Population genetics of the <scp>A</scp>merican eel (<i><scp>A</scp>nguilla rostrata</i>): <i><scp>F</scp></i><sub>ST</sub> = 0 and <scp>N</scp>orth <scp>A</scp>tlantic <scp>O</scp>scillation effects on demographic fluctuations of a panmictic species
Bibliographic record
Abstract
We performed population genetic analyses on the American eel (Anguilla rostrata) with three main objectives. First, we conducted the most comprehensive analysis of neutral genetic population structure to date to revisit the null hypothesis of panmixia in this species. Second, we used this data to provide the first estimates of contemporary effective population size (Ne ) and to document temporal variation in effective number of breeders (Nb ) in American eel. Third, we tested for statistical associations between temporal variation in the North Atlantic Oscillation (NAO), the effective number of breeders and two indices of recruit abundance. A total of 2142 eels from 32 sampling locations were genotyped with 18 microsatellite loci. All measures of differentiation were essentially zero, and no evidence for significant spatial or temporal genetic differentiation was found. The panmixia hypothesis should thus be accepted for this species. Nb estimates varied by a factor of 23 among 12 cohorts, from 473 to 10,999. The effective population size Ne was estimated at 10,532 (95% CI, 9312-11,752). This study also showed that genetically based demographic indices, namely Nb and allelic richness (Ar), can be used as surrogates for the abundance of breeders and recruits, which were both shown to be positively influenced by variation during high (positive) NAO phases. Thus, long-term genetic monitoring of American glass eels at several sites along the North American Atlantic coast would represent a powerful and efficient complement to census monitoring to track demographic fluctuations and better understand their causes.
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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 | 0.001 | 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) | 0.001 | 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".