The susceptibility of soil enzymes to inhibition by leaf litter tannins is dependent on the tannin chemistry, enzyme class and vegetation history
Bibliographic record
Abstract
By inhibiting soil enzymes, tannins play an important role in soil carbon (C) and nitrogen (N) mineralization. The role of tannin chemistry in this inhibitory process, in conjunction with enzyme classes and isoforms, is less well understood. Here, we compared the inhibition efficiencies of mixed tannins (MTs, mostly limited to angiosperms) and condensed tannins (CTs, produced mostly by gymnosperms) against the potential activity of β-glucosidase (BG), N-acetyl-glucosaminidase (NAG), and peroxidase in two soils that differed in their vegetation histories. Compared with CTs, MTs exhibited 50% more inhibition of almond (Prunus dulcis) BG activity and greater inhibition of the potential NAG activity in the gymnosperm-acclimatized soils. CTs exhibited lower BG inhibition in the angiosperm-acclimated soils, whereas both types of tannins exhibited higher peroxidase inhibition in the angiosperm soils than in gymnosperm soils. At all of the tested tannin concentrations, irrespective of the tannin type and site history, the potential peroxidase activity was inhibited two-fold more than the hydrolase activity and was positively associated with the redox-buffering efficiency of tannins. Our finding that the inhibitory activities and mechanisms of MTs and CTs are dependent on the vegetative history and enzyme class is novel and furthers our understanding of the role of tannins and soil isoenzymes in decomposition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".