Seasonal and Age-Dependent Dietary Partitioning between the Great Black-backed and Herring Gulls
Bibliographic record
Abstract
Studies of seabird diets may reveal subtle ways in which sympatric species partition resources to facilitate co-existence. We studied the variability and partitioning of diets between the Herring (Larus argentatus) and Great Black-backed Gulls (L. marinus), both generalist predators, during incubation and early chick rearing on Kent Island, Bay of Fundy, Canada. We assessed diets from pellets collected around nests, regurgitates from captured birds, and stable-isotope analysis of prey items and tissues (blood and feathers) obtained from chicks and adults. Pellet analyses indicated that both species relied primarily on fish (28 to 45% of identified prey items) and crabs (15 to 43%). Stable-isotope analyses showed that the Great Black-backed Gull fed at a higher trophic level than the Herring Gull, both species fed at higher trophic levels during breeding than during nonbreeding, and both species has similar preferences for feeding inshore vs. offshore and in terrestrial vs. marine habitats. Contrary to previous research, we found that chicks were fed from a lower trophic level than where adults feed. Models of isotopic mixing estimating the proportion of assimilated diets were generally consistent with the pellet analysis for adults but revealed that both species fed their chicks more krill (>60%; Meganyctiphanes norvegica) and mackerel (>20%; Scomber scombrus) than adults consumed; adults may selectively provision their young with easily digestible prey and prey of high energy content. Our results reveal evidence of dietary partitioning between species and age classes, and highlight the strengths and biases associated with techniques for sampling gulls' diet.
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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".