Determinants of vegetation regeneration on forest roads following restoration treatments: implications for boreal caribou conservation
Bibliographic record
Abstract
Linear features are increasing worldwide and, in many jurisdictions, their decommissioning has been identified as a way to restore wildlife habitat. Few studies have assessed restoration practices on forest roads, yet they are the main linear disturbance throughout most circumboreal forests. In boreal forests of eastern Canada, such knowledge would be especially valuable for the conservation of boreal populations of woodland caribou ( Rangifer tarandus caribou ). We assessed the short‐term establishment of vegetation following four treatments applied across 40 km of forest roads, along a restoration gradient involving additive treatments (i.e. each successive treatment included the treatments prior): closing the road to traffic, decompacting the soil, planting black spruce trees, and adding enriched soil. We linked lateral cover (a proxy of movement obstruction for wildlife) and the occurrence and abundance of plant species to road treatments and environmental covariates. Vegetation establishment 3–4 years after decommissioning was mostly influenced by treatments but also by road width and stand composition in the vicinity of roads. The combination of closing, decompacting, and planting was the most effective treatment to establish regeneration that would lead to suitable caribou habitat as it reduced food availability for moose and bears (i.e. lower presence of herbaceous species, fruit‐bearing shrubs, and deciduous trees). It also reduced the presence of plants competing with spruce, such as ericaceous shrubs. Our results suggest that the decommissioning of forest roads could benefit caribou, provided it is performed at a sufficiently broad scale, and accompanied by other habitat restoration and protection practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".