From microhabitat to ecosystem: identifying the biophysical factors controlling soil CO2 dynamics in a karst shrubland

The soil CO₂ efflux (Fₛ) remains the least constrained component of the terrestrial carbon cycle; its estimates are still largely uncertain, mainly because of its considerable variation related to the many factors that interact over different temporal and spatial scales. Therefore, our aims were to: (i) identify the biophysical factors that control the soil CO₂ molar fraction (χₛ) and characterize their time‐frequency patterns in a karst shrubland, (ii) explore χₛ variation with soil cover type (microhabitat) and (iii) estimate Fₛ at the ecosystem scale. Continuous measurements of aboveground variables, including net CO₂ exchanges at the ecosystem level, were compared with pedoclimatic variables collected from four microhabitats (Festuca scariosa (Lag.) Asch. & Graebn., Hormathophylla spinosa (L.) P. Küpfer, Genista pumila (Hervier) Vierh. and bare soil). The microhabitat‐scale controlling factors of χₛ were identified by a top‐down statistical analysis, and time‐frequency patterns were analysed by wavelet spectral decomposition. Finally, Fₛ was upscaled from the microhabitat to the ecosystem scale by considering the spatial heterogeneity of ground cover. We determined that in addition to soil water content and temperature, which are traditionally used to predict χₛ or Fₛ, the wind (friction velocity) can also have a significant effect. Furthermore, the sensitivity of χₛ to the main factors identified here varied with microhabitat and season. Over a year, Fₛ upscaled to the ecosystem level and its uncertainty was 175 ± 13 g C m⁻² compared with 155 ± 8 g C m⁻² estimated from ecosystem respiration. These results provide new insight into mechanisms of soil CO₂ production and transport that question and can improve models traditionally used to quantify ecosystem CO₂ emissions. HIGHLIGHTS: Explored the variation of soil CO₂ in relation to its biophysical controlling factors. Used a top‐down statistical analysis and a wavelet time‐frequency decomposition. Identified controlling factors of soil CO₂ dynamics: soil water content, temperature, wind, microhabitat and season. Provides new insight into soil CO₂ production and transport, and improves CO₂ emission modelling.