Genetic analysis of the mating system and opportunity for sexual selection in northern water snakes (Nerodia sipedon)
Bibliographic record
Abstract
We used data collected over 3 years at two study sites to quantify the rates and consequences of multiple paternity and to determine the opportunity for selection on male and female northern water snakes (Nerodia sipedon). We sampled litters from 45 females that gave birth to 811 offspring. Using eight microsatellite DNA loci (probability of exclusion of nonparental males > 0.99), we assigned paternity to 93% of neonates from one study population and 69% of neonates from the other population. Observations of participation in mating aggregations predicted individual reproductive success poorly for two reasons. First, males regularly courted nonreproductive females. Second, more than half of all sexually mature males obtained no reproductive success each year, despite the fact that many of them participated in mating aggregations. The number of sires per litter ranged from one to five, with 58% of all litters sired by more than one male. Multiple paternity increased with female size, apparently both because bigger females mated with more males and because the larger litters of big females provide paternity opportunities to more males. Multiple paternity was also more prevalent in years with shorter mating seasons. We detected no advantage to multiple paternity in reducing either the number of unfertilized ovules or stillborn young. Despite the majority of males siring no young each year, some males fathered young with as many as three different females in one year. Male reproductive success increased by more than 10 offspring for each additional mate, whereas female success increased by fewer than 2 offspring for each additional mate. The opportunity for sexual selection was more than five times higher in males than females.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".