Chimpanzee behavioural diversity is spatially structured and negatively associated with genetic variation
Bibliographic record
Abstract
Abstract The question of how behavioural diversity in humans and other animals is shaped by the combined influence of demography, genetics, culture, and the environment receives much research attention. We take a macro-ecological approach to evaluate how chimpanzee ( Pan troglodytes ) behavioural diversity is spatially structured and associated with genetic diversity (i.e. heterozygosity as a proxy for effective population size) and contemporary and historic environmental context. We integrate the largest available chimpanzee behavioural and genomic datasets and apply spatially explicit Bayesian Generalised Linear Mixed Models to derive marginal effects for putative drivers and range wide spatial predictions of probability to observe behavioural traits. Contrary to expectations from neutral models of behavioural evolution, we observed a negative association of genetic diversity with behavioural diversity. This result suggests that behavioural traits may impact fitness. In contrast, we observed weaker associations of chimpanzee behavioural diversity with contemporary and historic environmental context. The very strong spatial structuring of behavioural traits is consistent with cultural transmission playing a major role in shaping chimpanzee behavioural diversity. Our analytical approach can be flexibly extended by additional candidate drivers of chimpanzee behavioural diversity, and offers a novel framework for testing competing ecological-evolutionary hypotheses across a wide variety of animal cultures.
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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 | 0.001 |
| 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) | 0.002 | 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".