Contemporary and historical patterns of connectivity among populations of an inland river fish species inferred from genetics and otolith chemistry
Bibliographic record
Abstract
Patterns of dispersal in riverine populations of Australian smelt ( Retropinna semoni ) were examined using otolith chemistry (Mg:Ca, Mn:Ca, Sr:Ca, Ba:Ca) and genetic markers (allozymes, mtDNA, microsatellite DNA). During a period of prolonged low flows, young-of-year smelt were collected from 13 streams within three catchments of the southern Murray–Darling Basin, Australia. Spatial differences in otolith core and edge chemical signatures and high levels of genetic assignment to sampling locations were observed, suggesting that most recruits were retained in natal areas after spawning. Following a subsequent period of hydrological connection, the same cohort was sampled as 1-year-olds. Maximum likelihood estimation using otolith core chemistry data from these fish suggested that retention in natal areas was highly variable between years and a similar, though less pronounced, pattern was evident in genetic assignments. Partitioning of genetic variation among catchments was not significant (FCT < 0.004) and probably reflects disequilibrium between migration and genetic drift due to an historical population expansion (~270 000 years ago). Taken together, otolith chemistry and genetic analyses suggest that contemporary dispersal of smelt within these catchments is relatively restricted and may be mediated by changes in hydrological connectivity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".