Characterizing Grass Functional Diversity and Traits using Evolutionary Relatedness
This study used a wide range of plant measurements to develop grass functional trait values of dominant lineages to improve land model representations. This method opens the door to the development of new vegetation models by using phylogenetic relatedness to create lineage-based functional types (LFTs) and provide a realistic representation of functional diversity.
The approach of linking phylogeny and multiple traits in six classes (physiological, structural, biochemical, anatomical, phenological, and disturbance-related) provides a realistic representation of functional diversity and opens the door to improve vegetation models integrated in land models.
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 harbor large functional diversity, yet most Land Surface Models (LSMs) summarize 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 a wide range of reported measurements, the study developed grass functional trait values (physiological, structural, biochemical, anatomical, phenological, and disturbance-related) of dominant lineages to improve LSM representations. The 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.