Comparative Analysis of the Vocal Repertoire of Eulemur: A Dynamic Time Warping Approach

Who you live with and what you duet for: a review of the function of primate duets in relation to their social organization

Duets are one of the most fascinating displays in animal vocal communication, where two animals fine-tune the timing of their emissions to create a coordinated signal. Duetting behavior is widespread in the animal kingdom and is present in insects, birds, and mammals. Duets are essential to regulate activities within and between social units. Few studies assessed the functions of these vocal emissions experimentally, and for many species, there is still no consensus on what duets are used for. Here, we reviewed the literature on the function of duets in non-human primates, investigating a possible link between the social organization of the species and the function of its duetting behavior. In primates and birds, social conditions characterized by higher promiscuity might relate to the emergence of duetting behavior. We considered both quantitative and qualitative studies, which led us to hypothesize that the shift in the social organization from pair living to a mixed social organization might have led to the emergence of mate defense and mate guarding as critical functions of duetting behavior. Territory/resource ownership and defense functions are more critical in obligate pair-living species. Finally, we encourage future experimental research on this topic to allow the formulation of empirically testable predictions.

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.

Coevolution of social and communicative complexity in lemurs

The endemic lemurs of Madagascar (Lemuriformes: Primates) exhibit great social and communicative diversity. Given their independent evolutionary history, lemurs provide an excellent opportunity to identify fundamental principles in the coevolution of social and communicative traits. We conducted comparative phylogenetic analyses to examine patterns of interspecific variation among measures of social complexity and repertoire sizes in the vocal, olfactory and visual modality, while controlling for environmental factors such as habitat and number of sympatric species. We also examined potential trade-offs in signal evolution as well as coevolution between body mass or brain size and communicative complexity. Repertoire sizes in the vocal, olfactory and visual modality correlated positively with group size, but not with environmental factors. Evolutionary changes in social complexity presumably antedated corresponding changes in communicative complexity. There was no trade-off in the evolution of signals in different modalities and neither body mass nor brain size correlated with any repertoire size. Hence, communicative complexity coevolved with social complexity across different modalities, possibly to service social relationships flexibly and effectively in pair- and group-living species. Our analyses shed light on the requirements and adaptive possibilities in the coevolution of core elements of social organization and social structure in a basal primate lineage. This article is part of the theme issue 'Cognition, communication and social bonds in primates'.

Introducing the Software CASE (Cluster and Analyze Sound Events) by Comparing Different Clustering Methods and Audio Transformation Techniques Using Animal Vocalizations

Simple Summary

Unsupervised clustering algorithms are widely used in ecology and conservation to classify animal vocalizations, but also offer various advantages in basic research, contributing to the understanding of acoustic communication. Nevertheless, there are still some challenges to overcome. For instance, the quality of the clustering result depends on the audio transformation technique previously used to adjust the audio data. Moreover, it is difficult to verify the reliability of the clustering result. To analyze bioacoustic data using a clustering algorithm, it is, therefore, essential to select a reasonable algorithm from the many existing algorithms and prepare the recorded vocalizations so that the resulting values characterize a vocalization as accurately as possible. Frequency-modulated vocalizations, whose frequencies change over time, pose a particular problem. In this paper, we present the software CASE, which includes various clustering methods and provides an overview of their strengths and weaknesses concerning the classification of bioacoustic data. This software uses a multidimensional feature-extraction method to achieve better clustering results, especially for frequency-modulated vocalizations.

Abstract

Unsupervised clustering algorithms are widely used in ecology and conservation to classify animal sounds, but also offer several advantages in basic bioacoustics research. Consequently, it is important to overcome the existing challenges. A common practice is extracting the acoustic features of vocalizations one-dimensionally, only extracting an average value for a given feature for the entire vocalization. With frequency-modulated vocalizations, whose acoustic features can change over time, this can lead to insufficient characterization. Whether the necessary parameters have been set correctly and the obtained clustering result reliably classifies the vocalizations subsequently often remains unclear. The presented software, CASE, is intended to overcome these challenges. Established and new unsupervised clustering methods (community detection, affinity propagation, HDBSCAN, and fuzzy clustering) are tested in combination with various classifiers (k-nearest neighbor, dynamic time-warping, and cross-correlation) using differently transformed animal vocalizations. These methods are compared with predefined clusters to determine their strengths and weaknesses. In addition, a multidimensional data transformation procedure is presented that better represents the course of multiple acoustic features. The results suggest that, especially with frequency-modulated vocalizations, clustering is more applicable with multidimensional feature extraction compared with one-dimensional feature extraction. The characterization and clustering of vocalizations in multidimensional space offer great potential for future bioacoustic studies. The software CASE includes the developed method of multidimensional feature extraction, as well as all used clustering methods. It allows quickly applying several clustering algorithms to one data set to compare their results and to verify their reliability based on their consistency. Moreover, the software CASE determines the optimal values of most of the necessary parameters automatically. To take advantage of these benefits, the software CASE is provided for free download.

Parent-offspring turn-taking dynamics influence parents’ song structure and elaboration in a singing primate

Parent-offspring interactions are essential to interpret animal social evolution and behavior, but their role in mediating acoustic communication in animals that interact vocally is still unclear. Increasing evidence shows that primate vocal communication is way more flexible than previously assumed, and research on this topic can provide further information on how the social environment shaped vocal plasticity during the evolution of the Primate order. Indris communicate through elaborated vocal emissions, usually termed songs. Songs are interactive vocal displays in which all members of the family group alternate their emissions, taking turns during chorusing events. We aimed to understand whether specific rules regulate the turn-taking of different group members and investigate the flexibility of indris’ vocal behavior when co-singing with their offspring. We found that social factors can influence the turn-taking organization in a chorus, as offspring were more likely to drop out from the parents’ duet than join in, and we speculate that overlap might signal competition by members of the same-sex. The duet between the reproductive pair was the most common type of singing organization, followed by a duet between mothers and sons and the triadic interaction between mother, father, and son. Interestingly, parents’ solo singing seems to stimulate offspring to vocalize, and we also found that mothers and fathers simplify, at least in part, song elaboration when chorusing with offspring. Our results indicate that indris can perform short-time adjustments to the number of co-emitters and their identity: our approach is advantageous in highlighting the multilevel influences on primate vocal flexibility. Moreover, it provides evidence that some aspects of our vocal plasticity were already present in the lemur lineage.

Increased signal complexity is associated with increased mating success

The evolution of complex signals has often been explored by testing multiple functional hypotheses regarding how independent signal components provide selective benefits to offset the costs of their production. In the present study, we take a different approach by exploring the function of complexity per se. We test the hypothesis that increased vibratory signal complexity-based on both proportional and temporal patterning-provides selective benefits to courting male Schizocosa stridulans wolf spiders. In support of this hypothesis, all of our quantified metrics of vibratory signal complexity predicted the mating success of male S. stridulans. The rate of visual signalling, which is mechanistically tied to vibratory signal production, was also associated with mating success. We additionally found evidence that males can dynamically adjust the complexity of their vibratory signalling. Together, our results suggest that complexity per se may be a target of female choice.

Comparative Analysis of the Vocal Repertoires of the Indri (Indri indri) and the Diademed Sifaka (Propithecus diadema)

Strepsirrhine vocalisations are extraordinarily diverse and cross-species comparisons are needed to explore how this variability evolved. We contributed to the investigation of primate acoustic diversity by comparing the vocal repertoire of two sympatric lemur species, Propithecus diadema and Indri indri. These diurnal species belong to the same taxonomic family and have similar activity patterns but different social structures. These features make them excellent candidates for an investigation of the phylogenetic, environmental, and social influence on primate vocal behavior. We recorded 3 P. diadema groups in 2014 and 2016. From 1,872 recordings we selected and assigned 3814 calls to 9 a priori call types, on the basis of their acoustic structure. We implemented a reproducible technique performing an acoustic feature extraction relying on frequency bins, t-SNE data reduction, and a hard-clustering analysis. We first quantified the vocal repertoire of P. diadema, finding consistent results for the 9 putatively identified call types. When comparing this repertoire with a previously published repertoire of I. indri, we found highly species-specific repertoires, with only 2% of the calls misclassified by species identity. The loud calls of the two species were very distinct, while the low-frequency calls were more similar. Our results pinpoint the role of phylogenetic history, social and environmental features on the evolution of communicative systems and contribute to a deeper understanding of the evolutionary roots of primate vocal differentiation. We conclude by arguing that standardized and reproducible techniques, like the one we employed, allow robust comparisons and should be prioritized in the future.

Utilizing DeepSqueak for automatic detection and classification of mammalian vocalizations: a case study on primate vocalizations

Bioacoustic analyses of animal vocalizations are predominantly accomplished through manual scanning, a highly subjective and time-consuming process. Thus, validated automated analyses are needed that are usable for a variety of animal species and easy to handle by non-programing specialists. This study tested and validated whether DeepSqueak, a user-friendly software, developed for rodent ultrasonic vocalizations, can be generalized to automate the detection/segmentation, clustering and classification of high-frequency/ultrasonic vocalizations of a primate species. Our validation procedure showed that the trained detectors for vocalizations of the gray mouse lemur (Microcebus murinus) can deal with different call types, individual variation and different recording quality. Implementing additional filters drastically reduced noise signals (4225 events) and call fragments (637 events), resulting in 91% correct detections (N_total = 3040). Additionally, the detectors could be used to detect the vocalizations of an evolutionary closely related species, the Goodman’s mouse lemur (M. lehilahytsara). An integrated supervised classifier classified 93% of the 2683 calls correctly to the respective call type, and the unsupervised clustering model grouped the calls into clusters matching the published human-made categories. This study shows that DeepSqueak can be successfully utilized to detect, cluster and classify high-frequency/ultrasonic vocalizations of other taxa than rodents, and suggests a validation procedure usable to evaluate further bioacoustics software.

Filling in the gaps: Acoustic gradation increases in the vocal ontogeny of chimpanzees (Pan troglodytes)

Human vocal ontogeny is considered to be a process whereby a large repertoire of discrete sounds seemingly emerges from a smaller number of acoustically graded vocalizations. While adult chimpanzee vocal behavior is highly graded, its developmental trajectory is poorly understood. In the present study, we therefore examined the size and structure of the chimpanzee vocal repertoire at different stages of ontogeny. Audio recordings were collected on infant (N = 13) and juvenile (N = 13) semi-wild chimpanzees at Chimfunshi Wildlife Orphanage, Zambia, using focal and ad libitum sampling. All observed call types were acoustically measured. These were predominantly grunts, whimpers, laughs, screams, hoos, and barks and squeaks. A range of spectral and temporal acoustic parameters were extracted, and fuzzy c-means clustering was used to quantify the size and structure of the repertoire. The infant and juvenile vocal repertoires were both best described with the same number of clusters. However, compared to infants, juvenile call clusters were less distinct from one another and could be extracted only when a low level of overlap between call clusters was permitted. Moreover, the acoustic overlap between call clusters was significantly higher for juveniles. Overall, this pattern shows greater acoustic overlap in juvenile vocalizations compared to infants, suggesting a trend toward increased acoustic gradation in chimpanzee vocal ontogeny. This may imply in contrast to humans, chimpanzees become increasingly proficient in using graded signals effectively rather than developing a larger repertoire of more discrete sounds in ontogeny.

Finding Meanings in Low Dimensional Structures: Stochastic Neighbor Embedding Applied to the Analysis of Indri indri Vocal Repertoire

Simple Summary

The description of the vocal repertoire represents a critical step before deepening other aspects of animal behaviour. Repertoires may contain both discrete vocalizations—acoustically distinct and distinguishable from each other—or graded ones, with a less rigid acoustic structure. The gradation level is one of the causes that make repertoires challenging to be objectively quantified. Indeed, the higher the level of gradation in a system, the higher the complexity in grouping its components. A large sample of Indri indri calls was divided into ten putative categories from the acoustic similarity among them. We extracted frequency and duration parameters and then performed two different analyses that were able to group the calls accordingly to the a priori categories, indicating the presence of ten robust vocal classes. The analyses also showed a neat grouping of discrete vocalizations and a weaker classification of graded ones.

Abstract

Although there is a growing number of researches focusing on acoustic communication, the lack of shared analytic approaches leads to inconsistency among studies. Here, we introduced a computational method used to examine 3360 calls recorded from wild indris (Indri indri) from 2005–2018. We split each sound into ten portions of equal length and, from each portion we extracted spectral coefficients, considering frequency values up to 15,000 Hz. We submitted the set of acoustic features first to a t-distributed stochastic neighbor embedding algorithm, then to a hard-clustering procedure using a k-means algorithm. The t-distributed stochastic neighbor embedding (t-SNE) mapping indicated the presence of eight different groups, consistent with the acoustic structure of the a priori identification of calls, while the cluster analysis revealed that an overlay between distinct call types might exist. Our results indicated that the t-distributed stochastic neighbor embedding (t-SNE), successfully been employed in several studies, showed a good performance also in the analysis of indris’ repertoire and may open new perspectives towards the achievement of shared methodical techniques for the comparison of animal vocal repertoires.

Human Non-linguistic Vocal Repertoire: Call Types and Their Meaning

Recent research on human nonverbal vocalizations has led to considerable progress in our understanding of vocal communication of emotion. However, in contrast to studies of animal vocalizations, this research has focused mainly on the emotional interpretation of such signals. The repertoire of human nonverbal vocalizations as acoustic types, and the mapping between acoustic and emotional categories, thus remain underexplored. In a cross-linguistic naming task (Experiment 1), verbal categorization of 132 authentic (non-acted) human vocalizations by English-, Swedish- and Russian-speaking participants revealed the same major acoustic types: laugh, cry, scream, moan, and possibly roar and sigh. The association between call type and perceived emotion was systematic but non-redundant: listeners associated every call type with a limited, but in some cases relatively wide, range of emotions. The speed and consistency of naming the call type predicted the speed and consistency of inferring the caller’s emotion, suggesting that acoustic and emotional categorizations are closely related. However, participants preferred to name the call type before naming the emotion. Furthermore, nonverbal categorization of the same stimuli in a triad classification task (Experiment 2) was more compatible with classification by call type than by emotion, indicating the former’s greater perceptual salience. These results suggest that acoustic categorization may precede attribution of emotion, highlighting the need to distinguish between the overt form of nonverbal signals and their interpretation by the perceiver. Both within- and between-call acoustic variation can then be modeled explicitly, bringing research on human nonverbal vocalizations more in line with the work on animal communication.

Modeling individual vocal differences in group-living lemurs using vocal tract morphology

Vocal individuality is widespread in social animals. Individual variation in vocalizations is a prerequisite for discriminating among conspecifics and may have facilitated the evolution of large complex societies. Ring-tailed lemurs Lemur catta live in relatively large social groups, have conspicuous vocal repertoires, and their species-specific utterances can be interpreted in light of source-filter theory of vocal production. Indeed, their utterances allow individual discrimination and even recognition thanks to the resonance frequencies of the vocal tract. The purpose of this study is to determine which distinctive vocal features can be derived from the morphology of the upper vocal tract. To accomplish this, we built computational models derived from anatomical measurements collected on lemur cadavers and compared the results with the spectrographic output of vocalizations recorded from ex situ live individuals. Our results demonstrate that the morphological variation of the ring-tailed lemur vocal tract explains individual distinctiveness of their species-specific utterances. We also provide further evidence that vocal tract modeling is a powerful tool for studying the vocal output of non-human primates.