Kimpara, R. | bioRxiv Science

bioRxiv2026

Pervasive ethanol in the fruits and floral nectars consumed by tropical frugivores

Dietary ethanol has long been proposed as a selective force shaping the diets and physiology of tropical frugivores and nectarivores, yet this idea has rested on measurements from only a few species and sites. Here, we present the first global survey of ethanol in wild fruits and flowers (591 and 37 species, respectively, across 18 countries) and test ecological and phylogenetic predictors of its variation. We detected ethanol in the ripened fruits of 72% of angiosperm species (211 species, N [≥] 3 per species; 0.021% - 3.9% alcohol by volume (ABV)). Ethanol was also detected in the flowers of 12 species (0.037% - 0.73% ABV). Maximum fruit ethanol varied with temperature, humidity, and phylogenetic relatedness, and it was higher in ripe fruits collected from the forest floor relative to their canopy counterparts. We then extrapolated potential daily ethanol consumption for 10 primate species. Estimated maximum daily ethanol ingestion for primates ranged from negligible to 0.72 g/kg/day on average (equivalent to 5.1 international standard drinks, 10 g ethanol each, for a 70 kg human). Our findings show that ethanol is widespread in plant reproductive tissues and shaped by both phylogeny and environment, supporting the hypothesis that dietary ethanol has been a pervasive selective pressure on fruit- and nectar-feeding animals.

ecology↗

bioRxiv2025

Analysis of individual identification and age-class classification of wild macaque vocalizations without pitch- and formant-based acoustic parameter measurements

In recent years, deep learning has achieved high performance in bioacoustic classification tasks by leveraging automatically processed acoustic features for large datasets. However, few performance evaluations of automatically processed acoustic features have been conducted on small-scale data because deep learning requires large datasets. To test whether mel spectrograms (an automatically processed acoustic features) are effective for classifying relatively small acoustic data, we evaluated the performance of two classification machines (random forest and support vector machine) using mel-spectrograms of 651 coo calls of six wild female Japanese macaques on two tasks: 1) individual identification and 2) age-class classification between younger (<10 yrs) and the older animals (>20 yrs). For the individual identification task, the mean balanced accuracy was 0.81 for random forest and 0.82 for support vector machine. For the age-class classification task, the mean balanced accuracy was 0.91 for random forest and 0.93 for support vector machine. Considering that of all the calls were recorded in the wild, methods using automatically processed acoustic features, such as mel spectrogram, are effective in classifying small acoustic data for the individual identification task. The high performance in the age-class classification task might be attributable to the potential of mel spectrograms to capture the characteristics of older individuals (e.g. harshness).

animal behavior and cognition↗