Lineage‐based functional types: characterising functional diversity to enhance the representation of ecological behaviour in Land Surface Models. Issue 1 (31st July 2020)

• Record Type: Journal Article
• Authors: Griffith, Daniel M.; Osborne, Colin P.; Edwards, Erika J.; Bachle, Seton; Beerling, David J.; Bond, William J.; Gallaher, Timothy J.; Helliker, Brent R.; Lehmann, Caroline E. R.; Leatherman, Lila; Nippert, Jesse B.; Pau, Stephanie; Qiu, Fan; Riley, William J.; Smith, Melinda D.; Strömberg, Caroline A. E.; Taylor, Lyla; Ungerer, Mark; Still, Christopher J.
• Is Part Of: New phytologist. Volume 228:Issue 1(2020)
• Page Start: 15
• Page End: 23
• Publication Date: 2020-07-31
• Subjects: C4 photosynthesis -- Earth system models -- evolution -- grass biogeography -- land surface models -- plant functional types -- vegetation models; Botany -- Periodicals; 580
• DOI: 10.1111/nph.16773
• Languages: English
• ISSNs: 0028-646X

Abstract

Summary: 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 C3 grasses and (sub)tropical C4 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.