Dataset DOI: 10.5061/dryad.1vhhmgr71

Description of the data and file structure

The data were collected from a series of controlled greenhouse and field experiments designed to investigate phenotypic trait variation, functional trait coordination, and the influence of environmental and genetic factors in Andropogon gerardi. In the greenhouse, plants from multiple natural populations were grown under standardized conditions to quantify morphological, physiological, and phenological traits across development. A subset of plants was subjected to experimental drought to assess trait plasticity and drought responses. Parallel field measurements were collected from the same populations to capture trait expression in natural environmental conditions. In addition, genotyping-by-sequencing (GBS) was conducted to obtain genome-wide SNP data, which enabled the evaluation of genetic structure and its relationship with trait variation. The experimental design allowed integration of trait, climate, and genetic data to examine the ecological and evolutionary drivers of intraspecific trait variation.

Files and variables

This repository contains datasets and analysis code used to study plant performance, physiology, and genetic variation under drought and environmental gradients.

Data Files

Drought_Experiment.xlsx

Description:
Dataset from a controlled drought experiment measuring biomass allocation, physiological traits, and growth responses of different populations. Includes treatment information and environmental variables.

Variables:

• Master_ID – Unique identifier for each plant/sample
• Block – Experimental block (spatial replication)
• Pop – Population ID
• Treatment – Watering regime (e.g., drought vs. control)
• RhizomeBio – Rhizome biomass (g)
• RootBio – Root biomass (g)
• TotBelowBio – Total belowground biomass (g)
• Above_Below – Above: below biomass ratio (unitless)
• VegBio – Vegetative biomass (g)
• RepBio – Reproductive biomass (g)
• SeedBio – Seed biomass (g)
• StalkDiam – Stalk diameter (mm)
• NLeaves – Number of leaves (count)
• TotalAboveBio – Total aboveground biomass (g)
• Veg_Rep_Bio – Vegetative: reproductive biomass ratio (unitless)
• LeafAreaGR – Leaf area growth rate (cm² day⁻¹)
• HeightGR – Height growth rate (cm day⁻¹)
• SPAD – Chlorophyll content (SPAD units)
• WaterPot – Leaf water potential (MPa)
• Bolting – Presence/absence of bolting (days)
• Flowering – Presence/absence of flowering (days)
• LeafArea – Total leaf area (cm²)
• Photo – Photosynthetic rate (µmol CO₂ m⁻² s⁻¹)
• Transp – Transpiration rate (mmol H₂O m⁻² s⁻¹)
• WUE – Water-use efficiency (µmol CO₂ mmol H₂O⁻¹)
• Cond – Stomatal conductance (mol H₂O m⁻² s⁻¹)
• Ci – Intercellular CO₂ concentration (µmol mol⁻¹)
• Height – Plant height (cm)
• Thick – Leaf thickness (mm)
• Width – Leaf width (cm)
• Climate/Geographic covariates:
• MAT – Mean annual temperature (°C)
• MAP – Mean annual precipitation (mm)
• GSP – Growing season precipitation (mm)
• Aridity – Aridity index (C/mm)
• GS_Aridity – Growing season aridity index (C/mm)
• GSL – Growing season length (days)
• Evap_Index – Evapotranspiration index (unitless)
• GST – Growing season temperature (°C)
• Latitude, Longitude – Geographic coordinates

Main_Experiment.xlsx

Description:
Dataset from a multi-site main experiment comparing physiological and morphological traits across environmental gradients.

Variables:

• Site – Experimental site location
• Block – Block within site
• Bolting – Presence/absence of bolting (date)
• Flowering – Presence/absence of flowering (date)
• Stalk_Diameter – Stalk diameter (mm)
• N_Leaves – Number of leaves (count)
• SPAD – Chlorophyll content (SPAD units)
• Growth_Rate_Height – Height growth rate (cm day⁻¹)
• Growth_Rate_Leaf_Area – Leaf area growth rate (cm² day⁻¹)
• Water_Pot – Leaf water potential (MPa)
• Photo – Photosynthetic rate (µmol CO₂ m⁻² s⁻¹)
• WUE – Water-use efficiency (µmol CO₂ mmol H₂O⁻¹)
• Ci – Intercellular CO₂ concentration (µmol mol⁻¹)
• Cond – Stomatal conductance (mol H₂O m⁻² s⁻¹)
• Transp – Transpiration rate (mmol H₂O m⁻² s⁻¹)
• Leaf_Area – Leaf area (cm²)
• Height – Vegetative plant height (cm)
• Thick – Leaf thickness (mm)
• Width – Leaf width (cm)
• Climate/Geographic covariates:
  • MAT (C), MAP (mm/yr), GSP (mm/yr), Aridity (C/mm), GS_Aridity (C/mm), GSL, Evap_Index, GST (C), Latitude, Longitude

Supplemental_Data_Main_Experiment.xlsx

Description:
Supplementary dataset to the main experiment, containing additional trait or environmental data not included in the core dataset.

Variables:
(Structure similar to Main_Experiment; exact variable definitions depend on supplemental sheet contents.)

Code Files

Each .R file contains analysis code for specific research objectives.

• Objective_1_PTN_R_Code.R – R scripts for analyzing population trait networks (PTNs).
• Objective_1_Trait_Climate_JMP_Code.R – JMP scripts for linking trait variation with climate gradients.
• Objective_2_ANCOVA_GLMS_Drought_Experiment_JMP_Code.R – JMP code for ANCOVA and GLMs on drought experiment data.
• Objective_2_PTN_Drought_Experiment_R_Code.R – R code for PTN analysis specific to drought conditions.
• Objective_3_Genetic_Trait_PCA_JMP_Code.R – JMP code for trait-based PCA using genetic groupings.
• Objective_3_Genetic_PCA_R_Code.R – R code for genetic PCA analyses.
• Objective_4_Field_Greenhouse_Comparison_JMP_Code.R – JMP scripts comparing field vs. greenhouse performance.
• Supplemental_Data_Objective_1_Trait_Climate_JMP_Code.R – JMP code for supplemental trait–climate analyses.

Genetic Data

• Main_Experiment_Genotypes.vcf
  Description: A 
  Variant Call Format (VCF) file containing SNP genotype data for individuals included in the main experiment. This file is used for population genetic analyses and integration with trait datasets.

Code/software

Data Files

• Excel files (.xlsx)
  • Can be opened with any spreadsheet software (e.g., Microsoft Excel, LibreOffice Calc, or Google Sheets).
  • For analysis, they were primarily read into R (v4.2.0 or later) and JMP Pro (v17.0).
• VCF file (.vcf)
  • Standard text-based variant format.
  • Can be viewed in text editors (e.g., VS Code, Notepad++) or specialized software such as VCFtools (v0.1.16), TASSEL, or R packages (vcfR, adegenet).
• Word documents (.txt)
  • Contain analysis scripts (R code or JMP code).
  • Can be opened in any word processor (e.g., Microsoft Word, LibreOffice Writer, Google Docs).
  • Copy–paste code into the relevant software (R or JMP) to run analyses.

Analysis Software

R (v4.2.0 or later)

Used for statistical analyses, genetic PCA, and population trait network analyses.

Core packages loaded:

• tidyverse (data wrangling & plotting)
• vegan (ordination analyses, PCA)
• adegenet (genetic analyses with SNP/VCF data)
• vcfR (importing and handling VCF genotype files)
• igraph (network analyses for PTNs)
• car (ANCOVA/GLM utilities)
• ggplot2 (visualization)

JMP Pro (v17.0)

Used for trait–climate analyses, ANCOVA/GLMs on drought experiments, and PCA of genetic and trait data.

• JMP scripts are provided in .R files and must be run within the JMP environment.

Workflow Overview

1. Data Input:
   • Trait and climate data from Drought_Experiment.xlsx, Main_Experiment.xlsx, and Supplemental_Data_Main_Experiment.xlsx.
   • Genotype data from Main_Experiment_Genotypes.vcf.
2. Objective 1 – Trait & Climate Analyses
   • JMP scripts (Objective_1_Trait_Climate_JMP_Code.txt) analyze trait–climate relationships.
   • R scripts (Objective_1_PTN_R_Code.txt) build population trait networks (PTNs).
3. Objective 2 – Drought Experiment
   • JMP (Objective_2_ANOVA_GLMS_Drought_Experiment_JMP_Code.txt) runs ANCOVA/GLMs on treatment effects.
   • R (Objective_2_PTN_Drought_Experiment_R_Code.txt) performs PTN analyses under drought.
4. Objective 3 – Genetic Analyses
   • JMP (Objective_3_Genetic_Trait_PCA_JMP_Code.txt) for PCA using trait data.
   • R (Objective_3_Genetic_PCA_R_Code.txt) for genetic PCA using VCF genotype data.
5. Objective 4 – Field vs. Greenhouse Comparison
   • JMP (Objective_4_Field_Greenhouse_Comparison_JMP_Code.txt) compares trait performance in controlled vs. natural conditions.
6. Supplemental Analyses
   • Additional JMP scripts (Supplemental_Data_Objective_1_Trait_Climate_JMP_Code.txt) provide supplementary trait–climate models.

Missing Data

Some cells in the datasets, particularly withinDrought_Experiment.xlsx, are intentionally left blank to indicate missing, unavailable, or non-applicable values. These empty cells were preserved because replacing them with text strings, such as "NA", "null", or "n/a" interferes with downstream analysis scripts and software workflows used in this study.

Access information

Other publicly accessible locations of the data:

• Field-based data also used in this study are in Dryad doi:10.5061/dryad.905qfttzm