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<p>Accurate estimation of terrestrial ecosystem respiration (TER) is essential for refining global carbon budgets. Current large-scale TER models rely on empirical structures derived from site-scale observations, often driven solely by hydrothermal factors. However, incorporating ecosystem-scale information is critical for more accurate large-scale TER modeling. Such ecosystem-scale variables have not been well parameterized, since the mechanisms by which they affect TER remain unclear. To address this gap, here we developed a Causality constrained Interpretable Machine Learning model for TER estimation (named “CIML-TER”) which consider the ecosystem-scale information. CIML-TER exhibited higher estimation accuracy (reducing relative mean absolute error by approximately 15%) and overcame the “artificial discontinuities” phenomenon of traditional models. Meanwhile, we quantitatively revealed that although environmental factors, such as temperature and water, were still the dominant drivers of TER (contributing ~44.15% of global TER variability), biotic factors (e.g., vegetation structure, ~25.91%) and spatiotemporal variation factors (e.g., land cover and phenology, ~29.94%) were also critical.</p>

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Vegetation structure and phenology primarily shape the spatiotemporal pattern of ecosystem respiration

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月31日

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  • Cenliang Zhao,
  • Wenquan Zhu

摘要

Accurate estimation of terrestrial ecosystem respiration (TER) is essential for refining global carbon budgets. Current large-scale TER models rely on empirical structures derived from site-scale observations, often driven solely by hydrothermal factors. However, incorporating ecosystem-scale information is critical for more accurate large-scale TER modeling. Such ecosystem-scale variables have not been well parameterized, since the mechanisms by which they affect TER remain unclear. To address this gap, here we developed a Causality constrained Interpretable Machine Learning model for TER estimation (named “CIML-TER”) which consider the ecosystem-scale information. CIML-TER exhibited higher estimation accuracy (reducing relative mean absolute error by approximately 15%) and overcame the “artificial discontinuities” phenomenon of traditional models. Meanwhile, we quantitatively revealed that although environmental factors, such as temperature and water, were still the dominant drivers of TER (contributing ~44.15% of global TER variability), biotic factors (e.g., vegetation structure, ~25.91%) and spatiotemporal variation factors (e.g., land cover and phenology, ~29.94%) were also critical.