An Application of Autonomous Recorders for Gibbon Monitoring

The power of gibbon songs: Going beyond the research to inform conservation actions

Gibbons (Hylobatidae) are the smallest of the apes, known for their arboreal behavior and stereotyped songs. These species and sex-specific songs are often the subject of detailed studies regarding their evolution, responses to changing environments, involvement in social behavior, and used to design vocalization-based survey techniques to monitor population densities and trends. What is poorly understood is the value and impact of using the science and sound of gibbon vocalization and gibbon stories in education and outreach to complement nongovernmental organizations (NGOs) efforts. We present an example of how Borneo Nature Foundation, a NGOs based in Indonesia, is working to use the recordings of the songs of Bornean white-bearded gibbons (Hylobates albibarbis) to inform conservation actions and education efforts. Gibbons in Indonesia are often poorly known or understood by the public compared to orangutans (Pongo spp). We showcase how a field of study, namely primate acoustics, is an untapped resource to create digital content to engage with local, national and international communities and can be developed into educational tools in the form of storytelling, mobile apps and games, to highlight the plight of these threatened species and how to conserve them.

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.

Passive acoustic monitoring using smartphones reveals an alarming gibbon decline in a protected area in the central Annamite Mountains, Vietnam

Monitoring populations is critical for understanding how they respond to anthropogenic disturbance and for management of protected areas. The use of passive acoustic monitoring can improve monitoring efforts as it allows for collection of data on vocal animals at spatial and temporal scales that are difficult using only human observers. In this study, we used a multiseason occupancy model to monitor occurrence, apparent extinction, and colonization probabilities of a northern yellow-cheeked gibbon, Nomascus annamensis population with acoustic data collected from mobile smartphones in Dakrong Nature Reserve, Vietnam. Forty-five sites were randomly selected for repeated surveys in 2019 and 2022. At each site, a mobile smartphone was attached to a tree and recorded sounds for 4.2 days and 3.89 days on average, in 2019 and 2022, respectively. We manually annotated spectrograms for the presence of gibbon calls, and we detected gibbons at 24 and 12 recording posts in 2019 and 2022, respectively. Estimated local apparent extinction from occupancy models was high with 67% of occupied sites in 2019 becoming unoccupied in 2022. Apparent colonization was low with ~25% of unoccupied sites in 2019 becoming occupied in 2022. As a result, the apparent occurrence probability declined from 0.58 in 2019 to 0.30 in 2022. If the absence of calls indicates that cells are unoccupied this would mean an alarming decline of the gibbon population in the nature reserve. We suggest that in the areas with high hunting pressure, monitoring intervals should be shortened to at least yearly. In addition, urgent actions, such as patrolling, or gun confiscation, should be implemented to conserve the gibbon populations in Dakrong Nature Reserve and other protected areas with the same management context.

A workflow for the automated detection and classification of female gibbon calls from long-term acoustic recordings

Passive acoustic monitoring (PAM) allows for the study of vocal animals on temporal and spatial scales difficult to achieve using only human observers. Recent improvements in recording technology, data storage, and battery capacity have led to increased use of PAM. One of the main obstacles in implementing wide-scale PAM programs is the lack of open-source programs that efficiently process terabytes of sound recordings and do not require large amounts of training data. Here we describe a workflow for detecting, classifying, and visualizing female Northern grey gibbon calls in Sabah, Malaysia. Our approach detects sound events using band-limited energy summation and does binary classification of these events (gibbon female or not) using machine learning algorithms (support vector machine and random forest). We then applied an unsupervised approach (affinity propagation clustering) to see if we could further differentiate between true and false positives or the number of gibbon females in our dataset. We used this workflow to address three questions: (1) does this automated approach provide reliable estimates of temporal patterns of gibbon calling activity; (2) can unsupervised approaches be applied as a post-processing step to improve the performance of the system; and (3) can unsupervised approaches be used to estimate how many female individuals (or clusters) there are in our study area? We found that performance plateaued with >160 clips of training data for each of our two classes. Using optimized settings, our automated approach achieved a satisfactory performance (F1 score ~ 80%). The unsupervised approach did not effectively differentiate between true and false positives or return clusters that appear to correspond to the number of females in our study area. Our results indicate that more work needs to be done before unsupervised approaches can be reliably used to estimate the number of individual animals occupying an area from PAM data. Future work applying these methods across sites and different gibbon species and comparisons to deep learning approaches will be crucial for future gibbon conservation initiatives across Southeast Asia.

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.

Toward Passive Acoustic Monitoring of Lemurs: Using an Affordable Open-Source System to Monitor Phaner Vocal Activity and Density

Developing new cost-effective methods for monitoring the distribution and abundance of species is essential for conservation biology. Passive acoustic monitoring (PAM) has long been used in marine mammals and has recently been postulated to be a promising method to improve monitoring of terrestrial wildlife as well. Because Madagascar’s lemurs are among the globally most threatened taxa, this study was designed to assess the applicability of an affordable and open-source PAM device to estimate the density of pale fork-marked lemurs (Phaner pallescens). Using 12 playback experiments and one fixed transect of four automated acoustic recorders during one night of the dry season in Kirindy Forest, we experimentally estimated the detection space for Phaner and other lemur vocalizations. Furthermore, we manually annotated more than 10,000 vocalizations of Phaner from a single location and used bout rates from previous studies to estimate density within the detection space. To truncate detections beyond 150 m, we applied a sound pressure level (SPL) threshold filtering out vocalizations below SPL 50 (dB re 20 μPa). During the dry season, vocalizations of Phaner can be detected with confidence beyond 150 m by a human listener. Within our fixed truncated detection area corresponding to an area of 0.07 km2 (detection radius of 150 m), we estimated 10.5 bouts per hour corresponding to a density of Phaner of 38.6 individuals/km2. Our density estimates are in line with previous estimates based on individually marked animals conducted in the same area. Our findings suggest that PAM also could be combined with distance sampling methods to estimate densities. We conclude that PAM is a promising method to improve the monitoring and conservation of Phaner and many other vocally active primates.

Not by the light of the moon: Investigating circadian rhythms and environmental predictors of calling in Bornean great argus

Great argus pheasants are known for their elaborate visual mating displays, but relatively little is known about their general ecology. The use of passive acoustic monitoring-which relies on long-term autonomous recorders-can provide insight into the behavior of visually cryptic, yet vocal species such as the great argus. Here we report the results of an analysis of vocal behavior of the Bornean great argus (Argusianus argus grayi) in Sabah, Malaysia, using data collected with 11 autonomous recording units. Great argus regularly emitted two call types, the long call and the short call, and we found that although both call types were emitted throughout the day, the short calls were more likely to occur during the morning hours (06:00-12:00LT). Great argus were less likely to call if there was rain, irrespective of the time of day. A substantial portion of calls at our site (~20%) were emitted between the hours of 18:00-06:00LT. We found that for nighttime calls, calling activity increased during new moon periods and decreased during periods of rain. We attribute the negative influence of rain on calling to increased energetic costs of thermoregulation during wet periods, and propose that the influence of the lunar cycle may be related to increased predation risk during periods with high levels of moonlight. Little is known about the behavioral ecology of great argus on Borneo, so it is difficult to know if the results we report are typical, or if we would see differences in calling activity patterns depending on breeding season or changes in food availability. We advocate for future studies of great argus pheasant populations using paired camera and acoustic recorders, which can provide further insight into the behavior of this cryptic species.

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.

Estimating ecoacoustic activity in the Amazon rainforest through Information Theory quantifiers

Automatic monitoring of biodiversity by acoustic sensors has become an indispensable tool to assess environmental stress at an early stage. Due to the difficulty in recognizing the Amazon's high acoustic diversity and the large amounts of raw audio data recorded by the sensors, the labeling and manual inspection of this data is not feasible. Therefore, we propose an ecoacoustic index that allows us to quantify the complexity of an audio segment and correlate this measure with the biodiversity of the soundscape. The approach uses unsupervised methods to avoid the problem of labeling each species individually. The proposed index, named the Ecoacoustic Global Complexity Index (EGCI), makes use of Entropy, Divergence and Statistical Complexity. A distinguishing feature of this index is the mapping of each audio segment, including those of varied lengths, as a single point in a 2D-plane, supporting us in understanding the ecoacoustic dynamics of the rainforest. The main results show a regularity in the ecoacoustic richness of a floodplain, considering different temporal granularities, be it between hours of the day or between consecutive days of the monitoring program. We observed that this regularity does a good job of characterizing the soundscape of the environmental protection area of Mamirauá, in the Amazon, differentiating between species richness and environmental phenomena.

Monitoring occurrence, extinction, and colonization probabilities for gibbon populations

All gibbon species (Family: Hylobatidae) are considered threatened with extinction and recognized on the International Union for Conservation of Nature Red List of Threatened Species. Because gibbons are one of the most threatened families of primates, monitoring their status is now critically important. Long-term monitoring programs applying occupancy approaches, in addition to assessing occurrence probability, improves understanding of other population parameters such as site extinction or colonization probabilities, which elucidate temporal and spatial changes and are therefore important for guiding conservation efforts. In this study, we used multiple season occupancy models to monitor occurrence, extinction, and colonization probabilities for northern yellow-cheeked crested gibbon Nomascus annamensis in three adjacent protected areas in the Central Annamites mountain range, Vietnam. We collected data at 30 listening posts in 2012, 2014, and 2016 using the auditory point count method. Occurrence probabilities were highest in 2012 (0.74, confidence interval [CI]: 0.56-0.87) but slightly lower in 2014 (0.66, CI: 0.51-0.79) and 2016 (0.67, CI: 0.49-0.81). Extinction probabilities during the 2012-2014 and 2014-2016 intervals were 0.26 (0.14-0.44) and 0.25 (0.12-0.44), respectively. Colonization probabilities during 2012-2014 were 0.44 (0.19-0.73) and between 2014 and 2016 was 0.51 (0.26-0.75). Although local site extinctions have occurred, high recolonization probability helped to replenish the unoccupied sites and kept the occurrence probability stable. Long-term monitoring programs which use occurrence probability alone might not fully reveal the true dynamics of gibbon populations. We strongly recommend including multiple season occupancy models to monitor occurrence, extinction, and colonization probabilities in long-term gibbon monitoring programs.

Gibbons aren't singing in the rain: presence and amount of rainfall influences ape calling behavior in Sabah, Malaysia

Early morning calling occurs across diverse taxa, which may be related to optimal conditions for sound transmission. There exists substantial inter- and intra-specific variation in calling time which is influenced by intrinsic, social and/or environmental factors. Here, we investigate environmental predictors of calling in gibbons. We hypothesized that male solos- which occur earlier and tend to be longer than duets-would be more influenced by environmental variables, if earlier, longer calling bouts are energetically costly, and therefore limited by overnight energy expenditure. Our top model for male solo events included amount of rain in the previous 24 hours, and explained 30% of the variance, whereas the top model for duet events (which included presence and amount of rainfall) explained only 5% of the variance. Rain the previous night led to a later start time of male solos (~30 minutes), but our top model for duet start time did not include any reliable predictors. Male solo events appear to be more influenced by environmental factors, and duets may be influenced more by social factors. Our results are in line with previous studies that show that changes in overnight conditions -which may alter energy expenditure -can influence early morning calling behavior.