Login / Signup

Resolving the influence of lignin on soil organic matter decomposition with mechanistic models and continental-scale data.

Bo YiChaoqun LuWenjuan HuangWenjuan YuJihoon YangAdina C HoweSamantha R Weintraub-LeffSteven J Hall
Published in: Global change biology (2023)
Confidence in model estimates of soil CO 2 flux depends on assumptions regarding fundamental mechanisms that control the decomposition of litter and soil organic carbon (SOC). Multiple hypotheses have been proposed to explain the role of lignin, an abundant and complex biopolymer that may limit decomposition. We tested competing mechanisms using data-model fusion with modified versions of the CN-SIM model and a 571-day laboratory incubation dataset where decomposition of litter, lignin, and SOC was measured across 80 soil samples from the National Ecological Observatory Network. We found that lignin decomposition consistently decreased over time in 65 samples, whereas in the other 15 samples, lignin decomposition subsequently increased. These "lagged-peak" samples can be predicted by low soil pH, high extractable Mn, and fungal community composition as measured by ITS PC2 (the second principal component of an ordination of fungal ITS amplicon sequences). The highest-performing model incorporated soil biogeochemical factors and daily dynamics of substrate availability (labile bulk litter:lignin) that jointly represented two hypotheses (C substrate limitation and co-metabolism) previously thought to influence lignin decomposition. In contrast, models representing either hypothesis alone were biased and underestimated cumulative decomposition. Our findings reconcile competing hypotheses of lignin decomposition and suggest the need to precisely represent the role of lignin and consider soil metal and fungal characteristics to accurately estimate decomposition in Earth-system models.
Keyphrases
  • ionic liquid
  • healthcare
  • plant growth
  • squamous cell carcinoma
  • magnetic resonance
  • physical activity
  • mental health
  • risk assessment
  • big data
  • electronic health record
  • human health