Positive relationships between genetic diversity and abundance in fishes
Bibliographic record
Abstract
Molecular markers, such as mitochondrial DNA and microsatellite loci, are widely studied to assess population genetics and phylogeography; however, the selective neutrality of these markers is increasingly being questioned. Given the importance of molecular markers in fisheries science and conservation, we evaluated the neutrality of both mtDNA and microsatellite loci through their associations with population size. We surveyed mtDNA and microsatellite data from the primary literature and determined whether genetic diversity increased with abundance across a total of 105 marine and freshwater fishes, with both global fisheries catch data and body size as proxies for abundance (with an additional 57 species for which only body size data were assessed). We found that microsatellite data generally yielded higher associations with abundance than mtDNA data, and within mtDNA analyses, number of haplotypes and haplotype diversity were more strongly associated with abundance than nucleotide diversity, particularly for freshwater fishes. We compared genetic diversity between freshwater and marine fishes and found that marine fishes had higher values of all measures of genetic diversity than freshwater fishes. Results for both mtDNA and microsatellites generally conformed to neutral expectations, although weaker relationships were often found between mtDNA nucleotide diversity and 'abundance' compared to any other genetic statistic. We speculate that this is because of historical events unrelated to natural selection, although a role for selection cannot be ruled out.
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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 | 0.002 |
| 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".