Vegetation structure and phenology primarily shape the spatiotemporal pattern of ecosystem respiration
Abstract 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-sca...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
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| Series: | Communications Earth & Environment |
| Online Access: | https://doi.org/10.1038/s43247-025-02240-1 |
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| Summary: | Abstract 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. |
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| ISSN: | 2662-4435 |