A machine learning approach for classifying and quantifying acoustic diversity

Many morphs: Parsing gesture signals from the noise

Parsing signals from noise is a general problem for signallers and recipients, and for researchers studying communicative systems. Substantial efforts have been invested in comparing how other species encode information and meaning, and how signalling is structured. However, research depends on identifying and discriminating signals that represent meaningful units of analysis. Early approaches to defining signal repertoires applied top-down approaches, classifying cases into predefined signal types. Recently, more labour-intensive methods have taken a bottom-up approach describing detailed features of each signal and clustering cases based on patterns of similarity in multi-dimensional feature-space that were previously undetectable. Nevertheless, it remains essential to assess whether the resulting repertoires are composed of relevant units from the perspective of the species using them, and redefining repertoires when additional data become available. In this paper we provide a framework that takes data from the largest set of wild chimpanzee (Pan troglodytes) gestures currently available, splitting gesture types at a fine scale based on modifying features of gesture expression using latent class analysis (a model-based cluster detection algorithm for categorical variables), and then determining whether this splitting process reduces uncertainty about the goal or community of the gesture. Our method allows different features of interest to be incorporated into the splitting process, providing substantial future flexibility across, for example, species, populations, and levels of signal granularity. Doing so, we provide a powerful tool allowing researchers interested in gestural communication to establish repertoires of relevant units for subsequent analyses within and between systems of communication.

Artificial Intelligence for Climate Change Biology: From Data Collection to Predictions

In the era of big data, ecological research is experiencing a transformative shift, yet big-data advancements in thermal ecology and the study of animal responses to climate conditions remain limited. This review discusses how big data analytics and artificial intelligence (AI) can significantly enhance our understanding of microclimates and animal behaviors under changing climatic conditions. We explore AI's potential to refine microclimate models and analyze data from advanced sensors and camera technologies, which capture detailed, high-resolution information. This integration can allow researchers to dissect complex ecological and physiological processes with unprecedented precision. We describe how AI can enhance microclimate modeling through improved bias correction and downscaling techniques, providing more accurate estimates of the conditions that animals face under various climate scenarios. Additionally, we explore AI's capabilities in tracking animal responses to these conditions, particularly through innovative classification models that utilize sensors such as accelerometers and acoustic loggers. For example, the widespread usage of camera traps can benefit from AI-driven image classification models to accurately identify thermoregulatory responses, such as shade usage and panting. AI is therefore instrumental in monitoring how animals interact with their environments, offering vital insights into their adaptive behaviors. Finally, we discuss how these advanced data-driven approaches can inform and enhance conservation strategies. In particular, detailed mapping of microhabitats essential for species survival under adverse conditions can guide the design of climate-resilient conservation and restoration programs that prioritize habitat features crucial for biodiversity resilience. In conclusion, the convergence of AI, big data, and ecological science heralds a new era of precision conservation, essential for addressing the global environmental challenges of the 21st century.

Widespread cultural change in declining populations of Amazon parrots

Species worldwide are experiencing anthropogenic environmental change, and the long-term impacts on animal cultural traditions such as vocal dialects are often unknown. Our prior studies of the yellow-naped amazon (Amazona auropalliata) revealed stable vocal dialects over an 11-year period (1994-2005), with modest shifts in geographic boundaries and acoustic structure of contact calls. Here, we examined whether yellow-naped amazons maintained stable dialects over the subsequent 11-year time span from 2005 to 2016, culminating in 22 years of study. Over this same period, this species suffered a dramatic decrease in population size that prompted two successive uplists in IUCN status, from vulnerable to critically endangered. In this most recent 11-year time span, we found evidence of geographic shifts in call types, manifesting in more bilingual sites and introgression across the formerly distinct North-South acoustic boundary. We also found greater evidence of acoustic drift, in the form of new emerging call types and greater acoustic variation overall. These results suggest cultural traditions such as dialects may change in response to demographic and environmental conditions, with broad implications for threatened species.

Individual and ecological heterogeneity promote complex communication in social vertebrate group decisions

To receive the benefits of social living, individuals must make effective group decisions that enable them to achieve behavioural coordination and maintain cohesion. However, heterogeneity in the physical and social environments surrounding group decision-making contexts can increase the level of difficulty social organisms face in making decisions. Groups that live in variable physical environments (high ecological heterogeneity) can experience barriers to information transfer and increased levels of ecological uncertainty. In addition, in groups with large phenotypic variation (high individual heterogeneity), individuals can have substantial conflicts of interest regarding the timing and nature of activities, making it difficult for them to coordinate their behaviours or reach a consensus. In such cases, active communication can increase individuals' abilities to achieve coordination, such as by facilitating the transfer and aggregation of information about the environment or individual behavioural preferences. Here, we review the role of communication in vertebrate group decision-making and its relationship to heterogeneity in the ecological and social environment surrounding group decision-making contexts. We propose that complex communication has evolved to facilitate decision-making in specific socio-ecological contexts, and we provide a framework for studying this topic and testing related hypotheses as part of future research in this area. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

Vocal repertoire and individuality in the plains zebra (Equus quagga)

Acoustic signals are vital in animal communication, and quantifying them is fundamental for understanding animal behaviour and ecology. Vocalizations can be classified into acoustically and functionally or contextually distinct categories, but establishing these categories can be challenging. Newly developed methods, such as machine learning, can provide solutions for classification tasks. The plains zebra is known for its loud and specific vocalizations, yet limited knowledge exists on the structure and information content of its vocalzations. In this study, we employed both feature-based and spectrogram-based algorithms, incorporating supervised and unsupervised machine learning methods to enhance robustness in categorizing zebra vocalization types. Additionally, we implemented a permuted discriminant function analysis to examine the individual identity information contained in the identified vocalization types. The findings revealed at least four distinct vocalization types-the 'snort', the 'soft snort', the 'squeal' and the 'quagga quagga'-with individual differences observed mostly in snorts, and to a lesser extent in squeals. Analyses based on acoustic features outperformed those based on spectrograms, but each excelled in characterizing different vocalization types. We thus recommend the combined use of these two approaches. This study offers valuable insights into plains zebra vocalization, with implications for future comprehensive explorations in animal communication.

Vocalizations of the Pekin duck (Anas platyrhynchos domesticus): how stimuli, sex, and social groups affect their vocal repertoire

Pekin ducks are exposed to stressors such as heat stress, enteric pathogens, mycotoxins, and other environmental stressors. We know from wild bird literature that birds communicate through vocalizations. We hypothesized that Pekin ducks would have a diverse repertoire that is affected by the sex, social group, and specific stimuli. We utilized adult Pekin ducks to develop a vocal repertoire. We placed 1 to 4 ducks of varying sexes into a sound chamber with various stimuli used to encourage new vocalizations. Birds were recorded for 20 min with several variations of number and sexes of ducks. Once the ducks were recorded each vocalization that was clipped was named based on a predetermined naming system. We characterized the vocal system of the ducks under each stimulus and social treatment in 4 ways: overall call rates, call diversity, call repertoire, and call spectral properties. In all cases, normality of residuals and homogeneity of variances for GLM and ANOVA models were confirmed using Proc Univariate (SAS v9.4) where a p ≤ 0.05 was considered significant. We found that Pekin ducks produce up to 16 different vocalizations. The treatments had a significant effect on the overall rate of calls given by the ducks (ANOVA: F6,31 = 8.55, p < 0.0001). Ducks produced the most calls by far when someone was sitting in the chamber with them (30.04 ± 4.45 calls/min). For call diversity, we found that there was a significant main effect of hen number (F218 = 12.21, p = 0.0004) but no main effect of drake number (F3,18 = 3.04, p = 0.0555). Cluster analyses indicated that certain types of calls were given under specific conditions. There were generally 6 major clusters of vocal repertoires (R-square = 0.899, Cubic Clustering Criterion = 9.30). Our results suggest that Pekin ducks are affected by the types of stimuli and social environment in how much they vocalize and in the properties of the calls they use. In addition, males and females differ somewhat in the repertoire of the calls they use, and in the spectral properties of their calls.

Soundscape Characterization Using Autoencoders and Unsupervised Learning

Passive acoustic monitoring (PAM) through acoustic recorder units (ARUs) shows promise in detecting early landscape changes linked to functional and structural patterns, including species richness, acoustic diversity, community interactions, and human-induced threats. However, current approaches primarily rely on supervised methods, which require prior knowledge of collected datasets. This reliance poses challenges due to the large volumes of ARU data. In this work, we propose a non-supervised framework using autoencoders to extract soundscape features. We applied this framework to a dataset from Colombian landscapes captured by 31 audiomoth recorders. Our method generates clusters based on autoencoder features and represents cluster information with prototype spectrograms using centroid features and the decoder part of the neural network. Our analysis provides valuable insights into the distribution and temporal patterns of various sound compositions within the study area. By utilizing autoencoders, we identify significant soundscape patterns characterized by recurring and intense sound types across multiple frequency ranges. This comprehensive understanding of the study area's soundscape allows us to pinpoint crucial sound sources and gain deeper insights into its acoustic environment. Our results encourage further exploration of unsupervised algorithms in soundscape analysis as a promising alternative path for understanding and monitoring environmental changes.

GesturalOrigins: A bottom-up framework for establishing systematic gesture data across ape species

Current methodologies present significant hurdles to understanding patterns in the gestural communication of individuals, populations, and species. To address this issue, we present a bottom-up data collection framework for the study of gesture: GesturalOrigins. By "bottom-up", we mean that we minimise a priori structural choices, allowing researchers to define larger concepts (such as 'gesture types', 'response latencies', or 'gesture sequences') flexibly once coding is complete. Data can easily be re-organised to provide replication of, and comparison with, a wide range of datasets in published and planned analyses. We present packages, templates, and instructions for the complete data collection and coding process. We illustrate the flexibility that our methodological tool offers with worked examples of (great ape) gestural communication, demonstrating differences in the duration of action phases across distinct gesture action types and showing how species variation in the latency to respond to gestural requests may be revealed or masked by methodological choices. While GesturalOrigins is built from an ape-centred perspective, the basic framework can be adapted across a range of species and potentially to other communication systems. By making our gesture coding methods transparent and open access, we hope to enable a more direct comparison of findings across research groups, improve collaborations, and advance the field to tackle some of the long-standing questions in comparative gesture research.

Retracted: Tarsier islands: Exploring patterns of variation in tarsier duets from offshore islands of North Sulawesi

Retraction: Clink, D. J., Comella, I. A., Tasirin, J. S., & Klinck, H. (2022). Tarsier islands: Exploring patterns of variation in tarsier duets from offshore islands of North Sulawesi. American Journal of Primatology, (https://doi.org/10.1002/ajp.23410). The above article, published online on 27 June 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the authors, the journal Editor in Chief, Karen Bales, and Wiley Periodicals LLC. The retraction has been agreed due to the fact that the article included data for which there was no data sharing agreement in place.

Higher-order dialectic variation and syntactic convergence in the complex warble song of budgerigars

Dialectic signatures in animal acoustic signals are key in the identification of and association with group members. Complex vocal sequences may also convey information about behavioral state, and may thus vary according to social environment. Some bird species, such as psittaciforms, learn and modify their complex acoustic signals throughout their lives. However, the structure and function of vocal sequences in open-ended vocal learners remains understudied. Here, we examined vocal sequence variation in the warble song of budgerigars, and how these change upon contact between social groups. Budgerigars are open-ended vocal learners which exhibit fission-fusion flock dynamics in the wild. We found that two captive colonies of budgerigars exhibited colony-specific differences in the syntactic structure of their vocal sequences. Individuals from the two colonies differed in the propensity to repeat certain note types, forming repetitive motifs which served as higher-order signatures of colony identity. When the two groups were brought into contact, their vocal sequences converged, and these colony-specific repetitive patterns disappeared, with males from both erstwhile colonies now producing similar sequences with similar syntactic structure. We present data suggesting that the higher-order temporal arrangement of notes/vocal units is modified throughout life by social learning as groups of birds continually associate and dissociate. Our study sheds light on the importance of examining signal structure at multiple levels of organization, and the potential for psittaciform birds as model systems to examine the influence of learning and social environment on acoustic signals.

Sex Differences in the Neural Song Circuit and Its Relationship to Song Acoustic Complexity in House Wrens (Troglodytes aedon)

The song circuit in passerine birds is an outstanding model system for understanding the relationship between brain morphology and behavior, in part due to varying degrees of sex differences in structure and function across species. House wrens (Troglodytes aedon) offer a unique opportunity to advance our understanding of this relationship. Intermediate sex differences in song rate and complexity exist in this species compared to other passerines, and, among individual females, song complexity varies dramatically. Acoustic complexity in wild house wrens was quantified using a new machine learning approach. Volume, cell number, cell density, and neuron soma size were then measured for three song circuit regions, Area X, HVC (used as a proper name), and the robust nucleus of the arcopallium (RA), and one control region, the nucleus rotundus (Rt). For each song control area, males had a larger volume with more cells, larger somas, and lower cell density. Male songs had greater acoustic complexity than female songs, but these distributions overlapped. In females, increased acoustic complexity was correlated with larger volumes of and more cells in Area X and RA, as well as larger soma size in RA. In males, song complexity was unrelated to morphology, although our methods may underestimate male song complexity. This is the first study to identify song control regions in house wrens and one of few examining individual variation in both sexes. Parallels between morphology and the striking variability in female song in this species provide a new model for understanding relationships between neural structure and function.

Individual identity information persists in learned calls of introduced parrot populations

Animals can actively encode different types of identity information in learned communication signals, such as group membership or individual identity. The social environments in which animals interact may favor different types of information, but whether identity information conveyed in learned signals is robust or responsive to social disruption over short evolutionary timescales is not well understood. We inferred the type of identity information that was most salient in vocal signals by combining computational tools, including supervised machine learning, with a conceptual framework of "hierarchical mapping", or patterns of relative acoustic convergence across social scales. We used populations of a vocal learning species as a natural experiment to test whether the type of identity information emphasized in learned vocalizations changed in populations that experienced the social disruption of introduction into new parts of the world. We compared the social scales with the most salient identity information among native and introduced range monk parakeet (Myiopsitta monachus) calls recorded in Uruguay and the United States, respectively. We also evaluated whether the identity information emphasized in introduced range calls changed over time. To place our findings in an evolutionary context, we compared our results with another parrot species that exhibits well-established and distinctive regional vocal dialects that are consistent with signaling group identity. We found that both native and introduced range monk parakeet calls displayed the strongest convergence at the individual scale and minimal convergence within sites. We did not identify changes in the strength of acoustic convergence within sites over time in the introduced range calls. These results indicate that the individual identity information in learned vocalizations did not change over short evolutionary timescales in populations that experienced the social disruption of introduction. Our findings point to exciting new research directions about the robustness or responsiveness of communication systems over different evolutionary timescales.

Chinese striped-neck turtles vocalize underwater and show differences in peak frequency among different age and sex groups

Background

Turtle vocalizations play an important role throughout their lives by expressing individual information (position, emotion, or physiological status), reflecting mating preferences, and synchronizing incubation. The Chinese striped-neck turtle (Mauremys sinensis) is one of the most widely distributed freshwater turtles in China, whose wild population is critically endangered. However, its vocalization has not been studied, which can be the basis for behavioral and ecological studies.

Methods

Five different sex-age groups of turtles were recorded underwater in a soundproof room. Cluster analysis and principal component analysis for classification of Chinese striped-neck turtle calls were unreasonable. The turtle calls were manually sought using visual and aural inspection of the recordings in Raven Pro 1.5 software and classified according to differences perceived through auditory inspection and the morphological characteristics of the spectrograms. The results of similarity analysis verified the reliability of manual classification. We compared the peak frequency of the calls among different age and sex groups.

Results

We identified ten M. sinensis call types, displayed their spectra and waveforms, and described their auditory characteristics. Most calls produced by the turtles were low-frequency. Some high-frequency call types, that are common in other turtle species were also produced. Similar to other turtles, the Chinese striped-neck turtle generates harmonic vocalizations. Courtship behaviors were observed when one of the call types occurred in the mixed-sex group. Adult females produced more high-frequency call types, and subadult males had higher vocalizations than other groups. These results provide a basis for future research on the function of vocalizations, field monitoring, and conservation of this species.

The relative speed of the glucocorticoid stress response varies independently of scope and is predicted by environmental variability and longevity across birds

The acute glucocorticoid response is a key mediator of the coordinated vertebrate response to unpredictable challenges. Rapid glucocorticoid increases initiate changes that allow animals to cope with stressors. The scope of the glucocorticoid response - defined here as the absolute increase in glucocorticoids - is associated with individual differences in performance and varies across species with environment and life history. In addition to varying in scope, responses can differ enormously in speed; however, relatively little is known about whether speed and absolute glucocorticoid levels covary, how selection shapes speed, or what aspects of speed are important. We used corticosterone samples collected at 5 time points from 1750 individuals of 60 species of birds to ask i) how the speed and scope of the glucocorticoid response covary and ii) whether variation in absolute or relative speed is predicted by environmental context or life history. Among species, faster absolute glucocorticoid responses were strongly associated with a larger scope. Despite this covariation, the relative speed of the glucocorticoid response (standardized within species) varied independently of absolute scope, suggesting that selection could operate on both features independently. Species with faster relative glucocorticoid responses lived in locations with more variable temperature and had shorter lifespans. Our results suggest that rapid changes associated with the speed of the glucocorticoid response, such as those occurring through non-genomic receptors, might be an important determinant of coping ability and we emphasize the need for studies designed to measure speed independently of absolute glucocorticoid levels.

Toward a Computational Neuroethology of Vocal Communication: From Bioacoustics to Neurophysiology, Emerging Tools and Future Directions

Recently developed methods in computational neuroethology have enabled increasingly detailed and comprehensive quantification of animal movements and behavioral kinematics. Vocal communication behavior is well poised for application of similar large-scale quantification methods in the service of physiological and ethological studies. This review describes emerging techniques that can be applied to acoustic and vocal communication signals with the goal of enabling study beyond a small number of model species. We review a range of modern computational methods for bioacoustics, signal processing, and brain-behavior mapping. Along with a discussion of recent advances and techniques, we include challenges and broader goals in establishing a framework for the computational neuroethology of vocal communication.

Correlated evolution of distinct signals associated with increased social selection in female white-shouldered fairywrens

Conspicuous female signals have recently received substantial scientific attention, but it remains unclear if their evolution is the result of selection acting on females independently of males or if mutual selection facilitates female change. Species that express female, but not male, phenotypic variation among populations represents a useful opportunity to address this knowledge gap. White-shouldered fairywrens (Malurus alboscapulatus) are tropical songbirds with a well-resolved phylogeny where female, but not male, coloration varies allopatrically across subspecies. We explored how four distinct signaling modalities, each putatively associated with increased social selection, are expressed in two populations that vary in competitive pressure on females. Females in a derived subspecies (M. a. moretoni) have evolved more ornamented plumage and have shorter tails (a signal of social dominance) relative to an ancestral subspecies (M. a. lorentzi) with drab females. In response to simulated territorial intrusions broadcasting female song, both sexes of M. a. moretoni are more aggressive and more coordinated with their mates in both movement and vocalizations. Finally, M. a. moretoni songs are more complex than M. a. lorentzi, but song complexity does not vary between sexes in either population. These results suggest that correlated phenotypic shifts in coloration and tail morphology in females as well as song complexity and aggression in both sexes may have occurred in response to changes in the intensity of social selection pressures. This highlights increased competitive pressures in both sexes can facilitate the evolution of complex multimodal signals.

Is Seeing Still Believing? Leveraging Deepfake Technology for Livestock Farming

Deepfake technologies are known for the creation of forged celebrity pornography, face and voice swaps, and other fake media content. Despite the negative connotations the technology bears, the underlying machine learning algorithms have a huge potential that could be applied to not just digital media, but also to medicine, biology, affective science, and agriculture, just to name a few. Due to the ability to generate big datasets based on real data distributions, deepfake could also be used to positively impact non-human animals such as livestock. Generated data using Generative Adversarial Networks, one of the algorithms that deepfake is based on, could be used to train models to accurately identify and monitor animal health and emotions. Through data augmentation, using digital twins, and maybe even displaying digital conspecifics (digital avatars or metaverse) where social interactions are enhanced, deepfake technologies have the potential to increase animal health, emotionality, sociality, animal-human and animal-computer interactions and thereby productivity, and sustainability of the farming industry. The interactive 3D avatars and the digital twins of farm animals enabled by deepfake technology offers a timely and essential way in the digital transformation toward exploring the subtle nuances of animal behavior and cognition in enhancing farm animal welfare. Without offering conclusive remarks, the presented mini review is exploratory in nature due to the nascent stages of the deepfake technology.