DOI: 10.5061/dryad.xgxd254np
title: Data from: Trait variation and performance across varying levels of drought stress in cultivated sunflower (Helianthus annuus L.)

description: Background and Aims: Drought is a major agricultural challenge that is
 expected to worsen with climate change. A better understanding of drought
 responses has the potential to inform efforts to breed more tolerant
 plants. We assessed leaf trait variation and covariation in cultivated
 sunflower (Helianthus annuus L.) in response to water limitation. Methods:
 Plants were grown under four levels of water availability and assessed for
 environmentally induced plasticity in leaf stomatal and vein traits as
 well as biomass (performance indicator), mass fractions, leaf area, leaf
 mass per area, and chlorophyll content. Key Results: Overall, biomass
 declined in response to stress; these changes were accompanied by
 responses in leaf-level traits including decreased leaf area and stomatal
 size, and increased stomatal and vein density. The magnitude of trait
 responses increased with stress severity and relative plasticity of
 smaller-scale leaf anatomical traits was less than that of larger-scale
 traits related to construction and growth. Across treatments, where
 phenotypic plasticity was observed, stomatal density was negatively
 correlated with stomatal size and positively correlated with minor vein
 density, but the correlations did not hold up within treatments. Four leaf
 traits previously shown to reflect major axes of variation in a large
 sunflower diversity panel under well-watered conditions (i.e., stomatal
 density, stomatal pore length, vein density, and leaf mass per area)
 predicted a surprisingly large amount of the variation in biomass across
 treatments, but trait associations with biomass differed within
 treatments. Additionally, the importance of these traits in predicting
 variation in biomass is mediated, at least in part, through leaf size.
 Conclusions: Our results demonstrate the importance of leaf anatomical
 traits in mediating drought responses in sunflower, and highlight the role
 that phenotypic plasticity and multi-trait phenotypes can play in
 predicting productivity under complex abiotic stresses like drought.
description: Detailed methods can be found in publication "Trait
 variation and performance across varying levels of drought stress in
 cultivated sunflower (<em>Helianthus annuus</em> L.)" in
 <em>AoB Plants</em>
description: Data from paper entitled "Trait variation and performance
 across varying levels of drought stress in cultivated sunflower
 (<em>Helianthus annuus </em>L.)" in <em>AoB
 Plants</em>. Raw images are included for leaf scans, microscope
 images of stomata and veins and scanned images of cleared leaves.
 Spreadsheets are included for the experimental design, genotype averaged
 data, and a trait list.
description: # Leaf traits predict performance under varying levels of drought stress
 in cultivated sunflower (Helianthus annuus L.) Journal: AoB Plants
 Corresponding author: John Burke -
 [jmburke@uga.edu](mailto:jmburke@uga.edu) ## Description of the data and
 file structure **Experimental Design.csv** File listing genotypes (SAM
 lines), blocks and treatments for all 64 plants **TraitInfo.xlsx** Table
 listing all traits and units (also Table 1 in manuscript) *Code_and_Output
 -&gt; Data **DataAvg.csv** Plant level data for all traits (see file
 TraitInfo.xlsx for traits and units) **DataAvg_2.csv** Processed data
 (processing shown in script 01_Data_Wrangling.R)
 **Watering_BeforeAfter_Per.csv** Table with percentages of field capacity
 for watering each day (before and after watering) **RDPI_for_plots.csv**
 Plant level data used for plasticity (RDPI) analyses in manuscript Figure
 2 *Code_and_Output -&gt; Code* *R scripts used for all analyses #####
 01\_Data\_Wrangling.R Reformatting dataframe to be used in downstream
 analysis and figures ##### 02\_ANOVAs.R Two-way ANOVAs with interaction on
 ranked scale data to determine any significant treatment, genotype, and
 treatment x genotype interactions. Output reported in Tables 2 and S1, and
 used to determine posthoc treatment differences in Figure 1. #####
 03\_Boxplots.R Code for creating Figure 1, boxplots on a subset of the
 traits presented in Table 1. Treatment differences follow ANOVA results in
 Tables 2 and S1. ##### 04\_RDPI\_plotting.R Code for creating Figure 2,
 depicting RDPI (i.e., plasticity) for traits in each drought treatment
 relative to the well-watered control. Traits are grouped into functional
 groupings following Table 1 (each trait group was saved individually, and
 collated into a single figure using a graphics editor). See main text for
 how RDPI was calculated using estimated genotypic means (marginal means)
 for each treatment. ##### 05\_Corr\_Mats.R Code for creating Figure 3B
 (correlation matrix across all treatments) and Figures 4B,D,F,H
 (correlation matrices within each treatment) using all traits from Table
 1, except using averages for stomatal density, stomatal pore length, and
 stomatal guard cell width. Additionally, code for mantel tests comparing
 each within treatment correlation matrix against each other. #####
 06\_PCAs.R Code for creating Figure 3A (PCA across all treatments; first 2
 PC axes) and Figures 4A,C,E,G (PCAs within each treatment; first 2 PC
 axes) using all traits from Table 1, except using averages for stomatal
 density, stomatal pore length, and stomatal guard cell width (with trait
 loadings for PCs 1-3 reported in Table S2). Additionally, code for
 Hotelling's t tests comparing each within treatment against each
 other in Figure 3A, using the first 2 PC axes. #####
 07\_Trait\_Performance\_Regressions.R Code to perform the
 trait-performance multiple regression analyses using the Bayesian package
 'brms'. See main text for details on model fits. Additionally,
 code to create Figure 5 and Tables S3 & S4 (which report estimated
 associations for each trait and biomass within each treatment along with
 credible intervals), and S5 which reports the model comparison results
 from the models with and without leaf area. ##### 08\_WateringData\_Plot.R
 Code for creating Supplemental Figure 1, showing percent water content
 across the experiment across all treatments. \**Code_and_Output -&gt;
 Output* *mod_brm_1 and mod_brm_2: Saved brms model files
 \***StomataDensity_Raw** Images taken to measure stomatal density. File
 Names = -, ex: 1-B1 for pot 1
 and bottom stomata density image 1 Scale: 20X, 5.87 px/um
 \***IndividualStomata_Raw** Includes images of individual stomata used for
 stomatal size measurements. Images were taken on two difference
 microscopes, designated 1 and 2 File names =
 -S, ex: 1-BS1 is Pot 1, Bottom
 Stomata image 1 Microscope 1 Scale: 100X, 29.45 px/um Microscope 2 Scale:
 40X, 15.96 px/um \***LeafScans_Raw** Images of scans taken on flatbed
 scanner at 300dpi for measurement of LMA and leaf area File Names =
 Pilot_, ex: Pilot1_1 for pot 1,
 image 1 \***MinorVeins_Raw** Images taken with light microscope and used
 in neural network to calculate VLA. File Names = -, ex: 1-1 is pot 1, image 1 Microscope 1 Scale: 5X, 1.4688 px/um
 \***MajorVeinsScans_Raw** Images taken on flatbed scanner at 2400 dpi for
 measurement of second and major veins *Note:* \**Any missing values (NAs)
 in the dataframes indicate either the plant died during the experiment or
 accurate measures could not be made from the images taken. ## Code and
 Software Analyses were conducted using R v43.24.13 and RStudio v1.3.1093.
 All scripts are in folder Code_and_Output -&gt; Code