Investigation of causes and effects of predation by herring (Larus argentatus) and great black-backed gulls (L. marinus) on black-legged kittiwakes (Rissa tridactyla) on Gull Island, Newfoundland
Bibliographic record
Abstract
In previous studies it has been observed that herring gulls (Larus argentatus) and great black-backed gulls (L. marinus) depredated breeding black-legged kittiwakes (Rissa tridactyla) that nest along the southeastern coast of Newfoundland, Canada. However, the causes and effects of large gull predation on kittiwakes was never extensively investigated nor quantified. In this study, herring gull and great black-backed gull predation on black-legged kittiwakes at Gull Island, southeastern Newfoundland was quantified at four study plots in relation to the timing of the annual spawning arrival of capelin (Mallotus villosus), the size of kittiwake sub-colonies (number of nests), kittiwake nest-site characteristics, and wind conditions. I also investigated the impact of large gull predation on kittiwake breeding performance during 1998 and 1999. -- I compared large gulls' predation attempt frequency among three periods: before mean gull hatching, between mean gull hatching and the arrival of capelin, and following capelin arrival. In both years, the frequency of gull predation attempts on kittiwakes differed significantly among the three periods, with highest levels of predation occurring after gull chicks hatched but before capelin arrival. Overall gull predation attempt levels were lower in 1999, when capelin spawned earlier, than in 1998. -- Nesting density and the location on the cliff were kittiwake nest-site characteristics that affected significantly the risk of predation. Breeding success (number of successful nests) was influenced by nesting density and ledge width. Additionally, I found that both risk of predation and breeding success varied significantly among plots. Individual kittiwake nests at the smallest plot experienced a higher probability of attack by large gulls than nests at larger plots. Hence, the percentage of failed nests was highest at the smallest plot and decreased as the size of the plots increased. Regardless of wind conditions both gull species attacked nest sites located on upper parts to a higher likelihood than sites located on middle and lower parts of the cliffs. However, during calm conditions, roofs over nest sites reduced the risk of predation by herring gulls, whereas sites located on narrow ledges were less likely to be attacked by great black-backed gulls. During windy conditions, nesting density affected which sites were attacked by great black-backed gulls. -- The level of gull predation behaviour was significantly correlated with the percentage of kittiwake eggs and chicks that disappeared within a week. I estimated that 43% of kittiwake eggs and chicks at Gull Island were taken by gulls in 1998 and 30% in 1999. My results demonstrated that kittiwakes have been indirectly (through increased predation by gulls) affected by the delayed arrival and lower abundance of capelin, and that kittiwake nest-site characteristics, and the size of a sub-colony were significantly correlated with the risk of depredation.
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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) | 0.000 | 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".