Extensive Overlap in the Selection of Wild Fruits by Chimpanzees and Humans: Implications for the Management of Complex Social-Ecological Systems
Bibliographic record
Abstract
Understanding the capacity for humans to share resources (crops, wild foods, space) with large-bodied wildlife is vital for biodiversity conservation and human wellbeing, and requires comprehensive examination of their temporal interactions over fine spatial scales. We combined ecological (plant identification, wild fruit availability plots, animal faecal and trace sampling) and social science (free-listing, semi-structured interviews, participant observation) methods to systematically and simultaneously collect data on the availability and selection of fruits from wild plants by humans and critically-endangered chimpanzees (Pan troglodytes verus), a national conservation flagship species at Cantanhez National Park, Guinea-Bissau. Within an area of 12.7 km2, we demonstrate that local people’s monthly use of wild fruits was driven by its overall availability in the habitat, whereas chimpanzees, as ripe fruit specialists, sought out fruits year-round. Humans and chimpanzees overlap in the selection of fruits from at least 27 wild plant species. The ranked use of fruits from species which were used by both chimpanzees and humans was significantly positively correlated, suggesting they preferentially target fruits of the same wild plant species. Each month, humans and chimpanzees selected three to six of the same wild fruit species. Chimpanzees fed significantly more on wild fruit species that were available for longer periods, with no effect of that plant species density. Neither plant density nor number of fruiting months impacted human selection of fruit from a plant species, suggesting people might seek out desired resources irrespective of a species’ abundance in the landscape. These findings are important for the development of a shared knowledge base to establish culturally relevant conservation management strategies. We recommend the active management of plant species that are exploited for their fruits by both humans and chimpanzees at Cantanhez National Park, including figs (Ficus spp), oil-palm (Elaeis guineensis) and velvet tamarind (Dialium guineense). This can be achieved through supporting traditional resource management practices and the strategic replanting of shared plants in deforested areas and degraded corridors between forest fragments. This situation is representative of human-chimpanzee coexistence scenarios found across West Africa; the importance of shared resource use should be incorporated into local, national and regional conservation strategies.
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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".