Evolution in reproductive tempo and investment across the <i>Peromyscus</i> radiation
Bibliographic record
Abstract
Mammals display diverse reproductive strategies, however, the ultimate and proximate mechanisms that underlie this diversity and its composite traits remain poorly understood from both evolutionary and physiological perspectives. The Peromyscus genus of rodents, which is found throughout the north and central Americas, has diversified along life history gradients, varying both within and among species in reproductive strategies. This variation provides a useful model for studying reproductive diversity. Here, we combine a literature review with new analyses of captive colony breeding records from six Peromyscus species to assess our current understanding of how plasticity and local adaptation contribute to diversity in two classes of reproductive traits: phenology and litter investment. There is substantial evidence that many traits underlying phenology and litter investment have diverged among populations in ways that are likely to be locally adaptive, though plasticity in these traits remains common. However, these conclusions are largely based on data collected from the two most widespread Peromyscus species: P. maniculatus and P. leucopus. The majority of Peromyscus species diversity remains understudied regarding reproductive phenology and litter traits. We conclude by discussing key challenges and considerations relevant to using Peromyscus as a mammalian model for reproductive trait diversity and evolution moving forward.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".