Applying collocation and APRIORI analyses to chimpanzee diets: Methods for investigating nonrandom food combinations in primate self-medication

Identifying novel medicinal resources in chimpanzee diets has historically presented challenges, requiring extensive behavioral data collection and health monitoring, accompanied by expensive pharmacological analyses. When putative therapeutic self-medicative behaviors are observed, these events are often considered isolated occurrences, with little attention paid to other resources ingested in combination. For chimpanzees, medicinal resource combinations could play an important role in maintaining well-being by tackling different symptoms of an illness, chemically strengthening efficacy of a treatment, or providing prophylactic compounds that prevent future ailments. We call this concept the self-medicative resource combination hypothesis. However, a dearth of methodological approaches for holistically investigating primate feeding ecology has limited our ability to identify nonrandom resource combinations and explore potential synergistic relationships between medicinal resource candidates. Here we present two analytical tools that test such a hypothesis and demonstrate these approaches on feeding data from the Sonso chimpanzee community in Budongo Forest, Uganda. Using 4 months of data, we establish that both collocation and APRIORI analyses are effective exploratory tools for identifying binary combinations, and that APRIORI is effective for multi-item rule associations. We then compare outputs from both methods, finding up to 60% agreement, and propose APRIORI as more effective for studies requiring control over confidence intervals and those investigating nonrandom associations between more than two resources. These analytical tools, which can be extrapolated across the animal kingdom, can provide a cost-effective and efficient method for targeting resources for further pharmacological investigation, potentially aiding in the discovery of novel medicines. Our paper presents two effective methodological tools for investigating nonrandom food combinations in nonhuman primate diets: Multiple distinctive collocation analysis and the APRIORI algorithm. This diagram shows an example of an APRIORI food network visualization using our online data exploration web-app, PANacea. These tools can be used to better understand and visualize nonrandom food combinations with possible implications for the study of nonhuman self-medication. image New methods needed for holistically evaluating chimpanzee diets. Collocation and APRIORI analyses are effective tools for finding nonrandom resource combinations. Relatively high level of agreement between methods. Effective tool for exploring feeding ecology data sets and discovering novel self-medicative resources.