Drought and defoliation affect soil extracellular enzyme activity in northern temperate grasslands
Bibliographic record
Abstract
Drought has the most significant impact on arid grassland ecosystems. Managed grazing, including the timing and intensity of defoliation, may interact with drought to differentially affect processes related to soil organic matter decomposition. Extracellular enzyme activity (EEA) provides integrated measure of soil microbial activity which affects nutrient cycling. This study examined EEAs in response to five defoliation regimes and drought at seven grasslands across temperate grasslands of Canada. All sites were dominated by perennial grasses and forbs, but differed in plant species, climate, and soils. Soil samples were analyzed for five EEAs involved in carbon (C), nitrogen (N) and phosphorus (P) cycling. Drought reduced activity of enzymes involved in C cycling, β-glucosidase and β-cellobiosidase by 16 and 17%, respectively, P cycling (acid phosphatase) by 11%, and N cycling (N-acetyl-β-glucosaminidase) by 12%. β-xylosidase showed close association with, and was not affected by drought, suggesting a reduction in C turnover under future drought. β-glucosidase activity was reduced by intermediate defoliation relative to both control and heavy. Acid phosphatase and N-acetyl-β-glucosaminidase were affected by three-way interaction of drought, defoliation and mean growing season precipitation, highlighting the complex mechanism underlying EEA responses. Findings suggest that EEA was affected by drought, but defoliation effects were largely dependent upon drought and local climate.
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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".