Bald Eagle and Priority Sea Ducks Interact Over Space and Time in the Salish Sea: A Transboundary Perspective
Bibliographic record
Abstract
Sea ducks are considered vital indicators of ecosystem health yet are experiencing long-term abundance declines in the transboundary waters of the Salish Sea in Canada and the United States. Identifying the mechanisms driving changes in abundance or causing redistributions within this region necessitates a transboundary effort. Our analysis compiled data from both the British Columbia Coastal Waterbird Survey (BCCWS) and the Puget Sound Seabird Survey (PSSS) between 2009–2022 to assess broad-scale predator-prey interactions between Bald Eagles and priority sea ducks, using a multispecies occupancy model. Our results suggest that Bald Eagles more often than chance overlap with priority sea ducks in the Salish Sea. However, there was no evidence of second-order effects of latitude, longitude, or year on sea duck occurrence in the presence or absence of Bald Eagles, suggesting that these factors are acting independently. Our results not only help resource managers better understand the broad-scale interplay between predators and prey and the co-occurrence probability of priority sea ducks, but this analytical framework and data resource compilation also provide researchers with a foundation from which multi-species interactions and the mechanisms responsible can be disentangled across these transboundary waters.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".