Population genetics of the zebra mussel, <i>Dreissena polymorpha</i> (Pallas): local allozyme differentiation within midwestern lakes and streams
Bibliographic record
Abstract
Several aquatic invertebrates with free-swimming larvae have paradoxically demonstrated fine-scale genetic heterogeneity. In this study, we tested for genetic heterogeneity in an exotic freshwater bivalve, the zebra mussel, Dreissena polymorpha (Pallas), which like many marine molluscs has a free-swimming larval stage. Zebra mussels were collected from 22 sites in the Great Lakes and from a small inland lake complex in southwestern Michigan and scored for 13 allozyme loci. Sites were sampled in a hierarchical fashion to assess the spatial scale of genetic variation. Zebra mussel populations exhibited significant genetic heterogeneity on a local scale within lakes, even though populations remained homogenous on a larger regional scale between lakes or lake complexes. The allozyme loci that exhibited heterogeneity differed from lake to lake. Populations also displayed significant heterozygote deficiencies from Hardy-Weinberg expectations for a majority of loci, implying population subdivision and (or) inbreeding on a fine scale. Our results suggest that local genetic differentiation for zebra mussels is both spatially and temporally fluid and is the product of stochastic processes, such as spawning asynchrony and uneven mixing of larval cohorts, rather than natural selection.
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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 | 0.000 | 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".