Genetic differentiation is correlated with spatial separation
To assess the biogeographic patterns of nodule populations, we
calculated the hierarchical F ST of samples at
different population levels. The top level was management, where we
compared organic fields (DKO) versus field trial sites, whereas plot and
field were the lowest levels for the field trials and DKO samples,
respectively (Figure 1 and Figure S1 ).S ignificance was tested by permutation. For example, when
comparing organic (DKO) versus conventional (field trial) management,
samples were moved from one management to the other to check if this
generally resulted in a lower F ST. For each
management type, we then tested for the effect of country for the field
trials or groupings for the DKO samples. We observed no differentiation
between the conventionally managed sites (DK+F+UK) and the organic DKO
population. The reason is that there is no overlap in Rhizobiumpopulations between the UK and DK+F trial sites. Therefore the
difference between the three trial sites is as high as the difference
between trial sites and organic fields. To test the effect of groupings
and field/plots on the differentiation, we then analysed field trial
sites (DK, F, and UK) and the organic fields (DKO) separately.
For the field trial subset, country (DK, F, and UK), had a significant
effect (Table 2 ). The block design and clover genotype
(Figure S1 ) did not have any effect onF ST with the exception of nodA , where
there was a small but highly significant effect. Permuting the plots
within the individual field trial sites showed that the plots are also
significantly associated with Rhizobium population
differentiation. Furthermore, we added the block design and clover
genotype level to the test (Figure S1 ) and found that both
block and clover genotype had a small but significant effect onnodA differentiation when samples were permuted within the same
country (Table 2 ).
In the DKO subset, the grouping (DKO1, DKO2, DKO3, DKO4, DKO5, DKO6),
based on geographic proximity (Figure 1 ), had a significant
effect on differentiation for the core genes, recA andrpoB , but not for the accessory genes, nodA andnodD (Table 2 ). Field had a significant effect for all
four genes within the DKO subset. We concluded that the grouping level
was a valid way to cluster the samples for the cores genes, and used the
DKO subset to calculate pairwise F ST between the
six DKO clusters (Figure 1 and Figure 4 ). The fields
differ in geographic placement, while the clover genotypes and the field
management were similar between sites, allowing us to explore the
geographic differences in nodule population within a homogeneous set of
managements (Table S1 ).
Pairwise F ST between the DKO clusters displays a
significant correlation between F ST and
geographic distance. The overall F ST is highest
for the two core genes, and the correlation is highest and most
significant for rpoB (Figure 4 ). The level of genetic
differentiation between populations increased with distance, with the
effect being most pronounced for chromosomal core genes, indicating that
the core gene population composition is related to geographic origin.
Since the samples are not independent, we performed a Mantel test with
5000 replicates to test if the correlation betweenF ST and geographical distance is significantly
different from randomised datasets. Three genes, rpoB ,recA , and nodA , have a significant correlation between
genetic differentiation and geographical distance (rpoB :
R2=0.737, p-value=0.002, slope=6.310e-07; recA :
R2=0.617, p-value=0.019, slope=4.156e-07; nodA :
R2=0.632, p-value=0.047, slope=1.748e-07), butF ST and geographical distance are not
significantly correlated for nodD (nodD :
R2=0.420, p-value=0.115, slope=7.058e-08).