Expanding the Range of Plant Functional Diversity Represented in Global Vegetation Models: Towards Lineage-based Plant Functional Types

Friday, 19 December 2014: 10:35 AM
Christopher J Still1, Daniel Griffith2, Erika Edwards3, Elizabeth Forrestel4, Caroline Lehmann5, Michael Anderson2, Joseph Craine6, Stephanie Pau7 and Colin Osborne8, (1)Oregon State University, Corvallis, OR, United States, (2)Wake Forest University, Biology, Winston-Salem, NC, United States, (3)Brown University, Providence, RI, United States, (4)Yale University, New Haven, CT, United States, (5)University of Edinburgh, School of GeoScienes, Edinburgh, United Kingdom, (6)Kansas State University, Manhattan, KS, United States, (7)Florida State University, Tallahassee, FL, United States, (8)University of Sheffield, Sheffield, United Kingdom
Variation in plant species traits, such as photosynthetic and hydraulic properties, can indicate vulnerability or resilience to climate change, and feed back to broad-scale spatial and temporal patterns in biogeochemistry, demographics, and biogeography. Yet, predicting how vegetation will respond to future environmental changes is severely limited by the inability of our models to represent species-level trait variation in processes and properties, as current generation process-based models are mostly based on the generalized and abstracted concept of plant functional types (PFTs) which were originally developed for hydrological modeling. For example, there are close to 11,000 grass species, but most vegetation models have only a single C4 grass and one or two C3 grass PFTs. However, while species trait databases are expanding rapidly, they have been produced mostly from unstructured research, with a focus on easily researched traits that are not necessarily the most important for determining plant function. Additionally, implementing realistic species-level trait variation in models is challenging. Combining related and ecologically similar species in these models might ameliorate this limitation. Here we argue for an intermediate, lineage-based approach to PFTs, which draws upon recent advances in gene sequencing and phylogenetic modeling, and where trait complex variations and anatomical features are constrained by a shared evolutionary history. We provide an example of this approach with grass lineages that vary in photosynthetic pathway (C3 or C4) and other functional and structural traits. We use machine learning approaches and geospatial databases to infer the most important environmental controls and climate niche variation for the distribution of grass lineages, and utilize a rapidly expanding grass trait database to demonstrate examples of lineage-based grass PFTs. For example, grasses in the Andropogoneae are typically tall species that dominate wet and seasonally burned ecosystems, whereas Chloridoideae grasses are associated with semi-arid regions. These two C4 lineages are expected to respond quite differently to climate change, but are often modelled as a single PFT.