Initial soil organic carbon stocks govern changes in soil carbon : Reality or artifact?
© 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.
| Veröffentlicht in: | Global change biology. - 1999. - 29(2023), 5 vom: 25. März, Seite 1239-1247 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2023
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| Zugriff auf das übergeordnete Werk: | Global change biology |
| Schlagworte: | Journal Article carbon cycle meta-analysis regression to the mean soil soil organic matter statistical artifact Soil Carbon 7440-44-0 |
| Zusammenfassung: | © 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd. 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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| Beschreibung: | Date Completed 06.02.2023 Date Revised 25.05.2023 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1365-2486 |
| DOI: | 10.1111/gcb.16491 |