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Lineage‐based functional types: characterising functional diversity to enhance the representation of ecological behaviour in Land Surface Models
Author(s) -
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.
Publication year - 2020
Publication title -
new phytologist
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.742
H-Index - 244
eISSN - 1469-8137
pISSN - 0028-646X
DOI - 10.1111/nph.16773
Subject(s) - biome , trait , ecology , lineage (genetic) , functional diversity , biology , vegetation (pathology) , ecosystem , functional ecology , range (aeronautics) , computer science , medicine , biochemistry , materials science , pathology , composite material , programming language , gene
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 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.