Environmental and population dependency of genetic variability‐fitness correlations in<i>Rana temporaria</i>
Bibliographic record
Abstract
Abstract Considerable effort has been invested in studying the relationship between fitness and genetic variability. While evidence exists both for and against positive genetic variability-fitness correlations (GFC), the possible environment and population-dependency of GFCs has seldom been tested. We investigated GFCs in common frog (Rana temporaria) tadpoles reared under different temperatures and feeding regimes in four replicate populations. Genetic variability in eight microsatellite loci in 238 parents was used to estimate heterozygosity (H) and mean expected d2 in 158-sibships (4515 offspring). Generalized linear mixed model analyses of offspring fitness traits (survival to metamorphosis, developmental and growth rate) revealed that offspring survival probability was positively correlated with H, and that relationships were similar in all four populations tested. However, significant interaction between other genetic variability measures (d2, relatedness) and treatment conditions indicated that GFCs were detectable in some, but not in all environments. Interestingly, GFCs between survival and both heterozygosity and relatedness were most pronounced in stressful environments (i.e. limited food). Developmental and growth rates were significantly associated with d2 but less with H and relatedness. Furthermore, many of these GFCs were population-specific. These results suggest--in line with the contention that expression of inbreeding depression can be environment dependent--that GFCs can also be highly sensitive to the environmental conditions under which they are measured. The results further suggest that the observed positive correlation between H and survival probability is likely to be explainable by the 'general', rather than by the 'local' or 'direct' effect hypotheses.
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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 | 0.000 |
| 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".