Premating Avoidance of Inbreeding Absent in Female Guppies (<i>Poecilia reticulata</i>)
Bibliographic record
Abstract
Abstract The recognition and avoidance of kin during mating can be an important means of reducing the potential for inbreeding depression in offspring. We report here that premating mechanisms to avoid inbreeding, either innate or learnt through juvenile experience, are at best weak in female guppies (Poecilia reticulata). Guppies are small, ovoviviparous, neo‐tropical freshwater fish, with a polygamous mating system where males actively court females and females are selective of their mates. In a series of mate‐choice experiments, naïve, virgin females of the Quare River population in Trinidad were given a choice between a brother and a non‐sib male from the same population. Initially, females were only provided olfactory cues upon which to base their choice and then subsequently both olfactory and visual cues. Despite the females displaying mate choice, we found no evidence of them discriminating between the male types in either experiment. There was thus no indication of inbreeding avoidance, suggesting that experiences after maturation or with mature males (e.g. rare male preference), dispersal and/or post‐mating mechanisms may be evolutionarily more important avoidance mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".