Habitat selection and population trends of the Torngat Mountains caribou herd
Bibliographic record
Abstract
ABSTRACT Understanding why species at risk select certain habitat and what components of their life history influence changes in numbers can help mitigate population declines. The Torngat Mountains caribou (Rangifer tarandus) herd in northern Quebec‐Labrador, Canada, is declining, and few studies have examined the potential causes of this decline. We fitted 9 Argos and 26 global positioning system (GPS)‐collars on 35 adult caribou (25 female, 10 male) between 2011 and 2016 to assess seasonal habitat selection at 2 spatial scales, current and future population trends, and interactions with the neighboring Rivière‐George migratory caribou herd. The caribou of the Torngat Mountains herd selected areas with abundant food resources in winter and where prevalence of insects was lower in summer. They did not avoid areas where predation risk was high during calving. Spatial overlap with the Rivière‐George herd range decreased from 1990 to 2015 and was correlated with the size of the Rivière‐George population, which declined drastically during this period. The decline of the Torngat Mountains population was principally attributed to the low survival of adult females (0.72 annual survival rate) owing to subsistence harvest (9/24) and predation (7/24). Demographic models revealed that the growth rate of the population (λ) could vary from 0.83 (current) to 0.94 following a decrease in harvest pressure. Using demographic scenarios, we showed that the Torngat Mountains herd could continue to decrease if no management actions were taken to increase adult female survival. © 2018 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 | 0.000 | 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 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".