Community analysis
The community data of Frankia OTUs was standardized by
coverage-based rarefaction (Chao & Jost, 2012) and transformed to
presence/absence data. Non-metric multidimensional scaling (NMDS) was
performed to visualize Frankia community dissimilarity in this
study.
To elucidate whether filtering force drives symbiont assembly into host
plants, we generated 1000 null models of symbiont community by seven
random samplings from overall rhizosphere source community with
frequency distribution of OTUs in rhizosphere soil. We comparedFrankia OTU richness and composition in host’s root-nodules with
the null model, using generalized linear model (GLM) with Poisson
distribution and permutation MANOVA (PERMANOVA) with 9999 permutations,
using the lme4 1.1-21 package (Bates et al., 2019) and the vegan 2.5-5
package of the R 3.6.1 software, respectively.
To test how abiotic environmental factors and genetic variation in hosts
affect Frankia communities, we analyzed correlations ofFrankia community dissimilarity with Nei’s genetic distance of
hosts, soil pH and inorganic nitrogen contents in soil, using the vegan
2.5-5 package in the R 3.6.1 software.
To quantify variation in symbiont filtering force among hosts, we
defined the “symbiosis filtering” index as dissimilarity inFrankia community composition between root nodules and
rhizosphere soils in each river area. This symbiosis filtering index
indicates how certain Frankia genotypes were filtered from a
genetic pool in each river area. In order to quantify differences in
symbiosis filtering index, we calculated the symbiosis filtering indices
as Jaccard dissimilarities between root-nodules of host individuals and
rhizosphere soils in each river area, using 56 individuals of studied
alders which had Frankia community data for both the root nodules
and rhizosphere soils. Then, the symbiosis filtering indices were
averaged for each site. Next, we calculated the Euclidian distance of
the averaged symbiosis filtering indices among sites. We referred to the
distance as “symbiosis filtering differences.” In addition, to
estimate the genetic distance of alder populations among sites, we
calculated Nei’s genetic distance (Nei, 1978), based on 1,077 SNPs that
had been obtained by RAD-Seq (Kagiya et al., 2018). Finally, the
correlation between the Nei’s genetic distance of alders and the
symbiosis filtering differences was examined using Mantel tests. All the
community analyses were performed with the vegan 2.5–5 package in the R
software (version 3.6.1).
To quantify the relative contribution of genetic distance of hosts and
other abiotic factors in explaining the spatial patterns of symbiosis
filtering, accounting for spatial autocorrelation, generalized
dissimilarity modeling (GDM; Blois et al., 2013; Capinha et al., 2015;
Ferrier et al., 2007; Lasram et al., 2015) was performed. GDM models for
the symbiosis filtering differences included Nei’s genetic distance of
hosts, soil pH, and the total amount of inorganic nitrogen in soils,
Jaccard community dissimilarity of Frankia in rhizosphere soils,
and spatial distance as predictors. To test whether the genetic distance
of alders could explain the symbiosis filtering differences without
other factors, we estimated the unique and shared contribution of each
predictor to the total deviance explained by partitioning the predictors
with GDM models (Capinha et al., 2015; Fitzpatrick et al., 2013; Kagiya
et al., 2018). To calculate the unique contribution of genetic distance
and other factors, the deviance explained of the model removed each
predictor was subtracted from the deviance explained of the full model.
To calculate the shared contributions of both predictors, a value of
deviance explained of the model that was fitted by both predictors was
subtracted from the unique contributions of each predictor.