Evaluating functional recovery of habitat for threatened woodland caribou
Bibliographic record
Abstract
Abstract Habitat restoration is a core element for the recovery of many declining species. In western Canada, habitat restoration for the recovery of woodland caribou is focused on linear features ( LF s) created by oil and gas exploration. At present, the only established criterion for LF restoration is when vegetation structure on LF s is similar to surrounding vegetation. Human‐mediated habitat alteration impacts caribou population dynamics by increasing caribou predation rates in two ways: increasing alternate prey populations leading to higher predator numbers and increasing predator hunting efficiency. Linear features increase the movement rates—and may thus increase hunting efficiency—of wolves, a primary predator of caribou and a main hypothesized mechanism for population declines. One approach to determine LF recovery is to identify potential thresholds in the characteristics of regenerating LF s where efficiencies in wolf movement rates are no longer evident. We examined how vegetation affects wolf selection of, and movement on, LF s in northeastern Alberta using five‐minute Global Positioning System locations from 20 wolves. Wolves selected LF s with shorter vegetation and traveled faster on LF s with shorter, sparser vegetation and increased vegetation variability. Travel speeds were reduced by 1.5–1.7 km/h when vegetation exceeded heights of 0.50 m, but at least 30% of a LF required vegetation exceeding 4.1 m to slow movement rates to those traveled while in forest. Policy implications: Most of the movement efficiency afforded to wolves by LF s is mediated when vegetation exceeds 0.50 m, and therefore, active restoration could be focused in areas that have not met this value. Rather than treating this value as a clear threshold equating to functional recovery, multiple metrics across trophic levels must also be evaluated to assess population recovery for caribou.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".