Demographic consequences of harvesting: a case study from a small and isolated moose population
Bibliographic record
Abstract
Harvesting can have a substantial impact on population dynamics and individual performance in wild populations. While the direct consequences of harvest on individual survival and population growth rate are often apparent, harvesting can also have indirect and more subtle demographic consequences. Disentangling these consequences, however, requires in-depth knowledge of individual life histories of both females and males in the population. Here, we summarise demographic research on a population where such data exist: the Vega moose population in northern Norway. In this population, vital rates vary considerably among both females and males, and harvesting increases this variation by generating positive covariation between reproductive performance and survival. The skewed age and sex structure, which is typical of many harvested populations, also has demographic consequences: it reduces the ratio of effective to total population size and influences variation in vital rates in males and females. The moose harvest at Vega is structured by age- and sex-specific quotas, but it is not intentionally selective regarding size or other phenotypic characteristics. Still, harvest selection for earlier birth rates and larger calves was apparent, likely due to habitat-performance relationships and habitat-specific harvest mortality. Together, the bulk of research on this population shows that harvesting impacts population demography through many different pathways, with some being more subtle than others. These complex pathways influence the demographic variance and affect stochastic processes such as population growth, genetic drift, and rates of evolutionary change, and they must therefore be acknowledged in management plans to achieve sustainable harvesting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".