At-Colony Behaviour of Great Black-backed Gulls <i>Larus marinus</i> Following Breeding Failure
Bibliographic record
Abstract
Territoriality for breeding sites comes at an energetic cost—individuals actively defend the site from competitors and potential predators, thus precluding themselves from self-maintenance (e.g., foraging, preening) or offspring care. Breeding individuals are also constrained to central place foraging within a limited range of the territory. For these reasons, many seabirds do not spend extensive periods or make regular visits to the colony following breeding failure. To investigate behaviour following breeding failure, we studied colony and nest attendance and daily number of visits for six Great Black-backed Gulls Larus marinus that had failed to breed following global positioning system (GPS) tag attachment on the northeast coast of Newfoundland, Canada. Three failed breeders reduced colony and nest attendance by an average 6.32 h/d (95% confidence interval: 1.14) after the estimated date of failure. Conversely, three other failed breeders showed no decrease in attendance, and one individual increased colony attendance by 5.4 h/d. We predicted that failed breeders would be more likely to forage while attending the colony relative to active breeders (i.e., incubating or chick-rearing) due to their lack of offspring and territory to defend. During 18 two-hour nest watches of active and failed breeders, active breeders (n = 4) behaved more aggressively (e.g., predation, swooping) toward gulls at nearby sites in the colony, while failed breeders (n = 6) behaved mostly passively (e.g., preening, sitting, P = 0.029). Our findings indicate that failed breeders continue to attend the colony after breeding failure, indicating potential benefits (e.g., maintaining breeding territory and pair bonding). Our findings also reveal that using tracking data to indicate breeding failure may be misleading and, thus, we suggest researchers also use visual confirmation of breeding failure, when possible, in future studies. Finally, we warn researchers of the negative effects of tag attachment on gull reproductive success.
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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 | 0.000 | 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".