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Keywords:

  • disturbance gradient;
  • eddy covariance;
  • Pinus ponderosa;
  • soil CO2 efflux;
  • soil diffusion profile;
  • soil respiration

Abstract

Better understanding of variation in soil carbon dioxide (CO2) efflux caused by measurement techniques is needed, especially over gradients of site disturbance, to accurately estimate the global carbon cycle. We present soil CO2 efflux data from a gradient of disturbance to ponderosa pine (Pinus ponderosa C. Lawson var. scopulorum Engelm.) forests in northern Arizona, USA that were obtained using four different techniques: vented static chambers, a Licor 6400-09, and soil CO2 diffusion profiles using two different models (Moldrup, Millington–Quirk) to estimate soil gas diffusivity. We also compared soil CO2 efflux measured by the Moldrup and Millington–Quirk diffusion profile methods to nighttime total ecosystem respiration (TER) data from an eddy covariance tower. We addressed four questions: (1) Does the use of a given method to measure soil CO2 efflux bias results across a disturbance gradient? (2) Does the magnitude of difference between observed and modeled estimates of soil CO2 differ between methods and across sites? (3) What is the spatial variability of each method at each site? (4) Which method is closest to the estimate of TER measured by the eddy covariance tower? Although soil CO2 efflux varied significantly among methods the differences were consistent among sites. Measured and modeled total growing season fluxes were generally higher for the Licor 6400-09 and Millington–Quirk diffusion gradient methods compared with static chamber and the Moldrup diffusion gradient methods. A power analysis showed that the larger static chamber was the most efficient method at sampling spatial variation in soil CO2 efflux. Nighttime measurements of soil CO2 efflux from the Moldrup diffusion gradient method were most strongly related to nighttime TER assessed with eddy covariance. The use of a single, well-implemented method to measure soil CO2 efflux is unlikely to create bias in comparisons across a gradient of forest disturbance.