Correlating tropical climate with survival of an Arctic-breeding, trans-equatorial migrant seabird
Bibliographic record
Abstract
Extreme climate can negatively affect survival through increased physiological demands or by reducing prey availability. This can have significant population-level consequences for organisms with low reproductive rates, such as seabirds. As an Arctic-breeding trans-equatorial migrant, Sabine’s gull (Xema sabini) is exposed to a profound variety of climate regimes during the year. Therefore, its annual survival may be affected by broad-scale teleconnection patterns that influence regional climate variability. We used Program MARK to estimate apparent survival and resighting probabilities from 2007 to 2013 for adult Sabine’s gulls breeding at a High Arctic colony. We then combined capture–mark–recapture data for the High Arctic colony with those previously published from a Low Arctic colony (1998–2002) to examine influences of climate variability on survival. Mean ± standard error apparent survival estimated for the High Arctic colony was 0.90 ± 0.03, similar to that previously reported for the Low Arctic colony. We found a negative relationship between survival and the Tropical/Northern Hemisphere pattern, an atmospheric mode that is associated with the Pacific jet stream. Our study suggests that although Sabine’s gull survival was generally high and relatively constant over time, adult mortality may increase during years of extreme climate events in regions far beyond their Arctic breeding grounds.
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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 | 0.001 |
| 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 | 0.000 | 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".