The role of maternal effects on offspring performance in familiar and novel environments
Bibliographic record
Abstract
Abstract Maternal effects are an important evolutionary force that may either facilitate adaptation to a new environment or buffer against unfavourable conditions. The degree of variation in traits expressed by siblings from different mothers is frequently sensitive to environmental conditions. This could generate a Maternal-by-Environment interaction (M×E) that could inflate estimates of Genotype-by-Environment effects (G×E). We aimed to test for environment-specific maternal effects (M×E) using a paternal full-sib/half-sib breeding design in the seed beetle Callosobruchus maculatus, where we split and reared offspring from the same mother on two different bean host types – original and novel. Our quantitative genetic analysis indicated that maternal effects were very small on both host types for all the measured life-history traits. There was also little evidence that maternal oviposition preference for a particular host type predicted her offspring’s performance on that host. Further, additive genetic variance for most traits was relatively high on both hosts. While there was higher heritability for offspring reared in the novel host, there was no evidence for G×Es, and most cross-host genetic correlations were positive. This suggests that offspring from the same family ranked similarly for performance on both host types. Our results point to a genetic basis of host adaptation in the seed beetle, rather than maternal effects. Even so, we encourage researchers to test for potential M×Es because, due to a lack of testing, it remains unclear how often they arise.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".