Abstract. Soil bioturbation plays a key role in soil functions such as carbon and nutrient cycling. Despite its importance, fundamental knowledge on how different organisms and processes impact the rates and patterns of soil mixing during bioturbation is lacking. However, this information is essential for understanding the effects of bioturbation in present-day soil functions and on long-term soil evolution. Luminescence, a light-sensitive mineral property, serves as a valuable tracer for soil bioturbation. The luminescence signal resets (bleaches) when a soil particle is exposed to daylight at the soil surface and accumulates when the particle is buried in the soil, acting as a proxy for subsurface residence times. In this study, we compiled three luminescence-based datasets of soil mixing by different biota and compared them to numerical simulations of bioturbation using the soil-landscape evolution model ChronoLorica. The goal was to understand how different mixing processes affect depth profiles of luminescence-based metrics, such as the modal age, width of the age distributions and the fraction of bleached particles. We focus on two main bioturbation processes: mounding (advective transport of soil material to the surface) and subsurface mixing (diffusive subsurface transport). Each process has a distinct effect on the luminescence metrics, which we summarized in a conceptual diagram to help with qualitative interpretation of luminescence-based depth profiles. A first attempt to derive quantitative information from luminescence datasets through model calibration showed promising results, but also highlighted gaps in data that must be addressed before accurate, quantitative estimates of bioturbation rates and processes are possible. The new numerical formulations of bioturbation, which are provided in an accompanying modelling tool, provide new possibilities for calibration and more accurate simulation of the processes in soil function and soil evolution models.

中文翻译：

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混合信号：通过发光和数值建模解释不同土壤生物扰动过程的混合模式


摘要。土壤生物扰动在碳和养分循环等土壤功能中发挥着关键作用。尽管其重要性，但关于不同生物体和过程如何影响生物扰动期间土壤混合的速率和模式的基础知识仍然缺乏。然而，这些信息对于了解生物扰动对当今土壤功能和长期土壤演化的影响至关重要。发光是一种光敏矿物特性，可作为土壤生物扰动的有价值的示踪剂。当土壤颗粒暴露在土壤表面的日光下时，发光信号会重置（漂白），并在颗粒埋入土壤中时积累，充当地下停留时间的代表。在这项研究中，我们编制了三个基于发光的不同生物群土壤混合数据集，并将它们与使用土壤景观演化模型 ChronoLorica 的生物扰动数值模拟进行比较。目标是了解不同的混合过程如何影响基于发光的指标的深度剖面，例如模态年龄、年龄分布的宽度和漂白颗粒的比例。我们关注两个主要的生物扰动过程：堆积（土壤物质平流输送到地表）和地下混合（扩散地下输送）。每个过程对发光指标都有不同的影响，我们在概念图中总结了这一点，以帮助对基于发光的深度剖面进行定性解释。通过模型校准从发光数据集中获取定量信息的首次尝试显示了有希望的结果，但也强调了在对生物扰动速率和过程进行准确、定量估计之前必须解决的数据差距。 附带的建模工具中提供的新的生物扰动数值公式为校准和更准确地模拟土壤功能和土壤演化模型的过程提供了新的可能性。