Northern Gannets (<i>Morus bassanus</i>) breeding at their southern limit struggle with prey shortages as a result of warming waters
Bibliographic record
Abstract
Abstract Northern Gannet (Morus bassanus) colonies near the species’ southernmost limits are experiencing plateaued or declining population growth and prolonged poor productivity. These trends have been linked to reductions in the availability of the species’ key prey, the Atlantic mackerel (Scomber scombrus). Declines in mackerel availability have been associated with warming ocean temperatures and over-fishing. Here, we assessed the influence of prey availability, abundance, and sea surface temperature (SST) during the breeding season on Northern Gannet reproductive success over a multi-decadal time span at their southernmost colony at Cape St. Mary's, NL, Canada. We demonstrate that warming SST affects reproductive success differently in early vs. late chick-rearing, but that overall, declining mackerel availability (landings and biomass) due to warming SST and over-exploitation has resulted in poor productivity of Northern Gannets at their southernmost limit. Our study is consistent with previous findings in other colonies in Atlantic Canada and France, and contrasts with findings in more northern colonies where mackerel population increases and range expansion are coinciding with gannet population growth. This implies that warming SST is having opposing influences on Northern Gannets and mackerel at the different extremes of the gannets’ breeding range.
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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".