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decomposing leaf

Credit: CC BY-NC 2.0 Alison Hurt

It’s kind of amazing what you can learn by taking a fresh look at old data. A re-analysis of data from a large and influential decomposition experiment suggests that—at least in arid lands—the degradation of organic matter by light plays a much bigger role than previously understood.

Back in 1990, an ambitious group of LTER scientists packed ten standard types of leaf and root litter—with widely varying chemistry—into thousands of mesh bags and asked their colleagues at 28 research sites to put them out in the field to decompose. Collected and analyzed every year over the next decade, this rich data set from the Long-term Intersite Decomposition Experiment Team (LIDET) study had tremendous influence on how ecologists understand and predict decomposition.

The “biotic” model that emerged from the LIDET study considered the ratios of cellulose, lignin, and soluble carbon in litter, as well as initial nitrogen content, climate, and soil conditions. It did a good job of predicting decomposition for most sites and the approach was incorporated into many standard ecosystem models.

In the nearly two decades since the LIDET results appeared, photodegradation of organic matter has come to be recognized as a potentially important driver of decomposition, especially at arid sites. This new study uses the LIDET data from three arid sites in the original study to evaluate 59 models that incorporate photodegradation.

One model stood out as a much better descriptor of decomposition dynamics for these sites. Its success suggests that, even now, the current understanding of photodegradation is incomplete. Key features of this successful model include losses from cellulose and lignin pools that do not accrue to the fast-turnover labile pool. It also allowed UV radiation to slow microbial decomposition rates and soil infiltration to “shade” litter from the effects of UV light.

The two types of models performed similarly in the first four years of decomposition, but diverged significantly in years 4-10, with the new photodegradation model coming much closer to the measured results. As drylands expand and get drier, better predictions of carbon turnover will depend on better understanding of photodegradation. Good thing they knew where to find that data!

Source: Adair, E. C., W. J. Parton, J. Y. King, L. A. Brandt, and Y. Lin. 2017. Accounting for photodegradation dramatically improves prediction of carbon losses in dryland systems. Ecosphere 8(7):e01892. 10.1002/ecs2.1892