Diversifying and correlational selection on behavior toward conspecific and heterospecific competitors in brook stickleback (<i><scp>C</scp>ulaea inconstans</i>)
Bibliographic record
Abstract
Behaviors toward heterospecifics and conspecifics may be correlated because of shared mechanisms of expression in both social contexts (nonadaptive covariation) or because correlational selection favors adaptive covariation. We evaluated these hypotheses by comparing behavior toward conspecifics and heterospecifics in brook stickleback (Culaea inconstans) from three populations sympatric with and three allopatric from a competitor, the ninespine stickleback (Pungitius pungitius). Behavioral traits were classified into three multivariate components: overt aggression, sociability, and activity. The correlation of behavior between social contexts for both overt aggression and activity varied among populations in a way unrelated to sympatry with ninespine stickleback, while mean aggression was reduced in sympatry. Correlations in allopatric populations suggest that overt aggression and activity may genetically covary between social contexts for nonadaptive reasons. Sociability was rarely correlated in allopatry but was consistently correlated in sympatry despite reduced mean sociability, suggesting that correlational selection may favor a sociability syndrome in brook stickleback when they coexist with ninespine stickleback. Thus, interspecific competition may impose diversifying selection on behavior among populations, although the causes of correlated behavior toward conspecifics and heterospecifics and whether it can evolve in one social context independent of the other may depend on the type of behavior.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".