Scholarlink

<p>High-resolution maps of plant functional traits are crucial for understanding terrestrial ecosystem processes; however, their integration into ecosystem models has been hindered by uncertainties and a lack of spatially detailed data. Here we combine optical remote sensing, global crowd-sourced biodiversity records and plant trait databases to map community trait distributions worldwide at 1-km resolution, estimating community-weighted means (CWMs) and higher-order moments (standard deviation, skewness, and kurtosis) for specific leaf area (SLA), leaf nitrogen (LNC) and leaf phosphorus (LPC) concentrations. Benchmarking against sPlotOpen plot-level CWMs shows low explained variance (<i>R</i><sup>2</sup>&#xa0;=&#xa0;0.10–0.27 across traits), indicating limited plot-scale predictive skill under current limited open global benchmarks and scale mismatches. Agreement increases when using a canopy-weighted comparator (TWM; <i>R</i><sup>2</sup>&#xa0;=&#xa0;0.22–0.38; relative RMSE &#xa0;≈&#xa0;12–18%), consistent with the top-of-canopy sensitivity of optical sensors. By providing spatially explicit trait distributions and their higher-order moments, our findings deliver improved detail for understanding biodiversity patterns and ecosystem functioning and provide landscape-scale insights into trait-mediated coexistence. This work enhances ecological modeling and offers a foundation for assessing the impacts of global environmental changes, advancing our understanding of plant functional diversity’s role in ecosystem resilience and sustainability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月21日

查看全文
  • Álvaro Moreno-Martínez,
  • Jordi Muñoz-Marí,
  • Jose E. Adsuara,
  • Benjamin Dechant,
  • Jens Kattge,
  • Teja Kattenborn,
  • Francesco Maria Sabatini,
  • Fabian D. Schneider,
  • Gregory Duveiller,
  • Peter M. van Bodegom,
  • Ethan E. Butler,
  • Miguel D. Mahecha,
  • Emma Izquierdo-Verdiguier,
  • Josep Peñuelas,
  • Gerhard Boenisch,
  • Ryan Pavlick,
  • Philip A. Townsend,
  • Nuno Carvalhais,
  • Daniel Lusk,
  • Gustau Camps-Valls

摘要

High-resolution maps of plant functional traits are crucial for understanding terrestrial ecosystem processes; however, their integration into ecosystem models has been hindered by uncertainties and a lack of spatially detailed data. Here we combine optical remote sensing, global crowd-sourced biodiversity records and plant trait databases to map community trait distributions worldwide at 1-km resolution, estimating community-weighted means (CWMs) and higher-order moments (standard deviation, skewness, and kurtosis) for specific leaf area (SLA), leaf nitrogen (LNC) and leaf phosphorus (LPC) concentrations. Benchmarking against sPlotOpen plot-level CWMs shows low explained variance (R2 = 0.10–0.27 across traits), indicating limited plot-scale predictive skill under current limited open global benchmarks and scale mismatches. Agreement increases when using a canopy-weighted comparator (TWM; R2 = 0.22–0.38; relative RMSE  ≈ 12–18%), consistent with the top-of-canopy sensitivity of optical sensors. By providing spatially explicit trait distributions and their higher-order moments, our findings deliver improved detail for understanding biodiversity patterns and ecosystem functioning and provide landscape-scale insights into trait-mediated coexistence. This work enhances ecological modeling and offers a foundation for assessing the impacts of global environmental changes, advancing our understanding of plant functional diversity’s role in ecosystem resilience and sustainability.