Non-native earthworms preferentially promote bacterial rather than fungal denitrification in northern temperate deciduous forests
Bibliographic record
Abstract
We undertook a study to determine if non-native earthworm populations occurring in sugar maple (Acer saccharum Marsh.) forests of eastern Canada could potentially alter denitrification rates driven by bacteria and fungi. We measured earthworm abundances and collected surface mineral soil samples from 38 sugar maple forests, 14 that were colonized by earthworms (EW+) and 24 that were earthworm-free (EW-). In each soil sample, we measured (1) fungal, bacterial and total microbial biomass, (2) fungal, bacterial and total potential denitrification, (3) the abundances of bacterial and fungal denitrifying genes, and (4) soil physicochemical properties known to influence denitrification rates. Earthworms decreased forest floor depth and soil C:N ratio, but increased mineral soil pH, total N, bacterial and fungal biomass, and nitrification rates. Earthworms increased bacterial-driven denitrification more so than fungal-driven denitrification. Accordingly, specific denitrification rates (i.e., denitrification-to-biomass ratio) increased for bacteria and decreased for fungi with the presence of earthworms. The relative abundances of bacterial denitrifying genes (nirK, nirS and nosZ) increased with the presence of earthworms, whereas the fungal denitrifying gene (P450nor) was not affected. Taken collectively, our results suggest that earthworms increase N2O emissions in sugar maple forest soils mainly by promoting the bacterial denitrification pathway. This increase is due to physicochemical changes in the soil environment promoting bacterial activity and to changes in the functional diversity of bacterial communities.
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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) | 0.000 | 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".