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

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 similar to 44.15% of global TER variability), biotic factors (e.g., vegetation structure, similar to 25.91%) and spatiotemporal variation factors (e.g., land cover and phenology, similar to 29.94%) were also critical.