Effects of burning on vegetation, soil physicochemistry and prokaryotic microbial communities in surface and subsurface peat
Bibliographic record
Abstract
Prescribed burning is a common management strategy in peatlands that has the potential to affect soil physicochemistry, alter biogeochemical cycles and trigger changes in vegetation structure. How burning affects prokaryotic community composition across different soil profiles is not well understood. This study explored the effects of prescribed burning on the diversity of prokaryotic communities in peat soils. Soil samples were collected from Moor House Nature Reserve, UK, a long-term monitoring site initiated in 1954 subject to three burning treatments: Burning at short rotations every 10 years, burning at long rotations every 20 years and a non-burn control. Observed species richness for archaea was highest in the topsoil of the non-burn control plots and highest for bacteria in the topsoil of the non-burn control and plots under a long rotation regime. Community composition was significantly different between different burn treatments and soil depth. Archaeal community structure was shaped by NH 4 + and pH in the topsoil; by Pb, moisture and Al in the 20–40 cm profile; and by total N, total C, Al, Ca, Fe and pH in the 40–60 cm profile. Bacterial community structure was shaped by NH 4 + , heather cover, pH and Mg in the topsoil; by Fe, K and Pb in the 20–40 cm profile; and by Al, Ca and Fe in the 40–60 cm profile. A co-occurrence network analysis revealed that the topsoil of the non-burn control plots had a more complex network structure with more positive links than those under a rotational burn, but a higher average connectivity with a higher number of negative links was observed in the long rotation 20–40 cm profile. The results provide a new insight into the response processes of soil prokaryotic communities to burning in peatland soils, providing valuable knowledge that can support the evaluation and management of ecosystem services in peatlands. • Prescribed burning altered aboveground vegetation and soil properties altering the structure of prokaryotic communities. • Co-occurrence networks show positive links in non-burn topsoil and antagonistic links in short-rotation burn treatments. • Prokaryotic communities show unique indicator species after prescribed burns, helping estimate the site's burning history. • The study highlights the potential impact on soil biogeochemical processes mediated by prokaryotic communities in peatlands.
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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".