Evidence for seasonal compensation of hunting mortalities in a long‐lived migratory bird
Bibliographic record
Abstract
Abstract Understanding whether hunting mortality is additive to or compensated by other mortality sources is at the heart of managing harvested populations. Long‐lived species are expected to exhibit hunting mortality additive to other sources of mortality, making them ideal candidates for population management through sport harvest. Previous studies on these processes have focussed on density‐dependent natural mortality compensating for hunting mortality, but when harvest occurs in distinct periods of the year, heterogeneity in hunting vulnerability between individuals could also lead to compensatory mortality between these periods. We explore this new idea using the case of the greater snow goose (Anser caerulescens atlantica), a harvested species whose population became overabundant in the late 20th century. To control this population, wildlife agencies liberalised hunting regulations with unprecedented actions such as special hunting seasons implemented in spring 1999 in Canada and in winter 2009 in the USA. To determine the relative impact of each measure on survival, we estimated survival of adult geese on a seasonal basis using 30 years of capture‐mark‐reencounter data in a joint live‐and‐dead‐encounter multievent model. We also used this quasi‐experimental set‐up to evaluate possible compensation in hunting mortality between seasons. We found that both special hunting seasons decreased goose survival in the seasons and periods in which they were implemented. However, survival increased during the spring hunting season after the establishment of the special winter hunting season in the USA in 2009. There was a negative relationship between annual spring and winter mortalities, suggesting that the increase in hunting mortality in winter was compensated by a reduction in spring mortality after 2009. Synthesis and applications: To our knowledge, we report the first documented instance of hunting mortality in one season being compensated by a reduction in hunting mortality in a subsequent season. We suggest that heterogeneity in hunting vulnerability among individuals, possibly linked to the presence of juveniles, may explain this phenomenon. A better knowledge of seasonal patterns and relationships between mortality components is needed to improve our understanding of population dynamics and management of harvested populations.
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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 | 0.000 | 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 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".