Using empirical measurements of tree branching architecture to scale whole-tree metabolism along a 4000 m elevation transect in the Peruvian Andes and Amazon

Plant scaling models use measurements of architecture (i.e., length, width, and order of branch or xylem segments) to ultimately predict whole-plant metabolism via mass and water-use allometries. The application of plant scaling models is broad, and holds potential to simplify forest modelling efforts. However little is known regarding the influence of the environment (e.g., temperature, light, etc) on variation in branching architecture traits and how this variation affects scaling. Furthermore, scaling model assumptions of a self-similar and symmetric branching network have not been extensively tested, especially in tropical forests. As such, it is still unclear to what extent tree communities can be approximated by simple geometrical models, and where important functional divergences from theory exist. Here we analyse novel tree architecture data from diverse species along a 4000m elevational gradient spanning the Andes to the Amazon in Peru. Specifically, we calculate and compare inter- and intra-specific scaling exponents related to branch segment length and width within a hierarchical Bayesian framework. Preliminary results indicate that branching architecture significantly varies among and within species especially with respect to light environments. As such, we explore the role of light in driving tree geometry by also analysing differences in light environment and crown shape. Then, we attempt to link branch architecture and crown shape. Using 6 branch-level and whole-tree traits (path length fraction, crown depth, crown width, crown volume, crown depth/width and crown width/depth) we are able to cluster 68 species of trees into 6 unique groups related to architecture and explain ~60% variability in these data. In the future, it will be important to relate these architectural groups to variation in leaf-level traits and physiology. Lastly, we discuss the implications of using these results to understand tropical forest responses to environmental change.