Disturbing to restore? Effects of mounding on understory communities on seismic lines in treed peatlands
Bibliographic record
Abstract
In peatlands, microtopography strongly affects understory plant communities. Disturbance can result in a loss of microtopographic variation, primarily through the loss of hummocks. To address this, mounding treatments can be used to restore microtopography. We examined the effects of mounding on the understory vegetation on seismic lines in wooded fens. Seismic lines are deforested linear corridors (∼3 to 8 m wide) created for oil and gas exploration. Our objectives were to compare the recovery of understory communities on unmounded and mounded seismic lines and determine how recovery varies with microtopographic position. Recovery was evident in the unmounded seismic lines, with higher shrub and total understory cover at the “tops” of the small, natural hummocks than at lower microtopographic positions — much like the trends in adjacent treed fens. In contrast, mounding treatments that artificially created hummocks on seismic lines significantly changed understory communities. Mounded seismic lines had higher forb cover, much lower bryophyte cover, less variation along the microtopographic gradient, and community composition less similar to that of the reference sites than unmounded seismic lines due to higher abundance of marsh-associated species. Our results suggest that mounding narrow seismic lines can be detrimental to the recovery of the understory communities in treed 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 | 0.001 |
| 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".