November 14, 2017

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The Power of Traditional Proxies for Measuring the Soil Carbon Cycle

Solid standbys like clay content should not be displaced by new imaging and genetics techniques.

The Science

Near-term land management and policy decisions depend on proxies, which are used as surrogates for soil features and processes and affect long-term projections of Earth system responses to change. In a new paper, soil ecologists from Pacific Northwest National Laboratory review and classify types of complex soil measurements—called proxies for the purposes of environmental research.

The Impact

Correlative and integrative proxies in soil carbon cycle measurements have continuing importance because they yield significant insight while being simpler, easier, and cheaper to measure than the actual feature being represented. For example, it is easier to measure clay content as an indicator of soil porosity or carbon storage potential, but understanding which feature is being inferred is important to interpreting the research. The thoughtful use of proxies can lead to new hypotheses and experiments to identify causative relationships; not using proxies may result in overweighting of correlations to explain research results and the misrepresentation of mechanisms.

Summary

In the long history of environmental, soil, and climate change sciences, researchers have always needed proxy variables to improve how complex variables and processes are measured and represented. They have used tree ring chronologies to infer past climate conditions, for instance. And both experimentalists and modelers widely use clay content as a proxy for properties such as bulk density, water-holding capacity, and soil organic matter.

Because of the complexity of processes and interactions within soil, measuring soil carbon dynamics is another case in which proxies are necessary.

In this realm, ecologists often use two types of proxies. Correlative proxies represent soil characteristics that cannot be directly measured. Integrative proxies aggregate information about multiple soil characteristics into one variable. Both of these proxies are useful for understanding the soil carbon cycle and are now being used to make predictions of the carbon fate and persistence under future climate scenarios. Still, the researchers point out, both proxies limit data interpretation.

Meanwhile, new advances in imaging and proteomics have added capabilities and variables to studying the soil carbon cycle. But so far, these methods are often more expensive and more difficult to measure directly.

The researchers advocate for the thoughtful use of appropriate proxies for predicting the soil carbon cycle. Proxies, they say, are simpler, easier, and cheaper to measure, and, if used wisely, can suggest new hypotheses and relationships for future study.

Principal Investigator

Vanessa Bailey
Pacific Northwest National Laboratory
vanessa.bailey@pnnl.gov

Program Manager

Daniel Stover
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
daniel.stover@science.doe.gov

Funding

This paper was the product of a working group assembled at a workshop sponsored by the Carbon Cycle Interagency Working Group via the U.S. Carbon Cycle Science Program under the auspices of the U.S. Global Change Research Program, “Celebrating the 2015 International Decade of Soil – Understanding Soil’s Resilience and Vulnerability,” Boulder, Colo., March 2016. VLB, BBL, RP, and KD were supported by grants from the Terrestrial Ecosystem Sciences program of the Office of Biological and Environmental Research, within the U.S. Department of Energy Office of Science. KL was supported by National Science Foundation DEB-1257032. KTB was supported by Linus Pauling Distinguished Postdoctoral Fellowship program, part of the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory.

References

Bailey, V. L., B. Bond-Lamberty, K. DeAngelis, and A. S. Grandy, et al. "Soil carbon cycling proxies: understanding their critical role in predicting climate change feedbacks". Global Change Biology 24 (3), 895–905  (2018). https://doi.org/10.1111/gcb.13926.