# Biotic homogenization, lower soil fungal diversity and fewer rare taxa in arable soils across Europe

Samiran Banerjee, Cheng Zhao, Gina Garland, Anna Edlinger, Pablo García-Palacios, Sana Romdhane, Florine Degrune, David Pescador, Chantal Herzog, Lennel Camuy-Velez, et al.

► To cite this version:

Samiran Banerjee, Cheng Zhao, Gina Garland, Anna Edlinger, Pablo García-Palacios, et al. Biotic homogenization, lower soil fungal diversity and fewer rare taxa in arable soils across Europe. Nature Communications, 2024, 15 (1), pp.327. 10.1038/s41467-023-44073-6. hal-04549870

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# Biotic homogenization, lower soil fungal diversity and fewer rare taxa in arable soils across Europe

Samiran Banerjee, Cheng Zhao, Gina Garland, Anna Edlinger, Pablo García-Palacios, Sana Romdhane, Florine Degrune, David Pescador, Chantal Herzog, Lennel Camuy-Velez, et al.

► To cite this version:

Samiran Banerjee, Cheng Zhao, Gina Garland, Anna Edlinger, Pablo García-Palacios, et al. Biotic homogenization, lower soil fungal diversity and fewer rare taxa in arable soils across Europe. Nature Communications, 2024, 15 (1), pp.327. 10.1038/s41467-023-44073-6. hal-04549870

HAL Id: hal-04549870

https://hal.inrae.fr/hal-04549870v1

Submitted on 17 Apr 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
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<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
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# Biotic homogenization, lower soil fungal diversity and fewer rare taxa in arable soils across Europe

Received: 1 March 2023

Accepted: 29 November 2023

Published online: 06 January 2024

<figure><img src="image_3.png" /></figure>

Check for updates

Samiran Banerjee Ⓔ<fnref n="1" />,²✉, Cheng Zhao Ⓔ³, Gina Garland², Anna Edlinger Ⓔ²,⁴, Pablo García-Palacios Ⓔ⁵,⁶, Sana Romdhane⁷, Florine Degrune⁸, David S. Pescador Ⓔ<fnref n="9" />,¹⁰, Chantal Herzog Ⓔ², Lennel A. Camuy-Velez<fnref n="1" />, Jordi Bascompte Ⓔ¹¹, Sara Hallin Ⓔ¹², Laurent Philippot⁷, Fernando T. Maestre Ⓔ¹³,¹⁴, Matthias C. Rillig Ⓔ⁸,¹⁵ & Marcel G. A. van der Heijden Ⓔ²,⁶✉

Soil fungi are a key constituent of global biodiversity and play a pivotal role in agroecosystems. How arable farming affects soil fungal biogeography and whether it has a disproportional impact on rare taxa is poorly understood. Here, we used the high-resolution PacBio Sequel targeting the entire ITS region to investigate the distribution of soil fungi in 217 sites across a 3000 km gradient in Europe. We found a consistently lower diversity of fungi in arable lands than grasslands, with geographic locations significantly impacting fungal community structures. Prevalent fungal groups became even more abundant, whereas rare groups became fewer or absent in arable lands, suggesting a biotic homogenization due to arable farming. The rare fungal groups were narrowly distributed and more common in grasslands. Our findings suggest that rare soil fungi are disproportionally affected by arable farming, and sustainable farming practices should protect rare taxa and the ecosystem services they support.

Soil fungi play a crucial role in agroecosystems by delivering essential functions such as mineralization of organic matter to plant-available nutrients, aggregate stability, carbon stabilization, and plant growth promotion<fnref n="1" />⁻⁴. However, there is often a trade-off between agricultural

production and biodiversity, i.e., intensive agriculture can disrupt belowground communities and result in biodiversity loss⁵,⁶. Studies performed at local scales have found a negative impact of intensive farming on the richness of specific fungal groups such as arbuscular

<footnote n="1">¹Department of Microbiological Sciences, North Dakota State University, Fargo, ND 58102, USA. ²Agroscope, Plant-Soil Interactions Group, 8046 Zurich, Switzerland. ³ETH Zurich, Institute for Environmental Decisions, 8092 Zurich, Switzerland. ⁴Wageningen Environmental Research, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands. ⁵Instituto de Ciencias Agrarias, Consejo Superior de Investigaciones Científicas, 28006 Madrid, Spain. ⁶University of Zurich, Department of Plant and Microbial Biology, 8057 Zurich, Switzerland. ⁷University Bourgogne Franche Comte, INRAE, Institut Agro Dijon, Agroecologie, Dijon, France. ⁸Freie Universität Berlin, Institute of Biology, Altensteinstr. 6, 14195 Berlin, Germany.</footnote>

<footnote n="9">⁹Departamento de Farmacología, Farmacognosia y Botánica, Facultad de Farmacia, Universidad Complutense de Madrid, 28940 Madrid, Spain. ¹⁰Departamento de Biología y Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos, 28933 Móstoles, Spain. ¹¹University of Zurich, Department of Evolutionary Biology and Environmental Studies, 8057 Zurich, Switzerland. ¹²Swedish University of Agricultural Sciences, Department of Forest Mycology and Plant Pathology, Box 7026, 750 07 Uppsala, Sweden. ¹³Departamento de Ecología, Universidad de Alicante, Carretera de San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Alicante, Spain. ¹⁴Instituto Multidisciplinar para el Estudio del Medio "Ramón Margalef", Universidad de Alicante, Carretera de San Vicente del Raspeig s/n, 03690 San Vicente, del Raspeig, Alicante, Spain. ¹⁵Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), 14195 Berlin, Germany. ✉e-mail: samiran.banerjee09@gmail.com; marcel.vanderheijden@agroscope.admin.ch</footnote>
<figure><img src="image_2.png" /></figure>

# Biotic homogenization, lower soil fungal diversity and fewer rare taxa in arable soils across Europe

Received: 1 March 2023

Accepted: 29 November 2023

Published online: 06 January 2024

<figure><img src="image_3.png" /></figure>

Check for updates

Samiran Banerjee Ⓔ<fnref n="1" />,²✉, Cheng Zhao Ⓔ³, Gina Garland², Anna Edlinger Ⓔ²,⁴, Pablo García-Palacios Ⓔ⁵,⁶, Sana Romdhane⁷, Florine Degrune⁸, David S. Pescador Ⓔ⁹,¹⁰, Chantal Herzog Ⓔ², Lennel A. Camuy-Velez<fnref n="1" />, Jordi Bascompte Ⓔ¹¹, Sara Hallin Ⓔ¹², Laurent Philippot⁷, Fernando T. Maestre Ⓔ¹³,¹⁴, Matthias C. Rillig Ⓔ⁸,¹⁵ & Marcel G. A. van der Heijden Ⓔ²,⁶✉

Soil fungi are a key constituent of global biodiversity and play a pivotal role in agroecosystems. How arable farming affects soil fungal biogeography and whether it has a disproportional impact on rare taxa is poorly understood. Here, we used the high-resolution PacBio Sequel targeting the entire ITS region to investigate the distribution of soil fungi in 217 sites across a 3000 km gradient in Europe. We found a consistently lower diversity of fungi in arable lands than grasslands, with geographic locations significantly impacting fungal community structures. Prevalent fungal groups became even more abundant, whereas rare groups became fewer or absent in arable lands, suggesting a biotic homogenization due to arable farming. The rare fungal groups were narrowly distributed and more common in grasslands. Our findings suggest that rare soil fungi are disproportionally affected by arable farming, and sustainable farming practices should protect rare taxa and the ecosystem services they support.

Soil fungi play a crucial role in agroecosystems by delivering essential functions such as mineralization of organic matter to plant-available nutrients, aggregate stability, carbon stabilization, and plant growth promotion<fnref n="1" />⁻⁴. However, there is often a trade-off between agricultural

production and biodiversity, i.e., intensive agriculture can disrupt belowground communities and result in biodiversity loss⁵,⁶. Studies performed at local scales have found a negative impact of intensive farming on the richness of specific fungal groups such as arbuscular

<footnote n="1">¹Department of Microbiological Sciences, North Dakota State University, Fargo, ND 58102, USA. ²Agroscope, Plant-Soil Interactions Group, 8046 Zurich, Switzerland. ³ETH Zurich, Institute for Environmental Decisions, 8092 Zurich, Switzerland. ⁴Wageningen Environmental Research, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands. ⁵Instituto de Ciencias Agrarias, Consejo Superior de Investigaciones Científicas, 28006 Madrid, Spain. ⁶University of Zurich, Department of Plant and Microbial Biology, 8057 Zurich, Switzerland. ⁷University Bourgogne Franche Comte, INRAE, Institut Agro Dijon, Agroecologie, Dijon, France. ⁸Freie Universität Berlin, Institute of Biology, Altensteinstr. 6, 14195 Berlin, Germany. ⁹Departamento de Farmacología, Farmacognosia y Botánica, Facultad de Farmacia, Universidad Complutense de Madrid, 28940 Madrid, Spain. ¹⁰Departamento de Biología y Geología, Física y Química Inorgánica, Universidad Rey Juan Carlos, 28933 Móstoles, Spain. ¹¹University of Zurich, Department of Evolutionary Biology and Environmental Studies, 8057 Zurich, Switzerland. ¹²Swedish University of Agricultural Sciences, Department of Forest Mycology and Plant Pathology, Box 7026, 750 07 Uppsala, Sweden. ¹³Departamento de Ecología, Universidad de Alicante, Carretera de San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Alicante, Spain. ¹⁴Instituto Multidisciplinar para el Estudio del Medio "Ramón Margalef", Universidad de Alicante, Carretera de San Vicente del Raspeig s/n, 03690 San Vicente, del Raspeig, Alicante, Spain. ¹⁵Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), 14195 Berlin, Germany. ✉e-mail: samiran.banerjee09@gmail.com; marcel.vanderheijden@agroscope.admin.ch</footnote>
mycorrhizal fungi<sup>4,7,8</sup>. However, the effect of arable farming on the overall soil fungal communities should also be determined at large scales. Owing to the difference in the levels of resources and inputs in ecosystems (e.g., arable lands vs. grasslands), the response of different functional groups to land-use change might also vary. The impact of production systems on biodiversity is not dichotomous, and land use effects on ecological communities can be nuanced and require large-scale investigations<sup>9,10</sup>. Assessing such effects of land-use intensification on soil fungal biogeography across a wide range of climatic and soil conditions and understanding its consequences for agroecosystem functioning is important. Such information can be used for designing sustainable farming practices that support fungi-mediated ecosystem services and bolster food security. Consequently, there has been a surge of fungal biogeography studies at large scales<sup>4,8,11-14</sup>, revealing groups of fungi that are rare or restricted to a few sites. Beyond investigating the overall distribution, a fundamental goal of microbial biogeography studies is to identify the environmental factors that shape microbial distribution patterns<sup>15-18</sup>. Identifying such factors can also reveal why some fungal groups display environmental filtering or provincialism while others are more widespread<sup>1,19</sup>, helping develop models on soil fungal responses to land-use intensification at large scales.

The effect of agricultural practices may differ across prevalent and rare taxa<sup>20</sup>. For example, while prevalent groups may not show any noticeable response to intensive practices, rare taxa, groups with a narrow niche breadth<sup>1,12,15</sup>, may undergo considerable distributional changes and be present at specific locations or be exclusive to a particular practice. While the influence of anthropogenic activities on plant rarity has been studied for decades<sup>21</sup>, little is known regarding their effects on soil microbial communities. This is of particular concern since numerically rare microbial taxa have been shown to play a key role in many soil biogeochemical processes including nitrification, denitrification and methanogenesis<sup>22-24</sup>. Rare taxa are an important constituent of the soil biodiversity reservoir, and their contribution is critical to sustainable ecosystem functioning. In the case of fungi, multiple comprehensive studies have shown that most fungal species are not cosmopolitan and that over 80% of the taxa are rare and likely vulnerable to extinction<sup>8,21</sup>. Intensive management practices may exert a strong homogenizing effect on fungal communities, reducing the occurrence of rare taxa<sup>5,25</sup>. Such biotic homogenization would result in a lower fungal diversity in intensively managed ecosystems<sup>6,14</sup> with implications for fungi-mediated processes. In contrast, rare taxa may be more common in grasslands because of less disturbance and/or the availability of more niches and heterogeneous resources due to greater aboveground diversity in grasslands, especially at a large geographic range. However, the impact of anthropogenic land-use intensification on fungal biogeography and rare taxa is still unclear.

Here, we aimed to address the following research questions: (a) How do fungal diversity and community composition vary across land use and countries? (b) What factors drive fungal biogeographical patterns at continental scale? and (c) Are abundant and rare fungal groups differentially affected by land use? To answer these questions, we used the high-resolution PacBio Single Molecule, Real-Time (SMRT) Sequel sequencing targeting the entire internal transcribed spacer (ITS) region and assessed fungal biogeographical patterns in 156 arable and 61 grassland sites across a 3000 km North-South gradient in Europe (Fig. S1). By incorporating climatic, management, and soil properties, we identified the drivers of soil fungal biogeography. We assessed the prevalence and rarity of soil fungi and how arable farming specifically influences rare fungal groups. Rarity can be defined in different ways to identify taxa that are permanently rare, conditionally rare, and transiently rare<sup>22,26</sup>. In this study, we identified rare taxa by selecting the OTUs present in two or fewer sites. Finally, we assessed the importance of rare taxa for soil ecosystem processes to elucidate whether the loss of such groups can have any functional implications.

We hypothesized that the effect of land use differs between abundant and rare fungal groups. Based on our findings from the root mycobiome<sup>27</sup>, we also hypothesized that agricultural intensity has a negative influence on the overall soil fungal diversity.

## Results

### Fungal diversity and composition

Arable lands comprised significantly (Kruskal-Wallis chi-squared = 36.016; *P* < 0.001) less diverse soil fungal communities compared to grasslands across five countries (Kruskal-Wallis chi-squared = 48.86; *P* < 0.001) in Europe (Figs. 1A, S2, S3). This difference was consistent when fungal richness was computed for the same number of samples for grasslands and arable lands (Fig. S4; *n* = 61). Overall, on average, arable lands harbored 20% fewer OTUs than grasslands (Table S1). Geographic proximity exerted a significant effect (PERMANOVA *F* = 14.369; *R*<sup>2</sup> = 0.206; *P* < 0.001) on fungal communities with sites clustering based on their distribution from Sweden in the north to Spain in the south (Figs. 1B, S5, S6). Within each cluster, the arable lands and grasslands also tended to group significantly (PERMANOVA *F* = 11.369; *R*<sup>2</sup> = 0.040; *P* < 0.001). The interactive effect of country and land use was also significant (PERMANOVA *F* = 2.335; *R*<sup>2</sup> = 0.032; *P* < 0.001). Major fungal classes also showed significant differences between land uses (Fig. S7; Table S2 and S3). In particular, Sordariomycetes, Mortierellomycetes and Tremellomycetes were relatively more abundant in arable lands than in grasslands (Fig. 1C). Several functional groups of soil fungi also varied significantly across the five countries (Fig. 1D). Arbuscular mycorrhizal fungal groups (Glomerallales; Table S2) showed a higher relative abundance in grasslands than in arable lands, which may be due to the availability of host plants in grasslands.

### Factors influencing fungal biogeography

The prevalent fungal classes showed distinct distribution patterns across the latitudinal gradient (Fig. S8). There were also positive associations between fungal richness and latitude and longitude, with the highest richness in mid-latitude (Fig. 2A–D). However, the associations were stronger for grasslands than arable lands. Although Switzerland had the highest richness among all countries, the relationships still held true when assessed without the Swiss samples (Fig. S9). Edaphic factors were an important driver of fungal richness in arable lands (Fig. 2C), with soil pH, carbon, nitrogen, and cation exchange capacity having the strongest correlations with fungal diversity across both arable lands and grasslands (Figs. S10, S11, S12). Climatic factors also displayed consistently strong associations in both arable and grasslands across the continent (Fig. 2D). Partial Mantel tests revealed that environmental parameters had a stronger influence in grasslands (*r*<sub>Env|Geo</sub> = 0.239, *p* < 0.001) than in arable lands (*r*<sub>Env|Geo</sub> = 0.112, *p* = 0.008). Similarly, when accounted for the geographic distance, a stronger effect of climate was observed for grasslands (*r*<sub>Clim|Geo</sub> = 0.141, *p* = 0.006) than arable lands (*r*<sub>Clim|Geo</sub> = 0.03, *P* = 0.23). Effects of soil properties were also significant in both arable (*r*<sub>Soil|Geo</sub> = 0.228, *p* = 0.001) and grassland (*r*<sub>Soil|Geo</sub> = 0.243, *p* = 0.002) sites. We further explored these factors through structural equation modeling (SEM), which (χ<sup>2</sup> test *p*<sub>arable</sub> = 0.294, *p*<sub>grasslands</sub> = 0.699) explained a larger proportion of variance in fungal richness in grasslands (R<sup>2</sup><sub>grasslands</sub> = 40.8%) than in arable lands (R<sup>2</sup><sub>arable</sub> = 24.7%) (Fig. S13). Nitrogen mineralization potential and mean annual temperature were the most important drivers of fungal diversity in grasslands while soil carbon content was the main driver in arable lands.

### Soil fungal rarity

Grasslands had a proportionally higher presence of rare taxa compared to arable lands (Fig. 3). Cumulative abundance was calculated by square-rooting the number of reads per sample, and then averaging and summing up the ordered relative abundances. The cumulative
mycorrhizal fungi<sup>4,7,8</sup>. However, the effect of arable farming on the overall soil fungal communities should also be determined at large scales. Owing to the difference in the levels of resources and inputs in ecosystems (e.g., arable lands vs. grasslands), the response of different functional groups to land-use change might also vary. The impact of production systems on biodiversity is not dichotomous, and land use effects on ecological communities can be nuanced and require large-scale investigations<sup>9,10</sup>. Assessing such effects of land-use intensification on soil fungal biogeography across a wide range of climatic and soil conditions and understanding its consequences for agroecosystem functioning is important. Such information can be used for designing sustainable farming practices that support fungi-mediated ecosystem services and bolster food security. Consequently, there has been a surge of fungal biogeography studies at large scales<sup>4,8,11-14</sup>, revealing groups of fungi that are rare or restricted to a few sites. Beyond investigating the overall distribution, a fundamental goal of microbial biogeography studies is to identify the environmental factors that shape microbial distribution patterns<sup>15-18</sup>. Identifying such factors can also reveal why some fungal groups display environmental filtering or provincialism while others are more widespread<sup>1,19</sup>, helping develop models on soil fungal responses to land-use intensification at large scales.

The effect of agricultural practices may differ across prevalent and rare taxa<sup>20</sup>. For example, while prevalent groups may not show any noticeable response to intensive practices, rare taxa, groups with a narrow niche breadth<sup>1,12,15</sup>, may undergo considerable distributional changes and be present at specific locations or be exclusive to a particular practice. While the influence of anthropogenic activities on plant rarity has been studied for decades<sup>21</sup>, little is known regarding their effects on soil microbial communities. This is of particular concern since numerically rare microbial taxa have been shown to play a key role in many soil biogeochemical processes including nitrification, denitrification and methanogenesis<sup>22-24</sup>. Rare taxa are an important constituent of the soil biodiversity reservoir, and their contribution is critical to sustainable ecosystem functioning. In the case of fungi, multiple comprehensive studies have shown that most fungal species are not cosmopolitan and that over 80% of the taxa are rare and likely vulnerable to extinction<sup>8,21</sup>. Intensive management practices may exert a strong homogenizing effect on fungal communities, reducing the occurrence of rare taxa<sup>5,25</sup>. Such biotic homogenization would result in a lower fungal diversity in intensively managed ecosystems<sup>6,14</sup> with implications for fungi-mediated processes. In contrast, rare taxa may be more common in grasslands because of less disturbance and/or the availability of more niches and heterogeneous resources due to greater aboveground diversity in grasslands, especially at a large geographic range. However, the impact of anthropogenic land-use intensification on fungal biogeography and rare taxa is still unclear.

Here, we aimed to address the following research questions: (a) How do fungal diversity and community composition vary across land use and countries? (b) What factors drive fungal biogeographical patterns at continental scale? and (c) Are abundant and rare fungal groups differentially affected by land use? To answer these questions, we used the high-resolution PacBio Single Molecule, Real-Time (SMRT) Sequel sequencing targeting the entire internal transcribed spacer (ITS) region and assessed fungal biogeographical patterns in 156 arable and 61 grassland sites across a 3000 km North-South gradient in Europe (Fig. S1). By incorporating climatic, management, and soil properties, we identified the drivers of soil fungal biogeography. We assessed the prevalence and rarity of soil fungi and how arable farming specifically influences rare fungal groups. Rarity can be defined in different ways to identify taxa that are permanently rare, conditionally rare, and transiently rare<sup>22,26</sup>. In this study, we identified rare taxa by selecting the OTUs present in two or fewer sites. Finally, we assessed the importance of rare taxa for soil ecosystem processes to elucidate whether the loss of such groups can have any functional implications.

We hypothesized that the effect of land use differs between abundant and rare fungal groups. Based on our findings from the root mycobiome<sup>27</sup>, we also hypothesized that agricultural intensity has a negative influence on the overall soil fungal diversity.

## Results

### Fungal diversity and composition

Arable lands comprised significantly (Kruskal-Wallis chi-squared = 36.016; *P* < 0.001) less diverse soil fungal communities compared to grasslands across five countries (Kruskal-Wallis chi-squared = 48.86; *P* < 0.001) in Europe (Figs. 1A, S2, S3). This difference was consistent when fungal richness was computed for the same number of samples for grasslands and arable lands (Fig. S4; *n* = 61). Overall, on average, arable lands harbored 20% fewer OTUs than grasslands (Table S1). Geographic proximity exerted a significant effect (PERMANOVA *F* = 14.369; *R*<sup>2</sup> = 0.206; *P* < 0.001) on fungal communities with sites clustering based on their distribution from Sweden in the north to Spain in the south (Figs. 1B, S5, S6). Within each cluster, the arable lands and grasslands also tended to group significantly (PERMANOVA *F* = 11.369; *R*<sup>2</sup> = 0.040; *P* < 0.001). The interactive effect of country and land use was also significant (PERMANOVA *F* = 2.335; *R*<sup>2</sup> = 0.032; *P* < 0.001). Major fungal classes also showed significant differences between land uses (Fig. S7; Table S2 and S3). In particular, Sordariomycetes, Mortierellomycetes and Tremellomycetes were relatively more abundant in arable lands than in grasslands (Fig. 1C). Several functional groups of soil fungi also varied significantly across the five countries (Fig. 1D). Arbuscular mycorrhizal fungal groups (Glomerallales; Table S2) showed a higher relative abundance in grasslands than in arable lands, which may be due to the availability of host plants in grasslands.

### Factors influencing fungal biogeography

The prevalent fungal classes showed distinct distribution patterns across the latitudinal gradient (Fig. S8). There were also positive associations between fungal richness and latitude and longitude, with the highest richness in mid-latitude (Fig. 2A–D). However, the associations were stronger for grasslands than arable lands. Although Switzerland had the highest richness among all countries, the relationships still held true when assessed without the Swiss samples (Fig. S9). Edaphic factors were an important driver of fungal richness in arable lands (Fig. 2C), with soil pH, carbon, nitrogen, and cation exchange capacity having the strongest correlations with fungal diversity across both arable lands and grasslands (Figs. S10, S11, S12). Climatic factors also displayed consistently strong associations in both arable and grasslands across the continent (Fig. 2D). Partial Mantel tests revealed that environmental parameters had a stronger influence in grasslands (*r*<sub>Env|Geo</sub> = 0.239, *p* < 0.001) than in arable lands (*r*<sub>Env|Geo</sub> = 0.112, *p* = 0.008). Similarly, when accounted for the geographic distance, a stronger effect of climate was observed for grasslands (*r*<sub>Clim|Geo</sub> = 0.141, *p* = 0.006) than arable lands (*r*<sub>Clim|Geo</sub> = 0.03, *P* = 0.23). Effects of soil properties were also significant in both arable (*r*<sub>Soil|Geo</sub> = 0.228, *p* = 0.001) and grassland (*r*<sub>Soil|Geo</sub> = 0.243, *p* = 0.002) sites. We further explored these factors through structural equation modeling (SEM), which (χ<sup>2</sup> test *p*<sub>arable</sub> = 0.294, *p*<sub>grasslands</sub> = 0.699) explained a larger proportion of variance in fungal richness in grasslands (R<sup>2</sup><sub>grasslands</sub> = 40.8%) than in arable lands (R<sup>2</sup><sub>arable</sub> = 24.7%) (Fig. S13). Nitrogen mineralization potential and mean annual temperature were the most important drivers of fungal diversity in grasslands while soil carbon content was the main driver in arable lands.

### Soil fungal rarity

Grasslands had a proportionally higher presence of rare taxa compared to arable lands (Fig. 3). Cumulative abundance was calculated by square-rooting the number of reads per sample, and then averaging and summing up the ordered relative abundances. The cumulative
<figure><img src="image_4.png" /><figcaption>**Fig. 1 | Diversity, structure, and composition of the soil mycobiome.** A Fungal richness in arable lands (n = 156) and grasslands (n = 61) across five countries in Europe. Countries are arranged from the south on the left side to the north on the right side. For each country, the left (orange) and right (green) boxes indicate richness in arable lands and grasslands, respectively. The overall richness of soil fungi is shown in the upper-left subplot. \* indicates significant (*P* < 0.05) difference between arable lands and grasslands. Small circles indicate individual data points, boxes mark the interquartile range, vertical lines indicate the whiskers, bold horizontal lines show the median and \*x\* indicates the mean value. B Principal Coordinate Analysis (PCoA) showing Bray-Curtis dissimilarity of soil fungi in arable lands and grasslands across five countries. Colors represent five European countries.</figcaption></figure>

Alluvial diagrams showing the relative abundance of the abundant classes and orders (C) of soil fungi and the potential functional groups (D) in arable lands and grasslands across five counties. In these diagrams, various blocks represent clusters, and the streams or flows represent changes in the composition. For each country, the height of the blocks represents the size of cluster of fungal groups. Relative abundance of a functional group was determined by summing up relative abundances of OTUs that belonged to that specific group. The effects of geographic location (in red color) and land use type (in blue color) as assessed by Kruskal Wallis rank sum test are shown on the sides. Statistical significance: \*p value < 0.05; \*\*p value < 0.01; \*\*\*p value < 0.001; n.s not significant.

abundance was similar for the most abundant OTUs, but the difference increased for the rare (lower ranked) OTUs, with the square-rooting of the total number of sequences per site reaching 707.4 for grasslands compared with 589.4 for arable lands (Fig. 3A). Arable lands had 16.7% fewer fungal OTUs than grasslands and this was largely due to fewer rare OTUs. Furthermore, 75% of the total reads in arable lands was made up of the 19 most prevalent OTUs, while it required the 30 most prevalent OTUs to achieve 75% reads in grasslands. The cumulative abundance slopes for the two land use types were similar near the origin due to the prevalent OTUs, but they diverged due to fewer less abundant OTUs in the arable lands (Fig. 3A), and this pattern was consistent across all countries (Fig. S14). There was a positive correlation between OTU abundance and site occupancy or commonness (the number of sites the OTUs were present at) with rare OTUs significantly (*t*-test *p* < 0.05) more common in grasslands (Fig. 3B). Agricultural intensity, calculated²⁸ based on the tillage intensity and the application of chemical fertilizers and pesticides, was negatively associated with both the overall fungal richness (*p* = -0.27; *P* < 0.001) as well as the richness of rare taxa (*p* = -0.24; *P* < 0.004). Consistent with our hypothesis, these results suggest a possible negative impact of agricultural intensification on the soil mycobiome (Figs. 3C; S16).

On average, 40.7% of all OTUs were present in both land use types, whereas 19.5% and 39.8% were specific to arable lands and grasslands,

respectively (Table S1). In line with our hypothesis, the richness of rare fungi was significantly (*P* < 0.001) lower in arable soils, and this was similar when compared with the same number (n = 61) of samples of both land-use types (Figs. 4A, S15). The total number of rare OTUs was also higher in grassland soils (535) compared to arable soils (331). Although Spain had the lowest fungal richness, its proportion of rare taxa (0.154) was considerably higher than that of the other countries, especially France (0.073) and Sweden (0.076), suggesting that rarity was not proportional to the overall diversity. Several orders of the rare taxa were more common in grasslands than arable lands, including Pleosporales, Thelephorales, Pezizales, and Hypocreales, which are known for their responsiveness to plant diversity (Fig. 4B). While a large majority of rare fungi were unclassified at the genus level, we found a noticeable presence of the members of Mortierella, Archaeorhizomyces, Entoloma, Pluteus, and Psathyrella genera. Arbuscular mycorrhizal fungi of the order Glomerales were also significantly (*P* < 0.001) more abundant in grasslands, while plant pathogens were relatively more abundant in arable lands (Fig. 4B). We also annotated the ITS sequences to KEGG functional categories to identify the molecular functions of rare fungal taxa (Fig. S16). The major functions were associated with metabolism of carbohydrates (25%), amino acids (22%), and lipids (10%). Another important functional pathway was the biosynthesis and metabolism of glycans, which are key constituents of
<figure><img src="image_4.png" /><figcaption>**Fig. 1 | Diversity, structure, and composition of the soil mycobiome.** A Fungal richness in arable lands (n = 156) and grasslands (n = 61) across five countries in Europe. Countries are arranged from the south on the left side to the north on the right side. For each country, the left (orange) and right (green) boxes indicate richness in arable lands and grasslands, respectively. The overall richness of soil fungi is shown in the upper-left subplot. * indicates significant (*P* < 0.05) difference between arable lands and grasslands. Small circles indicate individual data points, boxes mark the interquartile range, vertical lines indicate the whiskers, bold horizontal lines show the median and ‘x’ indicates the mean value. B Principal Coordinate Analysis (PCoA) showing Bray-Curtis dissimilarity of soil fungi in arable lands and grasslands across five countries. Colors represent five European countries.</figcaption></figure>

Alluvial diagrams showing the relative abundance of the abundant classes and orders (C) of soil fungi and the potential functional groups (D) in arable lands and grasslands across five counties. In these diagrams, various blocks represent clusters, and the streams or flows represent changes in the composition. For each country, the height of the blocks represents the size of cluster of fungal groups. Relative abundance of a functional group was determined by summing up relative abundances of OTUs that belonged to that specific group. The effects of geographic location (in red color) and land use type (in blue color) as assessed by Kruskal Wallis rank sum test are shown on the sides. Statistical significance: \*p value < 0.05; \*\*p value < 0.01; \*\*\*p value < 0.001; n.s not significant.

abundance was similar for the most abundant OTUs, but the difference increased for the rare (lower ranked) OTUs, with the square-rooting of the total number of sequences per site reaching 707.4 for grasslands compared with 589.4 for arable lands (Fig. 3A). Arable lands had 16.7% fewer fungal OTUs than grasslands and this was largely due to fewer rare OTUs. Furthermore, 75% of the total reads in arable lands was made up of the 19 most prevalent OTUs, while it required the 30 most prevalent OTUs to achieve 75% reads in grasslands. The cumulative abundance slopes for the two land use types were similar near the origin due to the prevalent OTUs, but they diverged due to fewer less abundant OTUs in the arable lands (Fig. 3A), and this pattern was consistent across all countries (Fig. S14). There was a positive correlation between OTU abundance and site occupancy or commonness (the number of sites the OTUs were present at) with rare OTUs significantly (*t*-test *p* < 0.05) more common in grasslands (Fig. 3B). Agricultural intensity, calculated²⁸ based on the tillage intensity and the application of chemical fertilizers and pesticides, was negatively associated with both the overall fungal richness (*p* = -0.27; *P* < 0.001) as well as the richness of rare taxa (*p* = -0.24; *P* < 0.004). Consistent with our hypothesis, these results suggest a possible negative impact of agricultural intensification on the soil mycobiome (Figs. 3C; S16).

On average, 40.7% of all OTUs were present in both land use types, whereas 19.5% and 39.8% were specific to arable lands and grasslands,

respectively (Table S1). In line with our hypothesis, the richness of rare fungi was significantly (*P* < 0.001) lower in arable soils, and this was similar when compared with the same number (n = 61) of samples of both land-use types (Figs. 4A, S15). The total number of rare OTUs was also higher in grassland soils (535) compared to arable soils (331). Although Spain had the lowest fungal richness, its proportion of rare taxa (0.154) was considerably higher than that of the other countries, especially France (0.073) and Sweden (0.076), suggesting that rarity was not proportional to the overall diversity. Several orders of the rare taxa were more common in grasslands than arable lands, including Pleosporales, Thelephorales, Pezizales, and Hypocreales, which are known for their responsiveness to plant diversity (Fig. 4B). While a large majority of rare fungi were unclassified at the genus level, we found a noticeable presence of the members of Mortierella, Archaeorhizomyces, Entoloma, Pluteus, and Psathyrella genera. Arbuscular mycorrhizal fungi of the order Glomerales were also significantly (*P* < 0.001) more abundant in grasslands, while plant pathogens were relatively more abundant in arable lands (Fig. 4B). We also annotated the ITS sequences to KEGG functional categories to identify the molecular functions of rare fungal taxa (Fig. S16). The major functions were associated with metabolism of carbohydrates (25%), amino acids (22%), and lipids (10%). Another important functional pathway was the biosynthesis and metabolism of glycans, which are key constituents of
<figure><img src="image_5.png" /></figure>

**Fig. 2 | Drivers of fungal biogeography.** A, B Latitudinal and longitudinal gradient of fungi richness in arable lands (orange) and in grasslands (green). First order polynomial models show that strong effects of latitude and longitude on fungal richness in arable lands and grasslands. C Relationship between fungal richness and the principal component (PC) 1 of soil properties and soil fungal richness. The

relationship was only significant in arable lands. D Relationship between fungal richness and the principal component 1 of bioclimatic variables. Fungal richness increased significantly with increasing PC1 in both land use types. Adjusted R2 and *p* values indicate statistical significance.

the fungal cell wall. Rare soil fungi were also positively and significantly correlated with important soil ecosystem functions across both land-use types (Fig. S17), indicating that the loss of such groups due to agricultural intensity might have functional implications. However, since these groups were a part of the rare mycobiome, the correlations were weak. Overall, our results show a similarity between relative abundance and commonness, with abundant groups being more common, while rare groups even rarer.

## Discussion

We used the high-resolution PacBio Sequel targeting the entire ITS region to dissect fungal biogeographical patterns in 156 arable sites and 61 extensively managed grasslands across a 3000 km North-South gradient in Europe. We show that fungal diversity is consistently lower in arable lands than in grasslands across five countries. Rare fungi were affected or absent in arable lands, suggesting that biotic homogenization and a disproportionally negative impact of arable farming on the rare soil microorganisms.

## Biotic homogenization in arable lands

We found that, on an average, arable lands across the five European countries had nearly 25% lower fungal diversity than grasslands. One of the major impacts of intensive agriculture is the loss of biodiversity⁵,⁶. Practices such as excessive use of synthetic fertilizers and pesticides, monocultures, excessive tillage, and the homogenization of landscapes can negatively affect the local and regional pool of biodiversity⁵,²⁹. Indeed, agricultural intensification has been linked to a systematic decline in birds, invertebrates and amphibians in arable

lands⁶,³⁰. Importantly, biodiversity loss is not just a decrease in species number, but it also accompanies the loss of associations among various species, with the potential disruption of the network of mutual dependencies between species. For example, our previous report found that agricultural intensification has a negative impact on root endophytic fungi, and the associations among fungal members in conventional farmlands is 50% less than in organic lands²⁷. Recent studies also found that the abundance of beneficial mycorrhizal fungi is negatively associated with pesticide residues³¹ and pesticide application reduces the richness of mycorrhizal fungi and their ability to acquire phosphorus from the soil³². Despite this, we have limited knowledge of how intensive agricultural practices affect soil fungal diversity and distribution at large scales³³. Here, we show that fungal diversity peaked at mid-latitude, although it was consistently lower in arable lands. There was a negative relationship between fungal diversity and the agricultural management intensity calculated from tillage, agrochemicals, and pesticide information (Fig. 3C). This is in contrast to a recent study which found that fungal diversity was similar in grasslands and non-permanent croplands¹⁴, which could be due to the wider range of crops (over 20 different crops including cereals and vegetables) sampled in that study. Most of the arable lands assessed in this study practiced conventional tillage, which has been found to negatively affect mycelial networks³⁴, and may be a cause of the lower fungal diversity in arable soils. Furthermore, fertilization applications in arable lands can cause resource homogeneity, which may result in the dominance of copiotrophic microorganisms. Arable systems also have fewer hosts for symbiotic groups. Extensively managed grasslands are an important type of agricultural system that covers nearly
<figure><img src="image_5.png" /><figcaption>**Fig. 2 | Drivers of fungal biogeography.** A, B Latitudinal and longitudinal gradient of fungi richness in arable lands (orange) and in grasslands (green). First order polynomial models show that strong effects of latitude and longitude on fungal richness in arable lands and grasslands. C Relationship between fungal richness and the principal component (PC) 1 of soil properties and soil fungal richness. The</figcaption></figure>

relationship was only significant in arable lands. D Relationship between fungal richness and the principal component 1 of bioclimatic variables. Fungal richness increased significantly with increasing PC1 in both land use types. Adjusted R2 and *p* values indicate statistical significance.

the fungal cell wall. Rare soil fungi were also positively and significantly correlated with important soil ecosystem functions across both land-use types (Fig. S17), indicating that the loss of such groups due to agricultural intensity might have functional implications. However, since these groups were a part of the rare mycobiome, the correlations were weak. Overall, our results show a similarity between relative abundance and commonness, with abundant groups being more common, while rare groups even rarer.

## Discussion

We used the high-resolution PacBio Sequel targeting the entire ITS region to dissect fungal biogeographical patterns in 156 arable sites and 61 extensively managed grasslands across a 3000 km North-South gradient in Europe. We show that fungal diversity is consistently lower in arable lands than in grasslands across five countries. Rare fungi were affected or absent in arable lands, suggesting that biotic homogenization and a disproportionally negative impact of arable farming on the rare soil microorganisms.

## Biotic homogenization in arable lands

We found that, on an average, arable lands across the five European countries had nearly 25% lower fungal diversity than grasslands. One of the major impacts of intensive agriculture is the loss of biodiversity⁵,⁶. Practices such as excessive use of synthetic fertilizers and pesticides, monocultures, excessive tillage, and the homogenization of landscapes can negatively affect the local and regional pool of biodiversity⁵,²⁹. Indeed, agricultural intensification has been linked to a systematic decline in birds, invertebrates and amphibians in arable

lands⁶,³⁰. Importantly, biodiversity loss is not just a decrease in species number, but it also accompanies the loss of associations among various species, with the potential disruption of the network of mutual dependencies between species. For example, our previous report found that agricultural intensification has a negative impact on root endophytic fungi, and the associations among fungal members in conventional farmlands is 50% less than in organic lands²⁷. Recent studies also found that the abundance of beneficial mycorrhizal fungi is negatively associated with pesticide residues³¹ and pesticide application reduces the richness of mycorrhizal fungi and their ability to acquire phosphorus from the soil³². Despite this, we have limited knowledge of how intensive agricultural practices affect soil fungal diversity and distribution at large scales³³. Here, we show that fungal diversity peaked at mid-latitude, although it was consistently lower in arable lands. There was a negative relationship between fungal diversity and the agricultural management intensity calculated from tillage, agrochemicals, and pesticide information (Fig. 3C). This is in contrast to a recent study which found that fungal diversity was similar in grasslands and non-permanent croplands¹⁴, which could be due to the wider range of crops (over 20 different crops including cereals and vegetables) sampled in that study. Most of the arable lands assessed in this study practiced conventional tillage, which has been found to negatively affect mycelial networks³⁴, and may be a cause of the lower fungal diversity in arable soils. Furthermore, fertilization applications in arable lands can cause resource homogeneity, which may result in the dominance of copiotrophic microorganisms. Arable systems also have fewer hosts for symbiotic groups. Extensively managed grasslands are an important type of agricultural system that covers nearly
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

16% of all lands in Europe³⁵. Farmers are encouraged and often required to maintain a certain proportion of their lands as grasslands in order to receive government subsidies³⁶. The consistently higher functional diversity in grasslands supports the maintenance of grasslands as part of the farmlands to promote the soil mycobiome. We also found a greater abundance of mycorrhizal fungi and a relatively lower abundance of pathogens in grasslands. Overall, our results show that soil mycobiome displays contrasting biogeographical patterns between land-use types but a consistency across the 3000-km European gradient. However, plant community composition was not measured for grassland sites in this study and as a result, the importance of plant

**Fig. 3** | **Spatial commonness and rare fungi.** A The relationship between abundance and commonness of the overall soil fungal communities in arable lands (n = 156; orange) and grasslands (n = 61; green) across all five countries. Abundance is represented by the cumulative abundance of OTUs, which was calculated as the square-root transformed number of reads per sample. Cumulative abundance curves were created by averaging and summing up the ordered abundances. Commonness represents decreasing rank abundance of OTUs. Difference between the two land use types was mainly due to rare taxa, i.e., while abundant OTUs (higher ranked on the left) are similarly abundant in both land-use types, grasslands had a higher number of rare OTUs (lower ranked OTUs towards the end of the curves). B Spatial commonness or site occupancy of the overall soil fungal communities as revealed by the abundance of OTUs and the number of sites they were present at. Commonness represents the number of sites that the OTUs were present at, and it was calculated by square-root transforming the OTU abundance and then log-transforming to plot against the number of sites occupied. Common OTUs were present in a higher number of sites while rare OTUs were restricted in fewer sites. C Relationship between the overall fungal diversity and agricultural intensity for arable sites (n = 156). The Y axis represents the principal coordinate 1 of the overall OTUs using Bray-Curtis similarity whereas the X axis represents the agricultural intensity index calculated from agrochemical applications, tillage intensity, and crop diversity. Error bands indicate 95% confidence interval.

diversity could not be tested in our study. Moreover, we sampled extensive grasslands, which are different from pristine native grasslands in terms of their species composition. As shown in previous studies³⁷, plant diversity can be an important determinant of soil fungi as a diverse plant community influences soil physical properties with different root architectures, shapes the soil chemistry with diverse root exudates and residues, and thereby modulates the soil mycobiome. For example, labile resources will attract copiotrophic fungi while oligotrophic groups will settle for more recalcitrant resources. Indeed, resource heterogeneity may also vary based on the crop rotations in arable lands³⁸. Future studies should measure plant diversity when assessing arable and native ecosystems to dissect above- and belowground linkages.

## Drivers of fungal biogeography

Understanding the drivers of microbial distribution patterns is a fundamental goal of microbial ecology. Even in the global surveys of soil fungi⁴, agricultural systems are often not considered, and thus, our knowledge of the drivers of soil fungal biogeography in agricultural systems is still rudimentary. A large-scale investigation comparing fungal biogeography across intensive agricultural and extensive grassland systems can reveal the relative importance of climatic, geographical, and edaphic factors, and whether some drivers are consistent across intensive and extensive land-use types. Grassland and arable sites were paired in this study, making direct comparisons easier. We found that fungal diversity increased with latitude and longitude, with the highest diversity at mid-latitude (45°–50°). Importantly, this positive association was stronger in grassland soils, which is congruent with previous global reports on natural soils⁴,¹¹. Geographic distance and environment had a stronger influence in grasslands than in arable lands. The importance of spatial distance and soil environmental properties for structuring fungal communities has also been observed previously in non-agricultural soils¹²,³⁹. Owing to the inherent heterogeneity in soil resources, fungi communities vary across space and understanding of this community turnover can yield insights into the factors that govern their environmental filtering and dispersal limitations¹³. Climatic factors emerged as a strong control on soil fungi when geographic distance was accounted for. Previous reports on non-agricultural soils found a positive relationship between fungal diversity and mean annual temperature, with fungal diversity decreasing across the continent⁴,¹¹. In this study, both mean annual precipitation (366 mm–1296 mm) and temperature (2.8 °C–17.9 °C) varied considerably across the continent, and we found that fungal communities were more diverse in areas at mid-latitude (45°–50°) that
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

16% of all lands in Europe³⁵. Farmers are encouraged and often required to maintain a certain proportion of their lands as grasslands in order to receive government subsidies³⁶. The consistently higher functional diversity in grasslands supports the maintenance of grasslands as part of the farmlands to promote the soil mycobiome. We also found a greater abundance of mycorrhizal fungi and a relatively lower abundance of pathogens in grasslands. Overall, our results show that soil mycobiome displays contrasting biogeographical patterns between land-use types but a consistency across the 3000-km European gradient. However, plant community composition was not measured for grassland sites in this study and as a result, the importance of plant

**Fig. 3** | Spatial commonness and rare fungi. A The relationship between abundance and commonness of the overall soil fungal communities in arable lands (n = 156; orange) and grasslands (n = 61; green) across all five countries. Abundance is represented by the cumulative abundance of OTUs, which was calculated as the square-root transformed number of reads per sample. Cumulative abundance curves were created by averaging and summing up the ordered abundances. Commonness represents decreasing rank abundance of OTUs. Difference between the two land use types was mainly due to rare taxa, i.e., while abundant OTUs (higher ranked on the left) are similarly abundant in both land-use types, grasslands had a higher number of rare OTUs (lower ranked OTUs towards the end of the curves). B Spatial commonness or site occupancy of the overall soil fungal communities as revealed by the abundance of OTUs and the number of sites they were present at. Commonness represents the number of sites that the OTUs were present at, and it was calculated by square-root transforming the OTU abundance and then log-transforming to plot against the number of sites occupied. Common OTUs were present in a higher number of sites while rare OTUs were restricted in fewer sites. C Relationship between the overall fungal diversity and agricultural intensity for arable sites (n = 156). The Y axis represents the principal coordinate 1 of the overall OTUs using Bray-Curtis similarity whereas the X axis represents the agricultural intensity index calculated from agrochemical applications, tillage intensity, and crop diversity. Error bands indicate 95% confidence interval.

diversity could not be tested in our study. Moreover, we sampled extensive grasslands, which are different from pristine native grasslands in terms of their species composition. As shown in previous studies³⁷, plant diversity can be an important determinant of soil fungi as a diverse plant community influences soil physical properties with different root architectures, shapes the soil chemistry with diverse root exudates and residues, and thereby modulates the soil mycobiome. For example, labile resources will attract copiotrophic fungi while oligotrophic groups will settle for more recalcitrant resources. Indeed, resource heterogeneity may also vary based on the crop rotations in arable lands³⁸. Future studies should measure plant diversity when assessing arable and native ecosystems to dissect above- and belowground linkages.

## Drivers of fungal biogeography

Understanding the drivers of microbial distribution patterns is a fundamental goal of microbial ecology. Even in the global surveys of soil fungi⁴, agricultural systems are often not considered, and thus, our knowledge of the drivers of soil fungal biogeography in agricultural systems is still rudimentary. A large-scale investigation comparing fungal biogeography across intensive agricultural and extensive grassland systems can reveal the relative importance of climatic, geographical, and edaphic factors, and whether some drivers are consistent across intensive and extensive land-use types. Grassland and arable sites were paired in this study, making direct comparisons easier. We found that fungal diversity increased with latitude and longitude, with the highest diversity at mid-latitude (45°–50°). Importantly, this positive association was stronger in grassland soils, which is congruent with previous global reports on natural soils⁴,¹¹. Geographic distance and environment had a stronger influence in grasslands than in arable lands. The importance of spatial distance and soil environmental properties for structuring fungal communities has also been observed previously in non-agricultural soils¹²,³⁹. Owing to the inherent heterogeneity in soil resources, fungi communities vary across space and understanding of this community turnover can yield insights into the factors that govern their environmental filtering and dispersal limitations¹³. Climatic factors emerged as a strong control on soil fungi when geographic distance was accounted for. Previous reports on non-agricultural soils found a positive relationship between fungal diversity and mean annual temperature, with fungal diversity decreasing across the continent⁴,¹¹. In this study, both mean annual precipitation (366 mm–1296 mm) and temperature (2.8 °C–17.9 °C) varied considerably across the continent, and we found that fungal communities were more diverse in areas at mid-latitude (45°–50°) that
<figure><img src="image_9.png" /><figcaption>**Fig. 4** | Proportion and composition of rare fungi. A Rare fungal richness and proportion across land-use types and countries. Wilcoxon rank sum test was performed on the same number of sites (n = 61) for each land use. Left Panel: The richness of rare fungi per sample was significantly higher in grassland soils when compared with the same number of samples from arable lands. Small circles indicate individual data points, boxes mark the interquartile range, vertical lines indicate the whiskers, bold horizontal lines show the median and ‘x’ indicates the mean</figcaption></figure>

value. Middle Panel: The cumulative number of rare OTUs were also higher in grassland soils. Right Panel: Proportion of rare taxa was calculated by dividing the number of rare fungi with the total number of OTUs. B Taxonomic (upper panel) and functional (lower panel) composition of rare taxa in arable vs grassland soils. Rare taxa were identified from the original OTU table by identifying OTUs that are only present in one or two sites at respective land use types and Levin's niche breadth less than 0.55.

had low temperature but high precipitation. Indeed, higher temperatures reduced fungal diversity in both land-use types and fungal diversity was higher when the temperature change between seasons was more gradual. Collectively, these results show a consistent effect of geographic location and land-use on soil fungal communities across European countries.

## Fungal rarity and possible implications

The distribution and drivers of soil fungal biogeography may vary between agricultural systems that routinely receive amendments and extensively managed systems that do not. Thus, a large-scale investigation comparing fungal biogeography across agricultural and native systems would reveal if intensive agricultural practices can result in location-exclusivity of fungi, and whether such patterns are consistent across large spatial scales. Rare and abundant fungi can vary in their responses²⁰. Here, we found a disproportional effect of agricultural intensification on soil fungi in which prevalent taxa were unaffected, while rare fungi were rarer in arable lands compared to extensively managed grasslands. Consistently, agricultural intensity was negatively linked to the richness of both overall fungi and rare groups. Our observations are consistent with studies on macroorganisms, which report that rare species are vulnerable to anthropogenic disturbances and are at greater risk of extinction⁴⁰,⁴¹. While there is a strong emphasis on dominant taxa in recent studies²⁷,³⁸, our results suggest that the role of rare fungal taxa in soil ecosystems must not be overlooked. This is important because previous studies have found over 80% of fungi are rare or endemic and less than 100 groups are cosmopolitan¹,¹², and thus, rare members constitute a critical portion of belowground diversity. Although the overall richness was the lowest in Spain, it had the highest proportion of rare fungi, which might be pointing towards a greater vulnerability of rare fungi in dryland ecosystems. Indeed, a recent study found that fungi are vulnerable to drought at the global scale⁸. However, further studies are needed to compare such patterns in both dryland- and non-dryland ecosystems to obtain robust conclusions.

Rare taxa have been shown to play a key role in many soil geochemical processes including nitrification, denitrification and methanogenesis²²⁻²⁴,²⁶. In this study, we also found rare taxa were significantly associated with important soil processes such as basal respiration and aggregate stability, which are important indicators of soil health⁴². Thus, the loss of rare taxa might have functional implications for ecosystems. Conservation efforts must be made to protect such taxa and maintain their contributions to ecosystem functioning. For example, rare fungi display unique signatures compared to the abundant taxa in endangered Antarctic ecosystems⁴³. Rare members might be critically important for nutrient cycling in vulnerable ecosystems, and they might be indispensable for the survival of rare or endemic plants, hypotheses that can be tested in the future.

It is important to note that rare taxa can be dormant microbial members or a part of the relic DNA pool in the soil. Future studies may wish to identify active rare taxa by treating DNA samples with propidium monoazide (PMA), which binds to relic DNA and prevents subsequent amplification⁴⁴. It would also be of interest to see the relative contributions of eco-evolutionary factors to fungal rarity in agricultural systems. For example, an interesting question would be to explore if dispersal limitation exacerbates rare fungal distribution in arable lands. Future studies may also investigate how the loss of rare fungi affects functional plasticity and functional contingency of agroecosystems. Lastly, fungal biogeographical distribution can display temporal variability¹³, and thus, future studies should also assess fungal diversity over seasons or years to understand the temporal nature of biogeographical patterns. Temporal patterns of soil fungi would reveal if different types of rare taxa (e.g., permanent, transient, and conditional) are also disproportionally affected in arable lands.

## Concluding remarks

Intensive agriculture is a major cause of the loss of soil biodiversity of which soil fungi are an important constituent. Here we report that soil fungi display distinct biogeographical patterns at the continental scale with consistently lower diversity in arable lands than grasslands. We
<figure><img src="image_9.png" /><figcaption>**Fig. 4** | Proportion and composition of rare fungi. A Rare fungal richness and proportion across land-use types and countries. Wilcoxon rank sum test was performed on the same number of sites (n = 61) for each land use. Left Panel: The richness of rare fungi per sample was significantly higher in grassland soils when compared with the same number of samples from arable lands. Small circles indicate individual data points, boxes mark the interquartile range, vertical lines indicate the whiskers, bold horizontal lines show the median and ‘x’ indicates the mean</figcaption></figure>

value. Middle Panel: The cumulative number of rare OTUs were also higher in grassland soils. Right Panel: Proportion of rare taxa was calculated by dividing the number of rare fungi with the total number of OTUs. B Taxonomic (upper panel) and functional (lower panel) composition of rare taxa in arable vs grassland soils. Rare taxa were identified from the original OTU table by identifying OTUs that are only present in one or two sites at respective land use types and Levin's niche breadth less than 0.55.

had low temperature but high precipitation. Indeed, higher temperatures reduced fungal diversity in both land-use types and fungal diversity was higher when the temperature change between seasons was more gradual. Collectively, these results show a consistent effect of geographic location and land-use on soil fungal communities across European countries.

## Fungal rarity and possible implications

The distribution and drivers of soil fungal biogeography may vary between agricultural systems that routinely receive amendments and extensively managed systems that do not. Thus, a large-scale investigation comparing fungal biogeography across agricultural and native systems would reveal if intensive agricultural practices can result in location-exclusivity of fungi, and whether such patterns are consistent across large spatial scales. Rare and abundant fungi can vary in their responses²⁰. Here, we found a disproportional effect of agricultural intensification on soil fungi in which prevalent taxa were unaffected, while rare fungi were rarer in arable lands compared to extensively managed grasslands. Consistently, agricultural intensity was negatively linked to the richness of both overall fungi and rare groups. Our observations are consistent with studies on macroorganisms, which report that rare species are vulnerable to anthropogenic disturbances and are at greater risk of extinction⁴⁰,⁴¹. While there is a strong emphasis on dominant taxa in recent studies²⁷,³⁸, our results suggest that the role of rare fungal taxa in soil ecosystems must not be overlooked. This is important because previous studies have found over 80% of fungi are rare or endemic and less than 100 groups are cosmopolitan¹,¹², and thus, rare members constitute a critical portion of belowground diversity. Although the overall richness was the lowest in Spain, it had the highest proportion of rare fungi, which might be pointing towards a greater vulnerability of rare fungi in dryland ecosystems. Indeed, a recent study found that fungi are vulnerable to drought at the global scale⁸. However, further studies are needed to compare such patterns in both dryland- and non-dryland ecosystems to obtain robust conclusions.

Rare taxa have been shown to play a key role in many soil geochemical processes including nitrification, denitrification and methanogenesis²²⁻²⁴,²⁶. In this study, we also found rare taxa were significantly associated with important soil processes such as basal respiration and aggregate stability, which are important indicators of soil health⁴². Thus, the loss of rare taxa might have functional implications for ecosystems. Conservation efforts must be made to protect such taxa and maintain their contributions to ecosystem functioning. For example, rare fungi display unique signatures compared to the abundant taxa in endangered Antarctic ecosystems⁴³. Rare members might be critically important for nutrient cycling in vulnerable ecosystems, and they might be indispensable for the survival of rare or endemic plants, hypotheses that can be tested in the future.

It is important to note that rare taxa can be dormant microbial members or a part of the relic DNA pool in the soil. Future studies may wish to identify active rare taxa by treating DNA samples with propidium monoazide (PMA), which binds to relic DNA and prevents subsequent amplification⁴⁴. It would also be of interest to see the relative contributions of eco-evolutionary factors to fungal rarity in agricultural systems. For example, an interesting question would be to explore if dispersal limitation exacerbates rare fungal distribution in arable lands. Future studies may also investigate how the loss of rare fungi affects functional plasticity and functional contingency of agroecosystems. Lastly, fungal biogeographical distribution can display temporal variability¹³, and thus, future studies should also assess fungal diversity over seasons or years to understand the temporal nature of biogeographical patterns. Temporal patterns of soil fungi would reveal if different types of rare taxa (e.g., permanent, transient, and conditional) are also disproportionally affected in arable lands.

## Concluding remarks

Intensive agriculture is a major cause of the loss of soil biodiversity of which soil fungi are an important constituent. Here we report that soil fungi display distinct biogeographical patterns at the continental scale with consistently lower diversity in arable lands than grasslands. We
then show that arable farming leads to a homogenization of fungal
communities with prevalence of abundant groups in arable soils while
rare community members are rarer or even absent. We also report an
adverse effect of agricultural intensity on the richness of overall fungi
as well as rare groups. Our observation on soil fungi is consistent with
studies on macroorganisms reporting that rare species are particularly
susceptible to anthropogenic disturbances and at greater risk of
extinction⁴⁵,⁴⁶. This is important because global biodiversity con-
servation efforts recognize the vulnerability and irreplaceability of rare
and endemic plants and animals, but largely overlook soil
biodiversity⁴⁷. Further, many of the extinction factors have been shown
to act synergistically, with exacerbating effects on rare species⁴⁸. Soil is
one of the largest reservoirs of biodiversity⁴⁹,⁵⁰ and rare species are a
key constituent of that¹. Similar to plant- and animal hotspots, specific
ecoregions should also be identified for rare microbiota. One of such
ecoregions might be the dryland agroecosystems, where we found a
higher proportion of rare fungi, however, further studies are necessary
to understand the spatiotemporal variability and extinction dynamics
of such groups.

Methods

Site selection and soil sampling

This study was a part of the Digging Deeper Project conducted across five European countries Sweden, Germany, Switzerland, France, and Spain (Fig. 1). A total of 217 agricultural fields were chosen, including 156 arable sites and 61 extensively managed grasslands<sup>28,51</sup>. A majority (78%) of the arable sites were planted with wheat (<i>Triticum aestivum</i>) (<i>n</i> = 121) with the other cereal crops such as barley, <i>Hordeum vulgare</i> (<i>n</i> = 26); oat, <i>Avena sativa</i> (<i>n</i> = 6); rye, <i>Secale cereale</i> (<i>n</i> = 1); or triticale. Triticosecale sp. (<i>n</i> = 1) selected when wheat was unavailable. When possible, we paired agricultural fields with non-arable lands by sampling nearby extensively managed grasslands and marginal lands with permanent, predominantly herbaceous plant cover. These non-arable sites were mostly unfertilized and occasionally mowed. Many of these arable lands did not have nearby extensively managed grasslands, which resulted in an unbalanced design. However, to address whether the unbalanced design affected the outcome of this study, we computed fungal richness on the same number of arable lands (<i>n</i> = 61) by randomly selecting fields. Our analysis revealed that fungal richness was still lower for arable lands than grasslands (Fig. S4). Soil samples were collected in Spring 2017. At each site, eight soil cores were obtained in a circular pattern within a 10 m radius using a 5 cm diameter step-probe and to a depth of 20 cm. Soil samples were kept on ice until their transfer to the laboratory. Three of the cores were kept intact and used to measure bulk density and soil aggregation. The remaining soil cores were homogenized and sieved to 2 mm. Soil subsamples were air-dried for further processing for soil physical and chemical properties, stored at 4 °C for soil properties such as microbial biomass, and frozen at -18 °C for DNA extraction, mineral nitrogen content and potential N cycling rates.

Soil analyses

Soil properties were assessed according to Swiss Standard
Protocols³²,⁵² and conducted in Agroscope Reckenholz, Zurich. Gravi-
metric moisture was determined with a 10 g of field-moist soil at 105 °C
for 24 h. Soil pH was determined with a 20 g of soil in a 1:2.5 soil:water
solution. Soil texture was determined following the hydrometer
method⁵³. Mineral nitrogen content (NH₄⁺-N and NO₃⁻-N) was deter-
mined by extracting soil samples with 1 M KCl (soil: solution ratio of
1:10) and analyzed on a San ++ Automated Wet Chemistry Analyzer-
Continuous Flow Analyzer (CFA, Skalar, The Netherlands). Cation
exchange capacity was estimated with a 2.5 g of soil and the results
were expressed in mg/L. Total carbon and total nitrogen were analyzed
by combustion of a 250 mg of soil on a TruSpec CN Analyzer (LECO,
MI, USA). Soil organic carbon content was analyzed with a 0.5 g of soil

using the potassium-dichromate (K₂Cr₂O₇) oxidation method. Micro-
bial biomass carbon and microbial biomass nitrogen were measured
by the chloroform fumigation method⁵⁴. Chloroform fumigation was
performed with triplicates of 20 g of soil samples incubated for
24 hours. Basal respiration was measured by incubating soils with a
NaOH solution for 24 hours. Soil carbon and nitrogen concentration
were determined by combating a 250 mg of soil using TruSpec CN
Analyzer (LECO, MI, USA).

Amplicon sequencing

Soil DNA was extracted from 0.25 g soil using the PowerSoil DNA iso-
lation kit (MoBio, Carlsbad, CA) following the manufacturer's instruc-
tions. Fungal ITS region was amplified using the PacBio SMRT Sequel
platform with the primers ITSIF (CTTGGTCATTTAGAGGAAGTAA) and
ITS4 (TCCTCCGCTTATTGATATGC) targeting the entire ITS region⁵³. A
two-step PCR was conducted on a Biorad PCR Instrument (Biorad,
Hamburg, Germany) using the 5PRIME HotMaster Taq DNA Poly-
merase (Quantabio, Beverly, MA, USA) in 20 µl of reaction mixture⁵⁶.
Details on PCR conditions and library preparation can be found in
the Supplementary Information. The sequencing libraries were pre-
pared using P6/C4 chemistry (DNA/Polymerase Binding Kit P6, DNA
Sequencing Reagent 4.0) on the PacBio® Sequel Instrument available
at the Functional Genomic Centre Zurich (FGCZ, Zurich, Switzerland;
http://www.fgcz.ch). The PacBio SMRT Portal (https://www.pacb.com/
products-and-services/analytical-software/smrtdata/) was used to
process the raw sequences and extract the circular consensus
sequences of at least five passes⁵⁷.

Fastq files obtained from the PacBio runs were quality filtered
using the PRINSEQ-lite v0.20.4⁵⁸. Filtering parameters were: GC range
30–70, minimum mean quality score of 20, no ambiguous nucleotides,
low sequence complexity filter with a threshold of 30 in the DUST
algorithm. In a next step, the reads were demultiplexed using an in-
silico PCR approach as part of USEARCH v1¹⁵⁹ allowing max 1 mismatch
in the barcode-primer sequence but not at the 3-prime ends. The
amplicon size range was set to 100–2000 but all amplicons with
additional primer sites (concatenated amplicons - multi-primer arte-
facts) were removed. Sequences were then clustered into operational
taxonomic units (OTUs), based on 97% similarity, using the UPARSE
pipeline (Edgar 2013). Taxonomical information was predicted to
OTUs based on the UNITE database (V7.2)⁶⁰ using the SINTAX
classifier⁶¹. Singletons (OTUs that only occur once) and OTUs with low
abundance (relative abundance less than 0.5% in total and less than
0.5% in each sample) were excluded. A total of 17 phyla, 42 classes, 90
orders, 187 families and 293 genera were classified. Only 6% OTUs were
identified at the species level. Nearly half (49.4%) of the OTUs were
classified to at least the class level, accounting for 80.2% of total
abundance. The OTU table was rarefied to 2000 reads per sam-
ple (Fig. S2).

Statistical analyses

All statistical analyses were conducted using packages in R (v.3.4.3).
Climatic data were acquired at a resolution of 10 min (around 18.5 km
at latitude 40°) including 19 bioclimatic variables (http://www.
worldclim.org)⁶². Details on climate data acquisition can be found in
the Supplementary Information. Alpha diversity indices such as rich-
ness (number of OTUs), Shannon-Wiener index and ACE index were
calculated using the *phyloseq* package version 1.16.2⁶³. We performed a
Kruskal-Wallis rank-sum test to assess whether fungal diversity differed
significantly between land use types. Information on putative func-
tions was obtained using the *FunGuild* database⁶⁴.

Land-use-specific taxa were identified as the OTUs only present in either arable lands or grasslands. To compare fungal relative abundance across two land-use types and five countries, OTU numbers were normalized by square-rooting number of sequence reads and ordered by decreasing relative abundance for each site. Then, cumulative
then show that arable farming leads to a homogenization of fungal
communities with prevalence of abundant groups in arable soils while
rare community members are rarer or even absent. We also report an
adverse effect of agricultural intensity on the richness of overall fungi
as well as rare groups. Our observation on soil fungi is consistent with
studies on macroorganisms reporting that rare species are particularly
susceptible to anthropogenic disturbances and at greater risk of
extinction⁴⁵,⁴⁶. This is important because global biodiversity con-
servation efforts recognize the vulnerability and irreplaceability of rare
and endemic plants and animals, but largely overlook soil
biodiversity⁴⁷. Further, many of the extinction factors have been shown
to act synergistically, with exacerbating effects on rare species⁴⁸. Soil is
one of the largest reservoirs of biodiversity⁴⁹,⁵⁰ and rare species are a
key constituent of that¹. Similar to plant- and animal hotspots, specific
ecoregions should also be identified for rare microbiota. One of such
ecoregions might be the dryland agroecosystems, where we found a
higher proportion of rare fungi, however, further studies are necessary
to understand the spatiotemporal variability and extinction dynamics
of such groups.

Methods

Site selection and soil sampling

This study was a part of the Digging Deeper Project conducted across five European countries Sweden, Germany, Switzerland, France, and Spain (Fig. 1). A total of 217 agricultural fields were chosen, including 156 arable sites and 61 extensively managed grasslands<sup>28,51</sup>. A majority (78%) of the arable sites were planted with wheat (<i>Triticum aestivum</i>) (<i>n</i> = 121) with the other cereal crops such as barley, <i>Hordeum vulgare</i> (<i>n</i> = 26); oat, <i>Avena sativa</i> (<i>n</i> = 6); rye, Secale cereale (<i>n</i> = 1); or triticale. Triticosecale sp. (<i>n</i> = 1) selected when wheat was unavailable. When possible, we paired agricultural fields with non-arable lands by sampling nearby extensively managed grasslands and marginal lands with permanent, predominantly herbaceous plant cover. These non-arable sites were mostly unfertilized and occasionally mowed. Many of these arable lands did not have nearby extensively managed grasslands, which resulted in an unbalanced design. However, to address whether the unbalanced design affected the outcome of this study, we computed fungal richness on the same number of arable lands (<i>n</i> = 61) by randomly selecting fields. Our analysis revealed that fungal richness was still lower for arable lands than grasslands (Fig. S4). Soil samples were collected in Spring 2017. At each site, eight soil cores were obtained in a circular pattern within a 10 m radius using a 5 cm diameter step-probe and to a depth of 20 cm. Soil samples were kept on ice until their transfer to the laboratory. Three of the cores were kept intact and used to measure bulk density and soil aggregation. The remaining soil cores were homogenized and sieved to 2 mm. Soil subsamples were air-dried for further processing for soil physical and chemical properties, stored at 4 °C for soil properties such as microbial biomass, and frozen at -18 °C for DNA extraction, mineral nitrogen content and potential N cycling rates.

Soil analyses

Soil properties were assessed according to Swiss Standard Protocols³²,⁵² and conducted in Agroscope Reckenholz, Zurich. Gravimetric moisture was determined with a 10 g of field-moist soil at 105 °C for 24 h. Soil pH was determined with a 20 g of soil in a 1:2.5 soil:water solution. Soil texture was determined following the hydrometer method⁵³. Mineral nitrogen content (NH₄⁺-N and NO₃⁻-N) was determined by extracting soil samples with 1 M KCl (soil: solution ratio of 1:10) and analyzed on a San ++ Automated Wet Chemistry Analyzer-Continuous Flow Analyzer (CFA, Skalar, The Netherlands). Cation exchange capacity was estimated with a 2.5 g of soil and the results were expressed in mg/L. Total carbon and total nitrogen were analyzed by combustion of a 250 mg of soil on a TruSpec CN Analyzer (LECO, MI, USA). Soil organic carbon content was analyzed with a 0.5 g of soil

using the potassium-dichromate (K₂Cr₂O₇) oxidation method. Micro-
bial biomass carbon and microbial biomass nitrogen were measured
by the chloroform fumigation method⁵⁴. Chloroform fumigation was
performed with triplicates of 20 g of soil samples incubated for
24 hours. Basal respiration was measured by incubating soils with a
NaOH solution for 24 hours. Soil carbon and nitrogen concentration
were determined by combusting a 250 mg of soil using TruSpec CN
Analyzer (LECO, MI, USA).

Amplicon sequencing

Soil DNA was extracted from 0.25 g soil using the PowerSoil DNA isolation kit (MoBio, Carlsbad, CA) following the manufacturer's instructions. Fungal ITS region was amplified using the PacBio SMRT Sequel platform with the primers ITS1F (CTTGGTCATTTAGAGGAAGTAA) and ITS4 (TCCTCCGCTTATTGATATGC) targeting the entire ITS region⁵³. A two-step PCR was conducted on a Biorad PCR Instrument (Biorad, Hamburg, Germany) using the 5PRIME HotMaster Taq DNA Polymerase (Quantabio, Beverly, MA, USA) in 20 µl of reaction mixture⁵⁶. Details on PCR conditions and library preparation can be found in the Supplementary Information. The sequencing libraries were prepared using P6/C4 chemistry (DNA/Polymerase Binding Kit P6, DNA Sequencing Reagent 4.0) on the PacBio® Sequel Instrument available at the Functional Genomic Centre Zurich (FGCZ, Zurich, Switzerland; http://www.fgcz.ch). The PacBio SMRT Portal (https://www.pacb.com/products-and-services/analytical-software/smrtdata/) was used to process the raw sequences and extract the circular consensus sequences of at least five passes⁵⁷.

Fastq files obtained from the PacBio runs were quality filtered
using the PRINSEQ-lite v0.20.4⁵⁸. Filtering parameters were: GC range
30–70, minimum mean quality score of 20, no ambiguous nucleotides,
low sequence complexity filter with a threshold of 30 in the DUST
algorithm. In a next step, the reads were demultiplexed using an in-
silico PCR approach as part of USEARCH v1¹⁵⁹ allowing max 1 mismatch
in the barcode-primer sequence but not at the 3-prime ends. The
amplicon size range was set to 100–2000 but all amplicons with
additional primer sites (concatenated amplicons - multi-primer arte-
facts) were removed. Sequences were then clustered into operational
taxonomic units (OTUs), based on 97% similarity, using the UPARSE
pipeline (Edgar 2013). Taxonomical information was predicted to
OTUs based on the UNITE database (V7.2)⁶⁰ using the SINTAX
classifier⁶¹. Singletons (OTUs that only occur once) and OTUs with low
abundance (relative abundance less than 0.5% in total and less than
0.5% in each sample) were excluded. A total of 17 phyla, 42 classes, 90
orders, 187 families and 293 genera were classified. Only 6% OTUs were
identified at the species level. Nearly half (49.4%) of the OTUs were
classified to at least the class level, accounting for 80.2% of total
abundance. The OTU table was rarefied to 2000 reads per sam-
ple (Fig. S2).

Statistical analyses

All statistical analyses were conducted using packages in R (v.3.4.3).
Climatic data were acquired at a resolution of 10 min (around 18.5 km
at latitude 40°) including 19 bioclimatic variables (http://www.
worldclim.org)⁶². Details on climate data acquisition can be found in
the Supplementary Information. Alpha diversity indices such as rich-
ness (number of OTUs), Shannon-Wiener index and ACE index were
calculated using the *phyloseq* package version 1.16.2⁶³. We performed a
Kruskal-Wallis rank-sum test to assess whether fungal diversity differed
significantly between land use types. Information on putative func-
tions was obtained using the *FunGuild* database⁶⁴.

Land-use-specific taxa were identified as the OTUs only present in either arable lands or grasslands. To compare fungal relative abundance across two land-use types and five countries, OTU numbers were normalized by square-rooting number of sequence reads and ordered by decreasing relative abundance for each site. Then, cumulative
abundance curves for arable lands and grasslands were generated by averaging and summing up the ordered abundances, and this was conducted for the overall dataset as well as for each country separately. Welch's *t*-test was applied to test for each ordered OTU whether its relative abundances differed significantly between arable lands and grassland. First- and second-order polynomial models were fitted to the longitudinal and latitudinal distribution of soil fungal diversity, and the best fit was selected based on the corrected Akaike Information Criterion (AICc) using the *AICcmodavg* package<sup>65</sup>. Spearman's correlation coefficients between fungal diversity and bioclimatic variables and soil properties were calculated using the *corrplot* package. Since many variables for bioclimatic attributes and soil properties were correlated with each other, principal component analysis (PCA) was conducted, followed by linear regression analysis with the first principal component (PC1) as the explanatory variable and fungal diversity as the dependent variable. If two variables correlated too strongly with each other (Spearman's *r* > 0.95), only one was retained for the PCA analysis. Fungal community structure was assessed by performing the principal coordinate analysis (PCoA) with Bray-Curtis dissimilarity using the *ordinate* function in *phyloseq*. To examine fungal composition across countries, PERMANOVA was conducted with 999 permutations using the *adonis* function in *vegan*<sup>66</sup>. As PERMANOVA results were significant, canonical analysis of principal coordinates analysis (CAP) was conducted as a constrained ordination to quantify the effects of country and land use on soil fungal composition using the *ordinate* function. To investigate how soil fungal communities differed with increasing geographical and environmental dissimilarities, a partial Mantel test was conducted with the *mantelpartial* function in *vegan*. We performed SEM to investigate complex relationships among geographical locations, climatic conditions, soil properties and fungal communities using the *lavaan* package version 0.6–3<sup>67</sup>. An initial SEM was constructed based on the understanding of factors that shape soil fungal distribution (Fig. S18). Briefly, geographic locations influence soil fungal diversity through fungal dispersal limitations. Climate and soil properties are the two environmental filters on soil fungal communities. Texture, isothermality and mean temperature in the wettest season were also removed to achieve better model fit and parsimony. Samples containing missing values were removed, resulting in 143 observations for arable lands and 54 observations for grasslands, with 8 degrees of freedom. The initial SEM was modified sequentially by removing links that were not significant and hindered model fit, or by adding links that had high modification indices. The final SEM model had adequate fit ($p_{arable lands} = 0.294$; $p_{grasslands} = 0.699$; Fig. S13). A model was considered acceptable when the chi-square test *p*-value was greater than 0.05. The best model was preferentially selected based on parsimony, coefficient of determination of fungal diversity and goodness of fit using the maximum likelihood approach<sup>68</sup>.

Rare taxa were identified from the original OTU table by identifying OTUs that are only present at one or two sites of respective land use types and have a narrow niche breadth (<0.55). We computed niche breadth using the MicroNiche package<sup>69</sup>, which calculates the proportional occurrence of taxa across sites. A species is considered generalist when it utilizes all resources equally ($B_n = 1$) and the species has a broad, non-discriminatory niche<sup>69</sup>. On the other hand, a species is considered specialist when it has a narrow, discriminatory niche ($B_n < 0.5$). A null distribution with 999 permutations was used to determine the specialist taxa from the generalist. We used the *FunGuild* database<sup>64</sup> to identify the major functional groups that these rare taxa belong to and verified the functional guilds using the FungalTraits database<sup>70</sup>. To assess the cellular and molecular functions of rare fungi, we used FunFun<sup>71</sup>, which is a functional annotator that predicts the gene content of individual fungi from ITS sequencing data.

We created a single index of management intensity that could be compared across arable sites and countries, and this index was used in

a previous study<sup>28</sup>. For this, we used the management data obtained from the 2017 growing season. The specific management variables that were used in this index were the number of insecticide, herbicide and fungicide applications, the number of tillage events, the maximum tillage depth, and the total amount of mineral nitrogen applied. All of the above parameters were then included in a single management index using PCA and scaling the index between 0 and 1, with 0 indicating the minimum intensity of management practices and 1 indicating the maximum intensity.

## Reporting summary

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

## Data availability

All data are available on GitHub (https://github.com/sambanerjee2022/Agricultural-intensification-and-fungal-rarity.git). Sequences generated in this study are available through Sequence Read Archive (SRA) under BioProject accession number PRJNA1043689.

## Code availability

All scripts are available on GitHub (https://github.com/sambanerjee2022/Agricultural-intensification-and-fungal-rarity.git).

## References

1. Peay, K. G., Kennedy, P. G. & Talbot, J. M. Dimensions of biodiversity in the Earth mycobiome. *Nat. Rev. Microbiol.* **14**, 434–447 (2016).

2. van der Heijden, M. G. A., Bardgett, R. D. & Van Straalen, N. M. The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. *Ecol. Lett.* **11**, 296–310 (2008).

3. Crowther, T. W. et al. The global soil community and its influence on biogeochemistry. *Science* **365**, eaav0550 (2019).

4. Tedersoo, L. et al. Global diversity and geography of soil fungi. *Science* **34**, 1256688 (2014).

5. Gossner, M. M. et al. Land-use intensification causes multitrophic homogenization of grassland communities. *Nature* **540**, 266–269 (2016).

6. Tsiafouli, M. A. et al. Intensive agriculture reduces soil biodiversity across Europe. *Glob. Chang. Biol.* **21**, 973–985 (2015).

7. Helgason, T., Daniell, T. J., Husband, R., Fitter, A. H. & Young, J. P. Ploughing up the wood-wide web? *Nature* **394**, 431 (1998).

8. Tedersoo, L. et al. Global patterns in endemicity and vulnerability of soil fungi. *Glob. Chang. Biol.* **28**, 6696–6710 (2022).

9. Tuomisto, H. L., Hodge, I. D., Riordan, P. & Macdonald, D. W. Does organic farming reduce environmental impacts? - A meta-analysis of European research. *J. Environ. Manag.* **112**, 309–320 (2012).

10. Gabriel, D., Sait, S. M., Kunin, W. E. & Benton, T. G. Food production vs. biodiversity: comparing organic and conventional agriculture. *J. Appl. Ecol.* **50**, 355–364 (2013).

11. Bahram, M. et al. Structure and function of the global topsoil microbiome. *Nature* **560**, 233–237 (2018).

12. Talbot, J. M. et al. Endemism and functional convergence across the North American soil mycobiome. *Proc. Natl. Acad. Sci.* **111**, 6341–6346 (2014).

13. Averill, C., Cates, L. A. L., Dietze, M. C. & Bhatnagar, J. M. Spatial vs. temporal controls over soil fungal community similarity at continental and global scales. *ISME J.* **13**, 2082–2093 (2019).

14. Labouyrie, M. et al. Patterns in soil microbial diversity across Europe. *Nat. Commun.* **14**, 3311 (2023).

15. Hanson, C. A., Fuhrman, J. A., Horner-Devine, M. C. & Martiny, J. B. H. Beyond biogeographic patterns: Processes shaping the microbial landscape. *Nat. Rev. Microbiol.* **10**, 497–506 (2012).

16. Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. *Proc. Natl. Acad. Sci. USA* **103**, 626–631 (2006).
abundance curves for arable lands and grasslands were generated by averaging and summing up the ordered abundances, and this was conducted for the overall dataset as well as for each country separately. Welch's *t*-test was applied to test for each ordered OTU whether its relative abundances differed significantly between arable lands and grassland. First- and second-order polynomial models were fitted to the longitudinal and latitudinal distribution of soil fungal diversity, and the best fit was selected based on the corrected Akaike Information Criterion (AICc) using the *AICcmodavg* package⁶⁵. Spearman's correlation coefficients between fungal diversity and bioclimatic variables and soil properties were calculated using the *corrplot* package. Since many variables for bioclimatic attributes and soil properties were correlated with each other, principal component analysis (PCA) was conducted, followed by linear regression analysis with the first principal component (PC1) as the explanatory variable and fungal diversity as the dependent variable. If two variables correlated too strongly with each other (Spearman's *r* > 0.95), only one was retained for the PCA analysis. Fungal community structure was assessed by performing the principal coordinate analysis (PCoA) with Bray-Curtis dissimilarity using the *ordinate* function in *phyloseq*. To examine fungal composition across countries, PERMANOVA was conducted with 999 permutations using the *adonis* function in *vegan*⁶⁶. As PERMANOVA results were significant, canonical analysis of principal coordinates analysis (CAP) was conducted as a constrained ordination to quantify the effects of country and land use on soil fungal composition using the *ordinate* function. To investigate how soil fungal communities differed with increasing geographical and environmental dissimilarities, a partial Mantel test was conducted with the *mantelpartial* function in *vegan*. We performed SEM to investigate complex relationships among geographical locations, climatic conditions, soil properties and fungal communities using the *lavaan* package version 0.6–3⁶⁷. An initial SEM was constructed based on the understanding of factors that shape soil fungal distribution (Fig. S18). Briefly, geographic locations influence soil fungal diversity through fungal dispersal limitations. Climate and soil properties are the two environmental filters on soil fungal communities. Texture, isothermality and mean temperature in the wettest season were also removed to achieve better model fit and parsimony. Samples containing missing values were removed, resulting in 143 observations for arable lands and 54 observations for grasslands, with 8 degrees of freedom. The initial SEM was modified sequentially by removing links that were not significant and hindered model fit, or by adding links that had high modification indices. The final SEM model had adequate fit ($p_{\text{arable lands}} = 0.294$; $p_{\text{grasslands}} = 0.699$; Fig. S13). A model was considered acceptable when the chi-square test *p*-value was greater than 0.05. The best model was preferentially selected based on parsimony, coefficient of determination of fungal diversity and goodness of fit using the maximum likelihood approach⁶⁸.

Rare taxa were identified from the original OTU table by identifying OTUs that are only present at one or two sites of respective land use types and have a narrow niche breadth (<0.55). We computed niche breadth using the MicroNiche package⁶⁹, which calculates the proportional occurrence of taxa across sites. A species is considered generalist when it utilizes all resources equally ($B_n = 1$) and the species has a broad, non-discriminatory niche⁶⁹. On the other hand, a species is considered specialist when it has a narrow, discriminatory niche ($B_n < 0.5$). A null distribution with 999 permutations was used to determine the specialist taxa from the generalist. We used the *FunGuild* database⁶⁴ to identify the major functional groups that these rare taxa belong to and verified the functional guilds using the FungalTraits database⁷⁰. To assess the cellular and molecular functions of rare fungi, we used FunFun⁷¹, which is a functional annotator that predicts the gene content of individual fungi from ITS sequencing data.

We created a single index of management intensity that could be compared across arable sites and countries, and this index was used in

a previous study²⁸. For this, we used the management data obtained from the 2017 growing season. The specific management variables that were used in this index were the number of insecticide, herbicide and fungicide applications, the number of tillage events, the maximum tillage depth, and the total amount of mineral nitrogen applied. All of the above parameters were then included in a single management index using PCA and scaling the index between 0 and 1, with 0 indicating the minimum intensity of management practices and 1 indicating the maximum intensity.

## Reporting summary

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

## Data availability

All data are available on GitHub (https://github.com/sambanerjee2022/Agricultural-intensification-and-fungal-rarity.git). Sequences generated in this study are available through Sequence Read Archive (SRA) under BioProject accession number PRJNA1043689.

## Code availability

All scripts are available on GitHub (https://github.com/sambanerjee2022/Agricultural-intensification-and-fungal-rarity.git).

## References

1. Peay, K. G., Kennedy, P. G. & Talbot, J. M. Dimensions of biodiversity in the Earth mycobiome. *Nat. Rev. Microbiol.* **14**, 434–447 (2016).

2. van der Heijden, M. G. A., Bardgett, R. D. & Van Straalen, N. M. The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. *Ecol. Lett.* **11**, 296–310 (2008).

3. Crowther, T. W. et al. The global soil community and its influence on biogeochemistry. *Science* **365**, eaav0550 (2019).

4. Tedersoo, L. et al. Global diversity and geography of soil fungi. *Science* **34**, 1256688 (2014).

5. Gossner, M. M. et al. Land-use intensification causes multitrophic homogenization of grassland communities. *Nature* **540**, 266–269 (2016).

6. Tsiafouli, M. A. et al. Intensive agriculture reduces soil biodiversity across Europe. *Glob. Chang. Biol.* **21**, 973–985 (2015).

7. Helgason, T., Daniell, T. J., Husband, R., Fitter, A. H. & Young, J. P. Ploughing up the wood-wide web? *Nature* **394**, 431 (1998).

8. Tedersoo, L. et al. Global patterns in endemicity and vulnerability of soil fungi. *Glob. Chang. Biol.* **28**, 6696–6710 (2022).

9. Tuomisto, H. L., Hodge, I. D., Riordan, P. & Macdonald, D. W. Does organic farming reduce environmental impacts? - A meta-analysis of European research. *J. Environ. Manag.* **112**, 309–320 (2012).

10. Gabriel, D., Sait, S. M., Kunin, W. E. & Benton, T. G. Food production vs. biodiversity: comparing organic and conventional agriculture. *J. Appl. Ecol.* **50**, 355–364 (2013).

11. Bahram, M. et al. Structure and function of the global topsoil microbiome. *Nature* **560**, 233–237 (2018).

12. Talbot, J. M. et al. Endemism and functional convergence across the North American soil mycobiome. *Proc. Natl. Acad. Sci.* **111**, 6341–6346 (2014).

13. Averill, C., Cates, L. A. L., Dietze, M. C. & Bhatnagar, J. M. Spatial vs. temporal controls over soil fungal community similarity at continental and global scales. *ISME J.* **13**, 2082–2093 (2019).

14. Labouyrie, M. et al. Patterns in soil microbial diversity across Europe. *Nat. Commun.* **14**, 3311 (2023).

15. Hanson, C. A., Fuhrman, J. A., Horner-Devine, M. C. & Martiny, J. B. H. Beyond biogeographic patterns: Processes shaping the microbial landscape. *Nat. Rev. Microbiol.* **10**, 497–506 (2012).

16. Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. *Proc. Natl. Acad. Sci. USA* **103**, 626–631 (2006).
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18. Chen, Y., Kuang, J., Wang, P., Shu, W. & Barberán, A. Associations between human impacts and forest soil microbial communities. *Elementa* **8**, 1 (2020).

19. Martiny, J. B. H. et al. Microbial biogeography: putting micro-organisms on the map. *Nat. Rev. Microbiol.* **4**, 102–112 (2006).

20. Jiao, S. & Lu, Y. Abundant fungi adapt to broader environmental gradients than rare fungi in agricultural fields. *Glob. Chang. Biol.* **26**, 4506–4520 (2020).

21. Enquist, B. J. et al. The commonness of rarity: Global and future distribution of rarity across land plants. *Sci. Adv.* **5**, 1–14 (2019).

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23. Chen, Q. L. et al. Rare microbial taxa as the major drivers of ecosystem multifunctionality in long-term fertilized soils. *Soil Biol. Biochem.* **141**, 107686 (2020).

24. Xiong, C. et al. Rare taxa maintain the stability of crop mycobiomes and ecosystem functions. *Environ. Microbiol.* **23**, 1907–1924 (2021).

25. Delgado-Baquerizo, M. et al. Global homogenization of the structure and function in the soil microbiome of urban greenspaces. *Sci. Adv.* **7**, eabg5809 (2021).

26. Jia, X., Dini-Andreote, F. & Salles, J. F. Unravelling the interplay of ecological processes structuring the bacterial rare biosphere. *ISME Commun.* **2**, 1–11 (2022).

27. Banerjee, S. et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. *ISME J.* **13**, 1722–1736 (2019).

28. Garland, G. et al. Crop cover is more important than rotational diversity for soil multifunctionality and cereal yields in European cropping systems. *Nat. Food* **2**, 28–37 (2021).

29. Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. *Nat. Commun.* **6**, 8568 (2015).

30. Robinson, R. & Sutherland, W. J. Post-war changes in arable farming and biodiversity in Great Britain. *J. Appl. Ecol.* **39**, 157–176 (2002).

31. Riedo, J. et al. Widespread occurrence of pesticides in organically managed agricultural soils-The ghost of a conventional agricultural past? *Environ. Sci. Technol.* **55**, 2919–2928 (2021).

32. Edlinger, A. et al. Agricultural management and pesticide use reduce the functioning of beneficial plant symbionts. *Nat. Ecol.* **6**, 1145–1154 (2022).

33. Romdhane, S. et al. Land-use intensification differentially affects bacterial, fungal and protist communities and decreases microbiome network complexity. *Environ. Microbiomes* **17**, 1–15 (2022).

34. de Vries, F. T., Hoffland, E., van Eekeren, N., Brussaard, L. & Bloem, J. Fungal/bacterial ratios in grasslands with contrasting nitrogen management. *Soil Biol. Biochem.* **38**, 2092–2103 (2006).

35. Henle, K. et al. Identifying and managing the conflicts between agriculture and biodiversity conservation in Europe-A review. *Agric. Ecosyst. Environ.* **124**, 60–71 (2008).

36. d'Andrimont, R. et al. Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union. *Sci. Data* **7**, 1–15 (2020).

37. Delgado-Baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. *Science* **359**, 320–325 (2018).

38. Egidi, E. et al. A few Ascomycota taxa dominate soil fungal communities worldwide. *Nat. Commun.* **101**, 1–9 (2019).

39. Meyer, K. M. et al. Why do microbes exhibit weak biogeographic patterns? *ISME J.* **12**, 1404–1413 (2018).

40. Davies, K. F., Margules, C. R. & Lawrence, J. F. A synergistic effect puts rare, specialized species at greater risk of extinction. *Ecology* **85**, 265–271 (2004).

41. Hedlund, B. P. & Staley, J. T. Microbial Endemism and Biogeography. in *Microbial Diversity and Bioprospecting* 225–231 (ASM Press, 2004). https://doi.org/10.1128/9781555817770.ch22

42. Bünemann, E. K. et al. Soil quality - A critical review. *Soil Biol. Biochem.* **120**, 105–125 (2018).

43. Cox, F., Newsham, K. K. & Robinson, C. H. Endemic and cosmopolitan fungal taxa exhibit differential abundances in total and active communities of Antarctic soils. *Environ. Microbiol.* **21**, 1586–1596 (2019).

44. Carini, P. et al. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. *Nat. Microbiol.* **2**, 1–6 (2016).

45. Johnson, C. N. et al. Biodiversity losses and conservation responses in the Anthropocene. *Science* **356**, 270–275 (2017).

46. Guerra, C. A. et al. Global hotspots for soil nature conservation. *Nature* **610**, 693–698 (2022).

47. Myers, N., Mittermeier, R. A., Mittermeier, C. G., Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. **403**, 853–858 (2000).

48. Brook, B. W., Sodhi, N. S. & Bradshaw, C. J. A. Synergies among extinction drivers under global change. *Trends Ecol. Evol.* **23**, 453–460 (2008).

49. Banerjee, S. & Heijden, M. G. A. Soil microbiomes and one health. *Nat. Rev. Microbiol.* **19**, (2022).

50. Anthony, M. A., Bender, S. F. & van der Heijden, M. G. Enumerating soil biodiversity Mark. *Proc. Natl. Acad. Sci.* **120**, e2304663120 (2023).

51. Edlinger, A. et al. The impact of agricultural management on soil aggregation and carbon storage is regulated by climatic thresholds across a 3000 km European gradient. *Glob. Chang. Biol.* **29**, 3177–3192 (2023).

52. Agroscope. Schweizerische Referenzmethoden der Forschungsanstalten Agroscope. Agroscope, Zürich. (1996).

53. Ashworth, J., Keyes, D., Kirk, R. & Lessard, R. Standard procedure in the hydrometer method for particle size analysis. *Commun. Soil Sci. Plant Anal.* **32**, 633–642 (2001).

54. Vance, E. D., Brookes, P. C. & Jenkinson, D. S. An extraction method for measuring soil microbial biomass C. *Soil Biol. Biochem.* **19**, 703–707 (1987).

55. Gardes, M. & Bruns, T. D. ITS primers with enhanced specificity for basidiomycetes-application to the identification of mycorrhizae and ruts. *Mol. Ecol.* **2**, 113–118 (1993).

56. Lindahl, B. D. et al. Fungal community analysis by high-throughput sequencing of amplified markers-a user's guide. *N. Phytol.* **199**, 288–299 (2013).

57. Schlaeppi, K. et al. High-resolution community profiling of arbuscular mycorrhizal fungi. *N. Phytol.* **212**, 780–791 (2016).

58. Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. *Bioinformatics* **27**, 863–864 (2011).

59. Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. *Bioinformatics* **26**, 2460–2461 (2010).

60. Köljalg, U. et al. UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. *N. Phytol.* **166**, 1063–1068 (2005).

61. Bengtsson-Palme, J. et al. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. *Methods Ecol. Evol.* **4**, 914–919 (2013).

62. Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. *Int. J. Climatol.* **37**, 4302–4315 (2017).

63. McMurdie, P. J. & Holmes, S. Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. *PLoS One* **8**, e61217 (2013).

64. Nguyen, N. H. et al. FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. *Fungal Ecol.* (2016). https://doi.org/10.1016/j.funeco.2015.06.006
17. Yang, T., Lupwayi, N., Marc, S. A., Siddique, K. H. M. & Bainard, L. D. Anthropogenic drivers of soil microbial communities and impacts on soil biological functions in agroecosystems. *Glob. Ecol. Conserv.* **27**, e01521 (2021).

18. Chen, Y., Kuang, J., Wang, P., Shu, W. & Barberán, A. Associations between human impacts and forest soil microbial communities. *Elementa* **8**, 1 (2020).

19. Martiny, J. B. H. et al. Microbial biogeography: putting micro-organisms on the map. *Nat. Rev. Microbiol.* **4**, 102–112 (2006).

20. Jiao, S. & Lu, Y. Abundant fungi adapt to broader environmental gradients than rare fungi in agricultural fields. *Glob. Chang. Biol.* **26**, 4506–4520 (2020).

21. Enquist, B. J. et al. The commonness of rarity: Global and future distribution of rarity across land plants. *Sci. Adv.* **5**, 1–14 (2019).

22. Jousset, A. et al. Where less may be more: how the rare biosphere pulls ecosystems strings. *ISME J.* **11**, 853–862 (2017).

23. Chen, Q. L. et al. Rare microbial taxa as the major drivers of ecosystem multifunctionality in long-term fertilized soils. *Soil Biol. Biochem.* **141**, 107686 (2020).

24. Xiong, C. et al. Rare taxa maintain the stability of crop mycobiomes and ecosystem functions. *Environ. Microbiol.* **23**, 1907–1924 (2021).

25. Delgado-Baquerizo, M. et al. Global homogenization of the structure and function in the soil microbiome of urban greenspaces. *Sci. Adv.* **7**, eabg5809 (2021).

26. Jia, X., Dini-Andreote, F. & Salles, J. F. Unravelling the interplay of ecological processes structuring the bacterial rare biosphere. *ISME Commun.* **2**, 1–11 (2022).

27. Banerjee, S. et al. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. *ISME J.* **13**, 1722–1736 (2019).

28. Garland, G. et al. Crop cover is more important than rotational diversity for soil multifunctionality and cereal yields in European cropping systems. *Nat. Food* **2**, 28–37 (2021).

29. Gámez-Virués, S. et al. Landscape simplification filters species traits and drives biotic homogenization. *Nat. Commun.* **6**, 8568 (2015).

30. Robinson, R. & Sutherland, W. J. Post-war changes in arable farming and biodiversity in Great Britain. *J. Appl. Ecol.* **39**, 157–176 (2002).

31. Riedo, J. et al. Widespread occurrence of pesticides in organically managed agricultural soils-The ghost of a conventional agricultural past? *Environ. Sci. Technol.* **55**, 2919–2928 (2021).

32. Edlinger, A. et al. Agricultural management and pesticide use reduce the functioning of beneficial plant symbionts. *Nat. Ecol.* **6**, 1145–1154 (2022).

33. Romdhane, S. et al. Land-use intensification differentially affects bacterial, fungal and protist communities and decreases microbiome network complexity. *Environ. Microbiomes* **17**, 1–15 (2022).

34. de Vries, F. T., Hoffland, E., van Eekeren, N., Brussaard, L. & Bloem, J. Fungal/bacterial ratios in grasslands with contrasting nitrogen management. *Soil Biol. Biochem.* **38**, 2092–2103 (2006).

35. Henle, K. et al. Identifying and managing the conflicts between agriculture and biodiversity conservation in Europe-A review. *Agric. Ecosyst. Environ.* **124**, 60–71 (2008).

36. d'Andrimont, R. et al. Harmonised LUCAS in-situ land cover and use database for field surveys from 2006 to 2018 in the European Union. *Sci. Data* **7**, 1–15 (2020).

37. Delgado-Baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. *Science* **359**, 320–325 (2018).

38. Egidi, E. et al. A few Ascomycota taxa dominate soil fungal communities worldwide. *Nat. Commun.* **101**, 1–9 (2019).

39. Meyer, K. M. et al. Why do microbes exhibit weak biogeographic patterns? *ISME J.* **12**, 1404–1413 (2018).

40. Davies, K. F., Margules, C. R. & Lawrence, J. F. A synergistic effect puts rare, specialized species at greater risk of extinction. *Ecology* **85**, 265–271 (2004).

41. Hedlund, B. P. & Staley, J. T. Microbial Endemism and Biogeography. in *Microbial Diversity and Bioprospecting* 225–231 (ASM Press, 2004). https://doi.org/10.1128/9781555817770.ch22

42. Bünemann, E. K. et al. Soil quality – A critical review. *Soil Biol. Biochem.* **120**, 105–125 (2018).

43. Cox, F., Newsham, K. K. & Robinson, C. H. Endemic and cosmopolitan fungal taxa exhibit differential abundances in total and active communities of Antarctic soils. *Environ. Microbiol.* **21**, 1586–1596 (2019).

44. Carini, P. et al. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. *Nat. Microbiol.* **2**, 1–6 (2016).

45. Johnson, C. N. et al. Biodiversity losses and conservation responses in the Anthropocene. *Science* **356**, 270–275 (2017).

46. Guerra, C. A. et al. Global hotspots for soil nature conservation. *Nature* **610**, 693–698 (2022).

47. Myers, N., Mittermeier, R. A., Mittermeier, C. G., Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. **403**, 853–858 (2000).

48. Brook, B. W., Sodhi, N. S. & Bradshaw, C. J. A. Synergies among extinction drivers under global change. *Trends Ecol. Evol.* **23**, 453–460 (2008).

49. Banerjee, S. & Heijden, M. G. A. Soil microbiomes and one health. *Nat. Rev. Microbiol.* **19**, (2022).

50. Anthony, M. A., Bender, S. F. & van der Heijden, M. G. Enumerating soil biodiversity Mark. *Proc. Natl. Acad. Sci.* **120**, e2304663120 (2023).

51. Edlinger, A. et al. The impact of agricultural management on soil aggregation and carbon storage is regulated by climatic thresholds across a 3000 km European gradient. *Glob. Chang. Biol.* **29**, 3177–3192 (2023).

52. Agroscope. Schweizerische Referenzmethoden der Forschungsanstalten Agroscope. Agroscope, Zürich. (1996).

53. Ashworth, J., Keyes, D., Kirk, R. & Lessard, R. Standard procedure in the hydrometer method for particle size analysis. *Commun. Soil Sci. Plant Anal.* **32**, 633–642 (2001).

54. Vance, E. D., Brookes, P. C. & Jenkinson, D. S. An extraction method for measuring soil microbial biomass C. *Soil Biol. Biochem.* **19**, 703–707 (1987).

55. Gardes, M. & Bruns, T. D. ITS primers with enhanced specificity for basidiomycetes-application to the identification of mycorrhizae and ruts. *Mol. Ecol.* **2**, 113–118 (1993).

56. Lindahl, B. D. et al. Fungal community analysis by high-throughput sequencing of amplified markers-a user's guide. *N. Phytol.* **199**, 288–299 (2013).

57. Schlaeppi, K. et al. High-resolution community profiling of arbuscular mycorrhizal fungi. *N. Phytol.* **212**, 780–791 (2016).

58. Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. *Bioinformatics* **27**, 863–864 (2011).

59. Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. *Bioinformatics* **26**, 2460–2461 (2010).

60. Köljalg, U. et al. UNITE: a database providing web-based methods for the molecular identification of ectomycorrhizal fungi. *N. Phytol.* **166**, 1063–1068 (2005).

61. Bengtsson-Palme, J. et al. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. *Methods Ecol. Evol.* **4**, 914–919 (2013).

62. Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. *Int. J. Climatol.* **37**, 4302–4315 (2017).

63. McMurdie, P. J. & Holmes, S. Phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. *PLoS One* **8**, e61217 (2013).

64. Nguyen, N. H. et al. FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. *Fungal Ecol.* (2016). https://doi.org/10.1016/j.funeco.2015.06.006
65. Mazerolle, M. Model selection and multimodel inference using the AICcmodavg package. https://mirror.marwan.ma/cran/web/packages/AICcmodavg/vignettes/AICcmodavg.pdf (2020).

66. Dixon, P. VEGAN, a package of R functions for community ecology. *J. Veg. Sci.* **14**, 927–930 (2003).

67. Rosseel, Y. lavaan: an R package for structural equation modeling and more. *J. Stat. Comput.* https://doi.org/10.18637/jss.v048.i02 (2012).

68. Tredennick, A. T., Hooker, G., Ellner, S. P. & Adler, P. B. A practical guide to selecting models for exploration, inference, and prediction in ecology. *Ecology* **102**, e03336 (2021).

69. Finn, D. R. et al. MicroNIChe: an R package for assessing microbial niche breadth and overlap from amplicon sequencing data. *FEMS Microbiol. Ecol.* **96**, 131 (2020).

70. Pölme, S. et al. FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. *Fungal Divers.* **105**, 1–16 (2020).

71. Krivonos, D. V., Konanov, D. N. & Ilina, E. N. FunFun: ITS-based functional annotator of fungal communities. *Ecol. Evol.* **13**, 1–7 (2023).

## Acknowledgements

We thank all the farmers and farm managers for allowing us to sample their fields and for completing our detailed questionnaires. We also thank A. Saghai, A. Held, A. Bonvicini, S. Müller, S. Zhao, V. Somerville, A. Brugger, O. Scholz, D. Bugmann, R. Heiz, B. Seitz and M. Roser for help with both field work and laboratory analyses. We thank Dr. Jean-Claude Walser at the ETH Zurich Genetic Diversity Center for his help with bioinformatics. The Digging Deeper project was funded through the 2015–2016 BiodivERsA COg call for research proposals, with the national funders Swiss National Science Foundation (grant 31BD30-172462), Deutsche Forschungsgemeinschaft (317895346), Swedish Research Council Formas contract 2016-0194), Ministerio de Economía y Com-petitividad (Digging_Deeper, reference PCIN-2016-028) and Agence Nationale de la Recherche (ANR, France, grant ANR-16-EBI3-0004-01). J.B. is funded by the Swiss National Science Foundation (grant 310030-197201).

## Author contributions

M.G.A.v.d.H., S.H., F.T.M., L.P. and M.C.R. designed the study and obtained research funding. S.B., G.G., A.E., P.G.P., C.H., D.S.P., S.R. and F.D. contributed to data collection and analysis. S.B., C.Z. and L.A.C.V.

conducted data analysis. S.B., C.Z., J.B. and M.G.A.v.d.H. were involved in the interpretation of results. S.B., C.Z. and M.G.A.v.d.H. drafted the manuscript with contributions from all co-authors. All authors commented on and approved the final manuscript.

## Competing interests

The authors declare no competing interests.

## Additional information

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41467-023-44073-6.

**Correspondence** and requests for materials should be addressed to Samiran Banerjee or Marcel G. A. van der Heijden.

**Peer review information** *Nature Communications* thanks Emilia Han-nula, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

**Reprints and permissions information** is available at http://www.nature.com/reprints

**Publisher's note** Springer Nature remains neutral with regard to jur-isdictional claims in published maps and institutional affiliations.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2024
65. Mazerolle, M. Model selection and multimodel inference using the AICcmodavg package. https://mirror.marwan.ma/cran/web/packages/AICcmodavg/vignettes/AICcmodavg.pdf (2020).

66. Dixon, P. VEGAN, a package of R functions for community ecology. *J. Veg. Sci.* **14**, 927–930 (2003).

67. Rosseel, Y. lavaan: an R package for structural equation modeling and more. *J. Stat. Comput.* https://doi.org/10.18637/jss.v048.i02 (2012).

68. Tredennick, A. T., Hooker, G., Ellner, S. P. & Adler, P. B. A practical guide to selecting models for exploration, inference, and prediction in ecology. *Ecology* **102**, e03336 (2021).

69. Finn, D. R. et al. MicroNorme: an R package for assessing microbial niche breadth and overlap from amplicon sequencing data. *FEMS Microbiol. Ecol.* **96**, 131 (2020).

70. Pölme, S. et al. FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. *Fungal Divers.* **105**, 1–16 (2020).

71. Krivonos, D. V., Konanov, D. N. & Ilina, E. N. FunFun: ITS-based functional annotator of fungal communities. *Ecol. Evol.* **13**, 1–7 (2023).

## Acknowledgements

We thank all the farmers and farm managers for allowing us to sample their fields and for completing our detailed questionnaires. We also thank A. Saghai, A. Held, A. Bonvicini, S. Müller, S. Zhao, V. Somerville, A. Brugger, O. Scholz, D. Bugmann, R. Heiz, B. Seitz and M. Roser for help with both field work and laboratory analyses. We thank Dr. Jean-Claude Walser at the ETH Zurich Genetic Diversity Center for his help with bioinformatics. The Digging Deeper project was funded through the 2015–2016 BiodivERsA COg call for research proposals, with the national funders Swiss National Science Foundation (grant 31BD30-172462), Deutsche Forschungsgemeinschaft (317895346), Swedish Research Council Formas contract 2016-0194), Ministerio de Economía y Com-petitividad (Digging_Deeper, reference PCIN-2016-028) and Agence Nationale de la Recherche (ANR, France, grant ANR-16-EBI3-0004-01). J.B. is funded by the Swiss National Science Foundation (grant 310030-197201).

## Author contributions

M.G.A.v.d.H., S.H., F.T.M., L.P. and M.C.R. designed the study and obtained research funding. S.B., G.G., A.E., P.G.P., C.H., D.S.P., S.R. and F.D. contributed to data collection and analysis. S.B., C.Z. and L.A.C.V.

conducted data analysis. S.B., C.Z., J.B. and M.G.A.v.d.H. were involved in the interpretation of results. S.B., C.Z. and M.G.A.v.d.H. drafted the manuscript with contributions from all co-authors. All authors commented on and approved the final manuscript.

## Competing interests

The authors declare no competing interests.

## Additional information

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41467-023-44073-6.

**Correspondence** and requests for materials should be addressed to Samiran Banerjee or Marcel G. A. van der Heijden.

**Peer review information** *Nature Communications* thanks Emilia Han-nula, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.

**Reprints and permissions information** is available at http://www.nature.com/reprints

**Publisher's note** Springer Nature remains neutral with regard to jur-isdictional claims in published maps and institutional affiliations.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

© The Author(s) 2024