Assessing ecological correlates of marine bird declines to inform marine conservation
Bibliographic record
Abstract
Identifying drivers of ecosystem change in large marine ecosystems is central for their effective management and conservation. This is a sizable challenge, particularly in ecosystems transcending international borders, where monitoring and conservation of long-range migratory species and their habitats are logistically and financially problematic. Here, using tools borrowed from epidemiology, we elucidated common drivers underlying species declines within a marine ecosystem, much in the way epidemiological analyses evaluate risk factors for negative health outcomes to better inform decisions. Thus, we identified ecological traits and dietary specializations associated with species declines in a community of marine predators that could be reflective of ecosystem change. To do so, we integrated count data from winter surveys collected in long-term marine bird monitoring programs conducted throughout the Salish Sea--a transboundary large marine ecosystem in North America's Pacific Northwest. We found that decadal declines in winter counts were most prevalent among pursuit divers such as alcids (Alcidae) and grebes (Podicipedidae) that have specialized diets based on forage fish, and that wide-ranging species without local breeding colonies were more prone to these declines. Although a combination of factors is most likely driving declines of diving forage fish specialists, we propose that changes in the availability of low-trophic prey may be forcing wintering range shifts of diving birds in the Salish Sea. Such a synthesis of long-term trends in a marine predator community not only provides unique insights into the types of species that are at risk of extirpation and why, but may also inform proactive conservation measures to counteract threats--information that is paramount for species-specific and ecosystem-wide conservation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".