4.8 Article

Initial soil organic carbon stocks govern changes in soil carbon: Reality or artifact?

期刊

GLOBAL CHANGE BIOLOGY
卷 -, 期 -, 页码 -

出版社

WILEY
DOI: 10.1111/gcb.16491

关键词

carbon cycle; meta-analysis; regression to the mean; soil; soil organic matter; statistical artifact

资金

  1. Lawrence Livermore National Laboratory's Laboratory Directed Research and Development (LLNL-LDRD) program [19-ERD-010]
  2. U.S. Department of Energy Early Career Award [SCW1711]
  3. LLNL-LDRD grant [21-ERD-045]
  4. DOE [DE-AC52-07NA27344]

向作者/读者索取更多资源

Changes in soil organic carbon storage can impact global climate, and identifying environments that can gain or lose SOC is important. However, the observation that SOC-poor soils tend to gain or retain carbon more readily than SOC-rich soils may be influenced by statistical artifacts. Applying statistical corrections and avoiding normalized metrics can address this issue and improve scientific inference for environmental management.
Changes in soil organic carbon (SOC) storage have the potential to affect global climate; hence identifying environments with a high capacity to gain or lose SOC is of broad interest. Many cross-site studies have found that SOC-poor soils tend to gain or retain carbon more readily than SOC-rich soils. While this pattern may partly reflect reality, here we argue that it can also be created by a pair of statistical artifacts. First, soils that appear SOC-poor purely due to random variation will tend to yield more moderate SOC estimates upon resampling and hence will appear to accrue or retain more SOC than SOC-rich soils. This phenomenon is an example of regression to the mean. Second, normalized metrics of SOC change-such as relative rates and response ratios-will by definition show larger changes in SOC at lower initial SOC levels, even when the absolute change in SOC does not depend on initial SOC. These two artifacts create an exaggerated impression that initial SOC stocks are a major control on SOC dynamics. To address this problem, we recommend applying statistical corrections to eliminate the effect of regression to the mean, and avoiding normalized metrics when testing relationships between SOC change and initial SOC. Careful consideration of these issues in future cross-site studies will support clearer scientific inference that can better inform environmental management.

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