Genetic implications of reduced survival of male red deer Cervus elaphus under harvest
Bibliographic record
Abstract
We use simple, multivariate evolutionary models to evaluate the short-term potential for size-selective harvest to reduce genetic variability and alter life history in cervids. These genetic effects limit sustainable levels of harvest of the animals because they determine how changes in sex ratio, generation length and traits contributing to fitness influence population growth rate and local adaptation. Our analysis of harvest-mediated adaptive evolution employs a genetic approach that parameterizes models with empirical data obtained from European red deer Cervus elaphus. The analysis indicates that harvest, if sufficiently high to reduce the breeding ratio of males to females to below about 15:100, can reduce effective population size to a level that threatens adaptive potential. The reduction in effective size is realized through decreases in both sex ratio of breeders and the age of breeding males. Predicted selective effects of harvest on body size indicates a weak potential to alter most life-history traits over 10 generations under two harvest scenarios; the patterns suggest that current modes of harvest are unlikely to produce substantial life-history changes in red deer over 10 or fewer generations unless the genetic influences on red deer traits are considerably higher than those predicted here. Nevertheless, male reproductive success is expected to decline detectably if male harvest rate is sufficiently high (> 30%). Collectively, our results imply that harvest methods should permit higher post-hunt male:female ratios (18:100 or higher) and ensure that a sufficient number of larger, older males survive the breeding season. The capacity of selective harvest to alter demography and life history depends heavily on the genetic covariance structure underlying variation in these traits, information that is unknown for many red deer populations. Prudent harvest management should therefore implement and monitor approaches to hunting that aim to conserve life-history variation; meanwhile, use of less selective methods can reduce the risk to long-term adaptive potential and may permit higher sustainable harvest rates.
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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) | 0.001 | 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 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".