continental, temperate dry, temperate humid), soil texture\n(clay, loam, sand), and soil pH (acidic, neutral, alkaline) (lines 130-133, 144-147). Thus,\nsoil texture is no longer treated as an accessory variable but rather placed at the core of\nour analysis. This allows us to directly test how the soil microbiome can serve as a\npredictor of ecosystem multifunctionality compared to soil properties (e.g., nutrient\ncontent, organic carbon, microbial biomass) across these diverse contexts. We now\ndiscuss on the impact of soil texture in section “Context-dependency of soil microbiome\ncontributions to multifunctionality” (e.g., lines 326, 333, 348, 416).\nReviewer #1 – Specific comment 3 (R1SC33)\nLine 261: have a critical look here, you measured proxies/processes that are part of bigger\nfunctions.\nResponse to R1SC33\nWe thank the reviewer for this observation. Following the comments from both Reviewer\n1 and Reviewer 2, we have clarified our terminology and now refer to functional proxies,\nwhich serve as indicators of ecosystem functions/processes that in turn can be grouped\ninto broader ecosystem service categories (lines 60-62, line 204). To make this explicit,\nwe have added a supplementary table (Table S1) that provides the rationale for our\nreasoning, showing how each proxy (e.g., enzymatic activities, soil aggregation, net\nprimary productivity) links to specific functions/processes and their associated service\ncategories.\nReviewer #1 – Specific comment 34 (R1SC34)\nLine 263-266: please carefully check the sentence and the text between brackets, this\ndoes not run smoothly.\nResponse to R1SC34\nThe text mentioned by the reviewer here does no longer appear in the new version of the\nmanuscript.\nReviewer #1 – Specific comment 35 (R1SC35)\nLine 268-269: “the soil microbiome explained a significant portion of variation of\nmultifunctionality across land uses”: this needs a bit more critical discussion. What\nexplains what, are we looking at correlation or causality? I would think you can find\npatterns/correlations, but not more than that at this point.\nResponse to R1SC35\nWe thank the reviewer for this important point. The manuscript has been substantially\nrevised to provide stronger evidence regarding the explanatory power of the soil\nmicrobiome. Following the comments from both Reviewer 1 and Reviewer 2, we now\nperform variance partitioning analyses to disentangle the unique contributions of the soil\nmicrobiome, soil properties, and climate in explaining variation in ecosystem\nmultifunctionality and individual functions across soils grouped by land use, climatic\nregion, soil texture, and pH (Figure 3, Figure S4-S9). Our results show that soil properties,\nparticularly total nitrogen content and microbial biomass, are the strongest predictors of\nmultifunctionality (see Figure 2), largely due to their influence on basal respiration and,\nto a lesser extent, soil aggregation (see Figure S8-S9). In contrast, for other individual\nfunctions such as enzymatic activities and primary productivity, the soil microbiome\nexplains the largest share of variation (see Figure S4-S7). Finally, while we acknowledge\nthe observational nature of our design (lines 427-431), we apply structural equation\nmodels to go beyond simple correlations and provide insights into potential causal\npathways (lines 295-300).\nReviewer #1 – Specific comment 36 (R1SC36)\nLine 273-274: “helping with the monitoring of soils as sentinels of function”: as\nmentioned before, stay closer to the work/data. This would need a lot more\ninformation/research, stay a bit closer to your work.\nResponse to R1SC36\nWe have re-worked the opening paragraph of the results/discussion section to stay closer\nto our data (lines 302-325). In the revised version, we now emphasize that the\ncontribution of the soil microbiome to ecosystem multifunctionality and individual\nfunctions is strongly context-dependent, varying across soil texture, climatic region, land\nuse, and soil pH. This way we remain focused on our results and avoid speculative\nstatements beyond the scope of our study.\nReviewer #1 – Specific comment 37 (R1SC37)\nonly in some cases does a soil microorganism (taxon) have significant impact on the z-\nscore.\nResponse to R1SC37\nIn the revised version of the manuscript, we have re-worked the analyses by grouping taxa\ninto co-occurring modules and using these modules as predictors of multifunctionality\nand individual functions (lines 190-203, line 204). Applying random forest models, we\nnow show that specific groups of taxa (i.e., modules) emerge as significant predictors of\necosystem functions (Figure 2), although their importance is highly context-dependent,\nvarying with land use, climatic region, soil pH, and soil texture. Using this approach,\nwe find that microbial contributions to multifunctionality are context-dependent and vary\nwith land use, soil texture, pH, and climate. We now provide details on the taxonomic\nidentity within these modules and point towards specific microbial taxa: Actinobacteria\n(e.g., Sphingomonas, Gaiella), Acidobacteria, and fungal taxa such as Mortierella (lines\n355-382, Figure S10-S11, supplementary dataset 1).\nReviewer #1 – Specific comment 38 (R1SC38)\nThe multifunctionality of grassland and woodlands cannot be told apart from each other.\nWhat does that mean? They have equal multifunctionality?\nResponse to R1SC38\nThe lack of a clear di]erence between grasslands and woodlands in our initial analysis is\nlikely due to the large variation in multifunctionality observed within woodland soils (see\nFigure 1, Table S3). To address this, we now provide additional groupings of samples by\nclimatic region, soil pH, and soil texture (lines 130-134, lines 144-147). Note that, when\ngrouped by climatic region, multifunctionality di]ers significantly: soils from temperate\nhumid regions show the highest multifunctionality, followed by continental and then\ntemperate dry soils (Figure 1, Table S3).\nReviewer #1 – Specific comment 39 (R1SC39)\nLine 279: “driven by” word choice – rather: correlated with?\nResponse to R1SC39\nWe have rephrased this section to remain closely aligned with our results and avoid\nspeculation or overstatement. We now emphasize that the soil microbiome accounts for\na unique share of the variance, particularly in explaining variation in enzymatic activities\nand primary productivity (see line 321-325, Figure S4-S7).\nReviewer #1 – Specific comment 40 (R1SC40)\nLine 281: avoid using the word “significant” when this is not statistically supported.\nResponse to R1SC40\nThe word “significant” does no longer appear in this section or anywhere else in the\nmanuscript unless it is supported by a statistical test.\nReviewer #1 – Specific comment 41 (R1SC41)\nLine 289: Acidobacteria Gp5 seems very interesting as potential indicator. I think you can\nput a bit more emphasis on this, since it is a very nice finding of your work. Also here, it is\nuseful to highlight on the taxonomic results, since this has been a major part of your work.\nResponse to R1SC41\nWe agree with the reviewer that Acidobacteria Gp5 is a particularly interesting finding. In\nthe revised version of the manuscript, we have given more emphasis to this result (lines\n366, 493). Importantly, we now work at the OTU level by grouping OTUs into co-occurring\nmodules, which allows us to highlight the role of Acidobacteria Gp5 and other taxa within\nthe broader taxonomic structure of microbial communities as predictors of\nmultifunctionality (line 355-382).\nReviewer #1 – Specific comment 42 (R1SC42)\nLine 300: “can indeed be used as predictors of multifunctionality” – this needs a bit more\nexplanation on the how.\nResponse to R1SC42\nIn the revised version of the manuscript, we now expand on how these taxa could be used\nas predictors. Specifically, we highlight that the taxa identified could be incorporated into\nqPCR assays, integrated into soil health indices, or used as features in predictive models\n(see line 384).\nReviewer #1 – Specific comment 43 (R1SC43)\nLine 309: Your study did not focus on the metabolic versatility, but rather on e.g. the wide\noccurrence.\nResponse to R1SC43\nRemoved. Thanks for pointing this out.\nReviewer #1 – Specific comment 44 (R1SC44)\nLine 311: I was a bit confused about the relevance of phototrophic bacteria in (dark) soils.\nAre you sure this is not caused by chloroplast / plant material presence in the sample?\nAnd, if this is not the case: how relevant do you think that phototrophic bacteria can be\nin soils?\nResponse to R1SC44\nIn the revised version of the manuscript, we no longer use taxonomy-based predictions\nof functional groups and instead focus on modules of co-occurring OTUs, in line with the\nreviewers’ recommendations. This section has therefore been removed from the\nmanuscript.\nReviewer #1 – Specific comment 45 (R1SC45)\nLine 320: you mention key drivers, but this is very tricky to conclude based on DNA\namplicon data (on relative abundances). Please formulate this more carefully.\nResponse to R1SC45\nWe thank the reviewer for this important remark. The sentence referring to “key drivers”\nhas been removed in the revised version of the manuscript. We have also carefully\nproofread the text throughout to ensure that we do not overstate our findings and that we\nremain closely aligned with the results.\nReviewer #1 – Specific comment 46 (R1SC46)\nLine 331: you mention that it is worsened by cropland management: what type of\nmanagement? There can also be positive management practices, this needs a specific\ncontext.\nResponse to R1SC46\nThe statement referred to the study by Sunnemann et al., which compared a relatively\nunaltered grassland with a cropland receiving fertilizers and pesticides. To be more\nprecise and avoid ambiguity, we have replaced “cropland management” with “fertilizer\nand pesticide application” in the revised manuscript (see line 444-445).\nReviewer #1 – Specific comment 47 (R1SC47)\nLine 332: also shortly discuss the trade-o]s as are also mentioned in e.g. the paper of\nVazquez et al. (2021) that you cite.\nResponse to R1SC47\nWe have expanded this in added a new relevant reference (line 436-440).\nReviewer #1 – Specific comment 48 (R1SC48)\nLine 333-334: you mention aboveground processes, but only 1 was included: primary\nproductivity.\nResponse to R1SC48\nTo be transparent here and avoid ambiguity, we have replaced “aboveground processes”\nby “primary productivity” here (see line 445, 455). Moreover, we now acknowledge as a\nlimitation/future research direction that future studies should include other aboveground\nprocesses (e.g., greenhouse gas emissions, line 456).\nReviewer #1 – Specific comment 49 (R1SC49)\nLine 335: “to enhance accuracy”: this needs a bit of support, how do you see this?\nResponse to R1SC49\nWe thank the reviewer for this helpful comment. In our study, productivity was estimated\nfrom satellite imagery, which provides broad-scale coverage but is limited by spatial\nresolution and indirect inference. Direct measurements of actual biomass (e.g.,\nkilograms of dry weight per hectare) would o]er a more accurate and site-specific\nestimate of productivity. We now clarify this in the manuscript (lines 447-453).\nReviewer #1 – Specific comment 50 (R1SC50)\nLine 339-340: please add a bit of perspective on how you see this. Do you have specific\nfunctions in mind that should be included based on your work? This can be very helpful\nfor future studies.\nResponse to R1SC50\nIn the revised version of the manuscript, we have added perspective on functions that\nshould be considered in future studies. In particular, we highlight greenhouse gas\nemissions, as they represent a key soil-mediated process with strong implications for\nclimate regulation and ecosystem multifunctionality (see line 456).\nReviewer #1 – Specific comment 51 (R1SC51)\nLine 351: “biodiversity” → soil biodiversity\nResponse to R1SC51\nAdded (line 469).\nReviewer #1 – Specific comment 52 (R1SC52)\nLine 358-361: in line with previous comments, this is very speculative. Stay a bit closer to\nyour work and what you have shown here.\nResponse to R1SC52\nWe have rephrased the last paragraph of the discussion to stay closer to our results (lines\n459-481).\nReviewer #1 – Specific comment 53 (R1SC53)\nLine 390: here nine ecosystem functions are mentioned, whereas the methods focuses\non six. Is this correct?\nResponse to R1SC53\nThis is a mistake. Six ecosystem functions were measured. This has been corrected\nthrough the manuscript (line 40, 205, 252, 259, 305, 454).\nReviewer #1 – Specific comment 54 (R1SC54)\nFigure 1 is visually very clear, but please check the contents. The means of Grasslands\nand Woodlands seems to be the same, whereas the text mentions z-scores of 0.13 and\n0.26, respectively. The error bars would suggest di]erent significance levels (a, b), please\ndouble check that this is correct.\nResponse to R1SC54\nWe have re-worked and improved the figure and performed Chi-squared tests again.\nGrasslands and woodlands indeed do not significantly di]er in terms of\nmultifunctionality from each other (see Figure 1). We also indicate in the figure caption\nhow to interpret the boxplot.\nReviewer #1 – Specific comment 55 (R1SC55)\nFigure 2: are these results linked to the individual plots in the appendix information (S2-\nS4)? The results in those figures do not always match with the results presented here.\nPlease carefully check this.\nResponse to R1SC55\nThank you for noting this. We have re-done all main and supplementary figures and tables\nto ensure full consistency. In the revised manuscript: Figure 2 presents the random forest\nresults for all grouping categories; Figure 3 shows the variance partitioning for ecosystem\nmultifunctionality; and Supplementary Figures S4–S9 report variance partitioning for\neach individual function. Captions and cross-references were updated accordingly.\nReviewer #1 – Specific comment 56 (R1SC56)\nFigure 3: based on this result it becomes clear that the link between bacteria and\nmultifunctionality is very challenging. This is not clear from the results/discussion\nsection. Make sure to also describe this well.\nResponse to R1SC56\nWe thank the reviewer for this valuable comment. In the revised version of the\nmanuscript, we now focus on the potential of the soil microbiome to explain variation in\nmultifunctionality across di]erent contexts (land use, climatic region, soil pH, and soil\ntexture). We make explicit that the link between bacteria and multifunctionality exists\nonly to a certain extent, as shown in the variance partitioning analyses (Figure 3, Figure\nS4-S9). Furthermore, we have re-worked the structural equation models and now apply\nmulti-group SEMs to provide a clearer comparison of the interdependencies among\nclimate, soil properties, and the soil microbiome for ecosystem multifunctionality\n(Figure 4).\nReviewer #1 – Specific comment 57 (R1SC57)\nTable 1 needs a careful check. The units column suggests primary productivity is\npresented in kg C per square meter, but it likely is g carbon? Otherwise values are\nextremely high. Primary productivity is highest in woodlands overall: measured over the\nseason? More younger/highly productive forest systems? Can be the case, but for\naverage values this seems unlikely.\nResponse to R1SC57\nWe thank the reviewer for this careful observation. Indeed, the units in Table 1 were\nincorrect: “kg” should have been “g,” and this has been corrected in the revised\nmanuscript. We have also moved the table to the supplementary information (see Table\nS3), since providing the exact values of each functional indicator per land use is not the\nprimary goal of this study. As for primary productivity, this was estimated from satellite\nimagery as described in Romero et al. (2024, Nature Ecology & Evolution,\nhttps://doi.org/10.1038/s41559-024-02511-8). To minimize seasonality e]ects,\nparticularly in grasslands and croplands, values were averaged over the six months\naround the sampling date. This methodological detail has now been explicitly included in\nthe methods section (see line 243). We also highlight the limitations associated to this in\nthe discussion (line 447-449).\nReviewer #1 – Specific comment 58 (R1SC58)\nEnzyme activity is in nmol per gram: dry weight?\nResponse to R1SC58\nYes, this has been corrected in the table (Table S3), and it was already indicated in the\nmain text (see line 235).\nReviewer #1 – Specific comment 59 (R1SC59)\nKruskal-Wallis test “0.000688”→ “6.88E-04”\nResponse to R1SC59\nThis has been addressed in the new version of the table (Table S3).\nReviewer #1 – Specific comment 60 (R1SC60)\nPlease check use of numbers behind comma for the table.\nResponse to R1SC60\nThis has been addressed in the new version of the table (Table S3).\nReviewer #1 – Specific comment 61 (R1SC61)\nFigure S1: TN is missing\nResponse to R1SC61\nTotal nitrogen appears now in Figure 2, since random forests are not a]ected by\nmulticollinearity. Note however that total nitrogen was excluded from structural equation\nmodels due to high correlation with soil organic carbon, this is indicated in line 292.\nReviewer #1 – Specific comment 62 (R1SC62)\nFigure S2: are the taxa represented in relative abundance? Please also mention this in the\ncaption. Linked to the discussion on the work: The first graph shows that choosing these\n6 parameters for multifunctionality is questionable. Sure, if there is more N in the system,\nthere might be more enzymatic activity, primary productivity and microbial biomass. But\ndoes it mean that in every system, there should be more N for higher multifunctionality?\nIt might make more sense to make these multifunctionality scores context-specific.\nResponse to R1SC62\nWe thank the reviewer for this comment. In the revised version of the manuscript, we no\nlonger present individual taxa in relative abundance. Instead, we apply the WGCNA\npackage to group OTUs into co-occurring modules, and use eigengene values, which\nsummarize the abundance patterns of OTUs within each module into a single\nrepresentative variable (lines 190-203). Regarding the question of context-specificity, we\nagree and have restructured our analyses accordingly. In the revised version, we group\nsoils into broader categories of land use, climatic region, soil texture, and pH, and focus\nour discussion on how multifunctionality varies across these contexts (lines 130-134,\n144-147).\nReviewer #1 – Specific comment 63 (R1SC63)\nFigure S5-S7: the resolution is too low to be able to read these figures.\nResponse to R1SC63\nWe have addressed this in the new submission.\nReviewer #2 – General comment (R2GC)\nThis study investigates the relationship between soil microbiome composition and\necosystem multifunctionality across European croplands, grasslands, and woodlands.\nWhile the manuscript is well-structured and employs robust statistical methods\n(Random Forest and SEM), the novelty and analytical depth of the study are limited\ncompared to existing literature. The link between microbial diversity and\nmultifunctionality has been extensively documented, including the roles of soil\norganisms (Fan et al., 2023. Soil biodiversity supports the delivery of multiple ecosystem\nfunctions in urban greenspaces), diversity (Delgado-Baquerizo et al., 2016. Microbial\ndiversity drives multifunctionality in terrestrial ecosystems), and land-use e]ects\n(Delgado-Baquerizo et al., 2020. Multiple elements of soil biodiversity drive ecosystem\nfunctions across biomes). The findings largely confirm prior knowledge rather than\no]ering new mechanistic insights. Additionally, the methodological approach to\nassessing the relationship of multifunctionality and soil microbiome lacks sophistication\ncompared to threshold-based or averaging methods used in previous studies, which may\nlimit the interpretability of the results. Conclusions lack su]icient support from the\ncurrent results. The current stage of this manuscript is not suitable to publish in Nature\nCommunications.\nResponse to R2GC\nWe sincerely thank the reviewer for the time and e]ort dedicated to evaluating our\nmanuscript. We greatly appreciate the constructive feedback, which has helped us\nimprove the quality, clarity, and novelty of the revised version. Below, we detail how we\naddressed the reviewer’s main concerns.\n1. Analytical depth and novelty\nWe agree that the analytical depth in the original submission could be improved. The\nrevised manuscript now extends beyond broad diversity–function relationships by\nintegrating additional dimensions of environmental context. Specifically, and in line with\nreviewer #1’s comments, we do not restrict analyses to land use categories (croplands,\ngrasslands, woodlands). Instead, we now also classify soils by soil texture (sand, clay,\nloam), climatic region (temperate dry, temperate humid, continental), and pH\n(acidic, neutral, alkaline). This broader framework (lines 130-134, 144-147) enables us\nto test whether di]erent microbial taxa are associated with multifunctionality in di]erent\nsoil environments, which is a highly novel aspect not currently covered in the scientific\nliterature at the continental scale as far as we know. Our results indeed show that\nindicator taxa of multifunctionality are not universal: for instance, the taxa linked to\nmultifunctionality di]ered across soil grouping categories (see lines 355-382, line 421).\nThis adds a novel contribution to the literature by demonstrating that a “one-size-fits-all”\nmicrobial indicator of multifunctionality is unlikely to exist, and that context-specific\nassessments are essential.\nWe agree with the reviewer that the previous version of the manuscript may have\nappeared limited in novelty, since the relationship between microbial diversity and\nmultifunctionality has already been documented in several important publications.\nThese earlier studies typically assessed broad-level patterns by grouping taxa at the\nphylum level (e.g., Fan et al., 2023), relying on general diversity indices such as Shannon’s\nindex (e.g., Delgado-Baquerizo et al., 2016; Yang et al., 2024), or classifying organisms\ninto functional guilds (e.g., saprotrophs, mycorrhizae; see our own previous work in\nNature Ecology & Evolution; Romero et al., 2024). While these approaches have\nadvanced the field, they do not provide the taxonomic resolution necessary to identify\nspecific microbial taxa with potential to serve as indicators in land management or\nrestoration contexts. Also, the current analysis contains many more sites (data points)\nand contrasting land use types, compared to earlier work which focused on limited sites\nor natural ecosystems.\nTo move beyond these limitations, in the revised version we now group bacterial and\nfungal OTUs into modules of co-occurring taxa and employ their eigengene values\n(summarizing module composition) as predictors of multifunctionality. This\napproach allows us to identify not just broad diversity e]ects but also the specific\nmicrobial taxa and co-occurring groups associated with multifunctionality (line 190). To\nour knowledge this is the first time such an approach has been implemented on a dataset\nof this size and geographic breadth. We therefore argue that the revised manuscript\nprovides a more novel and mechanistic perspective than before.\n2. Multifunctionality indices (methodological approach)\nWe recognize the reviewer’s concern that the initial manuscript employed a relatively\nnarrow approach to multifunctionality. In the revised version, we now include four\ncomplementary multifunctionality indices: simple averaging, z-scores, a weighted\nindex (to correct for the overrepresentation of enzymatic activities among our\nmeasured functions), and multiple-threshold approaches (25%, 50%, 75%) (see line\n252, Table S3). Additionally, we now present correlations among all indices to show their\nconsistency and justify why the weighted index was selected for downstream analyses\n(Figure S2, line 265-268). To support the conclusion that the soil microbiome contributes\nto variation in ecosystem multifunctionality, we now employ variance partition analyses\n(Figure 3) which were not included in the earlier submitted version. Furthermore, we now\nalso evaluate how the soil microbiome, soil properties, and climate explain variation not\nonly in multifunctionality but also in each individual functional proxy, thereby increasing\ninterpretability (Figure S4-S9).\n3. Tone and conclusions\nFinally, we have carefully rephrased and toned down several sections of the text to avoid\noverstating our results and to ensure that conclusions are firmly grounded in the data.\nIn particular, and in line with both reviewers, we now highlight limitations and future\nresearch directions more explicitly (lines 380, 447, 456). The revised conclusions\nemphasize the context-dependent role of the soil microbiome and the relative\ncontribution of soil properties and climate, rather than making broad or speculative\nclaims (lines 326).\nReviewer #2 – Specific comment 1 (R2SC1)\nThe manuscript does not appear to include a dedicated figure explicitly supporting the\ncentral claim that \"the soil microbiome explains ecosystem multifunctionality across\nEurope after accounting for climate and soil properties. (lines 259-261)\"\nResponse to R2SC1\nWe thank the reviewer for this comment. Indeed, we agree that the statement “the soil\nmicrobiome explains ecosystem multifunctionality across Europe after accounting for\nclimate and soil properties” might be seen as not fully supported by the analyses and\nfigures of the old version of our manuscript. We acknowledge that the relatively low\nimportance of the soil microbiome in the previous version of our manuscript was partly\ndue to clustering OTUs into broad taxonomic groups (classes/phyla) and grouping\nsamples only into croplands, grasslands, and woodlands. This approach overlooked key\nenvironmental di]erences, as it lumped together sites from very distinct regions (e.g.,\nNorthern vs. Southern Europe). In the revised version, we now account for climate, soil\ntexture, and soil pH following reviewer suggestions, to better capture meaningful\necological variation. We now employ a di]erent more robust method where we cluster\nbacterial and fungal OTUs into groups of co-occurring OTUs (modules). We then use\nmodule eigenvalues (equivalent to first component of a PCA) to explore the relationship\nbetween module community composition and function. Moreover, in the last version of\nour paper all sites were simply grouped by land use (croplands, woodlands, grasslands).\nNow, also following the advice of reviewer #1, we also group the samples according to pH\n(acidic, neutral, alkaline), soil texture (loam, clay, sand), climatic region (temperate dry,\ntemperate humid, continental). This two-way approach (clustering OTUs into modules\nand grouping samples according to land use, pH, soil texture, and climatic region)\nallowed us to show that for some cases the soil microbiome is the best predictor (better\nthan climate and soil properties). Our new Figure 3 shows the results of a variation\npartition analysis where readers can see the contribution of the soil microbiome to\nexplain variation in ecosystem multifunctionality. Moreover, our new supplementary\nfigures S4 to S9 show variance partition analyses for individual functions and\ndemonstrate that the soil microbiome is the main factor explaining variation in soil\nenzymatic activities and primary productivity. As far as we know, this is the first time such\nextensive analysis has been performed. We now also provide the taxonomic a]iliation in\neach module (Figure S10-S11, supplementary dataset 1), therefore providing a list of\ntaxa with potential to be used as bioindicators of multifunctionality (lines 383-387).\nReviewer #2 – Specific comment 2 (R2SC2)\nThe authors claim that \"the bacteriome predicts function in croplands while mycobiomes\nsupport woodlands,\" but this conclusion appears overstated given the currently\nevidence. First, the reliance on Random Forest (RF) variable importance metrics\n(%IncMSE) alone is insu]icient to establish causality or even robust predictive\nrelationships, especially since the actual performance metrics of the RF models (e.g.,\nout-of-bag error, cross-validated R²) are not reported, leaving the strength and reliability\nof these predictions unclear. Figure 2 shows that while Acidobacteria and Nitrospira are\nidentified as important predictors in croplands, their relative importance (%IncMSE) is\nnotably lower than abiotic factors like total nitrogen and organic carbon, which raises\nquestions about the justification for emphasizing bacteriomes as primary drivers.\nSimilarly, for woodlands, fungal taxa like ectomycorrhiza and Agaricomycetes are\nhighlighted, yet their importance values are comparable to or lower than edaphic factors\n(e.g., organic carbon), undermining the assertion that mycobiomes \"support\" woodlands\nmultifunctionality more significantly than other variables. Furthermore, the selection of\nspecific microbial taxa (e.g., Acidobacteria Gp5, Nitrospira, ectomycorrhiza) lacks\ntransparency—was this based on ecological relevance? The authors state: \"Random\nForest results aligned with linear regressions linking predictor variables to cropland\nmultifunctionality, with the strongest correlations observed for total nitrogen (R² = 0.29,\np < 0.001), soil organic carbon (R² = 0.14, p < 0.001), and Acidobacteria Gp5 abundance\n(R² = 0.13, p < 0.001).\" However, judging from the correlation coe]icients, the strongest\nassociations between predictor variables and multifunctionality are actually total\nnitrogen, soil organic carbon, and monthly precipitation in croplands.\nResponse to R2SC2\nRandom Forest: In our study, we used RF to assess the importance of predictors (soil\nproperties, climate, and the soil microbiome) on multifunctionality. In line with the\ncomment from the reviewer, we now report %IncMSE together with cross-validated R2,\nand out-of-bag error (Figure 2, lines 285-289).\nImportance of the soil microbiome: to assess this, also taking into account the\ncomments of reviewer #1, we have now (i) moved beyond taxonomy grouping at the Class\nlevel since we think it is more appropriate to our purpose and also more novel to use OTUs\nto build co-occurring modules (groups of co-occurring OTUs) and use the community\ncomposition of the modules as predictors, we did this using the WGCNA package (see\nlines 190-203). (ii) We have also now grouped the soils into di]erent categories other\nthan land use: soil texture, pH, climatic zone, in order to extent our analyses beyond the\n3 land cover types (woodland, grassland, cropland). Importantly, we have now included\na figure with the results of a variation partition analysis (Figure 3), where readers can see\nas we show upfront the unique variance explained by climate, soil properties, and the soil\nmicrobiomes, as well as the shared variance. As you will see now in Figure 3, soil\nproperties explain between 12.16% and 31.41% of unique variance, and the soil\nmicrobiome explains up to 14.08%. Additionally, we now provide results on variation\npartition analyses for individual functions, and we show and discuss the fact that the soil\nmicrobiome is the main contributor to variation in enzymatic activities and primary\nproductivity (Figures S4 to S9). To stay closer to our own results and avoiding\ngeneralizations, we now discuss the taxa that could be used as indicators of ecosystem\nmultifunctionality based on our results (lines 355-382). In line with this, ≈1% of the fungal\nOTUs were mycorrhizal (phylum Glomeromycota), and these were present in several\nmodules (e.g., fungal module 5, see line 376). We also identified other symbiotic fungi\n(Ectomycorrhiza) in module 5 (e.g., Archaeorhizomyces), and this module consistently\nemerged among the top predictors of multifunctionality in specific contexts (woodland\nsoils, sandy soils, neutral soils, and soils of continental origin). These points are now\nclarified in the revised manuscript (see lines 355-382), and full taxonomic details for\neach fungal and bacterial module are provided in Supplementary Dataset 1 and Figure\nS10-S11.\nReviewer #2 – Specific comment 3 (R2SC3)\nIn \"Ecosystem-level understanding of the microbiome-function relationship\" part:\nThe text lacks systematic description of Figure 3 (SEM analysis) results. Examination\nshows fungi do not significantly influence multifunctionality in any ecosystem,\ncontradicting the \"fungi strongly support woodlands multifunctionality \" claim.\nThe statement that \"…as our results show that certain classes within this phylum (i.e.,\nAcidobacteria Gp22) can indeed be used as predictors of multifunctionality (particularly\nin croplands) …\" is unsupported by Figure 3A, which shows only phototrophic bacteria\n(PHO) significantly directly a]ect multifunctionality.\nThe claim that \"…In grasslands, however, basal respiration was related to Acidobacteria\nGp22 and Actinobacteria...\" (line 308) lacks support, which shows no significant e]ect of\nthese taxa on basal respiration.\nThe conclusion that \" ectomycorrhizal fungi and Mortierellomycetes emerged as key\ndrivers of soil aggregation and plant productivity in woodlands \" (lines 320-321) need be\nfurther supported.\nResponse to R2SC3\nWe thank the reviewer for the careful reading of our manuscript and for pointing out where\nour interpretation of the SEM results was overstated or insu]iciently supported.\nRegarding the SEM analysis: In the revised version of the manuscript, the focus has\nshifted to evaluating how the relationship between the soil microbiome and\nmultifunctionality varies across multiple environmental contexts (land use, climatic\nregion, soil pH, and soil texture). To this end, we have re-run the SEMs as multi-group\nSEMs (see Figure 4). This allows us to explicitly compare pathways across soil categories\nand prevents overgeneralization. The results of the multi-group SEMs are now described\nin the main text and supplementary information (line 388, 410, supplementary dataset\n2), and we no longer claim uniform e]ects (e.g., fungi strongly supporting\nmultifunctionality in woodlands).\nRegarding Acidobacteria, ectomycorrhizal fungi, and Mortierellomycetes: We fully agree\nwith the reviewer that the earlier version may have overstated the contribution of certain\nmicrobial groups. This was partly since we were working at broad taxonomic levels (class,\nphylum), which limited precision. In the revised version, we avoid these issues by\nadopting a new approach: grouping OTUs into modules of co-occurring taxa (via the R\npackage WGCNA, see lines 190-203). This provides a more robust, data-driven basis for\nlinking microbial communities to multifunctionality. With this approach, we can identify\nconsistent contributions of specific taxa (e.g., Mortierella, Streptomyces, Gaiella,\nSphingomonas) within modules that significantly explain variation in multifunctionality\nor individual functions (lines 355-382). We now highlight these results in the text while\navoiding unsupported claims about broad groups (line 326).\nReviewer #2 – Specific comment 4 (R2SC4)\nThe manuscript includes multiple speculative recommendations for future studies “We\nargue that future studies need to address the impacts of Acidobacteria on ecosystem\nfunctions at (line 298); Future research should explore the role of phototrophic bacteria\nas drivers of ecosystem (line314); These extensive discussion of future research\ndirections appears unjustified since these proposed studies could and should have been\naddressed within the current experimental framework using the existing data.\nResponse to R2SC4\nThank you for raising this point. We probably were not precise enough when we made\nthese comments. In the revised manuscript, we have moved beyond broad phylum- or\nclass-level microbial categories and now provide taxonomic resolution down to the OTU\nlevel (see supplementary dataset 1). However, we acknowledge that the observational\nnature of our study, spanning 484 soils across 27 countries, does not allow us to\nexperimentally test the functional roles of individual microbial taxa. Instead, our aim is to\ngenerate hypotheses and guide future targeted experiments. While we agree that our\ndataset is rich and comprehensive, directly addressing mechanistic processes or causal\nrelationships (e.g., through functional assays or microbial manipulation experiments)\ngoes beyond the scope of our current field-based survey. To address this comment,\nwhich is also raisen by reviewer #1, we now make multiple (specific) recommendations\nfor future studies in our discussion section (see e.g., lines 380, 447, 456).\nReviewer #2 – Specific comment 5 (R2SC5)\nThe reliance on z-score and PCA-based indices is less informative than threshold-based\nor averaging approaches, which better capture trade-o]s and synergies among\nfunctions. Why were more advanced multifunctionality metrics (e.g., multiple threshold\nanalysis) not considered?\nResponse to R2SC5\nThank you for this important comment. We agree that multifunctionality metrics based\non multiple threshold analyses can o]er valuable insights into trade-o]s and synergies\namong ecosystem functions. Following your recommendation, we now performed a\nrobust and comprehensive assessment of multifunctionality, including 4 distinct\napproaches: (i) a simple average of all functions standardized between 0 and 1, (ii) a z-\nscore–based index, (iii) a weighted index that accounts for the fact that half of the\nmeasured functions are enzymatic activities, and (iv) a multiple-threshold analysis (lines\n252-268). We also provide a supplementary figure to clearly show how these indices\nrelate to each other (Figure S2) and justify the use of the weighted index (lines 263-268).\nSpecifically, the weighted index reduces the overrepresentation of enzymatic processes\nin the overall multifunctionality score. Moreover, we go beyond aggregated indices by\nanalyzing how the soil microbiome, soil properties, and climate individually relate to each\nof the six functional proxies (see Figures S4-S9). This allows us to identify specific biotic\nand abiotic drivers for each function and avoids over-reliance on composite indices. We\nbelieve this approach balances robustness and interpretability, given the nature of our\ndataset.\nReviewer #2 – Specific comment 6 (R2SC6)\nThe RF analysis lacks critical details:\nHow were hyperparameters (e.g., number of trees, mtry) optimized?\nWas out-of-bag (OOB) error or cross-validation used to assess model performance?\nWere variable interactions examined, given that microbiome-environment relationships\nare often non-linear?\nResponse to R2SC6\nIn the random forest analyses, the number of trees (ntree) was fixed at 1000 to ensure\nmodel stability, rather than being optimized. The number of variables tried at each split\n(mtry), however, was optimized automatically by the caret::train() function, which tests a\nsequence of candidate values and selects the one that maximizes the chosen\nperformance metric (in this case, cross-validated R²). Other hyperparameters, such as\nnodesize and maxnodes, were left at their default values in the randomForest package.\nIn the new version of the manuscript, we now report for each random forest model both\nthe cross-validated R² and the out-of-bag (OOB) error. All the code used to generate\nrandom forest models as well as all other statistical analyses and figures in our study will\nbe released after acceptance. This is indicated in the new figure and the methods (Figure\n2, lines 285-300).\nReviewer #2 – Specific comment 7 (R2SC7)\nThe authors did not explain the di]erence between the black dashed lines and gray\ndashed lines in Figure 3.\nResponse to R2SC7\nThis comment does no longer apply to the new version of the figure, where we show the\nresults of multi-group SEM (Figure 4).\nPoint-by-point response to reviewers\nIn this file, the line numbers associated with the reviewers’ comments (shown in black,\nreproduced verbatim) correspond to the previously submitted version of the\nmanuscript, whereas the line numbers in our responses (shown in blue) refer to the\ncurrent revised version (track-changes)\nReviewer #1 general comment (R1GC):\nDear authors, dear Ferran Romero,\nThank you for the detailed response to the comments. I have read through the revised\nmanuscript, and it has strongly improved compared to the previous version. It reads\nwell, the definition of multifunctionality within the context of the work is much better,\nand the impact of the work is phrased well. I am pleased to see how the comments\nwere thoughtfully incorporated. I fully recognize that the number and scope of the\ncomments made the revision process particularly challenging, and I really appreciate\nthe effort that went into addressing them so thoroughly.\nThe phrasing of the introduction has improved with the phrasing of microorganisms as\nindicators of changes in multifunctionality, as well as the inclusion of land use, soil\ntexture and climate as basis. The uncertainties regarding the soil microbiome and\nmultifunctionality are now also expressed in a clear manner.\nThe materials and methods section also received a major update, now containing the\nrequested details on specific methods and tools. Thank you for also elaborating on the\nfreeze-thaw cycles in the rebuttal. I agree this is the best you can do for the current\nstudy. It is also highlighted well in the manuscript (line 352-354).\nThe suggestions for future research within the context of this work (e.g. determining\nactual biomass production, measuring soil gas emissions) provide a better context of\nthe work.\nOne important comment from my side addresses the terminology around\nmultifunctionality. The results & discussion, and conclusion now mention “our study\nfound the highest multifunctionality” (line 307), or “showed the lowest\nmultifunctionality” (line 312). However, these are higher or lower indicator values for\nmultifunctionality. Here I suggest a general rephrasing to emphasize the use of\nindicator values.\nResponse to R1GC:\nThank you very much for taking the time to read our revised manuscript and for your\nkind and constructive feedback. We are very pleased to hear that you find the\nmanuscript has strongly improved and that the revisions have clarified the framing and\nimpact of our work. Following your final suggestion, we have rephrased lines 307 and\n312 to emphasize that these refer to indicator values for multifunctionality, as you\nrecommended (see lines 150 and 155). Once again, thank you for your time and\nvaluable input, which have greatly contributed to refining our study.\nReviewer #1 specific comment 1 (R1SC1):\nMaterials & methods and beyond: the pH classes “acidic”, “neutral” and “alkaline” need\nto be quantified. Can you provide the quantified pH ranges for each category? This\napplies to the materials and methods section, and the results section (and figure\ncaptions).\nResponse to R1GC1:\nIn this study, soils were classified as “acidic” when they had pH < 6.5, neutral when\nthey had pH between 6.5 and 7.3, and alkaline when they had pH > 7.3. This is now\nindicated in the methods section (line 146) and in the results (lines 342-344) and\ndiscussion (line 416).\nReviewer #1 specific comment 2 (R1SC2):\nLine 410-413: “SEMs showed that the direct effect…” This sentence is not yet clear to\nme. Do you mean: water limitation strongly regulates microbial communities?\nResponse to R1GC2:\nThank you for pointing this out and for helping us clarify this sentence. In this study,\nSEMs were used to investigate whether the effects of climate and soil properties on\necosystem multifunctionality were direct, or indirect via changes in the microbiome.\nWhat we mean in this part of the manuscript is that, when grouping our soils by their\nclimatic region of origin, soils from dry climates showed a stronger direct impact of\nclimate on multifunctionality compared to soils from continental or humid regions.\nHowever, as you correctly noted, the indirect effect of climate via the microbiome is\nalso higher in soils from dry climates. We have therefore slightly modified this sentence\nto make these relationships clearer and to ensure the phrasing reflects the results\npresented in Figure 4 (lines 333-335).\nReviewer #1 specific comment 3 (R1SC3):\nLine 417-418: “This reflects the balanced texture of loams, which combine good water\nretention, aeration, and nutrient availability” I agree, a suggestion for rephrasing:\nLoamy soils occupy the middle of the soil texture spectrum, reflecting a balanced\ncomposition of sand, silt, and clay. This provides a balanced combination of water\nretention, aeration, and nutrient availability.\nResponse to R1SC3:\nWe have re-phrased this sentence as suggested by the reviewer (lines 339-340).\nReviewer #2 general comment (R2GC):\nResponse to R2GC:\nWe thank the reviewer once again for taking the time to revise our manuscript.\nReviewer notes on manuscript NCOMMS-25-16381\nThe paper of Romero and co-workers provides an insight into the soil microbiome as indicator of function\nacross Europe. It is an actual topic that is important for the future of sustainable food systems. The work is\nnovel and impactful by looking at a large number of sites across Europe and by combining amplicon\nsequencing data (16S rRNA gene & ITS sequences) with indicators of microbial abundance (such as\nmicrobial biomass) and activity (enzymes). The large dataset on 500 soils across Europe is of interest for\nthe scientific community. The paper is overall well written and I enjoyed reading the work.\nHaving said this, I have some larger points of feedback, mainly focusing on the set-up of the study and the\nclaimed impact on soil multifunctionality and agriculture.\nMultifunctionality is a central point of the paper. Overall “multifunctionality” would benefit from a better\ndefinition at the start. What is multifunctionality within the context of your work, which main processes do\nyou target and how do the parameters link to that? It is important to stay close to your own work to avoid\nspeculating too much.\nRegarding the conclusions and claims, I miss a section on the limitations and perspectives of this work. In\nline with the more specific comments given below, it would be very useful to provide an overview of the\ncurrent limitations and challenges of this work. This would, in my opinion, better embed it within the current\nresearch field, and it would better highlight the added value of this work without overinterpretation of the\nwork / the risk of speculation.\nThe figures are overall clear, and they provide good visual support for the written text. I can see that this\nhas been a lot of work and I appreciate the efforts. The supplementary figures further support the work and\nthey facilitate future research on the topic.\nI have added my comments, linking to the points mentioned above under a major comments section. The\nminor comments section contains mainly minor textual and structural suggestions.\nMajor comments\n- Overall, the pooling of all sampled soils in three major categories seems rather rough, as you showed\nearlier that there are quite big differences in microbial composition between different woodland soils:\nConiferous wood and broadleaved wood was always significantly different in fungal and bacterial richness\nand Shannon diversity (Labouyrie 2023). In addition, it is known that you find major differences in microbial\ncommunities of clay soils and sandy soils. This is not addressed in the paper, but it does need some\nattention.\nIn addition, it is known that the pH has a strong effect on microbial communities. How does the relatively\nlow pH of e.g. pine forests affect the results?\nThe consequence of zooming out to these three major categories is that you obtain very general\nmicrobiomes.\n-The z-score needs to be better embedded/introduced. A range of functions is mentioned in the introduction,\nbut not all are represented in the z-score.\nIf I see it correctly, the non-weighted calculation of z-score is based for 50% on enzymatic OM decomposing\nactivity, and the rest on net primary productivity, microbial biomass and aggregation is used to draw the\nconclusion that multifunctionality is driven by microbial factors. Individual graphs per cover type do not\nsupport the claim that the z-score is driven by microbial factors (Supplementary figure 2), neither do the\nSEM, and rather point at edaphic parameters to be driving the z-score\nI miss the reasoning on why you think these three enzymes can define (half of) multifunctionality.\n-The sampling time in spring makes it challenging to compare (mainly) the cropland to the grassland and\nwoodland sites. Please also discuss this in the paper.\n-The paper stresses a lot on taxa as valuable multifunctionality indicators. The focus on three major areas\nhowever has several limitations, which should be better discussed. At the end the need for conservation\nstrategies that preserve microbial-driven functions in diverse land uses are highlighted. However, by\nfocusing on three major land use types and using DNA-based taxonomy at higher taxonomic level this feels\nlike a bridge too far. It is very speculative.\nThe link to the z-score is also relevant here: The z-score cannot tell multifunctionality from grasslands and\nforests apart, how useful is it then in practice?\n-Is there a specific reason/motivation to not include AMF in the work? AMF were e.g. included in the work of\nLabouyrie (2023), but not in this work. It is good to, at least, shortly mention the motivation for not\nincluding this here.\n-I could not find a link to the sequencing data (online archive). This needs to be included.\nMinor comments\n-Line 42-43: microbial biomass as driver of microbial-driven functionality – circular reasoning? Please\nrephrase.\n-Line 52-53: you focus on functions at different levels/magnitudes. It is useful to have a look at Creamer et\nal. (2020): The life of soils: Integrating the who and how of multifunctionality. E.g. nutrient cycling is a main\nfunction, but water holding capacity is a smaller part of “water regulation and purification”.\n-Line 58: “soil biodiversity tends to support higher multifunctionality” – rather, this is higher levels of\nfunctioning within these specific functions (that you consider for the study).\n-Line 61-61: it is not clear to me what you mean by “critical aspects of function”. Could you clarify this?\n-Line 61-69: this needs some nuance regarding studies on microbial activity vs studies on presence (e.g.\nDNA-based studies)\n-Line 71: “First, focus is often on their activity”→ First, the focus is often on their general activity\n-Line 71-72: you emphasize the focus on general activity rather than taxonomy. However, the activity is also\na large part of the z-score you use. How do you look at this in your work?\n-Line 81: Here you mention the “valuable multifunctionality indicators”. I would like this to be mentioned at\nthe start, to make it immediately clear that this is your focus.\n-Line 82-83: please elaborate on these other projects and highlight the novelty.\n-Line 85: this sentence needs references to studies in Australia, USA, China.\n-Line 85-86: The claim that Europe is one of the most important economic and ecological regions with one of\nthe longest histories in land management is quite Eurocentric, and not necessarily accurate. Formulate this\nin a more neutral way.\n-Line 87: this needs a reference.\n-Line 88-90: this is very general and not yet clear. What does it mean in the context of your study? Is this\nwhat you (want to) show?\n-Line 113-114: also shortly mention the motivation to go for 0-20 cm for all samples. Several studies\nperform different sample strategies for arable, grassland and forest soils due to expected differences in soil\nprofiles / rooting depth. What was your motivation to go for a general sampling depth?\nIn addition, multifunctionality at greater depth might be important (e.g. Xu et al., 2023\nhttps://doi.org/10.1016/j.scitotenv.2023.162972)\n-Line 127: this is the old soil pH protocol. New one: 10390:2021. In addition, also mention which extraction\nliquids were used: water, CaCl2 and/or KCl?\n-Line 137: please also mention here that this is a proxy for microbial biomass.\n-Line 144: as mentioned before, it is good to highlight that AMF were not taken into account, since ITS data\ndoes not capture them. In the paper by Labouryie it was mentioned that 18S rRNA gene analysis was\nperformed, it is good to refer to this work and also shortly mention why this data is not shown here.\n-Line 147: also shortly mention that this deviates from the standard 0.25 g.\n-Line 150: is the relatively low number of cycles based on an optimized protocol? Good to shortly mention.\n-Line 154-157: please also mention the 16S rRNA gene regions that were targeted by your approach.\n-Line 165: it is good to mention/stress that default parameters / standard settings were used, if this was the\ncase. Otherwise the specific modifications need to be mentioned here.\n-Line 167-168: not clear if this is for the total dataset, or per sample?\n-Line 169: what were the considerations to use this older reference dataset? I would expect the newer Silva\ndatabase (v138) to be a better reference, since it is 4 years newer.\n-Line 184: here it becomes clear that the samples were frozen, which will have an impact on the potential\nsoil basal respiration rate. Please emphasize this, and also let this come back in the results and discussion\nsection. This is not necessarily a major issue, but it needs to be clearly indicated for correct interpretation.\n-Line 231: Is an R2 of 0.83 sufficient to continue with the weighted z-score? It would be good to shortly\nmention this.\n-Line 260-261: “even after accounting for critical ecosystem aspects such as climate and soil properties”.\nHere I am missing a short discussion on the impact of soil texture. Could you elaborate on that?\n-Line 261: have a critical look here, you measured proxies/processes that are part of bigger functions.\n-Line 263-266: please carefully check the sentence and the text between brackets, this does not run\nsmoothly.\n-Line 268-269: “the soil microbiome explained a significant portion of variation of multifunctionality across\nland uses”: this needs a bit more critical discussion. What explains what, are we looking at correlation or\ncausality? I would think you can find patterns/correlations, but not more than that at this point.\n-Line 273-274: “helping with the monitoring of soils as sentinels of function”: as mentioned before, stay\ncloser to the work/data. This would need a lot more information/research, stay a bit closer to your work.\n- only in some cases does a soil microorganism (taxon) have significant impact on the z-score.\n- The multifunctionality of grassland and woodlands cannot be told apart from each other. What does\nthat mean? They have equal multifunctionality?\n-Line 279: “driven by” – word choice – rather: correlated with?\n-Line 281: avoid using the word “significant” when this is not statistically supported.\n-Line 289: Acidobacteria Gp5 seems very interesting as potential indicator. I think you can put a bit more\nemphasis on this, since it is a very nice finding of your work. Also here, it is useful to highlight on the\ntaxonomic results, since this has been a major part of your work.\n-Line 300: “can indeed be used as predictors of multifunctionality” – this needs a bit more explanation on\nthe how.\n-Line 309: Your study did not focus on the metabolic versatility, but rather on e.g. the wide occurrence.\n-Line 311: I was a bit confused about the relevance of phototrophic bacteria in (dark) soils. Are you sure\nthis is not caused by chloroplast / plant material presence in the sample? And, if this is not the case: how\nrelevant do you think that phototrophic bacteria can be in soils?\n-Line 320: you mention key drivers, but this is very tricky to conclude based on DNA amplicon data (on\nrelative abundances). Please formulate this more carefully.\n-Line 331: you mention that it is worsened by cropland management: what type of management? There can\nalso be positive management practices, this needs a specific context.\n-Line 332: also shortly discuss the trade-offs as are also mentioned in e.g. the paper of Vazquez et al.\n(2021) that you cite.\n-Line 333-334: you mention aboveground processes, but only 1 was included: primary productivity.\n-Line 335: “to enhance accuracy”: this needs a bit of support, how do you see this?\n-Line 339-340: please add a bit of perspective on how you see this. Do you have specific functions in mind\nthat should be included based on your work? This can be very helpful for future studies.\n-Line 351: “biodiversity” → soil biodiversity\n-Line 358-361: in line with previous comments, this is very speculative. Stay a bit closer to your work and\nwhat you have shown here.\n-390: here nine ecosystem functions are mentioned, whereas the methods focuses on six. Is this correct?\nFigures and supplementary materials\n-Figure 1 is visually very clear, but please check the contents. The means of Grasslands and Woodlands\nseems to be the same, whereas the text mentions z-scores of 0.13 and 0.26, respectively. The error bars\nwould suggest different significance levels (a, b), please double check that this is correct.\n-Figure 2: are these results linked to the individual plots in the appendix information (S2-S4)? The results in\nthose figures do not always match with the results presented here. Please carefully check this.\n-Figure 3: based on this result it becomes clear that the link between bacteria and multifunctionality is very\nchallenging. This is not clear from the results/discussion section. Make sure to also describe this well.\n-Table 1 needs a careful check. The units column suggests primary productivity is presented in kg C per\nsquare meter, but it likely is g carbon? Otherwise values are extremely high.\nPrimary productivity is highest in woodlands overall: measured over the season? More younger/highly\nproductive forest systems? Can be the case, but for average values this seems unlikely.\nEnzyme activity is in nmol per gram: dry weight?\nKruskal-Wallis test “0.000688”→ “6.88E-04”.\nPlease check use of numbers behind comma for the table.\n-Figure S1: TN is missing\n-Figure S2: are the taxa represented in relative abundance? Please also mention this in the caption.\nLinked to the discussion on the work: The first graph shows that choosing these 6 parameters for\nmultifunctionality is questionable. Sure, if there is more N in the system, there might be more enzymatic\nactivity, primary productivity and microbial biomass. But does it mean that in every system, there should be\nmore N for higher multifunctionality? It might make more sense to make these multifunctionality scores\ncontext-specific.\n-Figure S5-S7: the resolution is too low to be able to read these figures."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Dear authors, dear Ferran Romero,\nThank you for the detailed response to the comments. I have read through the revised manuscript and it has strongly\nimproved compared to the previous version. It reads well, the definition of multifunctionality within the context of the work is\nmuch better, and the impact of the work is phrased well. I am pleased to see how the comments were thoughtfully\nincorporated. I fully recognize that the number and scope of the comments made the revision process particularly\nchallenging, and I really appreciate the effort that went into addressing them so thoroughly.\nThe phrasing of the introduction has improved with the phrasing of microorganisms as indicators of changes in\nmultifunctionality, as well as the inclusion of land use, soil texture and climate as basis. The uncertainties regarding the soil\nmicrobiome and multifunctionality are now also expressed in a clear manner.\nThe materials and methods section also received a major update, now containing the requested details on specific methods\nand tools. Thank you for also elaborating on the freeze-thaw cycles in the rebuttal. I agree this is the best you can do for the\ncurrent study. It is also highlighted well in the manuscript (line 352-354).\nThe suggestions for future research within the context of this work (e.g. determining actual biomass production, measuring\nsoil gas emissions) provide a better context of the work.\nOne important comment from my side addresses the terminology around multifunctionality. The results & discussion, and\nconclusion now mention “our study found the highest multifunctionality” (line 307), or “showed the lowest multifunctionality”\n(line 312). However, these are higher or lower indicator values for multifunctionality. Here I suggest a general rephrasing to\nemphasize the use of indicator values.\nSome minor comments:\n-Materials & methods and beyond: the pH classes “acidic”, “neutral” and “alkaline” need to be quantified. Can you provide\nthe quantified pH ranges for each category? This applies to the materials and methods section, and the results section (and\nfigure captions).\n-Line 410-413: “SEMs showed that the direct effect…” This sentence is not yet clear to me. Do you mean: water limitation\nstrongly regulates microbial communities?\n-Line 417-418: “This reflects the balanced texture of loams, which combine good water retention, aeration, and nutrient\navailability” I agree, a suggestion for rephrasing: Loamy soils occupy the middle of the soil texture spectrum, reflecting a\nbalanced composition of sand, silt, and clay. This provides a balanced combination of water retention, aeration, and nutrient\navailability.", "reviewer_2": "This manuscript has been revised according to the reviewer's comments."}, "author_response": "Point-by-point response to reviewers\nIn this file, the line numbers associated with the reviewers’ comments (shown in black,\nreproduced verbatim) correspond to the previously submitted version of the manuscript,\nwhereas the line numbers in our responses (shown in blue) refer to the current revised\nversion (track-changes)\nReviewer #1 general comment (R1GC):\nDear authors, dear Ferran Romero,\nThank you for the detailed response to the comments. I have read through the revised\nmanuscript, and it has strongly improved compared to the previous version. It reads\nwell, the definition of multifunctionality within the context of the work is much better,\nand the impact of the work is phrased well. I am pleased to see how the comments\nwere thoughtfully incorporated. I fully recognize that the number and scope of the\ncomments made the revision process particularly challenging, and I really appreciate\nthe effort that went into addressing them so thoroughly.\nThe phrasing of the introduction has improved with the phrasing of microorganisms as\nindicators of changes in multifunctionality, as well as the inclusion of land use, soil\ntexture and climate as basis. The uncertainties regarding the soil microbiome and\nmultifunctionality are now also expressed in a clear manner.\nThe materials and methods section also received a major update, now containing the\nrequested details on specific methods and tools. Thank you for also elaborating on the\nfreeze-thaw cycles in the rebuttal. I agree this is the best you can do for the current\nstudy. It is also highlighted well in the manuscript (line 352-354).\nThe suggestions for future research within the context of this work (e.g. determining\nactual biomass production, measuring soil gas emissions) provide a better context of\nthe work.\nOne important comment from my side addresses the terminology around\nmultifunctionality. The results & discussion, and conclusion now mention “our study\nfound the highest multifunctionality” (line 307), or “showed the lowest\nmultifunctionality” (line 312). However, these are higher or lower indicator values for\nmultifunctionality. Here I suggest a general rephrasing to emphasize the use of\nindicator values.\nResponse to R1GC:\nThank you very much for taking the time to read our revised manuscript and for your\nkind and constructive feedback. We are very pleased to hear that you find the\nmanuscript has strongly improved and that the revisions have clarified the framing and\nimpact of our work. Following your final suggestion, we have rephrased lines 307 and\n312 to emphasize that these refer to indicator values for multifunctionality, as you\nrecommended (see lines 150 and 155). Once again, thank you for your time and\nvaluable input, which have greatly contributed to refining our study.\nReviewer #1 specific comment 1 (R1SC1):\nMaterials & methods and beyond: the pH classes “acidic”, “neutral” and “alkaline” need\nto be quantified. Can you provide the quantified pH ranges for each category? This\napplies to the materials and methods section, and the results section (and figure\ncaptions).\nResponse to R1GC1:\nIn this study, soils were classified as “acidic” when they had pH < 6.5, neutral when\nthey had pH between 6.5 and 7.3, and alkaline when they had pH > 7.3. This is now\nindicated in the methods section (line 146) and in the results (lines 342-344) and\ndiscussion (line 416).\nReviewer #1 specific comment 2 (R1SC2):\nLine 410-413: “SEMs showed that the direct effect…” This sentence is not yet clear to\nme. Do you mean: water limitation strongly regulates microbial communities?\nResponse to R1GC2:\nThank you for pointing this out and for helping us clarify this sentence. In this study,\nSEMs were used to investigate whether the effects of climate and soil properties on\necosystem multifunctionality were direct, or indirect via changes in the microbiome.\nWhat we mean in this part of the manuscript is that, when grouping our soils by their\nclimatic region of origin, soils from dry climates showed a stronger direct impact of\nclimate on multifunctionality compared to soils from continental or humid regions.\nHowever, as you correctly noted, the indirect effect of climate via the microbiome is\nalso higher in soils from dry climates. We have therefore slightly modified this sentence\nto make these relationships clearer and to ensure the phrasing reflects the results\npresented in Figure 4 (lines 333-335).\nReviewer #1 specific comment 3 (R1SC3):\nLine 417-418: “This reflects the balanced texture of loams, which combine good water\nretention, aeration, and nutrient availability” I agree, a suggestion for rephrasing:\nLoamy soils occupy the middle of the soil texture spectrum, reflecting a balanced\ncomposition of sand, silt, and clay. This provides a balanced combination of water\nretention, aeration, and nutrient availability.\nResponse to R1SC3:\nWe have re-phrased this sentence as suggested by the reviewer (lines 339-340).\nReviewer #2 general comment (R2GC):\nResponse to R2GC:\nWe thank the reviewer once again for taking the time to revise our manuscript."}]

Decision letter