Tree regeneration on industrial linear disturbances in treed peatlands is hastened by wildfire and delayed by loss of microtopography
Bibliographic record
Abstract
The world’s forests are highly fragmented by linear disturbances, and many forests have failed to recover decades after abandonment. Lack of recovery is common in unproductive forests, such as treed peatlands, because of conditions that limit tree growth, including simplification of microtopography (loss of microsites). The persistence of these features affects biodiversity, but of particular concern in Canada is the detrimental effects on threatened woodland caribou. Although natural regeneration of trees in peatlands occurs in some places, it is not an effective recovery strategy for restoring the habitat of woodland caribou. This has led to restoration activities with costs exceeding CAD$12 500/km. However, current restoration does not consider wildfires, which can destroy planted trees but also initiate early seral conditions that favor natural regeneration. Here we compared tree regeneration on seismic lines and adjacent forest controls for burnt (75 sites) and unburnt (68 sites) treed peatlands in northeast Alberta, Canada. Tree regeneration (stems with a DBH < 1 cm/ha) varied from 28 500 in burnt lines, 11 440 in unburnt lines, and 18 210 in burnt forest, to 9520 in unburnt forest. Wildfires promoted denser regeneration in sites with a greater proportion of serotinous species and water table depth. Microtopography and terrain wetness explained regeneration on burnt lines, but not unburnt lines. In burnt and unburnt lines, sunlight, microtopography, and depth of water table most affected tree regeneration patterns.
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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 | 0.001 | 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".