Early life of fathers affects offspring fitness in a wild rodent
Bibliographic record
Abstract
Intergenerational fitness effects on offspring due to the early life of the parent are well studied from the standpoint of the maternal environment, but intergenerational effects owing to the paternal early life environment are often overlooked. Nonetheless, recent laboratory studies in mammals and ecologically relevant studies in invertebrates predict that paternal effects can have a major impact on the offspring's phenotype. These nongenetic, environment-dependent paternal effects provide a mechanism for fathers to transmit environmental information to their offspring and could allow rapid adaptation. We used the bank vole Myodes glareolus, a wild rodent species with no paternal care, to test the hypothesis that a high population density environment in the early life of fathers can affect traits associated with offspring fitness. We show that the protein content in the diet and/or social environment experienced during the father's early life (prenatal and weaning) influence the phenotype and survival of his offspring and may indicate adaptation to density-dependent costs. Furthermore, we show that experiencing multiple environmental factors during the paternal early life can lead to a different outcome on the offspring phenotype than stimulated by experience of a single environmental factor, highlighting the need to study developmental experiences in tandem rather than independent of each other.
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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) | 0.001 | 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".