Cycles and drivers of spatiotemporal variation of forest soil respiration at the intra-landform scale
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Spatiotemporal variability of soil respiration (RS) is a primary driver of large uncertainties associated with forest carbon budgets. However, most studies focus on either spatial or temporal variations, not both. As the frequency and severity of extreme weather events, such as heavy rain and drought, increase it is crucial to understand how these stressors affect the spatial variation of RS to reduce uncertainties in carbon budgets. We analyzed a decades-long data set of continuous chamber measurements from a temperate deciduous forest in the central USA to examine spatiotemporal variations of RS and the influence of water status on these patterns. Our findings reveal that spatial variation of RS changes seasonally and is significantly affected by water status. During drought conditions, the mean daily spatial variation of summer RS increased from a CV of RS of 16.6 percent to a CV of RS of 28.1 percent, and the daily amplitude also rose. A power analysis indicated that spatial variation observed at the MOFLUX forest prevented detection of changes in RS between MOFLUX and a hypothetical forest. Spatial variability peaked at soil water content levels of 0.2 m^3 m^-3 and 0.5 m^3 m^-3, indicating that in the extremes, soil water content drives spatial variation. Additionally, our analysis showed that soil water content (SWC) and soil temperature (Tsoil) are key drivers of spatial variation of RS, with the influence of these drivers shifting seasonally. During the non-growing season, soil climatic variables explained more of the spatial variation among chambers compared to the growing season. These results highlight the seasonality in the drivers of spatial variation of RS, where soil climatic variables have greater explanatory power during the non-growing season. Our results demonstrate that both seasonality and extreme water conditions significantly influence the spatiotemporal variability of RS, providing valuable insights for improving understanding of forest RS spatial variation.
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M.S.