Isotopic analysis (δC and δH) of lignin methoxy groups in forest soils to identify and quantify lignin sources.

The relative apportion of above and below ground carbon sources is known to be an important factor in soil organic matter formation. Although lignin is the most abundant aromatic plant material in the terrestrial biosphere, our understanding of lignin source contributions to soil organic matter (SOM) is limited due to the complex molecular structure and analysis of lignin. In this study, we novelly apply the dual isotopic analysis (δC and δH values) of lignin methoxy groups (LMeO) with the Bayesian mixing model, MixSIAR, to apportion lignin sources in two contrasting soil types, a podzol and a stagnosol. Results of the isotopic analysis of LMeO demonstrate the ability of δH LMeO values to discriminate between above and below ground lignin sources, while δC LMeO values discriminated between photosynthesising and non-photosynthesising tissues. In the stagnosol subsurface horizons, a decreasing proportion of the leaf litter lignin was observed with increasing organic matter degradation, cumulating in the Ah horizon being dominated by lignin from roots. The podzol sites indicated a similar reduction in leaf litter lignin with an increase in organic matter degradation and depth. However, the Ah horizon was shown to accumulate lignin from the above ground woody material. Furthermore, given the significant abundance of LMeO groups in the terrestrial biosphere and the extremely depleted δC LMeO values in leaf litter, we employed a mass balance approach to determine the extent in which the C bulk enrichment generally associated with isotopic fractionation during organic matter decomposition can be attributed to the shift in lignin sources. Analysis reveals that 14 % and 11 % of bulk C enrichment can be attributed to the transition in LMeO sources from leaf litter to roots in the stagnosol and podzol, respectively. Thus, models relying on C enrichment with depth as an indicator of carbon turnover may be partially overestimating rates.