Linking Tropical Forest Function to Hydraulic Traits in a Size-Structured and Trait-Based Model

Abstract:

A major weakness of forest ecosystem models applied to Amazonia is their inability to capture the diversity of responses to changes in water availability commonly observed within and across forest communities, severely hampering efforts to predict the fate of Amazon forests under climate change. Such models often prescribe moisture sensitivity using heuristic response functions which are uniform across all individuals and lack important knowledge about trade-offs in hydraulic traits. We address this weakness by implementing a process representation of plant hydraulics into an individual- and trait-based model (Trait Forest Simulator; TFS) intended for application at discrete sites across Amazonia. The model represents a trade-off in the safety and efficiency of water conduction in xylem tissue through hydraulic traits, which then lead to variation in plant water use and growth dynamics. The model accounts for the buffering effects of leaf and stem capacitance on leaf water potential at short time scales, and cavitation-induced reductions in whole-plant conductance over longer periods of water stress. We explore multiple possible links between this hydraulic trait spectrum and other whole-plant traits, such as maximum photosynthetic capacity and wood density. The model is shown to greatly improve the diversity of tree response to seasonal changes in water availability as well as response to drought, as determined by comparison with sap flux and stem dendrometry measurements. Importantly, this individual- and trait-based framework provides a testbed for identifying both critical processes and functional traits needed for inclusion in coarse-scale Dynamic Global Vegetation Models, which will lead to reduced uncertainty in the future state of Amazon tropical forests.