CONSERVATION RISKS OF MALE-SELECTIVE HARVEST FOR MAMMALS WITH LOW REPRODUCTIVE POTENTIAL
Bibliographic record
Abstract
We used harvested, stochastic population models for grizzly bears (Ursus arctos) and polar bears (U. maritimus) to illustrate the propensity for male-biased harvesting to reduce mean male age and numbers of sexually mature males for species with relatively low reproductive potential. We compared our results with those obtained from caribou (Rangifer tarandus), an annually reproducing species with relatively high reproductive potential. Differences in the rate at which mean ages and numbers of sexually mature males were reduced by harvesting was a function of differences in species life history of species, but also the extent to which young animals were protected from hunting. For example, the length of each species' reproductive cycle (we modeled 1 year for caribou, 2 years for bears) determined the degree to which sex-biased harvest was also age-biased. Proportionately more young bears were protected from harvest than young caribou due to the multiannual, rather than annual, parental care afforded young bears (we assumed all females with accompanying offspring were invulnerable to harvest). This additional age-bias in the hunt served to direct offtake toward adult males; consequently, as male selectivity in the kill increased, mean ages of bears declined at rates that were higher than for caribou. For species with low reproductive potential, we believe there is a real possibility that persistence probabilities may be overestimated if, after prolonged sex-selective harvest, lack of sexually mature males in the population impairs fecundity. We encourage further development of population models that incorporate potentially negative, hunting-induced impacts on reproduction. In particular, we support the development of models that link the mean age of males or frequency of adult males in a population to the rate at which females are successfully mated.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".