Mixed signals: interpreting mixing patterns of different soil bioturbation processes through luminescence and numerical modelling

Abstract

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.