Noninvasive Technologies for Primate Conservation in the 21st Century

Applying machine learning to primate bioacoustics: Review and perspectives

This paper provides a comprehensive review of the use of computational bioacoustics as well as signal and speech processing techniques in the analysis of primate vocal communication. We explore the potential implications of machine learning and deep learning methods, from the use of simple supervised algorithms to more recent self-supervised models, for processing and analyzing large data sets obtained within the emergence of passive acoustic monitoring approaches. In addition, we discuss the importance of automated primate vocalization analysis in tackling essential questions on animal communication and highlighting the role of comparative linguistics in bioacoustic research. We also examine the challenges associated with data collection and annotation and provide insights into potential solutions. Overall, this review paper runs through a set of common or innovative perspectives and applications of machine learning for primate vocal communication analysis and outlines opportunities for future research in this rapidly developing field.

Future coexistence with great apes will require major changes to policy and practice

The great apes-bonobos, chimpanzees, gorillas and orangutans-are critically threatened by human activities. We have destroyed their habitats, hunted them and transmitted fatal diseases to them. Yet we also conduct research on them, try to protect them and live alongside them. They are endangered, and time is running out. Here we outline what must be done to ensure that future generations continue to share this planet with great apes. We urge dialogue with those who live with great apes and interact with them often. We advocate conservation plans that acknowledge the realities of climate change, economic drivers and population growth. We encourage researchers to use technology to minimize risks to great apes. Our proposals will require substantial investment, and we identify ways to generate these funds. We conclude with a discussion of how field researchers might alter their work to protect our closest living relatives more effectively.

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.

Unmanned aerial vehicles (UAVs) with thermal infrared (TIR) sensors are effective for monitoring and counting threatened Vietnamese primates

Monitoring the population size of threatened primate species with minimal disturbance is becoming an outstanding requirement for conservation and wildlife management. Drones with thermal infrared (TIR) and visible spectrum (RGB) imaging are increasingly used to survey arboreal primates, but ground-truthing is still required to assess the effectiveness of drone-based count estimates. Our pilot study aims to assess the ability of a drone with both TIR and RGB sensors to detect, count, and identify semi-wild population of four endangered species of langurs and gibbon in the Endangered Primate Rescue Center (EPRC) in northern Vietnam. We found that TIR imagery enabled higher detection rates compared to RGB imagery and obtained an accurate count with the TIR only after four drone flights. We could identify langurs species based on thermal signature at a flight height of 50 m from the ground level (max tree height = 15 m), via size and shape of the body. With TIR imagery, we were able to record inconspicuous behaviors such as foraging and play. While some individuals initially showed flight or avoidance behaviors when the drone was sighted, these behaviors decreased or were absent on following drone surveys. Our study suggests that monitoring and precisely counting langur and gibbon species populations could be successful with the use of thermal drones only.

Performance of autonomous recorders to detect a cryptic and endangered primate species, the black lion-tamarin (Leontopithecus chrysopygus)

Information about species distribution is important for conservation but the monitoring of populations can demand a high sampling effort with traditional methods (e.g., line transects, sound playback) that are poorly efficient for cryptic primates, such as the black lion tamarin (Leontopithecus chrysopygus). Here we investigated the effectiveness of passive acoustic monitoring (PAM) as an alternative method to identify the presence of vocalizing lion tamarins in the wild. We aimed to: (1) determine the maximum distance at which autonomous recorders (Song Meter 3) and Raven Pro acoustic software can respectively detect and identify lion tamarin long calls emitted by two captive subjects (ex situ study); and (2) determine the sampling effort required to confirm the presence of the species in the wild (in situ study). In captive settings, we recorded lion tamarin long calls with one to two autonomous recorders operating at increasing distances from the animals' enclosure (8-202 m). In a 515 ha forest fragment, we deployed 12 recorders in a grid, 300 m apart from each other, within the estimated 100 ha home range of one group, and let them record for 10 consecutive days, totaling 985 h. In the ex situ study, hand-browsing of spectrograms yielded 298 long calls emitted from 8 to 194 m, and Raven's Template Detector identified 54.6% of them, also emitted from 8 to 194 m. In the in situ study, we manually counted 1115 long calls, and the Raven's Template Detector identified 44.75% of them. Furthermore, the presence of lion tamarins was confirmed within 1 day using four randomly sorted recorders, whereas 5 days on average were necessary with only one device. While specific protocols still need to be developed to determine primate population size using this technology, we concluded that PAM is a promising tool when considering long term costs and benefits.

ecoSound-web: an open-source, online platform for ecoacoustics

Passive acoustic monitoring of soundscapes and biodiversity produces vast amounts of audio recordings, but the management and analyses of these raw data present technical challenges. A multitude of software solutions exist, but none can fulfil all purposes required for the management, processing, navigation, analysis, and dissemination of acoustic data. The field of ecoacoustics needs a software tool that is free, evolving, and accessible. We take a step in that direction and present ecoSound-web: an open-source, online platform for ecoacoustics designed and built by ecologists and software engineers. ecoSound-web can be used for storing, organising, and sharing soundscape projects, manually creating and peer-reviewing annotations of soniferous animals and phonies, analysing audio in time and frequency, computing alpha acoustic indices, and providing reference sound libraries for different taxa. We present ecoSound-web's features, structure, and compare it with similar software. We describe its operation mode and the workflow for typical use cases such as the sampling of bird and bat communities, the use of a primate call library, and the analysis of phonies and acoustic indices. ecoSound-web is available from: https://github.com/ecomontec/ecoSound-web.