Soil Respiration Variability and Correlation Across a Wide Range of Temporal Scales

The high temporal variability of the soil‐to‐atmosphere CO2flux (soil respiration, RS) has been studied at hourly to multiannual time scales but remains less well understood than RSspatial variability. How RSfluxes vary and are autocorrelated at various time lags has practical implications for sampling and more fundamentally for our understanding of its abiotic and biotic underlying mechanisms. We examined the variability, correlation, and sampling requirements of RSover a wide range of temporal scales in a temperate deciduous forest in eastern Maryland, USA, using both automated (temporally continuous, N= 30,036 over 10 months) and survey (spatially diverse, temporally sparse, N= 1,912 over 17 months) data. Data from a global RSdatabase were also used to examine interannual variability in comparable forests. The coefficient of variability of successive measurements generally varied from the minute (median coefficient of variation 16%) to hourly and daily (11–12%) time scales. Successive RSvalues measured at a given collar exhibited a strong hour‐to‐hour correlation (r= 0.931) and a moderate correlation at a 2‐hr lag (0.289); day‐to‐day (i.e., 24 hr lag) hourly observations were uncorrelated. Daily RSmeans were well correlated at a 1‐day lag (r= 0.856) but not at any further time lag. In a linear mixed‐effects model predicting RS, soil temperature and moisture exerted consistently strong effects regardless of time scale, and model coefficient of variability was generally high (>80%). These results provide new opportunities to explore the drivers and variability of RSfluxes, quantify sampling requirements, and improve error propagation. Soils give off carbon dioxide, generated by microbes and plant roots, to the atmosphere. How this “soil respiration” (RS) varies in time, as one measures at minute, hourly, daily, or longer time scales, is related to the processes driving it and has implications for how we estimate this flow of carbon across space and time. We measured RSin a coastal deciduous forest in Maryland, USA, and found that the variability of RS—how much it changed between successive measurements—varied at the different time scales. RSvalues quickly became increasingly random (i.e., uncorrelated with each other) as one measured repeatedly over time. These results help us understand the factors driving this large flow of carbon to the atmosphere and improve our ability to estimate RSat times and places not directly measured. Variability of soil respiration (RS) had different characteristics at subhourly to seasonal time scales in a coastal deciduous forestSuccessive hourly and daily measurements were rarely correlated beyond more than one or two time lagsSoil temperature and moisture consistently explained most of the RSvariability across all time scales