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Vocal complexity is central to many evolutionary hypotheses about animal communication. Yet, quantifying and comparing complexity remains a challenge, particularly when vocal types are highly graded. Male Bornean orangutans (Pongo pygmaeus wurmbii) produce complex and variable "long call" vocalizations comprising multiple sound types that vary within and among individuals. Previous studies described six distinct call (or pulse) types within these complex vocalizations, but none quantified their discreteness or the ability of human observers to reliably classify them. We studied the long calls of 13 individuals to: (1) evaluate and quantify the reliability of audio-visual classification by three well-trained observers, (2) distinguish among call types using supervised classification and unsupervised clustering, and (3) compare the performance of different feature sets. Using 46 acoustic features, we used machine learning (i.e., support vector machines, affinity propagation, and fuzzy c-means) to identify call types and assess their discreteness. We additionally used Uniform Manifold Approximation and Projection (UMAP) to visualize the separation of pulses using both extracted features and spectrogram representations. Supervised approaches showed low inter-observer reliability and poor classification accuracy, indicating that pulse types were not discrete. We propose an updated pulse classification approach that is highly reproducible across observers and exhibits strong classification accuracy using support vector machines. Although the low number of call types suggests long calls are fairly simple, the continuous gradation of sounds seems to greatly boost the complexity of this system. This work responds to calls for more quantitative research to define call types and quantify gradedness in animal vocal systems and highlights the need for a more comprehensive framework for studying vocal complexity vis-à-vis graded repertoires. [ABSTRACT FROM AUTHOR]

Nonhuman primate vocalizations have been traditionally described as stereotyped and most likely genetically determined. However, there is increasing evidence of flexibility with a wide variety of species demonstrating the ability to change aspects of their calls, such as note order and phrase duration. We assess patterns of variation in note order and temporal features in wild siamang (Symphalangus syndactylus) phrases. We used three types of analyses on 1,015 ululating scream phrases (US-II), containing 21,609 notes, produced by ten siamang groups at three field sites. First, to assess similarity or differences in the organization of notes in the US-II phrase, we calculated the Levenshtein distance (LD), which quantifies the similarity of sequences of strings through the number of insertions, deletions, and substitutions required to transform one into the other. Second, we used a supervised classification approach to see how well we could assign US-II phrases to their groups and sites. Third, we compared five unsupervised clustering algorithms to investigate the tendency to cluster in US-II phrase types. The note order of the US-II phrase was variable shown by relatively large LD values; there also were significant mean differences between groups. Supervised classification using support vector machine and leave-one-out cross-validation returned 89.5% accuracy for site and 53.1% for group. For unsupervised clustering, the most stable solution based on the Silhouette coefficient returned four unique clusters or classes of the US-II phrase in our dataset. Our findings indicate high levels of intragroup variation in the US-II phrase and are consistent with previous reports on siamang vocalizations being flexible. Quantifying variation in the siamang US-II phrase provides a crucial first step in understanding the evolutionary forces that shaped these signals and the potential information they convey. [ABSTRACT FROM AUTHOR]

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. Research highlight: We used mobile smartphones to record the sounds of gibbons in the nature reserve for 2 to 5 days at each recording post. We detected gibbons at 24 and 12 recording posts in 2019 and 2022, respectively.Apparent extinction was high with 67% of occupied sites in 2019 becoming unoccupied in 2022. Colonization was low with ~25% of unoccupied sites in 2019 becoming occupied in 2022. The occurrence probability declined from 0.58 in 2019 to 0.30 in 2022.Urgent actions such as gun confiscation and conservation education should be implemented to save the endangered gibbon population in the nature reserve. [ABSTRACT FROM AUTHOR]

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From quantifying daily path length and home range use to mapping the distributions of primate species across Asia, spatial analysis is relevant for questions related to primate behavior and ecology across scales. Over 75% of wild primate populations are in decline (Estrada et al., [3]), and 103 of the 193 primate species and subspecies included in I All Asian Primates i are endangered or critically endangered. Wes Sechrest notes in the foreword that the book may inspire action to protect and recover Asian primate species, and Boonratana posits that the book may help advocate for the conservation of Asian primates. [Extracted from the article]

Acoustic signals are ubiquitous across mammalian taxa. They serve a myriad of functions related to the formation and maintenance of social bonds and can provide conspecifics information about caller condition, motivation and identity. Disentangling the relative importance of evolutionary mechanisms that shape vocal variation is difficult, and little is known about heritability of mammalian vocalizations. Duetting––coordinated vocalizations within male and female pairs––arose independently at least four times across the Primate Order. Primate duets contain individual‐ or pair‐level signatures, but the mechanisms that shape this variation remain unclear. Here, we test for evidence of heritability in two call types (pulses and chirps) from the duets of captive coppery titi monkeys (Plecturocebus cupreus). We extracted four features––note rate, duration, minimum and maximum fundamental frequency––from spectrograms of pulses and chirps, and estimated heritability of the features. We also tested whether features varied with sex or body weight. We found evidence for moderate heritability in one of the features examined (chirp note rate), whereas inter‐individual variance was the most important source of variance for the rest of the features. We did not find evidence for sex differences in any of the features, but we did find that body weight and fundamental frequency of chirp elements covaried. Kin recognition has been invoked as a possible explanation for heritability or kin signatures in mammalian vocalizations. Although the function of primate duets remains a topic of debate, the presence of moderate heritability in titi monkey chirp elements indicates duets may serve a kin recognition function. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Passive acoustic monitoring (PAM) has the potential to greatly improve our ability to monitor cryptic yet vocal animals. Advances in automated signal detection have increased the scope of PAM, but distinguishing between individuals—which is necessary for density estimation—remains a major challenge. When individual identity is known, supervised classification techniques can be used to distinguish between individuals. Supervised methods require labelled training data, whereas unsupervised techniques do not. If the acoustic signals of individuals are sufficiently different, the number of clusters might represent the number of individuals sampled. The majority of applications of unsupervised techniques in animal vocalizations have focused on quantifying species‐specific call repertoires. However, with increased interest in PAM applications, unsupervised methods that can distinguish between individuals are needed.Here we use an existing dataset of Bornean gibbon female calls with known identity from five sites on Malaysian Borneo to test the ability of three different unsupervised clustering algorithms (affinity propagation, K‐medoids and Gaussian mixture model‐based clustering) to distinguish between individuals. Calls from different gibbon females are readily distinguishable using supervised techniques. For internal validation of unsupervised cluster solutions, we calculated silhouette coefficients. For external validation, we compared clustering results with female identity labels using a standard metric: normalized mutual information. We also calculated classification accuracy by assigning unsupervised cluster solutions to females based on which cluster had the highest number of calls from a particular female.We found that affinity propagation clustering consistently outperformed the other algorithms for all metrics used. In particular, classification accuracy of affinity propagation clustering was more consistent as the number of females increased, and when we randomly sampled females across sites.We conclude that unsupervised techniques may be useful for providing additional information regarding individual identity for PAM applications. We stress that although we use gibbons as a case study, these methods will be applicable for any individually distinct vocal animal. [ABSTRACT FROM AUTHOR]

As social animals, many primates use acoustic communication to maintain relationships. Vocal individuality has been documented in a diverse range of primate species and call types, many of which have presumably different functions. Auditory recognition of one's neighbors may confer a selective advantage if identifying conspecifics decreases the need to participate in costly territorial behaviors. Alternatively, vocal individuality may be nonadaptive and the result of a unique combination of genetics and environment. Pair‐bonded primates, in particular, often participate in coordinated vocal duets that can be heard over long distances by neighboring conspecifics. In contrast to adult calls, infant vocalizations are short‐range and used for intragroup communication. Here, we provide two separate but complementary analyses of vocal individuality in distinct call types of coppery titi monkeys (Plecturocebus cupreus) to test whether individuality occurs in call types from animals of different age classes with presumably different functions. We analyzed 600 trill vocalizations from 30 infants and 169 pulse‐chirp duet vocalizations from 30 adult titi monkeys. We predicted that duet contributions would exhibit a higher degree of individuality than infant trills, given their assumed function for long‐distance, intergroup communication. We estimated 7 features from infant trills and 16 features from spectrograms of adult pulse‐chirps, then used discriminant function analysis with leave‐one‐out cross‐validation to classify individuals. We correctly classified infants with 48% accuracy and adults with 83% accuracy. To further investigate variance in call features, we used a multivariate variance components model to estimate variance partitioning in features across two levels: within‐ and between‐individuals. Between‐individual variance was the most important source of variance for all features in adults, and three of four features in infants. We show that pulse‐chirps of adult titi monkey duets are individually distinct, and infant trills are less individually distinct, which may be due to the different functions of the vocalizations. Research Highlights: Coppery titi monkey (Plecturocebus cupreus) adults can be classified with 83% accuracy and infants can be classified with 48% accuracy based on features estimated from spectrograms of adult duets and infant trills.Differences in individuality may be due to functional differences in these call types. [ABSTRACT FROM AUTHOR]

Duetting, or the stereotypical, repeated and often coordinated vocalizations between 2 individuals arose independently multiple times in the Order Primates. Across primate species, there exists substantial variation in terms of timing, degree of overlap, and sex-specificity of duet contributions. There is increasing evidence that primates can modify the timing of their duet contributions relative to their partner, and this vocal flexibility may have been an important precursor to the evolution of human language. Here, we present the results of a fine-scale analysis of Gursky's spectral tarsier Tarsius spectrumgurskyae duet phrases recorded in North Sulawesi, Indonesia. Specifically, we aimed to investigate individual-level variation in the female and male contributions to the duet, quantify individual- and pair-level differences in duet timing, and measure temporal precision of duetting individuals relative to their partner. We were able to classify female duet phrases to the correct individual with an 80% accuracy using support vector machines, whereas our classification accuracy for males was lower at 64%. Females were more variable than males in terms of timing between notes. All tarsier phrases exhibited some degree of overlap between callers, and tarsiers exhibited high temporal precision in their note output relative to their partners. We provide evidence that duetting tarsier individuals can modify their note output relative to their duetting partner, and these results support the idea that flexibility in vocal exchanges—a precursor to human language—evolved early in the primate lineage and long before the emergence of modern humans. [ABSTRACT FROM AUTHOR]

Vocal individuality has been documented in a variety of mammalian species and it has been proposed that this individuality can be used as a vocal fingerprint to monitor individuals. Here we provide and test a classification method using Mel-frequency cepstral coefficients (MFCCs) to extract features from Bornean gibbon female calls. Our method is semi-automated as it requires manual pre-processing to identify and extract calls from the original recordings. We compared two methods of MFCC feature extraction: (1) averaging across all time windows and (2) creating a standardized number of time windows for each call. We analysed 376 calls from 33 gibbon females and, using linear discriminant analysis, found that we were able to improve female identification accuracy from 95.7% with spectrogram features to 98.4% accuracy when averaging MFCCs across time windows, and 98.9% accuracy when using a standardized number of windows. We divided our data randomly into training and test data-sets, and tested the accuracy of support vector machine (SVM) predictions over 100 iterations. We found that we could predict female identity in the test data-set with a 98.8% accuracy. Using SVM on our entire data-set, we were able to predict female identity with 99.5% accuracy (validated by leave-one-out cross-validation). Lastly, we used the method presented here to classify four females recorded during three or more recording seasons using SVM with limited success. We provide evidence that MFCC feature extraction is effective for distinguishing between female Bornean gibbons, and make suggestions for future vocal fingerprinting applications. [ABSTRACT FROM AUTHOR]

Acoustic signals serve important functions in mate choice, resource defense, and species recognition. Quantifying patterns and sources of variation in acoustic signals can advance understanding of the evolutionary processes that shape behavioral diversity more broadly. Animal vocalization datasets are inherently multivariate and hierarchical, wherein multiple features are estimated from calls of many individuals across different recording locations. Patterns of variation within different hierarchical levels—notwithstanding the challenges they present for modeling and inference—can provide insight into processes shaping vocal variation. The current work presents a multivariate, variance components model to investigate three levels of variance (within-female, between-female, and between-site) in Bornean gibbon calls. For six of the eight features estimated from call spectrograms, between-female variance was the most important contributor to total variance. For one feature, trill rate, there were site-level differences, which may be related to geographic isolation of certain gibbon populations. There was also a negative relationship between trill rate and duration of the introduction, suggesting trade-offs in the production of gibbon calls. Given substantial inter-individual variation in gibbon calls, it seems likely that there has been selection to confer information regarding caller identity, but mechanisms leading to site-level variation in trill rate remain to be determined. [ABSTRACT FROM AUTHOR]

Effectively characterizing primate diets is fundamental to understanding primate behavior, ecology and morphology. Examining temporal variation in a species’ diet, as well as comparing the responses of different species to variation in resource availability, can enhance understanding of the evolution of morphology and socioecology. In this study, we use feeding data collected over five years to describe the diets of two sympatric Southeast Asian primate species of similar body size: white-bearded gibbons (Hylobates albibarbis) and red leaf monkeys (Presbytis rubicunda rubida), in Gunung Palung National Park, West Kalimantan, Indonesia. Long-term data sets are especially important for characterizing primate diets in Southeast Asia, where the forests exhibit supra-annual mast fruiting events. We found that gibbons were mainly frugivorous, with fruit and figs comprising 70% of their 145 independent feeding observations, whereas leaf monkeys ate a substantial amount of seeds (26%), fruits and figs (26.5%) and leaves (30%, n = 219 independent feeding observations). Leaf monkeys consumed a higher number of plant genera, and this was due mostly to the non-frugivorous portion of their diet. To investigate resource selection by these primates we utilized two different approaches: the Manly Selectivity Ratio, which did not take into account temporal variation of resource availability, and a model selection framework which did incorporate temporal variation. Both species selected figs (Ficus) more than predicted based on their availability under the Manly Selectivity Ratio. Model selection allowed us to determine how these primates alter the proportion of leaves, flowers, seeds, figs and fruit in their diets in response to variation in fruit availability. When fruits were scarce, both gibbons and leaf monkeys incorporated more leaves and figs into their diets, indicating that these two food classes are fallback foods for these primates. We discuss how different measures of resource selection can provide seemingly contradictory results, and emphasize the importance of long term studies that combine independent feeding observations with rigorous assessment of temporal variation in resource availability when modelling feeding selectivity. [ABSTRACT FROM AUTHOR]

Across diverse systems including language, music and genomes, there is a tendency for longer sequences to contain shorter constituents; this phenomenon is known as Menzerath's Law. Whether Menzerath's Law is a universal in biological systems, is the result of compression (wherein shortest possible strings represent the maximum amount of information) or emerges from an inevitable relationship between sequence and constituent length remains a topic of debate. In non-human primates, the vocalizations of geladas, male gibbons and chimpanzees exhibit patterns consistent with Menzerath's Law. Here, we use existing datasets of three duetting primate species (tarsiers, titi monkeys and gibbons) to examine the wide-scale applicability of Menzerath's Law. Primate duets provide a useful comparative model to test for the broad-scale applicability of Menzerath's Law, as they evolved independently under presumably similar selection pressures and are emitted under the same context(s) across taxa. Only four out of the eight call types we examined were consistent with Menzerath's Law. Two of these call types exhibited a negative relationship between the position of the note in the call and note duration, indicating that adherence to Menzerath's Law in these call types may be related to breathing constraints. Exceptions to Menzerath's Law occur when notes are relatively homogeneous, or when species-specific call structure leads to a deterministic decrease in note duration. We show that adherence to Menzerath's Law is the exception rather than the rule in duetting primates. It is possible that selection pressures for long-range signals that can travel effectively over large distances was stronger than that of compression in primate duets. Future studies investigating adherence to Menzerath's Law across the vocal repertoires of these species will help us better elucidate the pressures that shape both short- and long-distance acoustic signals., Competing Interests: We declare we have no competing interests., (© 2020 The Authors.)