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 productivity, opening up possibilities to fully harness
remote sensing data and reliably scale up from traits to map global
productivity in a more direct, efficient, and cost-effective manner.