DOI: 10.5061/dryad.3xsj3txrs
title: Intraspecific functional trait variation and coordination in Schizachyrium scoparium

description: Plant functional traits are increasingly recognized as vital tools in
 ecological restoration and biodiversity conservation. While functional
 traits and functional diversity are increasingly being used to inform
 restoration efforts, challenges remain in the characterization of trait
 variation in many systems, including within-species. This dataset supports
 the investigation of intraspecific trait variation and coordination among
 five populations of Schizachyrium scoparium (little bluestem), a species
 commonly used for restoration, from different habitat types across a
 gradient from southern Wisconsin to Northern Illinois. The dataset
 contains leaf, root, and chemical functional trait measurements recorded
 at the individual plant level as well as identifying information
 associating trait measurements with individual plant genotype and
 population membership. In addition to the data, this repository contains
 an R script demonstrating the analytical workflow used to model trait
 variation and coordination in the system.
description: Trait values contained in this repository reflect measurments
 from plants that were germinated and propagated in vitro using tissue
 culture techniques (see manuscript for details). All plants were harvested
 for trait measurement after growing for 13 weeks <em>ex
 vitro</em>. Plants were removed from their pots, and the growing
 media was gently washed from the root systems. The above-ground and
 below-ground tissues were separated at the crown, and the root systems
 were temporarily wrapped in damp paper towels, stored in plastic bags, and
 refrigerated for subsequent root scanning. Five fully expanded leaves were
 randomly chosen from each plant for leaf trait measurements and removed at
 the leaf collar, taking only the lamina and leaving the sheath tissue
 behind. The selected leaves were scanned at 600 dpi, and the surface area
 (mm<sup>2</sup>) of each leaf was calculated using ImageJ
 software (Schneider et al. 2012). The selected leaves were then weighed to
 retrieve the fresh leaf mass (g), dried at 60°C for 72 hours, and
 re-weighed to retrieve the dry leaf mass (mg). With these measurements, we
 calculated specific leaf area (SLA) as the ratio of leaf area to leaf dry
 mass (mm<sup>2</sup>/mg) and leaf dry matter content (LDMC) as
 the ratio of leaf dry mass to leaf fresh mass (mg/g). The resulting five
 leaf trait values for SLA and LDMC were then averaged within each
 individual. After weighing, the dried leaf samples were pooled per plant
 and sent to the Danforth Plant Science Center in St. Louis, MO for
 chemical analysis where % N content was measured via combustion by an
 elemental analyzer (Elementar vario ISOTOPE cube). The
 cleaned root system of each plant was individually scanned at 600 dpi
 using an Epson Expression 10000XL large-format flatbed scanner with a
 transparency attachment, following the protocol from (York 2023). The root
 systems were floated in a 300mm x 420 mm x 20 mm acrylic box filled with
 ~400 ml of water for scanning (York 2020). The entire root system of each
 plant was scanned, though in some cases the root systems needed to be
 sectioned to fit in the scanning area and ensure that the roots remained
 submerged. Minor edits were made to the root images to remove the borders
 of the acrylic box and any shadows from partially submerged roots using
 the open source GIMP software (v. 2.10; (The GIMP Development Team 2019).
 Following the scanning procedure, roots were patted dry to remove surface
 moisture and weighed to retrieve fresh root weight (g). The root samples
 were then dried at 60°C for 72 hours and weighed to obtain dry root mass
 (mg). These measurements were used to calculate root dry matter content
 (RDMC) as the ratio of dry root biomass to fresh root biomass
 (mg/g). The root scan images were analyzed using the
 “broken roots” analysis method in RhizoVision explorer (v. 2.0.2;
 (Seethepalli and York 2020). Various settings were tested to analyze root
 images, and segmented images were previewed to assess accuracy. Following
 testing, all root images were analyzed using the maximum recommended
 pruning threshold of 20, a non-root object filter of 1, edge-smoothing
 disabled, and an image threshold of 180 to produce the clearest root
 skeletonization. We extracted total root length (m) and average root
 diameter (mm) from the RhizoVision analyses and calculated specific root
 length (SRL, m/g) for each plant by dividing the total root length (m) by
 the dry root mass (g). References: 
 Schneider, C. A., W. S. Rasband, and K. W. Eliceiri. 2012. NIH
 Image to ImageJ: 25 years of image analysis. Nature Methods
 9:671–675. Seethepalli, A., and L. M. York. 2020,
 October. RhizoVision Explorer - Interactive software for generalized root
 image analysis designed for everyone. Zenodo. The GIMP
 Development Team. 2019, June 12. GIMP. York, L. 2020,
 October. Plans for root scanning trays to use on flatbed scanners.
 Zenodo. York, L. 2023. Root scanning using a flatbed
 scanner with transparency unit.
description: # Intraspecific functional trait variation and coordination in
 Schizachyrium scoparium
 [https://doi.org/10.5061/dryad.3xsj3txrs](https://doi.org/10.5061/dryad.3xsj3txrs) ## Description of the data and file structure This repository contains the data and code demonstrating the analyses in Zeldin et al."Intraspecific functional trait variation and coordination in *Schizachyrium scoparium*", evaluating the structure of intraspecific functional trait variation within and among five populations of Schizachyrium scoparium (little bluestem) a wide-spread, perennial, warm season restoration-relevant C4 grass species native to the United States and southern Canada. The plants for this study were propagated from wild-collected seeds using tissue culture techniques to accomplish replication at the genotype level. The data consists of leaf, root, and chemical functional trait measurements recorded in the summer 2022 after 13 weeks of ex vitro growth in a greenhouse setting. Leaf trait data include specific leaf area (SLA) and leaf dry matter content (LDMC) averaged across 5 leaves per plant, as well as leaf %N derived from combustion analysis of samples pooled across the 5 leaves. Root data include specific root length (SRL) and average root diameter obtained from image analysis of scanned whole root systems using RhizoVision explorer, as well as root dry matter content (RDMC) of each plant. Multivariate analysis revealed population differentiation in trait space, but models of population level differences in individual functional traits was mixed. Structural analyses showed that functional variation within populations was as high, or higher than between population variation across traits. Multivariate correlation models indicated coordination in a number of trait pairs, including two above- and below-ground ground trait combinations, while others appeared to be uncoordinated. ### Files and variables #### File: s\_scoparium\_ITV\_whole\_plant\_data.csv **Description:** This file contains all of the relevant data required for the analyses reported in the manuscript including the Bayesian hierarchical models of trait variation, multivariate correlation models, and the principal component analysis. All trait measurements are reported at the individual plant level and identifying information regarding population and genotype membership required for the specification of the hierarchical models is also included. ##### Variables * *population *: population membership - full populations labels correspond to manuscript population codes as follows: DRUMLIN = A, ALBANY = B, HOSAH = C, NACH-PP = D1, NACH-IK = D2 * *genotype *: genotype membership - arbitrary numerical code specifying plant genotypes within population * *individual *: individual code - arbitrary numerical code distinguishing individuals (clones) within genotype * *uniq_gen* : unique genotype identifier - concatenated population and genotype codes to distinguish unique genotypes * *ldmc_av *: leaf dry matter content (LDMC) - ratio of leaf dry mass (mg, dried at 60°C for 72 hours) to leaf fresh mass (g), averaged across five leaves per individual plant - g/mg * *sla_av *: specific leaf area (SLA) - ratio of leaf area (mm2) to leaf dry mass (mg, at 60°C for 72 hours), averaged across five leaves per individual plant - mm2/mg * *rdmc *: root dry matter content (RDMC) - ratio of dry root mass (mg) to fresh root mass (g) - mg/g * *root_diam *: average root diameter (mm) of skeletonized root system as measured by RhizoVision explorer - mm * *srl *: specific root length - total root length (m) of the skeletonized root system as measured by RhizoVision explorer, divided by the total dry root mass (g) - m/g * *%N*: percent Nitrogen measured via combustion by an elemental analyzer (Elementar vario ISOTOPE cube), N percentage of leaf samples pooled across five leaves per individual plant - percentage #### File: s\_scopariumITV.R (Zenodo) **Description:** This file contains an R script demonstrating the analytical and visualization workflow employed in the manuscript. The script contains the code needed to reproduce the Bayesian hierarchical models, multivariate correlation model, and principal component analysis along with examples of the construction of posterior predictions and figures. ## Code/software ### Software: All data preparation and analyses were performed in R (v. 4.3.3, R Core Team 2024) #### Dependencies: The following R packages are required to replicate the analyses and visualizations in the accompanying R code- * bayesplot - 1.11.0 * brms - 2.21.0 * ggdist - 3.3.2 * ggokabeito - 0.1.0 * ggpubr - 0.6.0 * ggrepel - 0.9.5 * ggthemes - 5.1.0 * posterior - 1.6.0 * tidybayes - 3.0.7 * tidyverse - 2.0.0