Improving landscape-scale productivity estimates by integrating trait-based models and remotely-sensed foliar-trait and canopy-structural data

Assessing the impacts of anthropogenic degradation and climate change on global carbon cycling is hindered by a lack of clear, flexible, and easy-to-use productivity models along with scarce trait and productivity data for parameterizing and testing those models. We provide a simple solution: a mechanistic framework (RS-CFM) that combines remotely-sensed foliar-trait and canopy-structural data with trait-based metabolic theory to efficiently map productivity at large spatial scales. We test this framework by quantifying net primary productivity (NPP) at high-resolution (0.01-ha) in hyper-diverse Peruvian tropical forests (30,040 hectares) along a 3,322-m elevation gradient. Our analysis captures hotspots and elevational shifts in productivity more accurately and in greater detail than alternative empirical- and process-based models that use plant functional types. This result exposes how high-resolution, location-specific variation in traits and light competition drive variability in pr...