Mycophagy (fungi consumption) is an important animal-ecosystem interaction, and provides important nutrients for numerous mammalian taxa, such as primates. Although mushroom consumption is a widespread behaviour in about a quarter of all known primate species, surprisingly little is known about their use of mushrooms as either a staple or fallback food, and the relationship between mushroom availability and consumption. We used four years (2019-2023) of direct observational data on mushroom consumption in the diet of three sympatric primate species (Chimpanzees, Pan troglodytes schweinfurthii; Yellow baboon, Papio cynocephalus; Red-tailed monkey, Cercopithecus ascanius) in a mosaic woodland habitat in the Issa valley, western Tanzania to address these gaps. We analysed mushroom consumption patterns and assessed mushroom availability from line transects for a period of 15 months (Oct 2022 – Dec 2023). Our findings show that mushrooms were an important dietary component for all three species when availability was high in the early-mid wet season (chimpanzees – 3 %, baboons – 22 %, and red-tailed monkeys – 3 %), while baboons also continued to consume mushrooms (>10 % of their diet) even when availability was low later in the year. We conclude that mushrooms serve as a fallback resource for Issa chimpanzees and red-tailed monkeys, while they represent a preferred food for Issa baboons. We contextualise mushroom consumption as a potential strategy of niche partitioning to reduce inter-specific feeding competition as well as underscore the importance of mycophagy and its role in primate dietary ecology and human evolution.

1. Passive acoustic monitoring (PAM) is a powerful tool for wildlife monitoring, but the time and expertise required to process large volumes of data pose significant challenges. Automated acoustic detectors improve efficiency by speeding up data processing. Class imbalance, resulting from fewer target signals relative to noise, complicates development and can negatively impact performance. However, training datasets should also reflect the conditions of real-world PAM datasets. 2. We developed an automated acoustic detector for chimpanzee loud calls while addressing class imbalance. We predicted that greater data diversity and high-quality data (clear signals, minimal noise interference) would enhance network performance and that class imbalance, by supporting diversity, is essential for functionality. We built training datasets with data recorded in wild settings and applied a temporal convolutional neural network approach using Deep Audio Segmenter (DAS). We trained networks using datasets containing varying levels of noise (50%, 75%, 90%, 99%) and also tested the effectiveness of frequency removal in improving performance. 3. The network performances varied significantly, with F1 scores of 0.44 to 0.86, exceeding a previous study (5% F1). The most imbalanced dataset produced the best performing network, capturing 90% of pant-hoot events and annotating them with 90% (SD = 20.9) accuracy. The results showed that increased class size was associated with greater intraclass diversity and improved precision (0.41–0.83). The networks showed consistently high recall rates, especially when frequency removal was not applied (0.89–0.92). 4. This study stresses the importance of class size and diversity in developing automated acoustic detectors. It also highlights the value of high-quality data for accurate pattern recognition of the target signal and the importance of the noise class for effective class decoupling and detector functionality. This research supports the advancement of PAM in chimpanzee studies, opening new opportunities to integrate remote sensing for efficient wildlife monitoring.