Molecular ecological approaches to studying the evolutionary impact of selective harvesting in wildlife
Bibliographic record
Abstract
Harvesting of wildlife populations by humans is usually targeted by sex, age or phenotypic criteria, and is therefore selective. Selective harvesting has the potential to elicit a genetic response from the target populations in several ways. First, selective harvesting may affect population demographic structure (age structure, sex ratio), which in turn may have consequences for effective population size and hence genetic diversity. Second, wildlife-harvesting regimes that use selective criteria based on phenotypic characteristics (e.g. minimum body size, horn length or antler size) have the potential to impose artificial selection on harvested populations. If there is heritable genetic variation for the target characteristic and harvesting occurs before the age of maturity, then an evolutionary response over time may ensue. Molecular ecological techniques offer ways to predict and detect genetic change in harvested populations, and therefore have great utility for effective wildlife management. Molecular markers can be used to assess the genetic structure of wildlife populations, and thereby assist in the prediction of genetic impacts by delineating evolutionarily meaningful management units. Genetic markers can be used for monitoring genetic diversity and changes in effective population size and breeding systems. Tracking evolutionary change at the phenotypic level in the wild through quantitative genetic analysis can be made possible by genetically determined pedigrees. Finally, advances in genome sequencing and bioinformatics offer the opportunity to study the molecular basis of phenotypic variation through trait mapping and candidate gene approaches. With this understanding, it could be possible to monitor the selective impacts of harvesting at a molecular level in the future. Effective wildlife management practice needs to consider more than the direct impact of harvesting on population dynamics. Programs that utilize molecular genetic tools will be better positioned to assess the long-term evolutionary impact of artificial selection on the evolutionary trajectory and viability of harvested populations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".