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Relating Stomatal Conductance to Leaf Functional Traits
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Remotely sensed between-individual functional trait variation in a temperate forest.

Carla Guillén-Escribà1,2, Fabian D Schneider1,3, Bernhard Schmid1

  • 1Remote Sensing Laboratories Department of Geography University of Zürich Zürich Switzerland.

Ecology and Evolution
|August 25, 2021
PubMed
Summary
This summary is machine-generated.

This study combines remote sensing (RS) with individual tree analysis to assess plant functional traits in forests. This approach allows for a comprehensive understanding of how species, environment, and their interactions shape forest community structure and productivity.

Keywords:
airborne imaging spectroscopyairborne laser scanningfunctional traitsphylogenetic variationremote sensingwithin‐species variation

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Area of Science:

  • Ecology
  • Remote Sensing
  • Forest Science
  • Trait-based Ecology

Background:

  • Trait-based ecology aims to understand plant community dynamics, such as how functional diversity influences productivity.
  • Integrating field-based individual plant trait assessments (limited spatial coverage) with remote sensing (RS) data (complete spatial coverage) has been challenging.
  • Existing RS approaches often assess traits at the vegetation pixel level, not the individual tree level.

Purpose of the Study:

  • To combine individual-tree crown delineation with RS-derived trait measures to bridge the gap between field and RS approaches.
  • To estimate the influence of taxonomic and environmental variation on trait variation across contiguous forest space.
  • To analyze functional diversity at individual tree and community levels with complete spatial coverage.

Main Methods:

  • Airborne imaging spectroscopy and laser scanning were used to collect individual-tree RS data in a mixed forest.
  • Biochemical (chlorophyll, carotenoids, water content) and architectural (plant area index, foliage-height diversity, canopy height) traits were derived from RS data.
  • General linear models were applied to partition trait variation into taxonomic, environmental, and interaction components.

Main Results:

  • Taxonomic variation explained over 15% of biochemical trait variation and around 5% of architectural trait variation.
  • Environmental factors (light, soil, elevation) explained slightly more variation than taxonomy, influencing traits like plant area index and canopy height.
  • Significant interactions between taxonomic and environmental variation were observed for most traits, suggesting species-specific responses to environmental gradients.

Conclusions:

  • High-resolution RS data enables individual-tree crown delineation and assessment of RS-derived functional traits at the individual level.
  • This integrated approach allows for the application of field-based trait ecology tools to partition trait variation, offering insights into genetic, environmental, and interactive influences.
  • The proposed method provides a promising framework for assessing individual-based trait information with complete spatial coverage, enhancing the analysis of functional diversity and its role in forest ecosystem processes.