DOI: 10.5061/dryad.gqnk98sn3
title: The potential for genotype-by-environment interactions to maintain genetic variation in a model legume–rhizobia mutualism

description: The maintenance of genetic variation in mutualism-related traits is key
 for understanding mutualism evolution, yet the mechanisms maintaining
 variation remain unclear. We asked whether genotype-by-environment (G×E)
 interaction is a potential mechanism maintaining variation in the model
 legume–rhizobia system, Medicago truncatula–Ensifer meliloti. We
 planted 50 legume genotypes in a greenhouse under ambient
 light and shade to reflect reduced carbon availability for
 plants. We found an expected reduction under shaded conditions for plant
 performance traits, such as leaf number, aboveground and
 belowground biomass, and a mutualism-related trait, nodule number. We also
 found G×E for nodule number, with ∼83% of this interaction due to shifts
 in genotype fitness rank order across light environments, coupled with
 strong positive directional selection on nodule number regardless of light
 environment. Our results suggest that G×E can maintain genetic variation
 in a mutualism-related trait that is under consistent positive directional
 selection across light environments.
description: <strong>Raw Data</strong> We
 conducted a manipulative greenhouse experiment in the Earth Sciences
 Centre at the University of Toronto. We applied two light treatments:
 ambient and shade, where plants in the ambient treatment were exposed to
 normal greenhouse conditions (16:8 h light:dark cycle, 22deg C day and
 18deg C night temperatures), and those in the shade treatment were covered
 with neutral shade cloth that blocked 70% of light. The experiment was set
 up as a split-plot randomized design, where each block contained both the
 ambient and shade treatments separated into two racks. We used one
 individual per genotype, placed in random locations in each rack, for a
 total of 50 experimental individuals per rack. To test for contamination
 among plants, an uninoculated control from an extra genotype was included
 in each rack. The experiment was replicated using 10 blocks, for a total
 of N= 1020 plants (1000 experimentalplants and 20 contamination control
 plants). After 6 weeks of growth, we measured the
 number of leaflets per plant, and by 9 weeks we harvested the plants by
 removing them from their Cone-tainers and separating the aboveground
 structures (shoots and leaves) from the belowground structures (roots and
 nodules). We placed the aboveground portion of plants in a drying oven for
 about 2 weeks to ensure they were fully dried before weighing them for
 aboveground biomass. We placed the belowground portion in sealed plastic
 bags to prevent desiccation and stored them at 4deg C for counting and
 collecting rhizobial nodules. We counted the number of nodules present on
 plant roots as a proxy for investment in the mutualism by plant hosts and
 for rhizobial performance. We then dried and weighed the belowground
 portion to estimate belowground biomass.
 <strong>Data Analysis</strong> We
 used R (version 3.6.2, R Core Team, 2019) to conduct analyses for the
 effects of light environment and plant genotype on the plant performance
 traits, leaf number, aboveground and belowground biomass, and on the
 mutualism-related trait, nodule number. We used a linear mixed model (LMM)
 with log-transformed response variables using the lmer functionfrom the
 <em>lme4</em> package (Bates et al., 2015). Untransformed data
 did not meet parametric tests for normality, homoscedasticity, and
 linearity ("checkAssumptions.R", Wood et al., 2018), and model
 diagnostics for generalized LMMs showed a misfit with the data using the
 <em>DHARMa</em> package (version 0.2.7, Hartig, 2020). Our
 LMMs included light treatment as the fixed effect and random effects of
 genotype, block, and genotype-by-treatment and block-by-treatment
 interactions. We conducted significance tests using the Anova function
 from the <em>car</em> package (3rd edition, Fox and Weisberg,
 2019) with fixed effects analyzed using the F-statistic and type III sum
 of squares. We analyzed the significance of random effects with
 log-likelihood ratio tests using the chi-squared test statistic to compare
 models with and without the effect of interest. We halved the pvalues for
 log-likelihood ratio tests because they are one-tailed tests ofwhether a
 variance is greater than zero. We analyzed genetic correlations among
 traits and across treatments us-ing the rcorr function from the
 <em>Hmisc</em> package (version 4.4-0,Harrell,2020) to
 calculate a matrix of Pearson correlation coefficients andcorresponding p
 values. To assess whether significant GxE interactions were due to changes
 in rank order versuschanges in the magnitude of genetic variance, we used
 an equation originally described by Cockerham (1963) and applied by
 Batstone et al.(2020). To obtain genotypic variance components (Vg), we
 used LMMs of log-transformed data withineach treatment with genotype as
 the main random effect, as well as arandom effect of block. We obtained
 Pearson correlation coefficients be-tween environments (rij) using the cor
 function from the stats package(version 3.6.2,R Core Team, 2019), with raw
 line means that were alsolog-transformed to remain consistent across all
 G3E analyses. We determined genetic correlations across environments for
 all traits (see Supplemental Figure 1), but we only usedrijfrom traits
 that showedsignificant G3E for the rank versus variance
 analysis.
description: Poor germination and establishment led to a final sample of 666
 plants that survived until the end of the experiment. Only the
 surviving individuals are included in the dataset.