Journal
NEW PHYTOLOGIST
Volume 228, Issue 1, Pages 15-23Publisher
WILEY
DOI: 10.1111/nph.16773
Keywords
C(4)photosynthesis; Earth system models; evolution; grass biogeography; Land Surface Models; plant functional types; vegetation models
Categories
Funding
- NASA under the Surface Biology and Geology (SBG) Study
- USGS through the National Innovation Center
- National Science Foundation [1342703, 1926431, 1856587]
- NSF [1253713, 1342787, 1120750]
- Royal Society
- RUBISCO Scientific Focus Area in the Regional and Global Climate Modeling Program of the US Department of Energy, Office of Science, Office of Biological and Environmental Research [DE-AC2-5CH11231]
- Direct For Biological Sciences
- Division Of Environmental Biology [1926431, 1342703, 1120750, 1342787] Funding Source: National Science Foundation
- Directorate For Geosciences
- Division Of Earth Sciences [1253713] Funding Source: National Science Foundation
- Division Of Integrative Organismal Systems
- Direct For Biological Sciences [1856587] Funding Source: National Science Foundation
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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 C(3)grasses and (sub)tropical C(4)grasses. 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.
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