Abstract：
Global soil nitrogen (N) cycling remains poorly understood due to its complex driving mechanisms. Here, we present a comprehensive analysis of global soil δ15N, a stable isotopic signature indicative of the N input-output balance, using a machine-learning approach on 10,676 observations from 2670 sites. Our findings reveal prevalent joint effects of climatic conditions, plant N-use strategies, soil properties, and other natural and anthropogenic forcings on global soil δ15N. The joint effects of multiple drivers govern the latitudinal distribution of soil δ15N, with more rapid N cycling at lower latitudes than at higher latitudes. In contrast to previous climate-focused models, our data-driven model more accurately simulates spatial changes in global soil δ15N, highlighting the need to consider the joint effects of multiple drivers to estimate the Earth\'s N budget. These insights contribute to the reconciliation of discordances among empirical, theoretical, and modeling studies on soil N cycling, as well as sustainable N management.
摘要：
由于其复杂的驱动机制，全球土壤氮（N）循环仍然知之甚少。这里,我们对全球土壤δ15N进行了全面分析，表明N输入-输出平衡的稳定同位素特征，使用机器学习方法对2670个站点的10676个观测结果进行分析。我们的发现揭示了气候条件的普遍联合影响，植物N使用策略，土壤性质，以及其他对全球土壤δ15N的自然和人为强迫。多个驱动因素的联合作用控制着土壤δ15N的纬度分布，低纬度的氮循环比高纬度的快。与以前以气候为重点的模型相比，我们的数据驱动模型更准确地模拟了全球土壤δ15N的空间变化，强调需要考虑多个驱动因素的联合影响来估计地球的N预算。这些见解有助于调和经验，理论,以及土壤氮循环的建模研究，以及可持续的N管理。
