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<p>Forest diversity and dynamics are governed by the interactions between organismal function and the abiotic and biotic environment. Functional traits have been widely employed in forest ecology to estimate key functional tradeoffs related to tree performance in a given environment. Hyperspectral reflectance data are utilized in ecology to predict functional trait values at the individual tree or pixel scale on broad spatial extents, but the diversity of functions captured by these traits is limited. Here, we demonstrate a novel integration of reflectance and to gene expression data for processes of interest to ecologists. We show linkages between the expression of ecologically important genes and reflectance data and the potential to transform the depth at which ecologists can rapidly estimate functional diversity.</p>

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Linking leaf hyperspectral reflectance to gene expression

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月23日

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  • Yanni Chen,
  • Logan Monks,
  • Vanessa E. Rubio,
  • Alexander J. Cox,
  • Nathan G. Swenson

摘要

Forest diversity and dynamics are governed by the interactions between organismal function and the abiotic and biotic environment. Functional traits have been widely employed in forest ecology to estimate key functional tradeoffs related to tree performance in a given environment. Hyperspectral reflectance data are utilized in ecology to predict functional trait values at the individual tree or pixel scale on broad spatial extents, but the diversity of functions captured by these traits is limited. Here, we demonstrate a novel integration of reflectance and to gene expression data for processes of interest to ecologists. We show linkages between the expression of ecologically important genes and reflectance data and the potential to transform the depth at which ecologists can rapidly estimate functional diversity.