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.

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 (<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_max, in mm), vessel number per unit root cross-sectional area (N, in mm^-2), lumen fraction (F, in mm²/mm²), and theoretical axial conductivity (K_r, in kg m^-1 MPa^-1 s^-1), 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 (< 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).

Any program that can import .csv files can access the data.