Effect of prescribed fire on microbial indicators of soil biogeochemistry in a mixed-grass prairie
Bibliographic record
Abstract
Prescribed fire is a widely used management tool for restoring grassland ecosystems. This study assessed the effects of prescribed fire on soil microbial communities and physicochemical properties in both tame (non-native forage) and native pastures within a northern mixed-grass prairie in southwestern Saskatchewan, Canada. Soil samples (0–10 cm depth) were collected from burned and adjacent unburned areas in 2018 (first growing season post-fire) and in 2019 (1-year post-fire). Microbial abundance and community composition were analyzed using phospholipid fatty acid (PLFA) profiling. Prescribed fire caused minimal short-term changes in soil microbial communities and properties, with responses varying by pasture type. In the tame pasture, fire initially increased fungal abundance and the Gram-negative to Gram-positive bacterial ratio (GN:GP), while reducing microbial biomass C and N, and total C and N, indicating organic matter loss and nutrient volatilization. Although fungal abundance recovered by the following year, the elevated GN:GP ratio persisted, suggesting that post-fire conditions favored fast-growing GN bacteria over spore-forming GP bacteria. Microbial community composition in both pasture types was strongly correlated with the relative abundances of fungi, total bacteria, and Gram-type bacterial groups. Native pastures showed greater microbial and nutrient stability across both sampling years. Overall, seasonal and annual variability exerted a stronger influence on microbial communities and soil nutrients than fire. These findings demonstrate the resilience of soil microbial communities to low-intensity prescribed fire in mixed-grass prairie ecosystems and suggest that fire, under controlled conditions, can be a viable management tool with short-term disruption to soil biological function.
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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".