Search Results (2)

Scent-marking through odours from excreta and glandular secretions is widespread in mammals. Among primates, diurnal group-living lemurs show different deployment modalities as part of their strategy to increase signal detection. We studied the diademed sifaka (Propithecus diadema) in the Maromizaha New Protected Area, Eastern Madagascar. We tested whether the scent-marking deposition occurred using a sequential rubbing of different body parts. We also tested if glands (i.e., deposition of glandular secretions) were more frequently rubbed than genital orifices (i.e., deposition of excreta) by comparing different kinds of rubbing behaviour. We then investigated if the depositor’s rank and sex affected the sequence of rubbing behaviour, the height at which the scent-marking happened, and the tree part targeted. We found that glandular secretions were often deposited with urine, especially in dominant individuals. The probability of anogenital and chest marking was highest, but chest rubbing most frequently occurred in dominant males. Markings were deposited at similar heights across age and sex, and tree trunks were the most used substrate. Males exhibited long and more complex scent-marking sequences than females. Our results indirectly support the idea that diademed sifakas deploy a sex-dimorphic mixture of glandular secretions and excreta to increase the probability of signal detection by conspecifics. Full article

The growing concern for the ongoing biodiversity loss drives researchers towards practical and large-scale automated systems to monitor wild animal populations. Primates, with most species threatened by extinction, face substantial risks. We focused on the vocal activity of the indri (Indri indri) recorded in Maromizaha Forest (Madagascar) from 2019 to 2021 via passive acoustics, a method increasingly used for monitoring activities in different environments. We first used indris’ songs, loud distinctive vocal sequences, to detect the species’ presence. We processed the raw data (66,443 10-min recordings) and extracted acoustic features based on the third-octave band system. We then analysed the features extracted from three datasets, divided according to sampling year, site, and recorder type, with a convolutional neural network that was able to generalise to recording sites and previously unsampled periods via data augmentation and transfer learning. For the three datasets, our network detected the song presence with high accuracy (>90%) and recall (>80%) values. Once provided the model with the time and day of recording, the high-performance values ensured that the classification process could accurately depict both daily and annual habits of indris‘ singing pattern, critical information to optimise field data collection. Overall, using this easy-to-implement species-specific detection workflow as a preprocessing method allows researchers to reduce the time dedicated to manual classification. Full article