Wildfire as a key determinant of peatland microtopography
Bibliographic record
Abstract
Microtopography is a common attribute of wetlands, particularly boreal bog and fen peatlands. This self-organized patterning is primarily an autogenic process; however, the role of allogenic forces such as disturbance in the maintenance of microtopography is poorly understood. In this study, we quantify the effect of fire on the distribution of the microtopographic gradient in boreal bogs using a before–after wildfire natural experiment. We also quantify the change in spatial abundance of microforms in boreal treed peatlands over a 100-year successional chronosequence. Wildfire nearly doubled the range of the microtopographic gradient, increasing the relative abundance of low-elevation microforms (hollows), although the distribution of elevations was influenced by peatland ontogeny at the time of wildfire. Through succession, raised microforms (hummocks) became more abundant, presumably due to autogenic surface drying facilitating hummock species expansion into adjacent hollows. Although autogenic processes may be responsible for the development of self-organized spatial patterning in wetlands, disturbances such as wildfire are necessary for maintaining boreal peatland microtopography over extended time scales. Because of the tight linkage between microtopography, species diversity, and ecosystem function, these feedbacks between wildfire and microtopography are critical for understanding peatland dynamics and the potential impact of a changing environment.
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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 | 0.001 | 0.001 |
| 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".