Predation and food–weather interactions drive colony collapse in a managed metapopulation of Arctic Terns (<i>Sterna paradisaea</i>)
Bibliographic record
Abstract
Seabirds are considered bioindicators of bottom-up ecosystem processes, owing to seabirds’ dependence on marine prey. However, ground-nesting seabirds are susceptible to predation, which can limit their use as bioindicators. Machias Seal Island (MSI) supported the largest colony of Arctic Terns (Sterna paradisaea Pontoppidan, 1763) in the Gulf of Maine metapopulation, but prolonged breeding failure led ∼90% of terns to abandon the colony in 2006. We analyzed 12 years of food, weather, and predation data using logistic regression models to determine which had the strongest influence on breeding success. Food–weather interactions were important; under low rainfall, more euphausiids (northern krill, Meganyctiphanes norvegica (M. Sars, 1857)) in the diet increased breeding success, but euphausiids had a negative effect as rainfall became moderate or high. Predation by Herring Gulls (Larus argentatus Pontoppidan, 1763) increased following the cessation of lethal predator control; we identified a predation threshold of 25%, beyond which terns could not breed successfully. The collapse of MSI’s tern colony can be attributed entirely to gull predation. The breeding success of terns at MSI cannot be used as a bottom-up ecosystem bioindicator without accounting for predation. Managers of ground-nesting seabirds should consider predation and food as equally valid potential causes of population or reproductive declines.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".