Strain frequencies from first sequencing run

Strain frequencies of 101 rhizobial strains in pools of Medicago nodules inferred from high coverage WGS data. Raw data used to calculate strain frequencies are on NCBI (Accessions SRR6029825–SRR6029912) with code in a previous Dryad repository ( https://doi.org/10.5061/dryad.fp1bg)

Main R code: statistical analysis and figures

R code to recreate all figures and analysis included in the Evolution paper. This file takes as inputs freq1.tsv, freq2.tsv, and SingleStrain_phenotype_summary.tsv

Strain frequencies from second sequencing run

Strain frequencies of 101 rhizobial strains from pools of Medicago nodules inferred from high coverage WGS data. Raw data used to calculate strain frequencies are on NCBI (Accessions SRR6029825–SRR6029912) with code in a previous Dryad repository (https://doi.org/10.5061/dryad.fp1bg)

Plant phenotypes from single strain experiment

Plant phenotype data from a previously published single strain experiment (Burghardt et al, 2018, PNAS, https://doi.org/10.1073/pnas.1714246115). This file is also found in the Dryad repository for that publication ( https://doi.org/10.5061/dryad.fp1bg). The file is used as input for calculations of plant benefit of strain communities in the main R code

Data from: Modeling the influence of genetic and environmental variation on the expression of plant life cycles across landscapes

Organisms develop through multiple life stages that differ in environmental tolerances. The seasonal timing, or phenology, of life-stage transitions determines the environmental conditions to which each life stage is exposed and the length of time required to complete a generation. Both environmental and genetic factors contribute to phenological variation, yet predicting their combined effect on life cycles across a geographic range remains a challenge. We linked submodels of the plasticity of individual life stages to create an integrated model that predicts life-cycle phenology in complex environments. We parameterized the model for Arabidopsis thaliana and simulated life cycles in four locations. We compared multiple “genotypes” by varying two parameters associated with natural genetic variation in phenology: seed dormancy and floral repression. The model predicted variation in life cycles across locations that qualitatively matches observed natural phenology. Seed dormancy had larger effects on life-cycle length than floral repression, and results suggest that a genetic cline in dormancy maintains a life-cycle length of 1 year across the geographic range of this species. By integrating across life stages, this approach demonstrates how genetic variation in one transition can influence subsequent transitions and the geographic distribution of life cycles more generally

Environmental data, source code, and summarized results

This package contains the necessary environmental data (.csv) and R source code (.R) to recreate the life cycle results for six genotypes in four European locations highlighted in the paper "Modeling the influence of genetic and environmental variation on the expression of plant life cycles across landscapes". We also provide the summarized results for each of these 24 genotype x environment combinations so you do not actually have to run all the simulations to explore the results (.Rdata)