D4_WP4_10.5281.zenodo.14168775

Towards climate-smart sustainable management of agricultural soils AGROECOseqC AGROECOlogical strategies for an efficient functioning of plant - soil biota interactions to increase SOC sequestration Deliverable D4 Draft publication: Disentangling the influence of different agro ecological practices on dominant and rare microbial community in a gradient of European countries. Due date of deliverable: October 2024 Actual submission date: October 2024 Deliverable D4 Draft publication This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 2 GENERAL DATA Grant Agreement: 862695 Project acronym: EJP SOIL Programme title: Towards climate-smart sustainable management of agricultural soils Programme website: www.ejpsoil.eu Project title: AGROECOlogical strategies for an efficient functioning of plant - soil biota interactions to increase SOC sequestration Project website: www.ejpsoil.eu Start date of the project: February 1st, 2020 Project duration: 60 months Name of lead contractor: INRAE Funding source: H2020-SFS-2018-2020 / H2020-SFS-2019-1 Type of action: European Joint Project COFUND DELIVERABLE NUMBER: D4 DELIVERABLE TITLE: Draft publication DELIVERABLE TYPE: manuscript WORK PACKAGE N: WP4 WORK PACKAGE TITLE: Soil Microbial Functional diversity DELIVERABLE LEADER: Margarita Ros (CEBAS-CSIC) AUTHOR: DOI: Angel Carrascosa, Jose Antonio Pascual, Alessandra Trinchera, Sebastien Fontaine, Sara Sanchez-Moreno, Skaidrė Supronienė, Bruno Huyghebaert, Jim Rasmussen, Margarita Ros 10.5281/zenodo.14168775 DISSEMINATION LEVEL: CO Deliverable D4 Draft publication This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 3 ABSTRACT Sustainable agriculture mainly focuses on increasing the productivity of the soil and reducing the harmful effects of agricultural practices on climate, soil, water, environment and human health. Sustainable agriculture practices comprise practices, such as agroforestry, intercropping, crop rotation, green manuring, conservation tillage, cover crops, and adopting biofertilizers. Understanding the ecological attributes of common versus rare soil microbial community increase our ability to predict the respond of soil ecosystems functions and to determine the taxa that can be protected or if protecting only common taxa would be enough to protect ecosystem services. AGROECOseqC investigate real experimental fields in seven countries with different treatments where different sustainable managements are applying to investigate the changes of microbial subcommunities of bacteria and fungi Common and rare microbial communities (sub-communities) respond differently to agricultural practices intensities across the different core sites. Our data suggest that sub-communities of bacteria community respond similar to the changes, while dominant fungi are more sensitive. Results showed that the ECO practice increase more phyla related to sustainable agricultural practices such as related to AMF, and C or N cycle. However, none common beneficial microorganism has been increased due this practice. Deliverable D4 Draft publication This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 4 Table of Contents List of Tables ............................................................................................................................................ 4 List of Figures ........................................................................................................................................... 4 1. Introduction ..................................................................................................................................... 6 2. Materials and Methods ................................................................................................................... 7 2.1 Experimental design and sampling ............................................................................................... 7 2.2 Measure of physical, physicochemical and chemical soil properties ........................................... 7 2.3 Soil DNA extraction and sequencing ............................................................................................. 8 2.4 Sequencing data processing .......................................................................................................... 8 2.5 Climate data collection ................................................................................................................. 8 2.6 Statistical analysis ......................................................................................................................... 9 3. Results ............................................................................................................................................. 9 3.1 Relative abundance and percentage of sub-communities of soil bacteria and fungi ................... 9 3.2 Effect of sustainable practices on diversity indices of sub-communities of soil bacteria and fungi .................................................................................................................................................. 10 3.3 Effect of sustainable practices on sub-communities of soil bacteria and fungi communities structure and composition ................................................................................................................ 10 3.4 Relationship between core sites and bioclimatic and soil properties ........................................ 11 4. Discussion ...................................................................................................................................... 11 4.1 Dominant and rare microbial community in different European core sites ............................... 11 4.2 Diversity indexes under sustainable practices in different European core sites ........................ 11 4.3 Shift of sub-communities of bacteria and fungi under sustainable practices in different European core sites ........................................................................................................................... 12 5. Conclusion ..................................................................................................................................... 13 6. Tables............................................................................................................................................. 14 7. Figures ........................................................................................................................................... 15 8. List of references ........................................................................................................................... 20 List of Tables Table 1 Characteristics of core sites List of Figures Figure 1 Dominant and rare bacteria (A – B) and fungi (C – D) across sites. Numbers inside the bars indicates the number of individual ASVs (A – C) or the relative abundance (B – D) corresponding to dominant or rare taxa………………………………………………………………………………………………………………….. 14 Figure 2 Mean Log Ratio Response (LRR) values of alpha diversity indexes and their 95% confidence intervals for dominant bacteria (A), rare bacteria (B), dominant fungi (C) and rare fungi (D)………… 15 Figure 3 Principal Coordinate Analysis (PCoA) showing the Bray-Curtis dissimilarity matrix of dominant and rare sol bacteria (A – B) and dominant and rare soil fungi (C – D) across sites. Different shapes represent 7 European countries. Colours represents the 3 agricultural practices studied……………….16 Figure 4 Mean Log Ratio Response (LRR) values of bacterial and fungal phyla and their 95% confidence intervals for the dominant bacterial community (A), rare bacterial community (B), dominant fungal community (C) and rare fungal community (D)………………………………………………………………………………..17 Deliverable D4 Draft publication This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 5 Figure 5 Relationship between the different microbial community’s composition (principal coordinates analysis 1; PCoA1) studied and the latitudinal (A) and longitudinal (B) gradient, as well as the principal component 1 (PC1) of soil properties (C) and climatic conditions (D) determined by linear regression analysis. The lines show the linear regression and the adjusted R2 and p values indicate statistical significance……………………………………………………………………………………………………………………………………….18 Deliverable D4 Draft publication This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 6 1. Introduction Soil biota plays an important role in contributing to provide ecosystem services such as food production, climate regulation or pest control (Barrios, 2007), thanks to the key role that biota have in diverse agroecosystem processes such as soil nutrient cycling, organic matter decomposition, C stock regulation, plant productivity and pest control (Van Der Heijden et al., 2008). Land-use perturbation has been identified as one of the main anthropic pressures affecting soil microbial diversity, resulting in community composition shifts and should be considered when planning soil management (Gardi et al., 2013; Tsiafouli et al., 2015). The need to optimizing crop output in order to ensure global food security has been going on for many years where the use agrochemicals for optimizing yield has resulted in devastating environmental consequences to soil health and fertility (Tilman, 1999). Today, the greatest success in agriculture is to achieve the desired increase in production by reducing the negative environmental conditions by implementing sustainable methods and sustainable solutions in agriculture. The fact that the agricultural activities and practices are compatible with the environment and being permanent is of great importance in terms of contributing to the sustainability of the ecology. A long-term stability and efficiency are required. For this purpose, the minimum, most economical, and fastest way of implementation of each application in agriculture is one of the priorities that should be focused on the protection of agricultural areas and natural resources. At the functional level, land-use perturbation may alter the composition of soil functional groups altering carbon and nitrogen retention (Cuartero et al., 2022b; Hernández-Lara et al., 2022). Moreover, climate, soil properties and vegetation are known to strongly affect the potential functions provided by the microbial communities (Yang et al., 2022). According to Mehmet Tuğrul (2020) sustainable agriculture mainly focuses on increasing the productivity of the soil and reducing the harmful effects of agricultural practices on climate, soil, water, environment and human health. Reduces the use of non-renewable sources and inputs from petroleum-based products and uses renewable resources to generate production. In general, it focuses on the needs, knowledge, skills and socio-cultural values of the local people. Regular and schematic soil management becomes the backbone of sustainable agriculture that support long-term agriculture (Thiele-Bruhn et al., 2012). Sustainable agriculture practices comprise practices, such as agroforestry, intercropping, crop rotation, green manuring, conservation tillage, cover crops, and adopting biofertilizers (White et al., 2012). Crop rotation can help conserve, maintain, or replenish soil resources, including organic matter, nutrient inputs, and physical and chemical properties. It has an important influence on its microbial properties (Machado, 2009). The appropriate choice of crops within the rotation and their sequence is crucial if nutrient cycling within the field system is to be optimized and losses minimized over the short and long term. No-tillage practices lead to higher C and N concentrations and water content in the soil, microbial population size and diversity in agricultural soils can be affected by either tillage (Helgason et al., 2010; Schmidt et al., 2018) or crop residue retention (Lupwayi et al., 2018) as well as the combined effects (Li et al., 2019). Green manuring is the practice of incorporating undecomposed green plants from the same field or another into the soil to maintain the nutrient supply to the next crop (Nair, 1993). Besides, green manure crops increase microbial growth and their dynamics in soil by releasing nutrients and energy materials as root exudates and eventually enhance soil fertility and soil health (Suman et al., 2022). Understanding the potential of the microbiome into the agriculture. Lead us to use it as an inoculant or its manipulation, to select more efficient microbial groups for plant develop (Özbolat et al., 2023), reduce the incidence of plant disease (Hernández-Lara et al., 2022), increase agricultural production, reduce chemical inputs (Cuartero et al., 2022a), and reduce emissions of greenhouse gases resulting in more sustainable agricultural practices. Soil microbial communities are highly diverse and contain both dominant and rare taxa that are crucial for regulating multiple soil processes (Jousset et al., 2017). Both dominant and rare taxa are expected to play complementary fundamental roles in Deliverable D4 Draft publication This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 7 maintaining ecosystem functions. According to Smith and Knapp (2003), most of the energy flow through common species while rare species are important reservoirs of genetic diversity and support specific ecosystem functional traits (Starke et al., 2020; Wagg et al., 2019) and exhibit greater sensitivity to environmental factors than common species (Clarke and Murphy, 2006; Cuartero et al., 2022b). However, the processes that affect rare bacterial species remain largely unknown or are overlooked (Bickel and Or, 2021; Kurm et al., 2019; Meyer et al., 2018). Understanding the ecological attributes of common versus rare soil microbial community increase our ability to predict the respond of soil ecosystems functions and to determine the taxa that can be protected or if protecting only common taxa would be enough to protect ecosystem services (Dee et al., 2019). In this work, we analyse DNA sequences of soil bacteria/fungi from 6 European countries (6 cores sites, 72 soil samples) grouped per each core site by conventional practice; (ii) low sustainable agriculture practice; iii) high sustainable agriculture practices (Table 1). We assess European-scale effects of sustainable soil managements, soil properties and climate on microbial communities and potential functional groups. Thus, we hypothesise that i) the rare microbial communities are less abundant that the dominant ones. Ii) The common and rare microbial communities (sub-communities) respond differently to agricultural practices intensities across the different core sites. Ii) The ECO practice affects more the microbial community and beneficial genera. 2. Materials and Methods 2.1 Experimental design and sampling This study is part of the EJP Soil Project (AGROecoSeq). It was conducted across different European countries (Spain, Lithuania, Netherlands, Belgium, Denmark, France and Italy) representative of several pedo-climatic conditions (Fig S1). In each country, we chose one core site with different treatments i) conventional practices (CONV); ii) low sustainable agriculture practices (SUST); iii) high sustainable agriculture practices (ECO) (Table S1). The distribution of the treatments has been carried out according to the number of sustainable practices carried out on each treatment. CONV (no sustainable practices); SUST (at least one sustainable practice); ECO (between two and three sustainable practices) Table 1. Soil samples from different core sites (CSs) were collected at peak of green biomass (maximum nutrient uptake). For each core site, three selected agronomic practices (treatments) will be tested in four blocks (field replicates) for 12 plots (three treatments x four replicates = twelve). For each treatment and each block, one plot will be identified with a surface from a minimum of 5.0 m2 to 50.0 m2 After dividing each sub-plot into 4 quadrats (with a side of min. 0.5 m - max. 2.0 m), 4 soil subsamples will be collected, then mixed to form one composite sample. Soil samples were sieved at <2 mm and stored at 4ºC and -20 ºC for further analysis. 2.2 Measure of physical, physicochemical and chemical soil properties The pH of the soil was measured after water-soluble extract (1:10 w/v) and measured using a pH meter. Total nitrogen (TN) and total organic carbon (TOC) were determined using an elemental CHNS-O analyzer (Truspec CN, Leco, St. Joseph, Mich., USA). Nutrients were measured using ICP-MS (7500CE, Agilent, Santa Clara, CA, USA). P available was measured according to Korndorfer et al. (1995). Nitrates and ammonium were measured according to Italian Gazette n. 248 of 21/10/1999 “Official Methods for Soil Chemical Analysis” - Method 19, 1999. Deliverable D4 Draft publication This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 8 2.3 Soil DNA extraction and sequencing DNA was extracted with the DNeasy PowerSoil Pro kit (Quiagen, Germany) from 0.5 g of soil. The DNA was purified with the QIAquick Gel kit (Qiagen). To measure the quality of the DNA, electrophoresis was performed on a 1.5% agarose gel. In addition, a NanoDrop 2000 fluorospectrometer (Thermo Fisher Scientific, Waltham, MA, USA) was used to quantify the DNA extraction yield. DNA sequencing was performed at the Instituto de Parasitología y Biomedicina "Lopez-Neyra" (CSIC, Spain) with ILUMINA technology (MiSeq) using a paired 2x300bp (PE 300) strategy. The libraries were constructed with the Nextera XT v2 DNA Library Preparation kit (Illumina Inc., CA, USA). The V3-V4 region of bacterial 16SrRNA was amplified using the primer pair 341F (5′- CCTACGGGNBGCASCAG-3′) and 806R (5´-GACTACNVGGGTATCTAATCC-3′) (Takahashi et al., 2014) and the resulting amplicons were tagged to PNA PCR clamps to reduce mitochondrial and plastid DNA amplification (Lundberg et al., 2013). The ITS2 region of fungal 18S rRNA was amplified using the primer pair ITS2 – fiTS7 (5’-GTGARTCATCGAATCTTTG-3’) and ITS4 (5’-TCCTCCGCTTATTGATATGC-3’) (Ihrmark et al., 2012). 2.4 Sequencing data processing The obtained demultiplexed sequences quality was tested using the FASTQC program v 0.12.1 (Andrews, 2023). The raw sequences were trimmed, denoised, merged, checked for quimeras and removed the singletons using the DADA2 v 1.22 pipeline (Callahan et al., 2016) on R v4.1.2 (R Development Core Team, 2021) for Rocky Linux. For fungal sequences, primers were removed using cutadapt v4.9 (Martin, 2011) and for bacterial sequences, we trimmed the first 19 and 21 nucleotides of the sequences. Bacterial and fungal sequences were trimmed using a quality score threshold of five and two, respectively. The ASVs taxonomy assignment was performed using the SILVA v 138.1 (Quast et al., 2012) database for bacteria and the UNITE v 9.0 database for fungi (Abarenkov et al., 2024). ASVs that were not assigned to a known phylum and singletons were removed. In order to facilitate the comparison among samples, the ASVs tables were rarefied (Fig. S2) to the lowest sequencing, depth found among samples for both bacterial and fungal databases 21,704 and 9,071 reads, respectively. We identified dominant and rare taxa (sub-communities) within the bacterial and fungal communities for each sample as the taxa with a relative abundance > 0.1 % or <0.1 % respectively (Cuartero et al., 2022b) 2.5 Climate data collection The climatic data was obtained from the WorldClim database (Fick and Hijmans, 2017), using the data from 1970 – 2000 at a resolution of 30 seconds (~1 Km2). We extracted the following data: precipitations, annual minimum temperature and annual maximum temperature using the function rast from the terra package (Hijmans et al., 2024). The bioclimatic variables were obtained using the biovars function from the dismo package (Hijmans et al., 2023). Deliverable D4 Draft publication This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 9 2.6 Statistical analysis All statistical analyses were conducted using in R v 4.1.2. (R Development Core Team, 2021) for Windows. The sample data, ASVs reads and taxonomic assignation were handled using microeco objects (Liu et al., 2021). The original microeco objects of both bacteria and fungi were subset on the dominant and rare taxa. All the analysis was performed separately on each community subset (Dominant bacteria, rare bacteria, dominant fungi and rare fungi). The ratios of dominant and rare ASVs were calculated separately for each sample. Alpha diversity indices Shannon, Richness and Chao1 were calculated using the function cal_alphadiv from microeco. The community’s structure was calculated by performing a Principal Coordinate Analysis (PCoA) based on the Bray-Curtis dissimilarity matrix calculated through the microeco package. To study the effects of the geographic position and the agricultural systems used on the microbial communities we performed a two-way PERMANOVA for each community with 999 permutations using microeco. As the two-way PERMANOVA results were significant for both the site and the treatments, conducted a one-way PERMANOVA with 999 permutations to evaluate the effects of the treatments in each site. In order to study the effect size of sustainable agricultural practices on alpha diversity indices and the abundances of bacterial and fungal phyla we calculated the natural log of response ratios and their confidence intervals at a 95% confidence level using a modified version of the function logRespRatio from the package ARPobservation (Pustejovsky, 2023). To test if the effect size were significant, we conducted a Welch´s t-test using the t.test function with the argument ‘var.equal = FALSE’ from the stats package (R Development Core Team, 2021). We filtered the bacterial and fungal phyla LRR plotted using a -0.1 and 0.1 threshold. We conducted a Spearman´s correlation followed by a linear regression analysis using the function lmperm from the permuco package (Frossard and Renaud, 2024) and a linear regression analysis between the geographic coordinates (Latitude and longitude) and the dominant/rare bacterial and fungal communities’ composition using the first component of the PCoA analysis (PCoA1) based on the Bray-Curtis dissimilarity matrix. A principal component analysis (PCA) was performed for the bioclimatic variables obtained from the worldclim data and the soil properties (total carbon, organic carbon, total nitrogen, NH4, NO3, available phosphorous and pH) using and factoextra and FactoMineR packages (Husson et al., 2024; Kassambara and Mundt, 2020). We used the first principal component (PCA1) for the Spearman´s correlation coefficient and the linear regression analysis with the dominant/rare bacterial and fungal communities first component of the PCoA1. 3. Results 3.1 Relative abundance and percentage of sub-communities of soil bacteria and fungi The % ASVs and relative abundance (%) of dominant and rare soil bacteria and fungi (subcommunities) varied slightly among core sites (Fig. 1). In most cores’ sites, approximately 75% of bacterial ASVs classified as rare, contributing to 45% of the total relative abundance, while dominant bacteria showed ~25% of ASVs with a relative abundance of 55%. Although, for core sites in Belgium and Netherlands dominant bacteria comprised around 60% of rare ASVs, accounting for 20% of the total relative abundance (Fig. 1A-B). The classification of dominant and rare fungi was more consistent across core sites (Fig. 1 CD). On average, the 45% of ASVs correspond to rare fungal community, and contributed on an average of 5 % of relative abundance of fungal community, while dominant fungi showed 55% of ASVs and 95% of abundance (Fig. 1 C-D) Deliverable D4 Draft publication This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 16 Figure 2. Mean Log Ratio Response (LRR) values of alpha diversity indexes and their 95% confidence intervals for dominant bacteria (A), rare bacteria (B), dominant fungi (C) and rare fungi (D). Asterisks (*) indicates a significant effect (* p < 0.05, ** p < 0.01, *** p < 0.001) according to a Welch´s t-test. Deliverable D4 Draft publication This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 17 Figure 3. Principal Coordinate Analysis (PCoA) showing the Bray-Curtis dissimilarity matrix of dominant and rare sol bacteria (A – B) and dominant and rare soil fungi (C – D) across sites. Different shapes represent 7 European countries. Colours represents the 3 agricultural practices studied Deliverable D4 Draft publication This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 18 Figure 4. Mean Log Ratio Response (LRR) values of bacterial and fungal phyla and their 95% confidence intervals for the dominant bacterial community (A), rare bacterial community (B), dominant fungal community (C) and rare fungal community (D). Asterisks (*) indicates a significant effect (* p < 0.05, ** p < 0.01, *** p < 0.001) according to a Welch´s ttest. Deliverable D4 Draft publication This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 19 Figure 5. Relationship between the different microbial community’s composition (principal coordinates analysis 1; PCoA1) studied and the latitudinal (A) and longitudinal (B) gradient, as well as the principal component 1 (PC1) of soil properties (C) and climatic conditions (D) determined by linear regression analysis. The lines show the linear regression and the adjusted R2 and p values indicate statistical significance. Deliverable D4 Draft publication This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 20 8. List of references Abarenkov, K., Zirk, A., Piirmann, T., Pöhönen, R., Ivanov, F., Nilsson, R.H., Kõljalg, U., 2023. Full UNITE+INSD dataset for Fungi. Andrews, S., 2023. FastQC: A Quality Control Tool for High Throughput Sequence Data [WWW Document]. URL https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ Anthony, M.A., Tedersoo, L., De Vos, B., Croisé, L., Meesenburg, H., Wagner, M., Andreae, H., Jacob, F., Lech, P., Kowalska, A., Greve, M., Popova, G., Frey, B., Gessler, A., Schaub, M., Ferretti, M., Waldner, P., Calatayud, V., Canullo, R., Papitto, G., Marinšek, A., Ingerslev, M., Vesterdal, L., Rautio, P., Meissner, H., Timmermann, V., Dettwiler, M., Eickenscheidt, N., Schmitz, A., Van Tiel, N., Crowther, T.W., Averill, C., 2024. Fungal community composition predicts forest carbon storage at a continental scale. Nat Commun 15, 2385. https://doi.org/10.1038/s41467-024-46792-w Banerjee, S., van der Heijden, M.G.A., 2023. Soil microbiomes and one health. Nat Rev Microbiol 21, 6–20. https://doi.org/10.1038/s41579-022-00779-w Banerjee, S., Zhao, C., Garland, G., Edlinger, A., García-Palacios, P., Romdhane, S., Degrune, F., Pescador, D.S., Herzog, C., Camuy-Velez, L.A., Bascompte, J., Hallin, S., Philippot, L., Maestre, F.T., Rillig, M.C., van der Heijden, M.G.A., 2024. Biotic homogenization, lower soil fungal diversity and fewer rare taxa in arable soils across Europe. Nat Commun 15, 327. https://doi.org/10.1038/s41467-023-440736 Barrios, E., 2007. Soil biota, ecosystem services and land productivity. Ecological Economics 64, 269– 285. https://doi.org/10.1016/j.ecolecon.2007.03.004 Bickel, S., Or, D., 2021. The chosen few—variations in common and rare soil bacteria across biomes. ISME J 15, 3315–3325. https://doi.org/10.1038/s41396-021-00981-3 Bier, R.L., Daniels, M., Oviedo-Vargas, D., Peipoch, M., Price, J.R., Omondi, E., Smith, A., Kan, J., 2024. Agricultural soil microbiomes differentiate in soil profiles with fertility source, tillage, and cover crops. Agric Ecosyst Environ 368, 109002. https://doi.org/10.1016/j.agee.2024.109002 Buckley, D.H., Huangyutitham, V., Nelson, T.A., Rumberger, A., Thies, J.E., 2006. Diversity of Planctomycetes in Soil in Relation to Soil History and Environmental Heterogeneity. Appl Environ Microbiol 72, 4522–4531. https://doi.org/10.1128/AEM.00149-06 Buckley, D.H., Schmidt, T.M., 2003. Diversity and dynamics of microbial communities in soils from agro‐ ecosystems. Environ Microbiol 5, 441–452. https://doi.org/10.1046/j.1462-2920.2003.00404.x Callahan, B.J., McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson, A.J.A., Holmes, S.P., 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869 Chen, Q.-L., Ding, J., Zhu, D., Hu, H.-W., Delgado-Baquerizo, M., Ma, Y.-B., He, J.-Z., Zhu, Y.-G., 2020. Rare microbial taxa as the major drivers of ecosystem multifunctionality in long-term fertilized soils. Soil Biol Biochem 141, 107686. https://doi.org/10.1016/j.soilbio.2019.107686 Clarke, R.T., Murphy, J.F., 2006. Effects of locally rare taxa on the precision and sensitivity of RIVPACS bioassessment of freshwaters. Freshw Biol 51, 1924–1940. https://doi.org/10.1111/j.13652427.2006.01611.x Deliverable D4 Draft publication This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 21 Cuartero, J., Özbolat, O., Sánchez-Navarro, V., Egea-Cortines, M., Zornoza, R., Canfora, L., Orrù, L., Pascual, J.A., Vivo, J.-M., Ros, M., 2021. Changes in Bacterial and Fungal Soil Communities in Long-Term Organic Cropping Systems. Agriculture 2021, 11, 445. Cuartero, J., Özbolat, O., Sánchez-Navarro, V., Weiss, J., Zornoza, R., Pascual, J.A., Vivo, J.M., Ros, M., 2022a. Long-Term Compost Amendment Changes Interactions and Specialization in the Soil Bacterial Community, Increasing the Presence of Beneficial N-Cycling Genes in the Soil. Agronomy 12, 316. https://doi.org/10.3390/agronomy12020316 Cuartero, J., Pascual, J.A., Vivo, J.-M., Özbolat, O., Sánchez-Navarro, V., Weiss, J., Zornoza, R., MartínezMena, M., García, E., Ros, M., 2022b. Melon/cowpea intercropping pattern influenced the N and C soil cycling and the abundance of soil rare bacterial taxa. Front Microbiol 13, 1004593. https://doi.org/10.3389/fmicb.2022.1004593 Dee, L.E., Cowles, J., Isbell, F., Pau, S., Gaines, S.D., Reich, P.B., 2019. When Do Ecosystem Services Depend on Rare Species? Trends Ecol Evol 34, 746–758. https://doi.org/10.1016/j.tree.2019.03.010 Delgado‐Baquerizo, M., Trivedi, P., Trivedi, C., Eldridge, D.J., Reich, P.B., Jeffries, T.C., Singh, B.K., 2017. Microbial richness and composition independently drive soil multifunctionality. Funct Ecol 31, 2330– 2343. https://doi.org/10.1111/1365-2435.12924 Endress, M.G., Chen, R., Blagodatskaya, E., Blagodatsky, S., 2024. The coupling of carbon and energy fluxes reveals anaerobiosis in an aerobic soil incubation with a Bacillota-dominated community. Soil Biol Biochem 195, 109478, https://doi.org/10.1016/j.soilbio.2024.109478 Engelberts, J. P., Robbins, S. J., de Goeij, J. M., Aranda, M., Bell, S. C., Webster, N.S., 2020. Characterization of a sponge microbiome using an integrative genome-centric approach. ISME J. 14, 1100–1110. https://doi.org/10.1038/s41396-020-0591-9 Fick, S.E., Hijmans, R.J., 2017. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. Int J Climatol 37, 4302–4315. https://doi.org/10.1002/joc.5086 Frossard, J., Renaud, O., 2024. Permuco: Permutation Tests for Regression, (Repeated Measures) ANOVA/ANCOVA and Comparison of Signals. Gardi, C., Jeffery, S., Saltelli, A., 2013. An estimate of potential threats levels to soil biodiversity in EU. Glob Chang Biol 19, 1538–1548. https://doi.org/10.1111/gcb.12159 Hartmann, M., Frey, B., Mayer, J., Mäder, P., Widmer, F., 2015. Distinct soil microbial diversity under long-term organic and conventional farming. ISME J 9, 1177–1194. Hartmann, M., Widmer, F., 2006. Community Structure Analyses Are More Sensitive to Differences in Soil Bacterial Communities than Anonymous Diversity Indices. Appl Environ Microbiol 72, 7804–7812. https://doi.org/10.1128/AEM.01464-06 Helgason, B.L., Walley, F.L., Germida, J.J., 2010. Long-term no-till management affects microbial biomass but not community composition in Canadian prairie agroecosytems. Soil Biol Biochem 42, 2192–2202. https://doi.org/10.1016/j.soilbio.2010.08.015 Deliverable D4 Draft publication This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 22 Hendgen, M., Hoppe, B., Döring, J., Friedel, M., Kauer, R., Frisch, M., Dahl, A., Kellner, H., 2018. Effects of different management regimes on microbial biodiversity in vineyard soils. Sci Rep 8, 9393. https://doi.org/10.1038/s41598-018-27743-0 Hernández-Lara, A., Ros, M., Cuartero, J., Bustamante, M.Á., Moral, R., Andreu-Rodríguez, F.J., Fernández, J.A., Egea-Gilabert, C., Pascual, J.A., 2022. Bacterial and fungal community dynamics during different stages of agro-industrial waste composting and its relationship with compost suppressiveness. Sci Total Environ 805, 150330. https://doi.org/10.1016/j.scitotenv.2021.150330 Hijmans, R., Bivand, R., Dyba, K., Pebesma, E., Sumner, M., 2024. terra: Spatial Data Analysis. https://doi.org/10.1038/ismej.2014.210 Hijmans, R., Phillips, S., Leathwick, J., Elith, J., 2023. dismo: Species Distribution Modeling. https://doi.org/10.3390/agriculture11050445 Husson, F., Josse, J., Le, S., Mazet, J., 2024. FactoMineR: Multivariate Exploratory Data Analysis and Data Mining. Ihrmark, K., Bödeker, I.T.M., Cruz-Martinez, K., Friberg, H., Kubartova, A., Schenck, J., Strid, Y., Stenlid, J., Brandström-Durling, M., Clemmensen, K.E., Lindahl, B.D., 2012. New primers to amplify the fungal ITS2 region - evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol Ecol 82, 666–677. https://doi.org/10.1111/j.1574-6941.2012.01437.x Jiao, S., Wang, J., Wei, G., Chen, W., Lu, Y., 2019. Dominant role of abundant rather than rare bacterial taxa in maintaining agro-soil microbiomes under environmental disturbances. Chemosphere 235, 248– 259. https://doi.org/10.1016/j.chemosphere.2019.06.174 Jousset, A., Bienhold, C., Chatzinotas, A., Gallien, L., Gobet, A., Kurm, V., Küsel, K., Rillig, M.C., Rivett, D.W., Salles, J.F., van der Heijden, M.G.A., Youssef, N.H., Zhang, X., Wei, Z., Hol, W.H.G., 2017. Where less may be more: how the rare biosphere pulls ecosystems strings. ISME J 11, 853–862. https://doi.org/10.1038/ismej.2016.174 Kassambara, A., Mundt, F., 2020. factoextra: Extract and Visualize the Results of Multivariate Data Analyses. Korndorfer, G.H., Anderson, D. L., Portier, K. M., Hanlon, E.A., 1995. Phosphorus soil test correlation to sugarcane grown on Histosols in the Everglades. Soil Sci. Soc. America J. 59, 1655 – 1661 Kurm, V., van der Putten, W.H., de Boer, W., Naus‐Wiezer, S., Hol, W.H.G., 2017. Low abundant soil bacteria can be metabolically versatile and fast growing. Ecology 98, 555–564. https://doi.org/10.1002/ecy.1670 Kurm, V., van der Putten, W.H., Hol, W.H.G., 2019. Cultivation-success of rare soil bacteria is not influenced by incubation time and growth medium. PLoS One 14, e0210073. https://doi.org/10.1371/journal.pone.0210073 Li, X., Zhang, Q., Ma, J., Yang, Y., Wang, Y., Fu, C., 2020. Flooding Irrigation Weakens the Molecular Ecological Network Complexity of Soil Microbes during the Process of Dryland-to-Paddy Conversion. Int J Environ Res Public Health 17, 561. https://doi.org/10.3390/ijerph17020561 Deliverable D4 Draft publication This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 23 Li, Y., Li, Z., Cui, S., Jagadamma, S., Zhang, Q., 2019. Residue retention and minimum tillage improve physical environment of the soil in croplands: A global meta-analysis. Soil Tillage Res 194, 104292. https://doi.org/10.1016/j.still.2019.06.009 Liu, B., Arlotti, D., Huyghebaert, B., Tebbe, C.C., 2022. Disentangling the impact of contrasting agricultureal management practices on soil microbial Communities-Importance of rare bacterial community memebers. Soil Biol Biochem 166 (108573). Liu, C., Cui, Y., Li, X., Yao, M., 2021. Microeco: An R package for data mining in microbial community ecology. FEMS Microbiol Ecol 97, fiaa255. https://doi.org/https://doi.org/10.1093/femsec/fiaa255 Louca, S., Parfrey, L.W., Doebeli, M., 2016. Decoupling function and taxonomy in the global ocean microbiome. Science 353, 1272–1277. https://doi.org/10.1126/science.aaf4507 Lundberg, D.S., Yourstone, S., Mieczkowski, P., Jones, C.D., Dangl, J.L., 2013. Practical innovations for high-throughput amplicon sequencing. Nat Methods 10, 999–1002. https://doi.org/10.1038/nmeth.2634 Lupwayi, N.Z., May, W.E., Kanashiro, D.A., Petri, R.M., 2018. Soil bacterial community responses to black medic cover crop and fertilizer N under no-till. Appl Soil Ecol 124, 95–103. https://doi.org/10.1016/j.apsoil.2017.11.003 Machado, S., 2009. Does intercropping have a role in modern agriculture? J Soil Water Conserv 64, 55A-57A. https://doi.org/10.2489/jswc.64.2.55A Martin, M., 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 17, 10. https://doi.org/10.14806/ej.17.1.200 McDonald, M.D., Lewis, K.L., Blazier, J.C., Gentry, T.J., 2024. Semi-arid soil bacterial communities are refined by altered plant selection pressure under conservation management practices. Appl Soil Ecol 194, 105191. https://doi.org/10.1016/j.apsoil.2023.105191 Mehmet T, K., 2020. Soil Management in Sustainable Agriculture, In: Sustainable Crop Production. IntechOpen. https://doi.org/10.5772/intechopen.88319 Meyer, K.M., Memiaghe, H., Korte, L., Kenfack, D., Alonso, A., Bohannan, B.J.M., 2018. Why do microbes exhibit weak biogeographic patterns? ISME J 12, 1404–1413. https://doi.org/10.1038/s41396-018-0103-3 Nair, P.K.R., 1993. An introduction to agroforestry. Kluwer Academic Publishers, Dordrecht, The Netherlands. Nelson, M.B., Martiny, A.C., Martiny, J. B.H., 2016. Global biogeography of microbial nitrogen-cycling traits in soil PNAS, 113, 8033-8040, 10.1073/pnas.1601070113 Oladapo, P., Olayemi, J. P., Schneekloth, M.D., Wallenstein, P., Trivedi, F, Calderón, j., Corwin, J., Fonte. S., 2022. Soil macrofauna and microbial communities respond in similar ways to management drivers in an irrigated maize system of Colorado (USA). App Soil Ecol 178, 104562. https://doi.org/10.1016/j.apsoil.2022.104562 Deliverable D4 Draft publication This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 24 Olayemi, O.P., Schneekloth, J.P., Wallenstein, M.D., Trivedi, P., Calderón, F.J., Corwin, J., Fonte, S.J., 2022. Soil macrofauna and microbial communities respond in similar ways to management drivers in an irrigated maize system of Colorado (USA). Appl Soil Ecol 178, 104562. https://doi.org/10.1016/j.apsoil.2022.104562 Özbolat, O., Sánchez-Navarro, V., Zornoza, R., Egea-Cortines, M., Cuartero, J., Ros, M., Pascual, J.A., Boix-Fayos, C., Almagro, M., de Vente, J., Díaz-Pereira, E., Martínez-Mena M., 2023. Long-term adoption of reduced tillage and green manure improves soil physicochemical properties and increases the abundance of beneficial bacteria in a Mediterranean rainfed almond orchard. Geoderma, 429, 116218, https://doi.org/10.1016/j.geoderma.2022.116218. Põlme, S., Abarenkov, K., Henrik Nilsson, R., Lindahl, B.D., Clemmensen, K.E., Kauserud, H., Nguyen, N., Kjøller, R., Bates, S.T., Baldrian, P., Frøslev, T.G., Adojaan, K., Vizzini, A., Suija, A., Pfister, D., Baral, H.- O., Järv, H., Madrid, H., Nordén, J., Liu, J.-K., Pawlowska, J., Põldmaa, K., Pärtel, K., Runnel, K., Hansen, K., Larsson, K.-H., Hyde, K.D., Sandoval-Denis, M., Smith, M.E., Toome-Heller, M., Wijayawardene, N.N., Menolli, N., Reynolds, N.K., Drenkhan, R., Maharachchikumbura, S.S.N., Gibertoni, T.B., Læssøe, T., Davis, W., Tokarev, Y., Corrales, A., Soares, A.M., Agan, A., Machado, A.R., Argüelles-Moyao, A., Detheridge, A., de Meiras-Ottoni, A., Verbeken, A., Dutta, A.K., Cui, B.-K., Pradeep, C.K., Marín, C., Stanton, D., Gohar, D., Wanasinghe, D.N., Otsing, E., Aslani, F., Griffith, G.W., Lumbsch, T.H., Grossart, H.-P., Masigol, H., Timling, I., Hiiesalu, I., Oja, J., Kupagme, J.Y., Geml, J., Alvarez-Manjarrez, J., Ilves, K., Loit, K., Adamson, K., Nara, K., Küngas, K., Rojas-Jimenez, K., Bitenieks, K., Irinyi, L., Nagy, L.G., Soonvald, L., Zhou, L.-W., Wagner, L., Aime, M.C., Öpik, M., Mujica, M.I., Metsoja, M., Ryberg, M., Vasar, M., Murata, M., Nelsen, M.P., Cleary, M., Samarakoon, M.C., Doilom, M., Bahram, M., HaghDoust, N., Dulya, O., Johnston, P., Kohout, P., Chen, Q., Tian, Q., Nandi, R., Amiri, R., Perera, R.H., dos Santos Chikowski, R., Mendes-Alvarenga, R.L., Garibay-Orijel, R., Gielen, R., Phookamsak, R., Jayawardena, R.S., Rahimlou, S., Karunarathna, S.C., Tibpromma, S., Brown, S.P., Sepp, S.-K., Mundra, S., Luo, Z.-H., Bose, T., Vahter, T., Netherway, T., Yang, T., May, T., Varga, T., Li, W., Coimbra, V.R.M., de Oliveira, V.R.T., de Lima, V.X., Mikryukov, V.S., Lu, Y., Matsuda, Y., Miyamoto, Y., Kõljalg, U., Tedersoo, L., 2020. FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Divers 105, 1–16. https://doi.org/10.1007/s13225-020-00466-2 Pustejovsky, J.E., 2023. ARPobservation: Tools for Simulating Direct Behavioral Observation Recording Procedures Based on Alternating Renewal Processes. Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., Glöckner, F.O., 2012. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41, D590–D596. https://doi.org/10.1093/nar/gks1219 R Development Core Team, 2021. R: A Languaje and Environment for Statistical Computing. R Foundation for Statistical Computing. Schmidt, R., Gravuer, K., Bossange, A. V., Mitchell, J., Scow, K., 2018. Long-term use of cover crops and no-till shift soil microbial community life strategies in agricultural soil. PLoS One 13, e0192953. https://doi.org/10.1371/journal.pone.0192953 Semenov, M. V., Krasnov, G.S., Semenov, V.M., van Bruggen, A., 2022. Mineral and Organic Fertilizers Distinctly Affect Fungal Communities in the Crop Rhizosphere. Journal of Fungi 8, 251. https://doi.org/10.3390/jof8030251 Deliverable D4 Draft publication This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 862695 25 Smith, M.D., Knapp, A.K., 2003. Dominant species maintain ecosystem function with non‐random species loss. Ecol Lett 6, 509–517. https://doi.org/10.1046/j.1461-0248.2003.00454.x Smith, S.E., Readm D.J., 2008. Mycorrhizal Symbiosis, 3rd edn. Academic Press, New York Starke, R., Capek, P., Morais, D., Callister, S.J., Jehmlich, N., 2020. The total microbiome functions in bacteria and fungi. J Proteo 213, 103623. https://doi.org/10.1016/j.jprot.2019.103623 Suman, J., Rakshit, A., Ogireddy, S.D., Singh, S., Gupta, C., Chandrakala, J., 2022. Microbiome as a Key Player in Sustainable Agriculture and Human Health. Front Soil Sci 2, 821589. https://doi.org/10.3389/fsoil.2022.821589 Sun, Y., Deng, X., Tao, C., Liu, H., Shen, Z., Liu, Y., Li, R., Shen, Q., 2022. Temporal Dynamics of Rare and Abundant Soil Bacterial Taxa from Different Fertilization Regimes Under Various Environmental Disturbances. mSystems 7. https://doi.org/10.1128/msystems.00559-22 Takahashi, S., Tomita, J., Nishioka, K., Hisada, T., Nishijima, M., 2014. Development of a Prokaryotic Universal Primer for Simultaneous Analysis of Bacteria and Archaea Using Next-Generation Sequencing. PLoS One 9, e105592. https://doi.org/10.1371/journal.pone.0105592 Thiele-Bruhn, S., Bloem, J., de Vries, F.T., Kalbitz, K., Wagg, C., 2012. Linking soil biodiversity and agricultural soil management. Curr Opin Environ Sustain 4, 523–528. https://doi.org/10.1016/j.cosust.2012.06.004 Tilman, D., 1999. Global environmental impacts of agricultural expansion: The need for sustainable and efficient practices. Proceedings of the National Academy of Sciences 96, 5995–6000. https://doi.org/10.1073/pnas.96.11.5995 Tsiafouli, M.A., Thébault, E., Sgardelis, S.P., de Ruiter, P.C., van der Putten, W.H., Birkhofer, K., Hemerik, L., de Vries, F.T., Bardgett, R.D., Brady, M.V., Bjornlund, L., Jørgensen, H.B., Christensen, S., Hertefeldt, T.D., Hotes, S., Gera Hol, W.H., Frouz, J., Liiri, M., Mortimer, S.R., Setälä, H., Tzanopoulos, J., Uteseny, K., Pižl, V., Stary, J., Wolters, V., Hedlund, K., 2015. Intensive agriculture reduces soil biodiversity across Europe. Glob Chang Biol 21, 973–985. https://doi.org/10.1111/gcb.12752 Van Der Heijden, M.G.A., Bardgett, R.D., Van Straalen, N.M., 2008. The unseen majority: Soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol Lett 11, 296–310. https://doi.org/10.1111/j.1461-0248.2007.01139.x Vavourakis C. D., Andrei A. S., Mehrshad, M., Ghai, R., Sorokin, D. Y., Muyzer, G., 2018. A metagenomics roadmap to the uncultured genome diversity in hypersaline soda lake sediments. Microbiome 6, 168. https://doi.org/10.1186/s40168-018-0548-7 Vourlitis, G.L., Berry, P., Cabuco,J.M., Estrada,A., García, K.H., Hunter, B.T., Mastaglio, L., Murguia, E., Nacauili, N., Ponce, M., Saenz, L., Salah, Z., Shaffer, J., Solis, M., Thomas, S., Becket, E., 2024. Composition and activity of soil microbial communities in native and non-native vegetation of southern California. App Soil Ecol, 193, 105164. https://doi.org/10.1016/j.apsoil.2023.105164 Wagg, C., Schlaeppi, K., Banerjee, S., Kuramae, E.E., van der Heijden, M.G.A., 2019. Fungal-bacterial diversity and microbiome complexity predict ecosystem functioning. Nat Commun 10, 4841. https://doi.org/10.1038/s41467-019-12798-y