DOI: 10.5061/dryad.fbg79cp1q
title: Root functional traits and growth rates in savanna trees and grasses

description: Root-based functional traits are relatively overlooked as drivers of
 savanna plant community dynamics, an important gap in water-limited
 ecosystems. Recent work has shed light on patterns of trait coordination
 in roots, but less is known about the relationship between root functional
 traits, water acquisition, and plant demographic rates. Here, we
 investigated how fine-root vascular and morphological traits are related
 in two dominant PFTs (C3 trees and C4 grasses from the savanna biome),
 whether root traits can predict plant relative growth rate (RGR), and
 whether root trait relationships differ in trees and grasses. We used root
 data from 21 tree and 18 grass species grown under greenhouse conditions,
 and quantified a suite of vascular and morphological root traits. We used
 a principal components analysis (PCA) to identify common axes of trait
 variation, compared trait correlation matrices between the two PFTs, and
 investigated the relationship between PCA axes and individual traits and
 RGR. We found that there was no clear single axis integrating vascular and
 morphological traits, but found that vascular anatomy predicted RGR in
 both trees and grasses. Trait correlation matrices differed in trees and
 grasses, suggesting potentially divergent patterns of trait coordination
 between the two functional types. Our results suggested that, despite
 differences in trait relationships between trees and grasses, root
 conductivity may constrain maximum growth rate in both PFTs, highlighting
 the critical role that water relations play in savanna vegetation dynamics
 and suggesting that root water transport capacity is an important
 predictor of plant performance in the savanna biome.
description: We used data from seedlings of 21 tree and 18 grass species (n =
 up to 5 individuals per species) native to the southern African savanna
 biome, grown at the UGA Botany Greenhouse between January and October
 2018. At harvest (timed so that trees and grasses were of similar height,
 equal to the ~ 40-cm depth of the pot), we removed plants from containers
 and collected and washed 3-4 fine-root (&lt;2 mm) segments, which we
 stored refrigerated in formaldehyde alcohol acetic acid (FAA:10%
 formaldehyde (37%), 50% ethanol (95%), 5% glacial acetic acid, and 35% DI
 water) until processing. We rinsed, dried, and froze the remaining root
 systems until further processing of root morphology could occur. During
 processing, we dehydrated fine roots in ethanol (70%), embedded them in
 paraffin, stained them using Safranin O and Fast Green, and then thinly
 cross-sectioned and mounted them on microscope slides (Wargowsky et al.
 2021). We used ImageJ to identify and measure individual xylem vessels for
 each root cross section. We used this dataset to extract six anatomical
 variables, four of which were used in the current study to minimize
 redundancy: maximum xylem vessel diameter (D<sub>max</sub>, in
 mm), vessel number per unit root cross-sectional area (N, in
 mm<sup>-2</sup>), lumen fraction (F, in
 mm<sup>2</sup>/mm<sup>2</sup>), and theoretical
 axial conductivity (K<sub>r</sub>, in kg
 m<sup>-1</sup> MPa<sup>-1</sup>
 s<sup>-1</sup>), which represents flow per root
 cross-sectional area. We used the Hagen-Poiseuille equation to calculate
 Kr based on the number and diameter of individual vessels and root
 cross-sectional area (Tyree & Ewers 1991, Wargowsky et al.
 2021).             To measure morphological traits, we
 thawed and gently separated the frozen root samples. We divided roots into
 three components: a subset of intact fine root branches to be scanned, all
 remaining fine roots (&lt; 2mm), and coarse roots. We scanned the root
 branch subsets with a flatbed scanner (Plustek OpticPro A230L, Santa Fe
 Springs, CA, USA). After scanning, we dried all components of the root
 system at 65 °C for 48 hr and weighed components separately using an
 analytical balance to 0.0001 g (AL104, Mettler-Toledo, Columbus, OH, USA).
 We used Smartroot for ImageJ (Lobet et al. 2011, Schneider et al. 2012) to
 trace all individual roots from scanned samples and collect data on three
 fine-root morphological traits: branching intensity (BI), specific root
 length (SRL), mean root diameter (RD), and root length per unit mass
 (RLM).
description: Any program that can import .csv files can access the data.
description: # Root functional traits and growth rates in savanna trees and grasses
 Functional trait and growth data for individual tree and grass seedlings
 grown in pots under greenhouse conditions. ## Description of the Data and
 file structure There is a single .csv file, with rows containing
 individual seedlings, and columns as follows: UniqueID Species
 Functional\_Type: C3 tree or C4 grass RGR: relative growth rate (on mass
 basis for grasses, on diameter basis for trees) Dmax: maximum vessel
 diameter (mm) N: number of vessels per root cross-section F: lumen
 fraction (mm2/mm2) Kr: theoretical axial conductivity (kg m-1 MPa-1 s-1)
 RD: mean root diameter (mm) SRL: specific root length (m/g) BI: branching
 intensity = number of nodes (branch initiation points) divided by the
 total root length (mm-1) RLM: root length to total root mass (m/g) NA
 values indicate a lack of data for specific columns (either RGR, vascular,
 or morphological trait data) ## Sharing/access Information Data were
 derived from the following sources: Root vascular data: WARGOWSKY, I. K.,
 J. E. NESMITH, AND R. M. HOLDO. 2021. Root vascular traits differ
 systematically between African savanna tree and grass species, with
 implications for water use. American Journal of Botany 108: 83-90.
 doi:10.1002/ajb2.1597. RGR and root morphological data: measured in Botany
 Greenhouse (University of Georgia) and Odum School of Ecology