Weekend warriors: contrasting temporal patterns in the harvest of three species of boreal ungulates
Bibliographic record
Abstract
Understanding the drivers that shape hunter behaviour and success can help guide management decisions regarding hunting. Although there has been work on the socioeconomic and environmental drivers of hunter effort, less quantitative analysis of the temporal patterns of wildlife harvest has been available. Yet, knowing when hunters are most active may be useful for distributing the spatiotemporal allotment of hunting opportunities where real or perceived issues of hunt quality (e.g. hunter congestion) or negative impacts to local people or target and non‐target wildlife species are of concern. As a case study, we used generalized linear models to examine the effect of season, day of week (i.e. weekday versus weekends), and holidays on 26–28 years of harvest data for bison Bison bison , thinhorn sheep Ovis dalli , and moose Alces americanus in Yukon, Canada. These species are important in regional socioecological systems and highly prized by local hunters. For all three species, harvest was significantly greater on weekends than weekdays. Most of the harvest for thinhorn sheep occurred early in the season, consistent with an ‘opening day' phenomenon, whereas that for moose and bison started slowly and increased throughout the season. For all three species harvest was not significantly influenced by holidays. Bison harvest, however, was influenced in relation to public school holidays, with harvest decreasing over the winter (Christmas) break and increasing over spring break in March. Differences between these three species are likely due to species‐specific hunting strategies, the behaviour of each species, hunter competition, and seasonal climate. Identifying patterns in hunter effort and harvest can inform wildlife management decisions on permit allocation. However, our data indicate that species‐specific patterns vary substantially, even in the same region, and need to be understood for proposed changes to the timing of hunting opportunities to be effective.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".