Integrating scientific knowledge and local ecological knowledge (LEK) about common eiders (Somateria mollissima) in southern Labrador
Bibliographic record
Abstract
There has been little research on the history and status of Common Eiders in Labrador. In this thesis I explored how varying degrees of human exploitation over time on the south coast of Labrador specifically in St. Peter's Bay have influenced Common Eider populations. To do this, I collected Local Ecological Knowledge (LEK) from hunters, and compared this information to data generated through scientific surveys and experiments. -- Population estimates of nesting pairs of eiders in St. Peter's Bay during 2001 obtained from direct nest counts and from boat surveys of breeding males were within 10% of each other, 651 nests and 713 males, respectively. This finding suggests that invasive nest counts could at times be substituted with less invasive breeding male counts carried out from boats. From LEK and from comparisons of nesting population data collected in this thesis with previous data, it was determined that the nesting population of S.m. dresseri in St. Peter's Bay and Henley Harbour is increasing. From LEK it was found that there have been changes in nesting locations as eiders move out of high use areas (e.g .Mary's Harbour) and into areas of lower human activity (e.g. Henley Harbour). -- The range of two subspecies of Common Eider, a northern subspecies (Somateria mollissima borealis) and a southern subspecies {S.m. dresseri), overlaps in southern Labrador. S.m. dresseri breeds in southern Labrador, where S.m. borealis are the more common subspecies during winter. Visual assessments of eiders in St Peter's Bay indicated that 99% of the eiders nesting there were S.m. dresseri. Head collections of eiders obtained from hunters showed that 72% of the eiders wintering near St. Peter's Bay were S.m. borealis. Changes to hunting and egging practices combined with stricter enforcement has resulted in hunting pressure shifting from S.m. dresseri to S.m. borealis. Changes in both nesting distribution and nesting populations appear to result from a shift from much spring and summer hunting and disturbance to winter hunting and minimal spring and summer disturbance. Interviews with hunters confirm that they are concerned about ongoing poaching and egging, and feel that more wildlife officers are required on the south Labrador coast, especially during eider nesting seasons. The possibility that St. Peter's Bay should be reinstated as a Migratory Bird Sanctuary, is also apparently gaining greater approval among hunters.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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; 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".