From February to May 2018, we visited 267 European A. thaliana populations around the end of their vegetative growth and close to the onset of flowering11 (Fig. 1a,b). At each site we collected whole rosettes from two individuals, along with a neighbouring crucifer (family Brassicaceae, primarily Capsella bursa-pastoris), if present, and two soil samples. We evaluated A. thaliana life history traits (Fig. 1c and Extended Data Fig. 1) and extracted information on climate variables for the collection sites12. We assessed the microbial composition of the leaf and soil samples by sequencing the V3–V4 region of the 16S ribosomal RNA locus and identifying amplicon sequence variants (ASV) using DADA13. Each ASV was considered a distinct bacterial lineage or phylotype. Host genetics and absolute microbe abundance were assessed by shotgun sequencing plant tissue, which generates reads of host and microbial genomes14.

Fig. 1: Representative sampling of A. thaliana phyllosphere microbiomes across Europe.

a,b, A. thaliana plants were collected from distinct ecosystems. a, Examples of aspects of collection locations. b, Latitude/longitude of all locations. MOG is an acronym for Moguériec, France, and Vdc for Villaviciosa de Córdoba, Spain. c, Based on images of individual plants taken at each site, we assessed plant health and development. The x axis represents qualitative values (Methods), except for the rosette diameter, which is classified in intervals of (1) 0–1 cm, (2) 1–2 cm and so on. The disease index corresponds to different macroscopic disease symptoms as indicated (Hpa, Hyaloperonospora arabidopsidis). The central horizontal line in each box indicates the median, the bounds indicate the upper and lower quartiles and the number above the boxes indicates the individuals in each group.

Phyllosphere composition is distinct from the soil and is host species specific

There is considerable debate as to the origin of the microbes that colonize plants, although soil often has a measurable influence4,15,16. A study across 17 European A. thaliana populations4 found differentiation between root and non-root-associated microbes, but no significant differences between A. thaliana and neighbouring grasses4. Intra-species comparisons in a common garden experiment had suggested that host genetics can explain about 10% of the variance among A. thaliana leaf bacteria17. At the basis of these comparisons is the question of how much the host influences microbiome assembly, either because of active recruitment of specific microbes, or because of the differential ability of microbes to colonize their hosts.

To explicitly test for enrichment of specific taxa in the phyllosphere, we compared soil and plant leaves across all 267 sites via multi-dimensional scaling (MDS; Hellinger transformation). As expected, there was broad-scale separation between the phyllosphere and the soil (Fig. 2a,b). Modelling18 the effect of compartment on the microbial core phylotypes in the phyllosphere revealed differential abundance of 91% (524/575) of phylotypes between the A. thaliana phyllosphere and soil (False Discovery Rate (FDR) <0.01). Focusing on differences among host species18, we found 36% (205/575) of phylotypes to distinguish A. thaliana from neighbouring crucifers (Extended Data Fig. 2). This indicates that inter-host species differences in genetics or phenology have a strong influence on microbiome composition. On a phylotype-by-phylotype basis, abundance in A. thaliana was poorly predicted by a phylotype’s abundance in soil or in the surrounding companion plants (Extended Data Fig. 2).

Fig. 2: Two distinct microbiome types in A. thaliana along a latitudinal cline.

a,b, Ordination on a Hellinger transformation of the samples. Arabidopsis thaliana leaf microbiomes are significantly differentiated from that of surrounding soil (a) and less so, but still significantly, from surrounding crucifers (Brassicaceae) (b). c,d, k-means clustering (k = 2) (c) identified two microbiome types that turned out to have a north–south latitudinal cline (d). e, Distribution of higher taxonomic levels across the southern and northern clusters. f, Comparison of extent of seasonal variation in south-west Germany (winter and spring) with the European geographic variation (clusters 1 and 2). g, Absence of correlation in fold changes (FCs) in phylotype abundance between the northern and southern clusters (y axis) and between the winter and spring samples from south-western Germany (x axis). Colour indicates association with the two north–south clusters 1 and 2.

Phyllosphere microbial composition varies with latitude

We tested the geographic differentiation of microbiomes using dimensionality reduction for the entire community and assessment of the spatial distribution for each bacterial phylotype. The former reveals global trends in composition, while the latter provides information on individual microbes contributing to such trends. Loadings on both the first and second principal coordinate axes (Fig. 2c) correlated with latitude (Pearson’s r = 0.75, P = 2.2 × 10−16, and r = −0.24, P = 1.35 × 10−7, respectively), suggesting geographic structure in the phyllosphere microbiome. Because silhouette scoring19 indicated that A. thaliana phyllosphere microbiomes were best characterized as two distinct types, we used k-means clustering of the Hellinger-transformed counts table to classify our samples (Fig. 2c and Extended Data Fig. 3). We found that the two microbiome types were strongly differentiated by geography, with one dominating in Northern and the other in Southern Europe (Fig. 2d,e). Among individual phylotypes, the relative abundance of one third (33%) was significantly associated with latitude (linear regression, FDR <0. 01), but only a small minority, 2%, was correlated with longitude, confirming that Northern and Southern European A. thaliana reproducibly harbour different microbiota. One percent of the plant-associated phylotypes were also significantly correlated in the soil with latitude, suggesting that the latitudinal contrast is formed via colonization.

The phyllosphere changes with plant development and the seasons20. To test whether the observed latitudinal phyllosphere contrast could be explained by seasonal and developmental differences, we compared our samples with a multi-year dataset from a single location in Germany21. Projecting seasonal phylotype composition into the MDS biplots of our pan-European samples did not reveal any preferential association of collection season with microbiome type (Fig. 2f). Comparing changes in the abundance of single phylotypes between seasons and between the two major microbiome types (Fig. 2g) similarly did not point to the latitudinal contrast reflecting environmental variation being caused by local seasonal differences (Wald test of multinomial frequency estimates, P > 0. 01).

The association between latitude and phylotype abundance was phylotype specific, differing within and between bacterial families (Fig. 3a and Extended Data Fig. 3). Pseudomonas and Sphingomonas are abundant across A. thaliana populations21,22,23 and both genera can affect A. thaliana health21,24,25. Linear regression of each core phylotype onto latitude revealed that four of the five most abundant sphingomonads have latitudinal clines (Fig. 3a,b, FDR <0. 01), while the most abundant pseudomonad phylotypes did not show long-distance variation (Fig. 3b–e). Rhizobiaceae were also latitudinally differentiated. A consequence of phylotype-specific association with latitude was that the two major microbiome types were significantly differentiated at the phylotype level, but not at higher taxonomic levels (Fig. 2e and Extended Data Fig. 3). Thus, even though A. thaliana is colonized by different individual phylotypes in Northern and Southern Europe, the bacterial classes remain broadly the same (Fig. 2e).

Fig. 3: Latitudinal clines in microbial abundances and association of a host immune gene with microbiome type.

a, Linear relationships between relative abundance (RA) of the most common phylotypes. The y axis represents −log10-transformed FDR-corrected P values obtained when regressing the abundance of a phylotype on latitude (linear regression). Phylotypes are grouped by families, which are indicated on the bottom. b,c, There is a strong latitudinal cline for the RA of the most abundant sphingomonads (b) but not for the most abundant pseudomonads (c; note the difference in RA scale). d,e, Interpolation of the abundance of the top sphingomonad phylotype (d) and of ATUE5 (e), the top pseudomonad phylotype and a known opportunistic pathogen, revealed a continuous spatial gradient for the top sphingomonad (d), but a patchy distribution with regional hotspots for the top pseudomonad (e). f, The relationship between microbiome type and polymorphism in plant immune genes was assessed with the Fst population differentiation index. The most extreme Fst values were found in the immune regulator ACD6. Data in b and c are presented as the estimated regression value ± s.e.m. Chr, chromosome.

Common phylotypes differ in their geographic distributions

A single Pseudomonas phylotype, ATUE5 (previously OTU5), is a common opportunistic pathogen in local populations in south-west Germany, where it is an important driver of total microbial load21. Because ATUE5 was also the most abundant pseudomonad in our study, we wanted to learn how its distribution was geographically structured (Fig. 3c). ATUE5 was the seventh most common phyllosphere phylotype overall, with a relative abundance of up to 64% (mean of 1.8%). ATUE5 was found in 56% of samples, but without significant latitudinal differentiation (Pearson’s r = 0.01, P = 0.92).

Despite ATUE5 being a common phyllosphere member, its distribution was disjoint, and ordinary Kriging interpolation across the sampled range confirmed a very patchy presence (Fig. 3c). In contrast, the most frequent Sphingomonas phylotype (and most frequent phylotype overall) showed a significant latitudinal cline (Fig. 3b). High ATUE5 abundance was largely limited to single populations or populations very close to each other, with a spatial autocorrelation restricted to distances of under 50 km (Extended Data Fig. 6). In summary, the Pseudomonas pathogen ATUE5 is widely yet very unevenly distributed.

Drought metrics predict microbiome composition

Common garden experiments have indicated that environmental factors strongly shape bacterial microbiome composition17. Our continental-scale data enabled us to test which abiotic factors are most correlated with geographic structure of the phyllosphere microbiome.

We tested for associations between climate variables and microbiome composition, including developmental and health traits as potential confounders26. Altogether, we considered 39 covariates that could influence microbiome composition (Extended Data Fig. 7 and Extended Data Table 1). We first removed covariates that were highly correlated with others and then performed random forest classification using the two microbiome types as response variables (Fig. 4 and Extended Data Fig. 8). The covariate with greatest explanatory power was the Palmer Drought Severity Index (PDSI) mean from the six pre-collection months, a metric of recent dryness27. PDSI was similarly the best predictor for the loading of a sample on MDS1. In general, environmental covariates were better predictors than were plant traits. In contrast, environmental covariates (including PDS1) had poor predictive power for plant-associated phylotypes in the soil microbiome, explaining less than 1% of the variance in the loading on the first principal coordinate axis.

Fig. 4: PDSI is the best predictor of phyllosphere microbiome type.

a, Random forest modelling was used to determine environmental variables associated with microbiome type. The abbreviations are explained in Methods. b, PDSI of the location was the best predictor of microbiome type, explaining more than 50% of the variance. The upper and lower hinges of the boxes represent the first and third quartiles and the central line the median, with n = 269 plants in cluster 1 and n = 192 plants in cluster 2. c, The mean PDSI throughout Europe for January to April 2018.

Because PDSI is correlated with latitude, we tested whether information about both variables improves prediction outcomes. Inclusion of PDSI significantly improved predictive capacity (P = 4.2 × 10−7 for logistic regression with microbiome type and P = 2.7 × 10−7 for linear regression on MDS1), indicating that the association between microbiome type and PDSI extends beyond latitudinal correlation. PDSI was also predictive for microbiome composition within geographic regions and their corresponding sampling tours (P = 2.3 × 10−7 for logistic regression with cluster identity and P = 0. 047 for linear regression on MDS1).

From mixed-effects modelling, we estimated the marginal R2 for PDSI to be 50%. Together with previous work supporting the importance of water availability in determining host-associated microbiomes9, we conclude that water availability affects which microbes can access the host plant and/or proliferate on the host. Drought might do so directly by affecting plant physiology, indirectly by shaping host genetics or by a combination of the two. Additionally, drought affects the abundances of microbes in the abiotic environment, and hence which microbes are present for colonization.

Host genetics is associated with microbiome composition

Arabidopsis thaliana exhibits strong population structure across Europe, with a pattern of isolation by distance28 and greater latitudinal than longitudinal differentiation1. Climate-driven selective pressures, particularly water availability and drought29, along with different groups of insect predators30 have contributed to the geographic structure of A. thaliana genetic diversity.

To determine whether this extends to the phyllosphere microbiome, we extracted heritability estimates for phyllosphere phylotypes from eight common garden experiments in which 200 A. thaliana accessions had been grown in four Swedish locations across 2 years8. Two thirds (368/575; 64%) of our core phylotypes had been observed in this study8. We were able to obtain heritability estimates for 251 of these phylotypes, almost all of which (247; 98.4%) had significant positive heritability in at least one of the eight experiments. Genetic differences are therefore very likely to contribute to the observed geographic differentiation of the A. thaliana phyllosphere microbiome across Europe. However, heritability does not necessarily imply direct host control of each phylotype, as it can also be exerted indirectly via microbial hub taxa8.

To determine how microbiome composition in our study might be influenced by host genetics, which was representative of previous surveys1 (Extended Data Fig. 4), we fitted a mixed-effects model that included relatedness as a random effect and the loading on the first axis of the decomposition of the microbiome composition as the phenotypic response variable. Plant genotype alone explains 68% of the variance in the loading along MDS1 and 52% of the variance in the MDS2 loading (pseudo h2 0.68, standard error of the mean (s.e.m.) 0.10 for MDS1 and pseudo h2 0.52, s.e.m. 0.12 for MDS2). MDS1 explains 8% and MDS2 5% of the variance in microbiome composition, consistent with host genetics probably playing only a subordinate role in structuring the microbiome8,17,31. In a mixed-effects model, PDSI was associated with MDS1, whereas several genetic principal components were associated with MDS2 (Extended Data Tables 2–4).

Because immune genes are prime targets for interactions with microbes32,33, we tested whether specific immune gene alleles are associated with the two microbiome types. Among a generous, though not exhaustive, list of 1,103 genes with connection to pathogen response and defense34, the top single-nucleotide polymorphism (SNP) was in ACD6 (empirical P = 0.0001) (Fig. 3f and Extended Data Fig. 5). ACD6 alleles can differentially impact pathogen resistance through constitutive effects on immunity35. The full ACD6 haplotypes associated with each microbiome type have not yet been reconstructed, as the short reads used for genotypic comparisons did not allow for resolution of full-length alleles. Nonetheless, our results demonstrate a striking association between microbiome type and polymorphisms in a central regulator of immune activation. Whether resident microbiota select for ACD6 allele type, or instead ACD6 allele type influences microbiome type, remains to be determined.

Are genetic alleles responsible for microbiome variation across geography? For defense genes such as R genes, this is probably not the case as variation tends to be maintained within local populations of A. thaliana36,37. We do not know whether this extends to genes that control the non-pathogenic microbiota. A previous study found ~150 SNPs to be significantly associated with heritable microbiome composition in A. thaliana31. When we tested the geographic differentiation of these SNPs across Europe (Extended Data Fig. 5), we found that they had significantly higher global Fst values than the genome-wide background, consistent with different A. thaliana populations selecting for different microbiota.

Host adaptation to drought influences microbial abundance

To disentangle the impact of drought from that of plant genetics, we conducted a common garden field experiment in California. Using a setup similar to our previous work in Europe29, we grew A. thaliana accessions (Extended Data Table 5) under a high- and low-watering regimen. Focusing on accessions that had previously been identified as drought adapted or susceptible based on genetic loci associated with adaptation to drought29, we assessed differences in phyllosphere composition after drought stress. Of the 575 core phylotypes in the European field collections, 154 were present in California and 20 were sufficiently common to enable us to determine the relative influences of genetics and drought treatment on their relative abundances (Extended Data Tables 2–4). Of these 20 phylotypes, 3 were significantly influenced by host genetic classification of drought-adapted versus susceptible accessions, and 3/20 showed a significant interaction between drought treatment and host genotype (Extended Data Table 6). Two out of 20 showed a significant response to the abiotic drought treatment alone. The phylotypes that were significantly associated with plant genotype in the California field experiment accounted for an appreciable fraction of the total microbiome in the European wild collections—an average of 13.2% of the total microbial community in a plant and as high as 71.9% total relative abundance in a plant (Extended Data Fig. 9). The most abundant phylotype across the European collection (Extended Data Fig. 9) was significantly associated with plant genotypic classification. In total, these results indicate that genetic adaptation to drought has an impact on some of the most abundant bacteria that colonize a plant.

Common phylotypes alter drought effects on A. thaliana

Finally, we tested whether water availability can influence the abundance of a common phylotype, the opportunistic pathogen ATUE5. In growth chambers, we exposed 5-week-old plants of the Col-0 reference accession to a week-long drought, followed by syringe inoculation with the ATUE5 p25.c2 strain21. Three days after infection, we compared bacterial growth and green tissue in drought-stressed and well-watered plants. Drought significantly reduced the ability of ATUE5 to proliferate in planta (Extended Data Fig. 10; two-sided Wilcoxon rank-sum test, P = 0.003), a result consistent with Pseudomonas pathogens relying on water availability to spread and multiply38. Drought also significantly reduced the green, photosynthetically active leaf area (Extended Data Fig. 10), with ATUE5 infection blunting this negative effect of drought.

These results indicate that infection by an opportunistic pathogen may be conditionally beneficial, conferring drought tolerance under specific conditions. ATUE5 was previously shown to influence A. thaliana growth in a genotype-specific manner39, indicating that the interaction between drought and ATUE5 infection is likely to differ between plant populations. This is reminiscent of viral infection reducing drought-based mortality40 and in agreement with plant growth promoting effects of microbes under drought41, as discussed in a recent review42 of the diverse mechanisms of microbe-mediated drought tolerance. Moreover, there is precedence for cryptic A. thaliana pathogens providing environment-specific fitness benefits43.