Collective behavior diverges independently of the benthic-limnetic axis in stickleback
Bibliographic record
Abstract
Comparing populations across replicate environments or habitat types can help us understand the role of ecology in evolutionary processes. If similar phenotypes are favored in similar environments, parallel evolution may occur. Collective behavior, including collective movement (e.g., schooling, flocking) and social networks, can play a key role in the adaptation by animals to different environments. However, studies exploring the parallelism of collective behavior are limited, with research traditionally focusing on morphological traits. Here, we asked if collective behavior has evolved in parallel across replicate populations of benthic and limnetic three-spined stickleback (Gasterosteus aculeatus). There were repeatable, population-level differences in collective behavior in a common garden, with some populations forming groups that were more cohesive and with higher strength and clustering coefficients. This suggests that collective behavior can evolve. However, these differences were not predicted by ecotype (benthic vs. limnetic). We found no evidence that boldness or morphological traits – both of which are known to be associated with benthic-limnetic divergence – were correlated with collective behavior. Together, these results suggest that while collective behavior evolves in this system, it does not co-evolve with divergence along the benthic-limnetic axis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".