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Leveraging remote sensing and crowd-sourced biodiversity data for enhanced plant functional trait mapping.

Álvaro Moreno-Martínez1, Jordi Muñoz-Marí2, Jose E Adsuara2

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Summary

This study maps plant functional traits globally using remote sensing and biodiversity data, providing crucial insights into ecosystem processes and biodiversity patterns for improved ecological modeling and understanding global change impacts.

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Area of Science:

  • Ecology
  • Remote Sensing
  • Biodiversity Science

Background:

  • High-resolution maps of plant functional traits are vital for ecosystem process understanding.
  • Integration into ecosystem models is limited by data scarcity and uncertainty.

Purpose of the Study:

  • To map global community trait distributions and their moments at 1-km resolution.
  • To assess the predictive skill of trait maps against plot-level data.
  • To provide spatially explicit trait data for ecological modeling.

Main Methods:

  • Combined optical remote sensing, global crowd-sourced biodiversity records, and plant trait databases.
  • Estimated community-weighted means (CWMs) and higher-order moments for specific leaf area (SLA), leaf nitrogen (LNC), and leaf phosphorus (LPC).
  • Benchmarked model outputs against sPlotOpen plot-level CWMs and canopy-weighted comparators (TWM).

Main Results:

  • Limited explained variance (R² = 0.10–0.27) when benchmarking against plot-level CWMs, indicating scale mismatches.
  • Increased agreement (R² = 0.22–0.38) with canopy-weighted comparators, aligning with optical sensor sensitivity.
  • Provided spatially explicit distributions of trait moments, enhancing landscape-scale ecological insights.

Conclusions:

  • The study delivers improved detail for understanding biodiversity patterns and ecosystem functioning.
  • Findings offer landscape-scale insights into trait-mediated coexistence and enhance ecological modeling.
  • This work provides a foundation for assessing global environmental change impacts on plant functional diversity and ecosystem resilience.