Evolutionary potential under heat and drought stress at the southern range edge of North American Arabidopsis lyrata
Cite this dataset
Heblack, Jessica; Schepers, Judith R; Willi, Yvonne (2024). Evolutionary potential under heat and drought stress at the southern range edge of North American Arabidopsis lyrata [Dataset]. Dryad. https://doi.org/10.5061/dryad.2rbnzs7sw
Abstract
The warm edges of species’ distributions are vulnerable to global warming. Evidence is the recent range retraction from there found in many species. It is unclear why populations cannot easily adapt to warmer, drier, or combined hot and dry conditions and locally persist. Here, we assessed the ability to adapt to these stressors in the temperate species Arabidopsis lyrata. We grew plants from replicate seed families of a central population with high genetic diversity under a temperature and precipitation regime typical of the low-latitude margin or under hotter and/or drier conditions within naturally occurring amplitudes. We then calculated genetic variance-covariance (G-) matrices of traits depicting growth and allocation as well as selection vectors to compare the predicted adaptation potential under the different climate-stress regimes. We found that the sum of genetic variances and genetic correlations were not significantly different under stress as compared to benign conditions. However, under drought and heat drought, the predicted ability to adapt was severely constrained due to strong selection and selection pointing in a direction with less multivariate genetic variation. The much-reduced ability to adapt to dry and hot-dry conditions is likely to reduce the persistence of populations at the low-latitude margin of the species’ distribution and contribute to the local extinction of the species under further warming.
README: Evolutionary potential under heat and drought stress at the southern range edge of North American Arabidopsis lyrata
With the given dataset and R script, you can replicate a G-matrix calculation for four treatments via MCMCglmm as well as D-matrix and selection vector & selection response estimations.
To assess a pre-calculated dataset and obtain results and graphs please use the VCtype = "dummy" option.
To run the analysis please set the VCtype = "MCMCglmm" option.
Description of the data and file structure
Data files include original/raw data (.csv & .xlsx files) and step files (description below) for the G-matrix analysis.
R script:
- R markdown file with the reproducible code (G-matrix-analysis_dryad.Rmd)
- R HTML file with the reproducible "dummy" dataset
- "sourced functions" R script with necessary functions
data_files folder:
different raw data files and AIC-corrected data files
- "StressExp_JHJS_230131" raw data for all plants without allocation data.
- "Schepers_et_al_Data310323" raw allocation data for all plants.
- "StressExp_JHJS_230131_noice_corrected_AICbased" growth data of all MI1 plants noice corrected.
- "StressExp_JHJS_230612_noice_corrected_AICbased_extra_variables" allocation data of all MI1 plants noice corrected.
- "StressExp_JHJS_230131_allPopus_AICbased_noice_corrected" growth data of all plants from all populations noise corrected.
- "StressExp_JHJS_230612_allPopus_AICbased_noice_corrected_extra_variables" allocation data of all plants from all populations noise corrected.
- Important traits (with units) are: ger_final - germination date; death_date - date of death; survival (binary) - 0 (died during the experiment) & 1 (survived till harvest); bolting_date - bolting date; flowering_date - flowering date; days_ger - days till germination (from sowing); days_bol - days till bolting (from germination); days_flo - days till flowering (from germination); days_alive - days alive in the experiment (from germination till death or harvest); asym - asymptoitc (maximum) size (mm^2); xmid - time till half growth (days); ggrate - fastest growth (1/scale); fw_infl - fresh weight inflorescence (g); dw_inf - dry weight inflorescence (g); fw_rosette - fresh weight of rosette (g); dw_rosette - dry weight rosette (g); fw_ros_dead - fresh weight of dead rosette (g); dw_ros_dead - dry weight of dead rosette (g); rs_ratio - root to shoot ratio; SLA - specific leave/rosette area (mm^2/mg); LDMC - leaf dry matter content (mg/g)
growth_curves folder:
- "0_Data" includes data necessary to calculate growth curves: "env_data" gives the germination date for each plant;
- "gro_green&red_adapted_4th+2v_310123" are the final size measures for each plant on each date, when pictures were taken. This file was produced by the image analysis described under "Code/Software" and post-processed with the R scripts "1._merge_files_for_growth_analysis" and "2.growth_curves&_variables" in this growth_curve folder.
- "1_Output" includes all results from the growth analysis produced by the R script "3._181225_GrowthTrajectory_JH_210415" in the growth_curve folder.
VC.obs folder:
- "VC_obs" files are main G- (Vc.obs norm) and D-matrices (VC.obs.dmax obtained via MCMCglmm for growth traits, allocation traits (SLR ending), and all traits (AllTraits ending).
- "VC.DIC" includes the DIC comparison between models with/without covariances and variances (only of G-matrices).
posterior_distribution folder:
- "posterior distribution" includes the posterior distribution of the main G-matrixes. They function as statistical error tests.
- "Dmax posterior distribution" includes the posterior distribution of the main D-matrixes. They function as statistical error tests.
rand_matrices folder:
- "rand.G posterior" includes the randomised 3900 matrices dataset for G-matrices.
- "rand.D posterior" includes the randomised 3900 matrices dataset for D-matrices.
rand_variables folder:
- "rand posterior" includes the calculated variables, e.g., genetic variance, evolvability, and dimensionality, to replicate results.
Code/Software
Image analysis, to obtain used growth traits, was done with a Python script and can be assessed on Zenodo as part of this dataset. It includes dummy images to re-run the analysis.
Funding
Swiss National Science Foundation, Award: 310030_184763