It’s all relative: population estimates enhance kin recognition in the guppy
Bibliographic record
Abstract
Abstract Kin recognition plays a fundamental role in social evolution, enabling active inbreeding avoidance, nepotism, and promoting cooperative social organization. Many organisms recognize kin based on phenotypic similarity – a process called phenotype matching – by comparing information associated with their own phenotype against the phenotypes of conspecifics. However, recent theory demonstrates that to accurately judge phenotypic similarity (and hence, relatedness), individuals require estimates of the population’s distribution of phenotypes as a “frame of reference.” Here, I use the Trinidadian guppy ( Poecilia reticulata ) to provide the first empirical test of this population estimation theory. I varied the phenotypic distributions of the groups in which focal individuals developed and found that, as adults, their patterns of inbreeding avoidance and nepotistic intrasexual competition differed as predicted by population estimation theory. Individuals reared with conspecifics more similar to themselves treated novel conspecifics as less closely related, suggesting a shifted population estimate. Individuals reared with more phenotypically variable conspecifics exhibited less extreme kin discrimination, suggesting a broader population estimate. These results provide experimental evidence that population estimates inform phenotype matching, and are acquired plastically through social experience. By calibrating phenotype matching to the population distribution of phenotypes, population estimation enhances kin recognition, increasing opportunities for the evolution of inbreeding avoidance and nepotism.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".