BREEDING DISPERSAL AND SURVIVAL OF ARCTIC TERNS (<i>STERNA PARADISAEA</i>) NESTING IN THE GULF OF MAINE
Bibliographic record
Abstract
We used capture-mark-recapture (or re-encounter) analysis of a metapopulation to estimate the probability of survival, re-encounter, and dispersal of Arctic Terns (Sterna paradisaea) nesting in the Gulf of Maine and the Bay of Fundy.Before our study, there were only a few anecdotal accounts of breeding dispersal, and the only estimates of survival for this species were calculated in the 1950s and 1960s in the United Kingdom, using return rates unadjusted for recapture probability.Approximately 45% of the North American breeding population nests in the Gulf of Maine region; 95% of these nest on the four islands studied.Reencounter observations of 2,295 adult Arctic Terns banded on these four key islands were collected from 1999 to 2005.An informationtheoretic approach was used to determine the model best describing survival and movement patterns.Models using the program M-SURGE suggested that the apparent survival of adult Arctic Terns was colony-and year-specific, ranging from 0.704 to 0.960 when transient individuals were accounted for.Re-encounter probabilities were generally low, ranging from 0.12 to 0.74, depending on colony and year.Fidelity to previous breeding colonies was high; estimated probability of movement among colonies ranged from 0.000 to 0.015.Breeding dispersal was negatively correlated with distances among islands, but not with colony size.There was no difference between male and female Arctic Terns in survival, re-encounter, or breeding dispersal.
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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 | 0.001 | 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) | 0.000 | 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".