Genetic structuring in the freshwater mussel<i>Anodonta</i>corresponds with major hydrologic basins in the western United States
Bibliographic record
Abstract
Freshwater mussels (unionids) are increasingly recognized as important providers of ecosystem services, yet are among the most endangered fauna in the world. Because unionids are generally sessile and require specific fish hosts for development and dispersal, they are particularly vulnerable to habitat degradation. Surprisingly, little is known about the distribution of genetic diversity in freshwater mussels and this gap has a negative impact on taxonomy, monitoring, conservation and ecological research in these species. Here, we focus on western North American Anodonta, one of only three genera known to exist in this broad landscape and which contains three highly divergent lineages. We describe phylogeographical subdivision in the most widespread and diverse of these lineages, which includes Anodonta californiensis and Anodonta nuttalliana and occurs from Canada to Mexico. Using mitochondrial and nuclear data, we found that genetic structuring within this clade is inconsistent with morphologically based species designations, but instead follows patterns of vicariance among major hydrogeologic basins. Furthermore, there was a strong tendency for population diversity within drainage systems to increase downstream, implying greater habitat or host fish availability in this direction. Microsatellite results indicated that sampling locations were all genetically distinct, even at short distances. Many of our sample populations showed evidence of a recent demographic bottleneck, although this effect seemed to be very local and not drainage or basin-specific. This study provides a foundation for the establishment of appropriate management units and future research on adaptive differentiation and host fish relationships.
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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".