Sex and age‐specific differences in the performance of harvest indices as proxies of population abundance under selective harvesting
Bibliographic record
Abstract
Harvest indices are commonly used as proxies to direct population monitoring but sources of variability, including harvest effort and factors influencing detectability of animals to hunters, are rarely considered. Harvest indices may further be influenced by selective harvesting with regulatory differences in harvest effort across sex and age‐classes. To evaluate how sex and age‐specific harvests vary as proxies of abundance under selective harvesting, we assessed harvest–abundance relationships (H–A) for moose Alces alces bulls, cows and calves across 58 wildlife management units (WMUs) in Ontario, Canada. Selective harvesting in our study area resulted in more regulated harvest of bulls and cows than calves. We therefore predicted more proportional H–A for calves than bulls and cows, with variability in H–A influenced by harvest effort, in addition to weather and landscape features that may influence moose detectability to hunters. In contrast to our expectation, we found that H–A was more proportional for adult moose than calves. Additionally, we found harvest was proportionally highest for bulls, despite greater harvest effort for calves. A positive effect of harvest effort on harvest as moose abundance increased helped to explain proportional H–A for adult moose. However, the effect of harvest effort on harvest was curvilinear at high effort levels, indicating that harvest will underestimate abundance when effort by hunters is high. Additionally, we found evidence of lower harvest in relation to abundance in WMUs with higher levels of recent disturbance from wildfire burns and clear‐cuts. We demonstrate that the relationship between harvest and abundance can vary across selectively harvested sex and age‐classes, while variability in H–A can be attributed to spatial variability in harvest effort and the landscape. We caution that sources of variability in H–A, both across and among sex and age‐classes, should not be ignored when using harvest indices, especially for selectively harvested species.
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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) | 0.000 | 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".