Effects of selective harvest of non‐lactating females on chamois population dynamics
Bibliographic record
Abstract
Summary In large mammals, regulations or hunter preference often lead to selective harvest of non‐lactating females, with unknown impacts upon population growth. Because female survival has very high elasticity effects on population growth, an assessment of the magnitude and selectivity of female harvest is crucial to understand population dynamics of sport‐hunted ungulates. Hunting accounts for most mortality of adult chamois Rupicapra rupicapra and regulations typically discourage the harvest of lactating females as orphaned kids have reduced survival rates. We used an individual‐based model, produced from empirical data, to explore the effects of selective removal of non‐lactating females on chamois population dynamics. Harvest intensity had much stronger effects on female population dynamics than selectivity for reproductive status. Selective harvest of non‐lactating females had very weak effects on population size and then only at a high harvest rate and under strong selectivity. Assuming no difference in winter survival between orphaned and non‐orphaned kids, harvest of non‐lactating females decreased population size at equilibrium compared to a random female harvest, whereas the opposite was true when assuming 50% lower survival of orphaned kids. Selective harvest of non‐lactating females may avoid the negative effects of orphaning on juvenile survival, but increases mortality of pre‐reproductive females that normally enjoy high survival and have high reproductive value. Synthesis and applications . The impact of selective harvest of non‐lactating females upon population dynamics is likely to be more affected by the age structure of lactating and non‐lactating females rather than by the survival of orphan and non‐orphan juveniles. High harvest of pre‐reproductive females has undesirable effects on population dynamics, which must be considered alongside any cultural preferences of hunters when developing hunting regulations or policies.
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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 | 0.000 | 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) | 0.000 | 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".