Evaluating the impact of caribou habitat restoration on predator and prey movement
Bibliographic record
Abstract
Fragmentation of the boreal forest by linear features, including seismic lines, has destabilized predator-prey dynamics, resulting in the decline of woodland caribou (Rangifer tarandus caribou) populations. Restoration of human-altered habitat has therefore been identified as a critical management tool for achieving self-sustaining woodland caribou populations. However, only recently has testing of the response of caribou and other wildlife to restoration activities been conducted. Early work has centered around assessing changes in wildlife use of restored seismic lines. We evaluated whether restoration reduces the movement rates of predators and their associated prey, which is expected to decrease predator hunting efficiency and ultimately reduce caribou mortality. We developed a new method for using cameras to measure fine-scale movement by measuring speed as animals traveled between cameras in an array. We used our method to quantify speed of caribou, moose (Alces alces), bears (Ursus americanus), and wolves (Canis lupus) on treated (restored) and untreated seismic lines. Restoration treatments reduced travel speeds along seismic lines of wolves by 1.38 km/h, bears by 0.55 km/h, and caribou by 1.57 km/h, but did not reduce moose travel speeds. Reduced predator and caribou speeds on treated seismic lines are predicted to decrease encounter rates between predators and caribou and thus lower caribou kill rates. However, further work is needed to determine whether reduced movement rates result in reduced encounter rates with prey, and ultimately reduced caribou mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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 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".