Evaluating the Sustainability of Harvest Among Northern Common Eiders <i>Somateria Mollissima Borealis</i> in Greenland and Canada
Bibliographic record
Abstract
Sustainable harvest, the extraction of game without affecting population viability, is a desirable approach to the use of wildlife. However, overharvest has been responsible for the decline of many wildlife populations globally, so there is an urgent need to balance human requirements while avoiding the severe depletion of wild populations. Northern common eiders Somateria mollissima borealis are heavily hunted in Canada and Greenland, but the effect of this intensive harvest has not been examined. We developed a population model to investigate the sustainability of the reported harvest, which consisted of two wintering areas in Greenland and Atlantic Canada and three breeding populations. The model indicated that harvest in Atlantic Canada was sustainable, but a number of conditions could lead to slow declines. In contrast, the annual winter harvest of 55,000–70,000 eiders reported during 1993–2000 in Greenland was not sustainable, and this conclusion held under a wide range of alternate conditions. The model indicated that harvest during late winter may have a greater effect on populations than harvest in early winter. We further refined the model to assume that at some low population level the success of hunters would decline and that harvest became a function of population size (a rate). This scenario had the expected and undesirable result of stabilizing populations at very low levels. Overall, our model suggests that the high harvest reported in Greenland during 1993–2000 endangers the sustainable use of the northern common eider population and that management actions are required. Common eider harvest levels in Greenland should be reduced by at least 40% of the 1993–2000 levels to stop projected declines, and allow for recovery of the decimated Greenland breeding population. Encouragingly, new hunting regulations were introduced in Greenland in 2002–2004, and harvest levels appear to be decreasing. If these harvest reductions continue, our population model could be used to re‐evaluate the status of populations in the two countries.
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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".