Macroevolutionary evidence suggests trait‐dependent coevolution between behavior and life‐history
Bibliographic record
Abstract
Species with fast life-histories typically prioritize current over future reproductive events, compared to species with slow life-histories. These species therefore require greater energetic input into reproduction, and also likely have less time to realize their reproductive potential. Hence, behaviors that increase access to both resources and mating opportunities, at a cost of increased mortality risk, could coevolve with the pace of life-history. However, whether this prediction holds across species, remains untested under standardized conditions. Here, we test how risky behaviors, which facilitate access to resources and mating opportunities (i.e., activity, boldness, and aggression), along with metabolic rate, coevolve with the pace of life-history across 20 species of killifish that present remarkable divergences in the pace of life-history. We found a positive association between the pace of life-history and aggression, but interestingly not with other behavioral traits or metabolic rate. Aggression is linked to interference competition, and in killifishes is often employed to secure mates, while activity and boldness are more relevant for exploiting energetic resources. Our results suggest that the trade-off between current and future reproduction plays a more prominent role in shaping mating behavior, while behaviors related to energy acquisition may be influenced by ecological factors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".