Liberalized harvest regulations have not affected overabundant Snow Geese in Northern Manitoba
Bibliographic record
Abstract
Abstract Large amounts of money are spent each year to control overabundant species that imperil biodiversity and ecosystem functioning across the globe. Lesser Snow Geese (Anser caerulescens caerulescens) are emblematic of this issue, as their overabundance has affected a whole suite of plant, insect, and bird communities via a trophic cascade that managers have attempted to stop before it spreads further across the North American (sub)Arctic. To achieve this goal, liberalized harvest measures designed to decrease Lesser Snow Goose survival and abundance were implemented almost 2 decades ago. Our previous quantitative assessment of management effectiveness indicated that the growing Lesser Snow Goose population quickly overwhelmed a satiated hunter population despite liberalized harvest regulations, eventually reducing the fraction of Lesser Snow Geese being harvested each year. Consistent with the philosophy of adaptive resource management, we apply improved methods to additional years of monitoring data to evaluate the ongoing impact of harvest conservation efforts on Lesser Snow Goose harvest rates. Our previous results suggested little effect of liberalized harvest regulations on harvest rates, but our new findings suggest even less of an impact. Harvest rates have recently stabilized at ~3%, the lowest levels observed over the last 48 yr of our study. Barring adverse effects of environmental change on natural mortality or reproductive success, additional measures will need to be taken to reduce Lesser Snow Goose overabundance and their ecosystem damage.
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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) | 0.002 | 0.006 |
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; both teacher heads agree on what is shown here.
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".