Skill trade‐offs promote persistent individual differences and specialized tactics
Bibliographic record
Abstract
Individuals generally differ in their ability to perform challenging behaviours, but the causes of such variability remain incompletely understood. Because animals can usually use different behavioural tactics to achieve their goals, we might expect individual differences in skill to be maintained when the available tactics require different abilities to perform well. To explore this idea, I used the producer-scrounger (PS) paradigm, which considers interactions between foragers that may either invest effort in searching for resources (i.e. produce) or exploit others' discoveries (i.e. scrounge). Specifically, I tested whether individual differences in cognitive traits (i.e. the ability to find food) might result from a trade-off with competitiveness (i.e. the ability to steal food) that would exert disruptive selection pressure and, as such, might explain the coexistence of condition-dependent foraging tactics. If individuals differ in their competitiveness, with strong contestants being better able to monopolize food resources (and hence to scrounge), the model predicts that strong and weak competitors should rely more on scrounging and producing, respectively, especially when the finder's advantage is low. These findings indicate that the existence of individual differences in competitive abilities may be sufficient to explain short-term individual foraging tactic specialization. Yet, the degree of behavioural specialization is expected to depend on both the social and ecological context. Furthermore, persistent phenotypic differences, that are necessary for stable individual specialization, require the existence of a trade-off between competitive abilities that enable greater success as scroungers and cognitive abilities that are associated with better efficiency to detect and/or capture prey and, as such, enable greater success as producers. Therefore, this study further highlights the importance of considering the existence of alternative tactics to measure and predict the evolution of traits, including cognitive traits, within populations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".