Landscape attributes explain migratory caribou vulnerability to sport hunting
Bibliographic record
Abstract
ABSTRACT Human disturbances are increasing in Arctic regions and have been suggested as one of the main factors explaining caribou (Rangifer tarandus) decline. The cumulative effects of disturbances may negatively affect caribou habitat use, survival, and population dynamics. Thus, there is a need to evaluate the impact of various human disturbances, especially those that cause direct mortality (e.g., sport hunting). We evaluated the relative importance of caribou and hunter habitat selection and landscape characteristics on caribou vulnerability to sport hunting in northern Québec, Canada. We used resource selection functions to describe habitat selection of 223 caribou and 87 hunters. We then characterized >169,000 caribou harvest sites recorded over 17 years according to the relative probability of co‐occurrence of caribou and hunters, the relative probability of occurrence of hunters only, or the characteristics of the landscape (e.g., distance to human infrastructures, elevation, land cover type). Landscape characteristics better explained caribou vulnerability to sport hunting than habitat selection of caribou and hunters, or their co‐occurrence. Caribou were more vulnerable in proximity to hunting infrastructures (e.g., roads, outfitter camps) than elsewhere, but caribou strongly avoided roads. Caribou were also more vulnerable on frozen lakes than in other land cover types. Lakes were, however, avoided by caribou and not selected by hunters. Harvest was more likely in smoother terrain, even if caribou and hunters did not select for this characteristic. We demonstrated caribou were more vulnerable in areas with good accessibility (near roads) or where caribou were easily detectable (lakes, smoother terrain), which also represents areas that were either avoided or not selected by caribou or hunters. This discrepancy between harvest distribution and behaviors of caribou and hunters suggests that harvest may be an opportunistic event where visibility and accessibility increased chances of success for hunters. Managers could use this information to manipulate hunting success according to population estimates and harvest quota by establishing minimal distance to risky areas within which hunting would be prohibited. © 2016 The Wildlife Society.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".