Estimating Population Growth and Recruitment Rates Across the Range of American Common Eiders
Bibliographic record
Abstract
ABSTRACT Sound management of bird populations rests upon an adequate understanding of their population dynamics. Our study evaluated recruitment and population growth rates of 14 American common eider (Somateria mollissima dresseri) colonies from Labrador, Nova Scotia, Quebec, Canada, and Maine, USA, during various periods between 1970 and 2019. We used Pradel mark‐recapture models to estimate colony‐specific growth rates and the relative contributions of survival and recruitment on growth. We also validated this approach using annual nest counts (~8,000 pairs) conducted between 2003 and 2019 during down harvest operations in 3 colonies located in the Saint Lawrence estuary in Quebec. There was generally a good agreement between estimates derived using the 2 approaches. We considered that capture‐recapture data were suitable to estimate population trends of common eiders in other colonies, especially for colonies where accurate nest monitoring is impaired by dense vegetation. The breeding abundance declined at major colonies in Maine and Nova Scotia and increased or was stable in Quebec and Labrador. Female survival contributed the most to population growth, but variation in recruitment among colonies was more important than variation in survival to explain population growth. Management measures should thus strive to maximize local recruitment in colonies with declining populations. The assumption that apparent survival probabilities were homogeneous throughout an individual capture history was violated at several colonies in Quebec and Labrador. Using recaptures and band recoveries, we showed that the lower apparent survival for newly marked individuals compared to females that had been recaptured at least once was caused by a difference in site fidelity rather than true survival. But <1% of recaptured females dispersed to another colony for breeding, indicating that the lower site fidelity could be related to heterogeneity in capture probability among individuals. © 2021 The Wildlife Society.
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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 | 0.001 |
| 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".