Effects of exceptional conservation measures on survival and seasonal hunting mortality in greater snow geese
Bibliographic record
Abstract
Summary Large‐scale conservation measures aimed at reducing the abundance of a wild population offer a unique opportunity to study the impacts of human exploitation on demographic parameters. We examined the effects of a spring conservation harvest and liberalized autumn/winter regulations introduced recently to control the rapid population growth of greater snow geese Anser caerulescens atlantica . We predicted that these changes in regulations affected survival and recovery probabilities, as well as kill rates. This was evaluated by analysing data on geese shot by hunters before and after implementation of these measures. Annual survival and recovery probabilities were estimated using likelihood‐based ring recovery models, and we developed a new, flexible seasonal model to determine the specific effects of regulation changes made during different hunting seasons. We used harvest survey data to estimate ring‐reporting rate, and thus to convert recovery rates into kill rates. Adult recovery and kill rates increased considerably with the implementation of the new measures in 1998–99. The addition of the spring harvest in Québec and the liberalized winter regulations in the USA had the largest impacts, whereas autumn regulation changes in Québec apparently had little effect. Juvenile recovery and kill rates also showed a moderate increase, but were highly variable. We observed a decline in mean annual adult survival rate, from 83·0% to 72·5%, although the implementation of conservation measures did not fully explain temporal variation in adult survival; juvenile survival showed no change. Adult survival was negatively related to harvest rate in adults but not in juveniles. Synthesis and applications . We demonstrate that the spring conservation harvest led to a decrease in adult survival and an increase in hunting mortality, and probably contributed to the decline in abundance observed since its implementation. However, regulations during the traditional autumn and winter hunting seasons could also be manipulated for future population control once the spring conservation harvest is discontinued. Our approach for evaluating hunting mortality on a seasonal basis should be useful in the management of other migratory populations that move through several jurisdictions.
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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) | 0.000 | 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".