Reproductive costs of migration for males in a partially migrating, pond-breeding amphibian
Bibliographic record
Abstract
Migratory animals face costs and benefits related to traveling to another habitat and the timing of the journey. These trade-offs can be sex-specific, with male reproductive success expected to be influenced by arrival time at the breeding habitat. In this study, we examined mating success in a population of partially migrating Red-Spotted Newts ( Notophthalmus viridescens viridescens (Rafinesque, 1820)). We tested the hypothesis that migrant males are at a disadvantage for spring mating opportunities compared with resident males owing to (i) later arrival time at the breeding pond and (ii) delay in developing the aquatic tail fin, which reduces their competitiveness. We measured the tail heights of successfully courting males compared with the general male population, as well as the time required for migrating males to develop tail fins. Temporally, migrant males arrived at the breeding pond before the majority of mating activity. However, we found that the time required for migrating males to acquire tail-fin heights necessary to be competitive for mating opportunities places them at a significant reproductive disadvantage compared with resident males. For partial migration to be maintained in the population, a reproductive cost for migrants could either trade off with another life-history trait or migration could be condition-dependent.
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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".