Opportunistic predation of birds by breeding Herring Gulls (Larus argentatus)
Bibliographic record
Abstract
Dietary specialization, exploiting a small fraction of available food resources, is commonly reported for gulls and skuas. Predation of birds by these species is usually considered a specialist strategy employed by the minority of the population but non-specialists also predate birds and may actually have a greater impact on the prey species. To date, most studies have focused on predatory bird-specialists, down-playing the possible importance of opportunistic predation by non-specialists. We addressed this by studying diet (regurgitated pellets and prey remains) and behavior of breeding Herring Gulls (Larus argentatus) over three summers at Gull Island, a mixed-species breeding colony in Lake Ontario. One-third of all pellets analyzed contained bird remains, primarily the most numerous breeding bird: Ring-billed Gull (L. delawarensis) chicks (51%) and adults (36%). Although all but one pair of Herring Gulls ate birds, all pairs maintained broad and mostly similar diets, with birds accounting for at most one-third of prey. Behavior also indicated that Herring Gulls at Gull Island were not predatory bird-specialists because predation was too infrequent to meet energetic requirements, was largely unsuccessful and was only ever observed when Ring-billed Gulls strayed into Herring Gull breeding territories. Instead, bird predation appeared mainly opportunistic, increasing with seasonal availability, access to shoreline, proximity to nesting Ring-billed Gulls and breeding territory size. Compared with predatory specialist Herring Gulls in the same region, individuals that predated birds at Gull Island did not display specialist behaviors and killed six times fewer birds (0.1-0.4 per day, on average) but were over 20 times more numerous (98% of the population versus 4%). Thus, our results indicate that opportunistic predation by non-specialists may have important consequences for prey species. Since opportunistic predation cannot be effectively managed using techniques widely advocated for specialist predators, it is essential to investigate cause of predation by large gulls prior to lethal management.
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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 | 0.001 | 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".