Old age variably impacts chimpanzee engagement and efficiency in stone tool use
Bibliographic record
Abstract
We know vanishingly little about how long-lived apes experience senescence in the wild, particularly with respect to their foraging behaviors. Chimpanzees use tools during foraging, and given the cognitive and physical challenges presented by tool use, tool-use behaviors are potentially at a heightened risk of senescence, though this has never been investigated in wild individuals. Accordingly, we sampled data from a longitudinal video archive that contained footage of wild chimpanzees using stone hammers and anvils to crack hard-shelled nuts ( nut cracking ) at an ‘outdoor laboratory’ over a 17-year period (with focal chimpanzees aging from approximately 39–44 to 56–61 years across this period). Over time, elderly chimpanzees began attending experimental nut-cracking sites less frequently than younger individuals. Several elderly chimpanzees exhibited reductions in efficiency across multiple stages of nut cracking, including taking longer to both select stone tools prior to use and use tools to crack open nuts and consume the associated pieces of kernel. Two chimpanzees began using less streamlined behavioral sequences to crack nuts, including a greater number of actions (such as more numerous hammer strikes). Notably, we report interindividual variability in the extent to which elderly chimpanzees’ tool-use behaviors changed during our sample period – ranging from small to profound reductions in engagement and efficiency – as well as differences in the specific aspects of nut cracking that changed for each individual. We discuss the possible causes of these changes – and recommendations for future research – with reference to literature surrounding the senescence of captive and wild primates.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".