Ecological traits shape genetic diversity patterns across the Mediterranean Sea: a quantitative review on fishes
Bibliographic record
Abstract
Abstract Aim We set out to identify the determinants of the variation in genetic diversity among fish species and test whether multi‐species genetic diversity is randomly distributed in space. Location Mediterranean Sea. Methods We collected genetic diversity data from 39 published studies on Mediterranean fishes (31 species) along with the spatial coordinates of the sampling sites. We focused on microsatellite heterozygosity (151 data points) and mitochondrial haplotype diversity (201 data points). We used linear regressions to link genetic diversity and 11 ecological traits. We also tested for spatial autocorrelation and trends in the residuals. Results Among‐species variation in microsatellite heterozygosity was explained by three ecological traits: vertical distribution, migration type and body length. Variation in mitochondrial haplotype diversity was also explained by vertical distribution and migration type, and by reproductive strategy (semelparity). However, vertical distribution and migration type showed opposite effects on microsatellites and mitochondrial diversity. After accounting for the effects of ecological traits, no spatial pattern was detected, except for one of the species considered. Main conclusions Ecological factors explain an important proportion of the among‐species genetic diversity. These results suggest that life history strategies of the species influence the variation of microsatellite diversity indirectly through their effect on effective population size, while the spatial variations of genetic diversity seem to be too complex to be identified in our analysis. We found very different effects of traits on mitochondrial and nuclearDNAdiversity, which can be explained by the specificities of mitochondrialDNA(absence of recombination, maternal inheritance and non‐neutrality).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".