COS 54-10
Linking tropical forest function to hydraulic traits in a size-structured and trait-based model

Tuesday, August 11, 2015: 4:40 PM
343, Baltimore Convention Center
Bradley Christoffersen, School of GeoSciences, University of Edinburgh, Edinburgh, United Kingdom
Manuel Gloor, School of Geography, University of Leeds, Leeds, United Kingdom
Sophie Fauset, School of Geography, University of Leeds, Leeds, United Kingdom
Nikolaos Fyllas, Forest Research Institute, Hellenic Agricultural Organization, Greece
David Galbraith, School of Geography, University of Leeds, Leeds, United Kingdom
Timothy Baker, School of Geography, University of Leeds, Leeds, United Kingdom
Lucy Rowland, School of GeoSciences, University of Edinburgh, Edinburgh, United Kingdom
Rosie Fisher, Climate & Global Dynamics, National Center for Atmospheric Research, Boulder
Oliver Binks, School of GeoSciences, University of Edinburgh, Edinburgh, United Kingdom
Maurizio Mencuccini, ICREA - CREAF and University of Edinburgh, Edinburgh, United Kingdom
Sanna A. Sevanto, Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM
Chonggang Xu, Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM
Yadvinder Malhi, Environmental Change Institute, University of Oxford, Oxford, United Kingdom
Clément Stahl, Joint Research Unit Ecology of Guiana Forests, CIRAD, Kourou, French Guiana
Fabien Wagner, INPE National Institute for Space Research, São José dos Campos, Brazil
Damien Bonal, INRA Institut National de la Recherche Agronomique, Paris, France
ACL da Costa, Geosciences, Federal University of Para, Belém, Brazil
Leandro Ferreira, Museu Paraense Emilio Goeldi, Belém, Brazil
Nathan G. McDowell, Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, NM
Patrick Meir, School of GeoSciences, University of Edinburgh, Edinburgh, United Kingdom
Background/Question/Methods

A major weakness of forest ecosystem models is their inability to capture the diversity of responses to changes in water availability, severely hampering efforts to predict the fate of tropical forests under climate change. Such models often prescribe moisture sensitivity using heuristic response functions that 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. 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 (Cleaf and Cstem) on leaf water potential at short time scales, and cavitation-induced reductions in whole-plant conductance over longer periods of water stress. We conducted meta-analyses of drought tolerance traits (osmotic potential at full turgor πo, bulk elastic modulus ε, apoplastic fraction af) for leaves and stems in tropical forests to inform links between hydraulic trait spectra and other plant traits, such as maximum photosynthetic capacity (Amax),leaf mass per area (LMA) and wood density (WD).

Results/Conclusions

Meta-analysis revealed a significant negative and positive relationship of leaf πo and ε with WD (and to a lesser extent, LMA), respectively, while WD was a poor predictor sapwood πo and ε.  However, a strong negative relationship of Cstem with WD emerged, driven by predictable changes in the af and saturated sapwood moisture content with WD.  Incorporating these relationships in the model greatly improved 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 tropical forests.