Informing trait-based ecology by assessing remotely sensed functional diversity across a broad tropical temperature...

Area of Science:

• Ecology
• Remote Sensing
• Global Change Biology

Background:

• Predicting global change impacts on ecosystems requires spatially continuous data on functional diversity.
• Tropical forests exhibit high biodiversity, making trait-based ecology crucial for understanding ecosystem function.

Purpose of the Study:

• To estimate remotely sensed functional diversity in tropical forests using imaging spectroscopy and foliar traits.
• To evaluate the role of community assembly processes and environmental filtering along an elevation gradient.
• To determine if remotely sensed functional diversity can predict tropical forest productivity.

Main Methods:

• Applied imaging spectroscopy and foliar trait data to estimate functional diversity across an Amazon-to-Andes elevation gradient.
• Analyzed scale dependency of community assembly processes, including trait convergence and environmental filtering.
• Correlated single- and multi-trait remotely sensed functional diversity measures with net and gross primary productivity.

Main Results:

• Community functional richness decreased significantly with increasing elevation.
• Scale-dependent trait convergence, driven by environmental filtering, explained trait variation along the gradient.
• Remotely sensed functional diversity measures were significant predictors of primary productivity variations.

Conclusions:

• Remotely sensed functional diversity offers a powerful tool for trait-based ecology in hyperdiverse tropical forests.
• Findings demonstrate the potential to link trait diversity to ecosystem function using remote sensing.
• This approach can improve predictions of global change impacts on tropical forest ecosystems.