Tree crown economics: testing and scaling a functional-trait based theory

The relationship of tree form and function has long fascinated humans, and now, much of our ability to improve maps and forecasts of the vital interactions of forests and global change hinges on our ability to understand this adaptive tree crown architecture.  To help address this challenge, I revisit Henry Horn’s classic 1971 monograph “The Adaptive Geometry of Trees”, and blend his theoretical framework with a contemporary ecological theory of species’ functional traits.  Then, I describe how this trait-based theory tree crown architecture can be robustly tested using state-of-the-art hyper-remote sensing techniques.  This suite of imaging techniques from hyper-spatial (e.g. UAV and satellite imagery), hyper-spectral (e.g. AVIRIS imagery), hyper-temporal (e.g. phenocams and tree- or tower-mounted time-lapse cameras), and hyper-dimensional (terrestrial and UAV LiDAR) sensors now enables us to visualize and measure the spectral and architectural properties of individual trees with unprecedented accuracy and precision.  Through analysis of hyper-remote sensing datasets collected in forests across eastern North America, I highlight how this testable trait-based theory of tree crown economics is already providing fresh insights into several important, but heretofore unresolved patterns of spatial and temporal variability in forest functioning.