Within-group relatedness can lead to higher levels of exploitation: a model and empirical test
Bibliographic record
Abstract
When animals live in groups, individuals can invest in resources themselves or exploit the investments of other group members. Grouping with kin may reduce the frequency of exploitation because kin selection should favor individuals that imposed fewer costs on their kin. However, taking into account the gains of the exploited individual, allowing kin to exploit one's efforts may be less costly than allowing exploitation from nonkin. In this case, there may be higher frequencies of exploitative behaviors among related than unrelated individuals. In order to understand the net effect of genetic relatedness on intragroup exploitation, we developed a model that considers the inclusive fitness consequences of “producing” (searching for food) and “scrounging” (exploiting the food discoveries of others) when foraging with relatives, while simultaneously allowing individuals to show differential tolerance toward scrounging by kin versus nonkin. The model predicts that increased relatedness can lead to higher levels of exploitation when producers are kin-selected to be more tolerant of scrounging from relatives compared with unrelated scroungers, for example, by being more aggressive toward nonkin. We tested this prediction empirically in captive zebra finches (Taeniopygia guttata) foraging either in flocks with full siblings or in flocks of unrelated individuals. Flocks of related zebra finches had higher frequencies of scrounging and lower levels of aggressive interactions compared with flocks of unrelated zebra finches. The results suggest that producers may be kin-selected to allow relatives to scrounge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".