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 (C3trees and C4grasses from the savanna biome), whether root traits can predict plant relative growth rate (RGR), and whether root trait multivariate 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.
Process‐based vegetation models attempt to represent the wide range of trait variation in biomes by grouping ecologically similar species into plant functional types (PFTs). This approach has been successful in representing many aspects of plant physiology and biophysics but struggles to capture biogeographic history and ecological dynamics that determine biome boundaries and plant distributions. Grass‐dominated ecosystems are broadly distributed across all vegetated continents and harbour large functional diversity, yet most Land Surface Models (LSMs) summarise grasses into two generic PFTs based primarily on differences between temperate C3grasses and (sub)tropical C4grasses. Incorporation of species‐level trait variation is an active area of research to enhance the ecological realism of PFTs, which form the basis for vegetation processes and dynamics in LSMs. Using reported measurements, we developed grass functional trait values (physiological, structural, biochemical, anatomical, phenological, and disturbance‐related) of dominant lineages to improve LSM representations. Our method is fundamentally different from previous efforts, as it uses phylogenetic relatedness to create lineage‐based functional types (LFTs), situated between species‐level trait data and PFT‐level abstractions, thus providing a realistic representation of functional diversity and opening the door to the development of new vegetation models.more » « less
- NSF-PAR ID:
- Publisher / Repository:
- Date Published:
- Journal Name:
- New Phytologist
- Page Range / eLocation ID:
- p. 15-23
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Evolutionary history plays a key role driving patterns of trait variation across plant species. For scaling and modeling purposes, grass species are typically organized into C3vs C4plant functional types (PFTs). Plant functional type groupings may obscure important functional differences among species. Rather, grouping grasses by evolutionary lineage may better represent grass functional diversity.
We measured 11 structural and physiological traits
in situfrom 75 grass species within the North American tallgrass prairie. We tested whether traits differed significantly among photosynthetic pathways or lineages (tribe) in annual and perennial grass species.
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Our findings suggest grouping grass species by photosynthetic pathway overlooks variation in several functional traits, particularly for C4species. These results indicate that further assessment of lineage‐based differences at other sites and across other grass species distributions may improve representation of C4species in trait comparison analyses and modeling investigations.
C4grasses are distinct from C3grasses, because C4grasses respond in a different manner to light, temperature, CO2and nitrogen and often have higher resource‐use efficiencies. C3and C4grasses are typically represented in earth system models (ESMs) by different plant functional types (PFTs). The ability of ESMs to capture C4grass biogeography and ecology across differing time periods is important to assess, given the crucial role they play in ecosystems and their divergent responses to global change.
Last Glacial Maximum (LGM), historical modern period (
ca. 1850) and end of this century. Major taxa studied
Proxy data representing relative cover and productivity of C4grasses were collated, including carbon isotope ratios of soil carbon and animal grazer tissue, and vegetation plot data in undisturbed grasslands. We selected available model predictions of C4PFT percentage cover. Models were compared against one another and assessed against proxy data at key time points: the LGM, the historical modern period before widespread grassland conversion to agriculture, and the end of this century.
We highlight large differences among model predictions of percentage C4grass cover across North America: all pairwise combinations have correlations < .5, and most are < .2. Models also do not capture spatial patterns of the percentage C4grass cover from proxy data, during either the LGM or the historical modern period. Models generally under‐predict percentage C4grass cover, particularly during the historical modern period.
Earth system models do not accurately represent the biogeography of C4grasses across a range of time‐scales, and their outputs do not agree with one another. We suggest model improvements to represent this crucial functional type better, including more collection and greater integration of C3and C4grass trait data, explicit representations of tree–grass competition for water, and a greater focus on disturbance ecology.
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Spruce–fir forests on Whiteface Mountain, NY,
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