Multiple mating predicts intensity but not mechanism of kin recognition
Bibliographic record
Abstract
Understanding how animals recognize their kin has been a major challenge in biology. Most animals use one of 2 mechanisms: “familiarity” whereby kin are remembered from interactions early in life, such as in a nest, or “phenotype matching” whereby putative kin are compared with a template of what kin should look, smell, or sound like. Cross-species studies suggest that there is a link between which of these 2 mechanisms are used and the degree of female promiscuity (multiple mating). Phenotype matching is more likely to be used by promiscuous species because these species have lower average brood relatedness than monogamous species and familiarity is thus an unreliable cue of relatedness. However, it is unclear if this relationship holds within species, across populations that differ in their degree of promiscuity. Here, we take advantage of variation in brood relatedness across populations of guppies (Poecilia reticulata) to examine the relationship between kin recognition mechanisms and multiple mating within a single species. Contrary to the established hypothesis, we show that variation in recognition mechanism across populations is not governed by multiple mating. Instead, our data show that kin recognition, quantified as association preferences for shoalmates, is strongest when brood relatedness is high, consistent with Hamilton’s rule, but multiple mating does not otherwise influence the specific recognition mechanism used.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".