<scp>QTL</scp>‐Based Evidence of Population Genetic Divergence in Male Territorial Aggressiveness of the Japanese Freshwater Threespine Stickleback
Bibliographic record
Abstract
ABSTRACT Territorial aggression is widespread across the animal kingdom and is expressed in diverse ecological and social contexts. In addition, there are marked variations in the degree of male reproductive territoriality within and between species. These differences are often attributed to genetic components. However, the evolutionary genetic mechanisms in wild animals are poorly understood. This study explored the genetic basis of divergent male territorial aggressiveness between two Japanese freshwater populations, Gifu (GF) and Tomakomai (TM), in the threespine stickleback, which is a well‐known model system for both behavioral ecology and evolutionary genetics. First, our field survey indicated that the distribution of reproductive territories differed greatly across breeding habitats between the focal populations, and the density of reproductive territories was much greater in the GF population. Second, a one‐on‐one arena aquarium experiment on male–male combat using wild‐caught and common‐garden‐reared males revealed that GF males were genetically more aggressive than TM males. Finally, we performed quantitative trait loci (QTL) analysis using an F2 hybrid cross between the two populations to identify the causal genomic regions contributing to the divergence in male territorial aggressiveness. Our QTL analysis identified a single significant locus in an aggression‐related behavioral component, that is, the number of bites of focal F2 males toward a GF stimulus intruder. Two notable behavior‐related genes, HTR2A and MAO‐A, are found near this locus. These genes have often been suggested to influence of aggressive behavior in animals; therefore, they are regarded as important candidate genes for further functional analyses. Thus, we are the first to provide a QTL‐based genetic basis for population divergence in male territorial aggressiveness in the threespine stickleback.
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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 | 0.001 | 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) | 0.002 | 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".