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 mecha...

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Bibliographic Details
Published in:Ecography (Copenhagen) 2022-08, Vol.2022 (8), p.n/a
Main Authors: 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.
Format: Article
Language:English
Subjects:
Anthropogenic factors
Biomass
Canopies
Carbon
Carbon cycle
climate
Climate change
Ecosystems
Empirical analysis
Estimates
Forests
functional biogeography
Growth models
Human influences
Light
Metabolism
Net Primary Productivity
Nitrogen
Plant growth
Productivity
Remote sensing
trait-based ecology
Tropical forests
Vegetation
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Summary: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.
ISSN:0906-7590
1600-0587
DOI:10.1111/ecog.06078