Open AccessImproving landscape‐scale productivity estimates by integrating trait‐based models and remotely‐sensed foliar‐trait and canopy‐structural data
Author(s) -
Wieczynski Daniel J.,
Díaz Sandra,
Durán Sandra M.,
Fyllas Nikolaos M.,
Salinas Norma,
Martin Roberta E.,
Shenkin Alexander,
Silman Miles R.,
Asner Gregory P.,
Bentley Lisa Patrick,
Malhi Yadvinder,
Enquist Brian J.,
Savage Van M.
Publication year - 2022
Publication title -
ecography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.973
H-Index - 128
eISSN - 1600-0587
pISSN - 0906-7590
DOI - 10.1111/ecog.06078
Subject(s) - productivity , trait , canopy , environmental science , scale (ratio) , primary production , ecology , spatial ecology , competition (biology) , vegetation (pathology) , physical geography , remote sensing , computer science , geography , biology , ecosystem , cartography , economics , macroeconomics , programming language , medicine , pathology
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 (30040 hectares) along a 3322‐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.