Mixed Signals: soil bioturbation rates from luminescence and numerical modelling

Bioturbation has a large impact of carbon and nutrient cycling in soils and therefore plays a key role in current-day soil functioning and long-term soil evolution. Despite its importance, fundamental knowledge on the mechanisms and rates of different soil bioturbation processes is limited, which prohibits accurate modelling of these processes.

Luminescence, a light-sensitive mineral property of the two most abundant minerals at the Earth surface (quartz and feldspar), is a valuable tracer for deriving rates of long-term bioturbation. It measures the last exposure of siliclastic particles to daylight and can therefore act as a proxy of subsurface residence times. However, these luminescence tracers do not account for previous resurfacing of grains and subsurface transport without bleaching, and therefore only represent the net replacement of the particles. This leads to an underestimation of bioturbation rates when derived from luminescence tracers only.

In this presentation, we introduce the new simulation model named Mixed Signals, which simulates two main bioturbation processes and their impacts on luminescence tracers: mounding (advective transport to the surface) and subsurface mixing (diffusive transport within the subsurface). We applied the model to two published luminescence datasets, each from settings dominated by organisms with distinct burrowing behavior: termites and anecic earthworms. We calibrated the model using different statistics derived from the experimental and simulated luminescence age distributions to derive bioturbation rates and mixing characteristics for the two datasets.

The model produced bioturbation rates that are orders of magnitude larger than the rates derived from the luminescence datasets alone, yet they are consistent with rates derived from observations. While some limitations remain, such as the need for a better experimental understanding of light penetration in soils for particle bleaching, our findings show the potential of the Mixed Signals model to extract accurate bioturbation rates from luminescence data. The model greatly enhances our understanding of bioturbation dynamics and improves the use of luminescence as a tracer for soil processes.