Factors affecting call usage in wild black-and-white ruffed lemurs (Varecia variegata) at Mangevo, Ranomafana National Park

An integrated passive acoustic monitoring and deep learning pipeline for black-and-white ruffed lemurs (Varecia variegata) in Ranomafana National Park, Madagascar

The urgent need for effective wildlife monitoring solutions in the face of global biodiversity loss has resulted in the emergence of conservation technologies such as passive acoustic monitoring (PAM). While PAM has been extensively used for marine mammals, birds, and bats, its application to primates is limited. Black-and-white ruffed lemurs (Varecia variegata) are a promising species to test PAM with due to their distinctive and loud roar-shrieks. Furthermore, these lemurs are challenging to monitor via traditional methods due to their fragmented and often unpredictable distribution in Madagascar's dense eastern rainforests. Our goal in this study was to develop a machine learning pipeline for automated call detection from PAM data, compare the effectiveness of PAM versus in-person observations, and investigate diel patterns in lemur vocal behavior. We did this study at Mangevo, Ranomafana National Park by concurrently conducting focal follows and deploying autonomous recorders in May-July 2019. We used transfer learning to build a convolutional neural network (optimized for recall) that automated the detection of lemur calls (57-h runtime; recall = 0.94, F1 = 0.70). We found that PAM outperformed in-person observations, saving time, money, and labor while also providing re-analyzable data. Using PAM yielded novel insights into V. variegata diel vocal patterns; we present the first published evidence of nocturnal calling. We developed a graphic user interface and open-sourced data and code, to serve as a resource for primatologists interested in implementing PAM and machine learning. By leveraging the potential of this pipeline, we can address the urgent need for effective primate population surveys to inform conservation strategies.

Bioacoustic characterization of the black-and-white ruffed lemur (Varecia variegata) vocal repertoire

Ruffed lemurs (Varecia spp.) exhibit a unique suite of behavioral traits compared to other lemur species, which includes their fluid fission-fusion social dynamics, communal rearing of parked litters, and pronounced frugivory in their humid rainforest habitats. Given these traits, and the dense rainforests they inhabit, vocal communication may be key to maintaining social cohesion, coordinating infant care, and/or defending their high-quality food resources. Indeed, they are known for their raucous 'roar-shriek' calls. However, there has been surprisingly little research on vocal communication in Varecia species and only two previously published repertoires, both of which were qualitative descriptions of their calls. In this study, we quantitatively examined the vocal repertoire of wild black-and-white ruffed lemurs (Varecia variegata) at Mangevo, Ranomafana National Park, Madagascar. We characterized 11 call types using 33 bioacoustic parameters related to frequency, duration, tonality, and composition. We also used discriminant function analysis and hierarchical clustering to quantitatively and objectively classify call types within the black-and-white ruffed lemur vocal repertoire. The repertoire consists of both monosyllabic and multisyllabic calls that are individually given or emitted in contagious choruses. Eight of the 11 calls were also used in combination or in larger multi-call sequences. The discriminant function analysis correctly assigned call types with 87% success, though this varied greatly by call type (1-65%). Hierarchical clustering identified 3-4 robust clusters, indicating low clustering structure in the data and suggesting that V. variegata exhibits a graded vocal repertoire. Future work should consider the environmental and behavioral contexts in which calls are used to better understand the function of these call types and combinations.