Citizen science and collaborative programs help reveal coastal waterbird trends and understand the mechanisms underlying those trends in the Salish Sea and Pacific coast
Bibliographic record
Abstract
Waterbirds are often used as indicators of ecosystem function across broad spatial and temporal scales. Resolving which species are declining and the ecological characteristics they have in common can offer insights into ecosystem changes and their underlying mechanisms. Using 20 years of citizen science data collected by the British Columbia Coastal Waterbird Survey, we examined trends in abundance of coastal waterbird species, both in the Salish Sea and along the outer Pacific Ocean coast, representing the core wintering coastal bird community of British Columbia, Canada. Results suggest that most populations are stable in both the Salish Sea and Pacific coast regions. However, a quarter of the species displayed significant decline trends in the Salish Sea, compared with only 8% along the Pacific coast. Guild-specific trends indicated that waterbirds occupying the Salish Sea are faring significantly worse than those residing along the outer coastal regions, almost irrespective of dietary specialization or migration distance. Our results provide evidence that differential environmental pressures between the inner and outer coastal regions may be causing overall loss of wintering waterbirds within, or abundance shifts away from, the Salish Sea. We outline potential mechanisms responsible for these observed patterns, including environmental (e.g., climate) and human-induced (e.g., nutrient and chemical pollution) pressures. We also present ongoing collaborative efforts in bird monitoring (e.g., with Indigenous communities) and research priorities to help understand these mechanisms.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".