Stable social organization in a wide variety of organisms has been linked to kinship, which can minimize conflict due to the indirect fitness benefits from cooperating with relatives. In birds, kin selection has been mostly studied in the context of reproduction or in species that are social year round. Many birds however are migratory, and the role of kinship in the winter societies of these species is virtually unexplored. In a previous study, we discovered striking social complexity and stability in a wintering population of migratory golden-crowned sparrows (Zonotrichia atricapilla) - individuals repeatedly form close associations with the same social partners, including across multiple winters. Here, we test the possibility that kinship might be involved in these close and stable social affiliations. We examine the relationship between kinship and social structure for two of the consecutive wintering seasons from the previous study. We found no evidence that social structure was influenced by kinship. Relatedness between most pairs of individuals was at most that of first cousins (and mostly far lower). Genetic networks based on relatedness do not correspond to the social networks, and Mantel tests revealed no relationship between kinship and pairwise interaction frequency. Kinship also failed to predict social structure in more fine-grained analyses, including analyses of each sex separately (in the event that sex-biased migration might limit kin selection to one sex), and separate analyses for each social community. The complex winter societies of golden-crowned sparrows appear to be based on cooperative benefits unrelated to kin selection. © 2015 John Wiley & Sons Ltd.

Over the past decade, there has been an explosion of interest in network research across the physical and social sciences. For social scientists, the theory of networks has been a gold mine, yielding explanations for social phenomena in a wide variety of disciplines from psychology to economics. Here, we review the kinds of things that social scientists have tried to explain using social network analysis and provide a nutshell description of the basic assumptions, goals, and explanatory mechanisms prevalent in the field. We hope to contribute to a dialogue among researchers from across the physical and social sciences who share a common interest in understanding the antecedents and consequences of network phenomena.

Friend of a friend relationships, or the indirect connections between people, influence our health, well-being, financial success and reproductive output. As with humans, social behaviours in other animals often occur within a broad interconnected network of social ties. Yet studies of animal social behaviour tend to focus on associations between pairs of individuals. With the increase in popularity of social network analysis, researchers have started to look beyond the dyad to examine the role of indirect connections in animal societies. Here, I provide an overview of the new knowledge that has been uncovered by these studies. I focus on research that has addressed both the causes of social behaviours, i.e. the cognitive and genetic basis of indirect connections, as well as their consequences, i.e. the impact of indirect connections on social cohesion, information transfer, cultural practices and fitness. From these studies, it is apparent that indirect connections play an important role in animal behaviour, although future research is needed to clarify their contribution.

Advances over the last 15 years have made social network analysis (SNA) a powerful tool for the study of nonhuman primate social behavior. Although many SNA-based techniques have been only very recently adopted in primatological research, others have been commonly used by primatologists for decades. The roots of SNA also stem from some of the same conceptual frameworks as the majority of nonhuman primate behavioral research. The rapid development of SNA in recent years has led to questions within the primatological community of where and how SNA fits within this field. We aim to address these questions by providing an overview of the historical relationship between SNA and the study of nonhuman primates. We begin with a brief history of the development of SNA, followed by a detailed description of the network-based visualization techniques, analytical methods and conceptual frameworks which have been employed by primatologists since as early as the 1960s. We also introduce some of the latest advances to SNA, thereby demonstrating that this approach contains novel tools for the study of nonhuman primate social behavior which may be used to shed light on questions that cannot be addressed fully using more conventional methods.© 2011 Wiley-Liss, Inc.

Acoustic communication is crucial to humans and many other tetrapods, including birds, frogs, crocodilians, and mammals. However, large-scale patterns in its evolution are largely unstudied. Here, we address several fundamental questions about the origins of acoustic communication in terrestrial vertebrates (tetrapods), using phylogenetic methods. We show that origins of acoustic communication are significantly associated with nocturnal activity. We find that acoustic communication does not increase diversification rates, a surprising result given the many speciation-focused studies of frog calls and bird songs. We also demonstrate that the presence of acoustic communication is strongly conserved over time. Finally, we find that acoustic communication evolved independently in most major tetrapod groups, often with remarkably ancient origins (~100-200 million years ago). Overall, we show that the role of ecology in shaping signal evolution applies to surprisingly deep timescales, whereas the role of signal evolution in diversification may not.

During female mate choice, both the male's phenotype and resources (e.g. his nest) contribute to the chooser's fitness. Animals other than humans are not known to advertise resource characteristics to potential mates through vocal communication; although in some species of anurans and birds, females do evaluate male qualities through vocal communication. Here, we demonstrate that calls of the male Emei music frog (Babina dauchina), vocalizing from male-built nests, reflect nest structure information that can be recognized by females. Inside-nest calls consisted of notes with energy concentrated at lower frequency ranges and longer note durations when compared with outside-nest calls. Centre frequencies and note durations of the inside calls positively correlate with the area of the burrow entrance and the depth of the burrow, respectively. When given a choice between outside and inside calls played back alternately, more than 70 per cent of the females (33/47) chose inside calls. These results demonstrate that males of this species faithfully advertise whether or not they possess a nest to potential mates by vocal communication, which probably facilitates optimal mate selection by females. These results revealed a novel function of advertisement calls, which is consistent with the wide variation in both call complexity and social behaviour within amphibians.

Multiple regression quadratic assignment procedures (MRQAP) tests are permutation tests for multiple linear regression model coefficients for data organized in square matrices of relatedness among n objects. Such a data structure is typical in social network studies, where variables indicate some type of relation between a given set of actors. We present a new permutation method (called "double semi-partialing", or DSP) that complements the family of extant approaches to MRQAP tests. We assess the statistical bias (type I error rate) and statistical power of the set of five methods, including DSP, across a variety of conditions of network autocorrelation, of spuriousness (size of confounder effect), and of skewness in the data. These conditions are explored across three assumed data distributions: normal, gamma, and negative binomial. We find that the Freedman-Lane method and the DSP method are the most robust against a wide array of these conditions. We also find that all five methods perform better if the test statistic is pivotal. Finally, we find limitations of usefulness for MRQAP tests: All tests degrade under simultaneous conditions of extreme skewness and high spuriousness for gamma and negative binomial distributions.

Social network analysis has been widely used to investigate the dynamics of social interactions and the evolution of social complexity across a range of taxa. Anuran species are highly dependent on vocal communication in mate choice; however, these species have rarely been the subject of social network analysis. The present study used social network analysis to investigate whether vocal network structures are consistent in Emei music frog (Babina daunchina) after the introduction of a simulated exotic rival of varying competitiveness into the social group. We broadcasted six categories of artificial calls (either highly sexually attractive calls produced from inside male nests or calls of low sexual attractiveness produced outside nests with three, five or seven notes, respectively) to simulate an intruder with different levels of competitiveness. We then constructed vocal networks for two time periods (before and after the disturbance) and quantified three network metrics (strength, closeness, and betweenness) that measure different aspects of individual-level position. We used the mean values of these network metrics to evaluate group-level changes in network structure. We found that the mean strength, mean closeness and mean betweenness were consistent between two time periods in all ponds, despite the fact that the positions of some individuals had changed markedly after disturbance. In addition, there was no significant interaction effect between period and numbers of notes on the three network metrics. These finding suggest that the structure of vocal networks in Emei music frogs remain stable at the group level after a conspecific disturbance, regardless of the intruder's competitiveness.

Animals often interact non-randomly with conspecifics, and association preferences can differ across life-history stages to maximize individuals' fitness. Mongolian gerbils (Meriones unguiculatus) are a social rodent that live in highly seasonal habitats and display seasonal fluctuations in population density, growth rate and the size of overlapped home ranges. Nevertheless, whether gerbils modify their social relationships at different life-history stages remains unknown. Here, we used social network analysis to examine whether social associations differ between the sexes and between life-history stages in a wild population of Mongolian gerbils. We quantified social attributes at both group level (assortativity) and individual level (social differentiation and degree, closeness and betweenness centrality); these attributes reflect individuals' social preferences and their potential influence on others in the network. We found that both male and female gerbils established fewer inter-group social connections during the food-hoarding season than during the breeding season, revealing constraints on sociality. Similarly, during the food-hoarding season, degree centrality and social differentiation increased significantly whereas closeness and betweenness centrality decreased significantly. Together, these results suggest that gerbils have relatively more partners and preferred associations and decreased influence over others in the network during the food-hoarding season. In addition, we found no significant difference in any of the social attribute between males and females, but there was a significant interaction effect between sex and season on degree, closeness and betweenness centrality. Our results demonstrate that Mongolian gerbils adjust their association strategies to adapt to the changes of life history. Such adjustments may balance the costs/benefits associated with survival and reproduction.© 2017 The Authors. Integrative Zoology published by International Society of Zoological Sciences, Institute of Zoology/Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

A common result in recent linguistic studies on word association networks is that their topology can often be described by Zipf's law, in which most words have few associations, whereas a few words are highly connected. However, little is known about syntactic networks in more rudimentary communication systems, which could represent a window into the early stages of language evolution. In this study, we investigate the syntactic network formed by syllable associations in the song of the oscine bird Troglodytes musculus. We use methods recently developed in the context of the study of complex networks to assess topological characteristics in the syntactic networks of T. musculus. We found statistically significant evidence for nestedness in the syllable association network of T. musculus, indicating network organization around a core of commonly used notes, small-world features, and a non-random degree distribution. Our analyses suggest the possibility of a balance between the maintenance of core notes and the acquisition/loss of rare notes through both cultural drift and improvisation. These results underscore the usefulness of investigating communication networks of other animal species in uncovering the initial steps in the evolution of complex syntax networks. Copyright © 2014 Elsevier B.V. All rights reserved.

Permutation tests are widely used to test null hypotheses with animal social network data, but suffer from high rates of type I and II error when the permutations do not properly simulate the intended null hypothesis.Two common types of permutations each have limitations. Pre-network (or datastream) permutations can be used to control 'nuisance effects' like spatial, temporal or sampling biases, but only when the null hypothesis assumes random social structure. Node (or node-label) permutation tests can test null hypotheses that include nonrandom social structure, but only when nuisance effects do not shape the observed network.We demonstrate one possible solution addressing these limitations: using pre-network permutations to adjust the values for each node or edge before conducting a node permutation test. We conduct a range of simulations to estimate error rates caused by confounding effects of social or non-social structure in the raw data.Regressions on simulated datasets suggest that this 'double permutation' approach is less likely to produce elevated error rates relative to using only node permutations, pre-network permutations or node permutations with simple covariates, which all exhibit elevated type I errors under at least one set of simulated conditions. For example, in scenarios where type I error rates from pre-network permutation tests exceed 30%, the error rates from double permutation remain at 5%.The double permutation procedure provides one potential solution to issues arising from elevated type I and type II error rates when testing null hypotheses with social network data. We also discuss alternative approaches that can provide robust inference, including fitting mixed effects models, restricted node permutations, testing multiple null hypotheses and splitting large datasets to generate replicated networks. Finally, we highlight ways that uncertainty can be explicitly considered and carried through the analysis.© 2021 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.

1. Animal social networks are descriptions of social structure which, aside from their intrinsic interest for understanding sociality, can have significant bearing across many fields of biology. 2. Network analysis provides a flexible toolbox for testing a broad range of hypotheses, and for describing the social system of species or populations in a quantitative and comparable manner. However, it requires careful consideration of underlying assumptions, in particular differentiating real from observed networks and controlling for inherent biases that are common in social data. 3. We provide a practical guide for using this framework to analyse animal social systems and test hypotheses. First, we discuss key considerations when defining nodes and edges, and when designing methods for collecting data. We discuss different approaches for inferring social networks from these data and displaying them. We then provide an overview of methods for quantifying properties of nodes and networks, as well as for testing hypotheses concerning network structure and network processes. Finally, we provide information about assessing the power and accuracy of an observed network. 4. Alongside this manuscript, we provide appendices containing background information on common programming routines and worked examples of how to perform network analysis using the r programming language. 5. We conclude by discussing some of the major current challenges in social network analysis and interesting future directions. In particular, we highlight the under-exploited potential of experimental manipulations on social networks to address research questions.© 2015 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of the British Ecological Society.

Social networks are often inferred from spatial associations, but other parameters like acoustic communication are likely to play a central role in within group interactions. However, it is currently difficult to determine which individual initiates vocalizations, or who responds to whom. To this aim, we designed a method that allows analyzing group vocal network while controlling for spatial networks, by positioning each group member in equidistant individual cages and analyzing continuous vocal interactions semi-automatically. We applied this method to two types of zebra finch groups, composed of either two adult females and two juveniles, or four young adults (juveniles from the first groups). Young often co-occur in the same social group as adults but are likely to have a different social role, which may be reflected in their vocal interactions. Therefore, we tested the hypothesis that the social structure of the group influences the parameters of the group vocal network. We found that groups including juveniles presented periods with higher level of activity than groups composed of young adults. Using two types of analyses (Markov analysis and cross-correlation), we showed that juveniles as well as adults were more likely to respond to individuals of their own age-class (i.e. to call one after another, in terms of turn-taking, and within a short time-window, in terms of time delay). When juveniles turned into adulthood, they showed adult characteristics of vocal patterns. Together our results suggest that vocal behavior changes during ontogeny, and individuals are more strongly connected with individuals of the same age-class within acoustic networks.

The formalization of multilayer networks allows for new ways to measure sociality in complex social systems, including groups of animals. The same mathematical representation and methods are widely applicable across fields and study systems, and a network can represent drastically different types of data. As such, in order to apply analyses and interpret the results in a meaningful way the researcher must have a deep understanding of what their network is representing and what parts of it are being measured by a given analysis. Multilayer social networks can represent social structure with more detail than is often present in single layer networks, including multiple "types" of individuals, interactions, or relationships, and the extent to which these types are interdependent. Multilayer networks can also encompass a wider range of social scales, which can help overcome complications that are inherent to measuring sociality. In this paper, I dissect multilayer networks into the parts that correspond to different components of social structures. I then discuss common pitfalls to avoid across different stages of multilayer network analyses-some novel and some that always exist in social network analysis but are magnified in multi-layer representations. This paper serves as a primer for building a customized toolkit of multilayer network analyses, to probe components of social structure in animal social systems.© The Author(s) (2021). Published by Oxford University Press on behalf of Editorial Office, Current Zoology.

Network analysis has driven key developments in research on animal behaviour by providing quantitative methods to study the social structures of animal groups and populations. A recent formalism, known as, has advanced the study of multifaceted networked systems in many disciplines. It offers novel ways to study and quantify animal behaviour through connected 'layers' of interactions. In this article, we review common questions in animal behaviour that can be studied using a multilayer approach, and we link these questions to specific analyses. We outline the types of behavioural data and questions that may be suitable to study using multilayer network analysis. We detail several multilayer methods, which can provide new insights into questions about animal sociality at individual, group, population and evolutionary levels of organization. We give examples for how to implement multilayer methods to demonstrate how taking a multilayer approach can alter inferences about social structure and the positions of individuals within such a structure. Finally, we discuss caveats to undertaking multilayer network analysis in the study of animal social networks, and we call attention to methodological challenges for the application of these approaches. Our aim is to instigate the study of new questions about animal sociality using the new toolbox of multilayer network analysis.

Social networks describe the pattern of intraspecific interactions within a population. An individual's position in a social network often is expected to influence its fitness, but only a few studies have examined this relationship in natural populations. We investigated the fitness consequences of network position in a wild beetle population. Copulation success of male beetles positively covaried with strength (a measure of network centrality) and negatively covaried with clustering coefficient (CC) (a measure of cliquishness). Further analysis using mediation path models suggested that the activity level of individuals drove the relationships between strength and fitness almost entirely. In contrast, selection on CC was not explained by individual behaviours. Although our data suggest that social network position can experience strong sexual selection, it is also clear that the relationships between fitness and some network metrics merely reflect variation in individual-level behaviours.© 2011 The Authors. Journal of Evolutionary Biology © 2011 European Society For Evolutionary Biology.

Heterogeneity within pathogen species can have important consequences for how pathogens transmit across landscapes; however, discerning different transmission routes is challenging. Here, we apply both phylodynamic and phylogenetic community ecology techniques to examine the consequences of pathogen heterogeneity on transmission by assessing subtype-specific transmission pathways in a social carnivore. We use comprehensive social and spatial network data to examine transmission pathways for three subtypes of feline immunodeficiency virus (FIV ) in African lions (Panthera leo) at multiple scales in the Serengeti National Park, Tanzania. We used FIV molecular data to examine the role of social organization and lion density in shaping transmission pathways and tested to what extent vertical (i.e., father- and/or mother-offspring relationships) or horizontal (between unrelated individuals) transmission underpinned these patterns for each subtype. Using the same data, we constructed subtype-specific FIV co-occurrence networks and assessed what combination of social networks, spatial networks or co-infection best structured the FIV network. While social organization (i.e., pride) was an important component of FIV transmission pathways at all scales, we find that FIV subtypes exhibited different transmission pathways at within- and between-pride scales. A combination of social and spatial networks, coupled with consideration of subtype co-infection, was likely to be important for FIV transmission for the two major subtypes, but the relative contribution of each factor was strongly subtype-specific. Our study provides evidence that pathogen heterogeneity is important in understanding pathogen transmission, which could have consequences for how endemic pathogens are managed. Furthermore, we demonstrate that community phylogenetic ecology coupled with phylodynamic techniques can reveal insights into the differential evolutionary pressures acting on virus subtypes, which can manifest into landscape-level effects.© 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.

Vocal communication networks can be linked to social behaviour, allowing a deeper understanding of social relationships among individuals. For this purpose, the description of vocal dyads is fundamental. In group-living species, this identification is based on behavioural indicators which require a high level of reactivity during social interactions. In the present study, we alternatively established a proximity-based approach to investigate whether sex-specific differences in vocal communication reflect social behaviour in a species with rather loose social associations and low levels of reactivity: the Southern white rhinoceros (Ceratotherium simum simum). We performed audio- and video recordings of 30 captive animals from seven groups. Vocal networks for the four most common call types were constructed by considering conspecifics at close distance (≤ 1 body length) to the sender as potential receivers. The analysis of the resulting unidirectional structures showed that not only the sex of the sender but also the sex of the potential receiver, the quality of social interactions (affiliative or agonistic) as well as association strength predict the intensity of vocal interactions between group members. Thus, a proximity-based approach can be used to construct vocal networks providing information about the social relationships of conspecifics-even in species with loose social associations where behavioural indicators are limited.

Social network theory has made major contributions to our understanding of human social organisation but has found relatively little application in the field of animal behaviour. In this review, we identify several broad research areas where the networks approach could greatly enhance our understanding of social patterns and processes in animals. The network theory provides a quantitative framework that can be used to characterise social structure both at the level of the individual and the population. These novel quantitative variables may provide a new tool in addressing key questions in behavioural ecology particularly in relation to the evolution of social organisation and the impact of social structure on evolutionary processes. For example, network measures could be used to compare social networks of different species or populations making full use of the comparative approach. However, the networks approach can in principle go beyond identifying structural patterns and also can help with the understanding of processes within animal populations such as disease transmission and information transfer. Finally, understanding the pattern of interactions in the network (i.e. who is connected to whom) can also shed some light on the evolution of behavioural strategies.© Springer-Verlag 2007.

Animals often gather information from other species by eavesdropping on signals intended for others. We review the extent, benefits, mechanisms, and ecological and evolutionary consequences of eavesdropping on other species' alarm calls. Eavesdropping has been shown experimentally in about 70 vertebrate species, and can entail closely or distantly related species. The benefits of eavesdropping include prompting immediate anti-predator responses, indirect enhancement of foraging or changed habitat use, and learning about predators. Eavesdropping on heterospecifics can provide more eyes looking for danger, complementary information to that from conspecifics, and potentially information at reduced cost. The response to heterospecific calls can be unlearned or learned. Unlearned responses occur when heterospecific calls have acoustic features similar to that used to recognize conspecific calls, or acoustic properties such as harsh sounds that prompt attention and may allow recognition or facilitate learning. Learning to recognize heterospecific alarm calls is probably essential to allow recognition of the diversity of alarm calls, but the evidence is largely indirect. The value of eavesdropping on different species is affected by problems of signal interception and the relevance of heterospecific alarm calls to the listener. These constraints on eavesdropping will affect how information flows among species and thus affect community function. Some species are 'keystone' information producers, while others largely seek information, and these differences probably affect the formation and function of mixed-species groups. Eavesdroppers might also integrate alarm calls from multiple species to extract relevant and reliable information. Eavesdropping appears to set the stage for the evolution of interspecific deception and communication, and potentially affects communication within species. Overall, we now know that eavesdropping on heterospecific alarm calls is an important source of information for many species across the globe, and there are ample opportunities for research on mechanisms, fitness consequences and implications for community function and signalling evolution. © 2014 The Authors. Biological Reviews © 2014 Cambridge Philosophical Society.

Complex networks are studied across many fields of science. To uncover their structural design principles, we defined "network motifs," patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks. We found such motifs in networks from biochemistry, neurobiology, ecology, and engineering. The motifs shared by ecological food webs were distinct from the motifs shared by the genetic networks of Escherichia coli and Saccharomyces cerevisiae or from those found in the World Wide Web. Similar motifs were found in networks that perform information processing, even though they describe elements as different as biomolecules within a cell and synaptic connections between neurons in Caenorhabditis elegans. Motifs may thus define universal classes of networks. This approach may uncover the basic building blocks of most networks.

By studying animal vocalizations such as birdsong, our capacity to record and interpret acoustic data has opened many opportunities for objective studies of animal behaviour, song evolution, physiology and ecology. However, the analysis of such data sets is often complex, and can vary between research teams, study species and theoretical approach. We test the use of network analysis in categorical birdsong data sets, focusing on four main applications: (1) quantifying and describing patterns of song sharing between individuals and/or populations; (2) understanding factors driving, and the implications of, an individual's position within a song-sharing network; (3) analysing (song or syllable) transfer patterns; and (4) investigating how environmental factors influence song-sharing networks. This exercise provides an excellent example of the advantages of sharing methodologies across disciplines within behavioural ecology and will hopefully inform future studies focused on different aspects of song sharing and cultural evolution by providing new tools and techniques for analysis. (C) 2019 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd.

We present spatsoc, an r package for conducting social network analysis with animal telemetry data. Animal social network analysis is a method for measuring relationships between individuals to describe social structure. Proximity-based social networks are generated from animal telemetry data by grouping relocations temporally and spatially, using thresholds that are informed by the characteristics of the species and study system. spatsoc fills a gap in r packages by providing flexible functions, explicitly for animal telemetry data, to generate edge lists and gambit-of-the-group data, perform data-stream randomization, and generate group by individual matrices. The implications of spatsoc are that current users of animal telemetry or otherwise georeferenced data for movement or spatial analyses will have access to efficient and intuitive functions to generate social networks.

Natal dispersal is an important life history trait driving variation in individual fitness, and therefore, a proper understanding of the factors underlying dispersal behaviour is critical to many fields including population dynamics, behavioural ecology and conservation biology. However, individual dispersal patterns remain difficult to quantify despite many years of research using direct and indirect methods. Here, we quantify dispersal in a single intensively studied population of the cooperatively breeding chestnut-crowned babbler (Pomatostomus ruficeps) using genetic networks created from the combination of pairwise relatedness data and social networking methods and compare this to dispersal estimates from re-sighting data. This novel approach not only identifies movements between social groups within our study sites but also provides an estimation of immigration rates of individuals originating outside the study site. Both genetic and re-sighting data indicated that dispersal was strongly female biased, but the magnitude of dispersal estimates was much greater using genetic data. This suggests that many previous studies relying on mark-recapture data may have significantly underestimated dispersal. An analysis of spatial genetic structure within the sampled population also supports the idea that females are more dispersive, with females having no structure beyond the bounds of their own social group, while male genetic structure expands for 750 m from their social group. Although the genetic network approach we have used is an excellent tool for visualizing the social and genetic microstructure of social animals and identifying dispersers, our results also indicate the importance of applying them in parallel with behavioural and life history data.© 2012 Blackwell Publishing Ltd.

Animal vocalizations serve a wide range of functions including territorial defense, courtship, social cohesion, begging, and vocal learning. Whereas many insights have been gained from observational studies and experiments using auditory stimulation, there is currently no technology available for the selective control of vocal communication in small animal groups. We developed a system for real-time control of vocal interactions among separately housed animals. The system is implemented on a field-programmable gate array (FPGA) and it allows imposing arbitrary communication networks among up to four animals. To minimize undesired transitive sound leakage, we adopted echo attenuation and sound squelching algorithms. In groups of three zebra finches, we restrict vocal communication in circular and in hierarchical networks and thereby mimic complex eavesdropping and middleman situations.

Identifying the individuals within a population can generate information on life history parameters, generate input data for conservation models, and highlight behavioural traits that may affect management decisions and error or bias within census methods. Individual animals can be discriminated by features of their vocalisations. This vocal individuality can be utilised as an alternative marking technique in situations where the marks are difficult to detect or animals are sensitive to disturbance. Vocal individuality can also be used in cases were the capture and handling of an animal is either logistically or ethically problematic. Many studies have suggested that vocal individuality can be used to count and monitor populations over time; however, few have explicitly tested the method in this role. In this review we discuss methods for extracting individuality information from vocalisations and techniques for using this to count and monitor populations over time. We present case studies in birds where vocal individuality has been applied to conservation and we discuss its role in mammals.

Culturally transmitted communication signals - such as human language or bird song - can change over time through cultural drift, and the resulting dialects may consequently enhance the separation of populations. However, the emergence of song dialects has been considered unlikely when songs are highly individual-specific, as in the zebra finch (Taeniopygia guttata). Here we show that machine learning can nevertheless distinguish the songs from multiple captive zebra finch populations with remarkable precision, and that 'cryptic song dialects' predict strong assortative mating in this species. We examine mating patterns across three consecutive generations using captive populations that have evolved in isolation for about 100 generations. We cross-fostered eggs within and between these populations and used an automated barcode tracking system to quantify social interactions. We find that females preferentially pair with males whose song resembles that of the females' adolescent peers. Our study shows evidence that in zebra finches, a model species for song learning, individuals are sensitive to differences in song that have hitherto remained unnoticed by researchers.© 2022. The Author(s).

Social network analysis (SNA) is a framework used to study the structure of societies. As an umbrella term that encompasses various tools of graph theory and mathematical models to visualize networks, SNA allows researchers to detect and quantify patterns in social networks. Within SNA, individuals are not independent, but are symbiotic or linked with one another in a network. Given its powerful analytical tools, SNA is capable of addressing a range of animal behaviors, and has accordingly become increasingly popular in behavioral ecology studies examining such notions as mate choice/sexual selection, cooperation, information flow and disease transition, behavioral strategies of individuals, fitness consequences of sociality and network stability. Nevertheless, SNA it relatively underutilized among Chinese behavioral ecologists. This study aims at highlighting the benefits of SNA in studying animal behaviors in order to promote greater utilization of SNA within Chinese studies. By first introducing social network theory and demonstrating how social networks can influence individual and collective behaviors, this paper provide a prospective overview of SNA's general utilization for the study of animal behavioral ecology as well as promising directions in the overall use of SNA.