Annual survival of North American Razorbills (Alca torda) varies with ocean climate indices
Bibliographic record
Abstract
According to life-history theory, survival in long-lived animals is not expected to vary greatly with environmental conditions. However, recent studies of a number of seabirds have shown that ocean climate indices such as the Pacific Decadal Oscillation index and sea surface temperature are correlated with adult survival. Here we evaluated whether annual adult survival of Razorbills (Alca torda L., 1758) at two breeding colonies covaried with oceanographic conditions in the North Atlantic during 1995–2006. We also examined pre-breeder survival (from fledging to age 2). The relationship between local adult survival and the North Atlantic Oscillation and two oceanographic variables (Labrador Current temperature profile and Bay of Fundy sea surface temperature) were evaluated using the program MARK. Capture–mark–recapture data fit the assumptions of the program MARK reasonably well with ĉ values ranging from 1.390 to 2.404. Pre-breeder survival rates were high for Machias Seal Island (Ô = 0.778 ± 0.041) and low for the Gannet Islands (Ô = 0.482 ± 0.033), likely the result of hunting pressure. On Machias Seal Island, constant adult survival (Ô = 0.967 ± 0.028) was the best model; however, time-dependent models that included a climate covariate performed nearly as well. On the Gannet Islands, adult survival (Ô = 0.890 ± 0.053) was negatively correlated with Labrador Current temperature. Overall, our results show significant differences in survival rates across sites over the same time period and highlight the importance of multisite studies and smaller scale climate indices for local populations. In addition, our study contributes to the growing body of evidence that survival in long-lived animals may vary with environmental conditions more than previously expected.
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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".