Large herbivores impact carbon cycling in myriad ways, including by impacting a landscape’s assembly and structure. It is therefore necessary to explore carbon cycling at the scale of landscape features, to better predict how large herbivore impacts may scale up. However, it is often prohibitive (logistically, climatically, financially) to measure carbon dynamics at fine spatial and temporal scales using traditional manually-operated methods.
I interrogated the effects of large herbivores on carbon cycling in a large-scale exclosure experiment in central Kenya by manually measuring in situ soil carbon flux. However, these data and supporting datasets illuminated the need for significantly greater spatial and temporal resolution to characterize soil carbon flux in this savanna.
To capture such resolution I developed and deployed a network of inexpensive, DIY, autonomous soil carbon flux chambers (“fluxbots”). Fluxbots collected hourly flux measurements at each of three key landscape features (open soil patches, the surface of active termite mounds, and beneath the canopy of the dominant tree species Acacia drepanolobium), across experimental treatments that allow all large vertebrate herbivores or none, for 2.5 months spanning a transition from dry to wet season.
Results/Conclusions
In situ soil carbon flux data manually collected over three dry seasons reveal treatment-level effects that vary from year to year; from increasing flux rate with decreasing large herbivore presence, to no difference between treatments. This variability is likely influenced by changing environmental conditions, like drought and rainfall. Complementary datasets demonstrate differences between landscape features within herbivore treatments, like higher soil microbial biomass and respiration rates from soils beneath tree canopies, and higher soil carbon at termite mounds. Such results indicate that highly-resolved data, temporal and spatial, is necessary to characterize soil carbon flux.
A synchronized network of fluxbots captured 2.5 months (Aug-Oct 2019) of hourly flux data, across herbivore treatments and the features within each. Atmospheric CO2 and flux rates follow cyclical diurnal patterns, indicating that flux is temporally dynamic, and that this high-elevation, seasonally dry savanna’s changing environmental conditions are influential. Such highly-resolved data could not have been collected manually, demonstrating the importance of innovating inexpensive sensors in improving accessibility and resolution of flux data. These and correlated data demonstrate that savanna carbon flux is extremely dynamic, and likely influenced by patterns in environmental conditions over time and the spatial distribution of features.