Hunting and evolution: theory, evidence, and unknowns
Bibliographic record
Abstract
Intense selective harvest of large mammals who carry the largest weapons may lead to an evolutionary shrinkage of those weapons. Currently, evidence suggesting evolutionary effects of harvest is limited to a few species of Bovidae and only 1 study has obtained data indicating a genetic effect. To have an evolutionary impact, harvest must be intense, persistent over time, similar over a large area without an effective source of unselected immigrants, and remove large individuals before they have a chance to breed. Many current harvest schemes do not fulfill all of these requirements, and they are unlikely to cause evolution. Before changes in weapon size over time are attributed to evolution, potential environmental sources of change, mainly density and climate, must be considered. We suggest that the role of weapon size in determining reproductive success, especially in interaction with male age, will determine whether or not intensive selective harvests may have evolutionary consequences. Age at harvest is a very important variable to consider. Changes in age structure over time may reveal underlying changes in harvest pressure or selectivity. A lack of data hampers our ability to assess the potential evolutionary effects of selective hunting. We provide a list of research hypotheses required to advance our ability to assess the evolutionary sustainability of current management practices.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".