Ecological role of hunting in population dynamics and its implications for co-management of the Porcupine caribou herd
Bibliographic record
Abstract
At a present population size of 160 000 animals, the Porcupine caribou herd has been subjected to an annual harvest rate of 2% for the past couple of decades. We modeled potential sensitivity of herd population dynamics to hunting and used that relation as a basis for a herd monitoring system. Maximum number of adult cows that could be harvested without causing a subsequent decline in herd size was calculated as a function of total number of adult cows in the herd and recruitment of calves to yearling age-class. Maximum cow harvest, therefore, is a threshold above which hunting has destabilizing effects on herd dynamics. Actual harvest in relation to theoretical maximum harvest provides a basis for prediction of herd sensitivity to hunting. Maximum harvest is a linear function of recruitment. Herd dynamics are especially sensitive to low recruitment, however, when combined with low herd size. The two relations involving recruitment and herd size provide the basis for predicting herd dynamics and sensitivity to hunting. Herd size is best estimated by aerial census, while an index of recruitment can be predicted by monitoring autumn body condition of adult females. Body condition can be estimated on the basis of a few simple metrics measured by hunters in the field. The hunters' data on body composition, combined with aerial census data on herd size, provide a useful tool for managers and co-management boards to devise policies and regulations to manage the herd. The population model and monitoring system can operate on the Internet and be accessible to all users in villages within the range of the Porcupine caribou herd.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".