Resource partitioning in Atlantic puffins and razorbills facing declining food: an analysis of feeding areas and dive behaviour in relation to diet
Bibliographic record
Abstract
Multi-species communities of closely-related seabirds present opportunities to determine how such species coexist. Machias Seal Island (MSI), New Brunswick, Canada, is a migratory bird sanctuary where several seabird species breed, including the largest number of Atlantic puffins Fratercula arctica and razorbills Alca torda in the Gulf of Maine/Bay of Fundy ecosystem. The species differ in nest sites, body size and wing-loading, as well as life history (specifically post-natal care); they take different proportions of a similar range of prey species, and recent studies show limited overlap in foraging areas at other sites. We wished to expand our understanding of resource partitioning at MSI by measuring differences in foraging areas and behaviour, in the context of recent declines in availability of key prey species and concomitant decreasing breeding success of both species, which suggest that carrying capacity may have been reached. Using GPS loggers in 2 breeding seasons, and long-term chick-diet data collected over 20 yr, we investigated differences in horizontal and vertical foraging distributions and prey that allow these 2 species to breed sympatrically. Logger data collected from puffins (n = 7) and razorbills (n = 8) revealed that razorbills fed in shallower water than puffins and took shorter foraging trips. Prey brought to chicks at control nests showed higher proportions of high-energy fish in razorbill diet compared with puffins. Foraging behaviour is likely affected by declining availability of high-quality food and increasing temperature since 2010.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".