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.

中文翻译：

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多种自然和人为驱动因素对土壤氮循环联合影响的全球证据


由于其复杂的驱动机制，全球土壤氮（N）循环仍然知之甚少。在这里，我们使用机器学习方法对 2670 个地点的 10,676 个观测值进行了对全球土壤 δ15N 的全面分析，δ15N 是一种稳定的同位素特征，表明氮输入输出平衡。我们的研究结果揭示了气候条件、植物氮利用策略、土壤特性以及其他自然和人为强迫对全球土壤 δ15N 的普遍联合影响。多种驱动因素的共同影响控制着土壤δ15N的纬度分布，低纬度地区的氮循环比高纬度地区更快。与之前以气候为中心的模型相比，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共同影响来估计地球的氮预算。这些见解有助于协调土壤氮循环以及可持续氮管理的实证、理论和模型研究之间的不一致。