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.

The ongoing recolonization of Europe by wolves ( Canis lupus ) has generated substantial societal attention reflected in media narratives, where attitudes vary in their framing of conservation success and human–wildlife conflict. We examined factors influencing media attitudes toward wolves by analyzing over 4000 online news articles from the Italian Alpine regions across a decade, employing both human and neural network classifications. Bayesian modeling identified clear spatial and temporal patterns: negative media sentiment prevailed in recently recolonized areas, whereas positive attitudes emerged in regions with established wolf populations and at the national level. Negative sentiment correlated strongly with seasonal peaks in predation on livestock and proximity to regional, national, and European elections. We revealed how spatial recolonization dynamics, human–wildlife conflicts, and electoral cycles collectively shape media framing of a recolonizing controversial carnivore. These findings inform conservation strategies that address human–wildlife conflict dynamics while acknowledging the politicized dimension surrounding the species. Graphical abstract