Temperate trees locally engineer decomposition and litter‐bound microbiomes through differential litter deposits and species‐specific soil conditioning
Bibliographic record
Abstract
Leaf decomposition varies widely across temperate forests, shaped by factors like litter quality, climate, soil properties, and decomposers, but forest heterogeneity may mask local tree influences on decomposition and litter-associated microbiomes. We used a 24-yr-old common garden forest to quantify local soil conditioning impacts on decomposition and litter microbiology. We introduced leaf litter bags from 10 tree species (5 arbuscular mycorrhizal; 5 ectomycorrhizal) to soil plots conditioned by all 10 species in a full-factorial design. After 6 months, we assessed litter mass loss, C/N content, and bacterial and fungal composition. We hypothesized that (1) decomposition and litter-associated microbiome composition would be primarily shaped by the mycorrhizal type of litter-producing trees, but (2) modified significantly by underlying soil, based on mycorrhizal type of the conditioning trees. Decomposition and, to a lesser extent, litter-associated microbiome composition, were primarily influenced by the mycorrhizal type of litter-producing trees. Interestingly, however, underlying soils had a significant secondary influence, driven mainly by tree species, not mycorrhizal type. This secondary influence was strongest under trees from the Pinaceae. Temperate trees can locally influence underlying soil to alter decomposition and litter-associated microbiology. Understanding the strength of this effect will help predict biogeochemical responses to forest compositional change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".