Fungal composition associated with host tree identity mediates nutrient addition effects on wood microbial respiration
Bibliographic record
Abstract
Fungi are key decomposers of deadwood, but the impact of anthropogenic changes in nutrients and temperature on fungal community and its consequences for wood microbial respiration are not well understood. Here, we examined how nitrogen and phosphorus additions (field experiment) and warming (laboratory experiment) together influence fungal composition and microbial respiration from decomposing wood of angiosperms and gymnosperms in a subtropical forest. Nutrient additions significantly increased wood microbial respiration via fungal composition, but effects varied with nutrient types and taxonomic groups. Specifically, phosphorus addition significantly increased wood microbial respiration (65%) through decreased acid phosphatase activity and increased abundance of fast-decaying fungi (e.g., white rot), while nitrogen addition marginally increased it (30%). Phosphorus addition caused a greater increase in microbial respiration in gymnosperms than in angiosperms (83.3% vs. 46.9%), which was associated with an increase in Basidiomycota:Ascomycota operational taxonomic unit abundance in gymnosperms but a decrease in angiosperms. The temperature dependencies of microbial respiration were remarkably constant across nutrient levels, consistent with metabolic scaling theory hypotheses. This is because there was no significant interaction between temperature and wood phosphorus availability or fungal composition, or the interaction among the three factors. Our results highlight the key role of tree identity in regulating nutrient response of wood microbial respiration through controlling fungal composition. Given that the range of angiosperm species may expand under climate warming and forest management, our data suggest that expansion will decrease nutrient effects on forest carbon cycling in forests previously dominated by gymnosperm species.
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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".