A critical consequence of joining social groups is the possibility of social transmission of information related to novel behaviours or resources. Network-based diffusion analysis (NBDA) has emerged as a leading frequentist framework for inferring and quantifying social transmission, particularly in non-human animal populations. NBDA has been extended several times to account for multiple diffusions, multiple networks, individual-level variables and complex transmission functions. Bayesian versions of NBDA have been proposed before, although these implementations have seen limited usage and have not kept pace with the evolving ecosystem of Bayesian methods. There is not yet a user-friendly package to implement a Bayesian NBDA. Here, we present a unified framework for performing Bayesian analysis of social transmission using NBDA-type models, implemented in the widely used Stan programming language. We provide a user-friendly R package 'STbayes' (ST: social transmission) for other researchers to easily use this framework. STbayes accepts user-formatted data, but can also import data directly from the existing NBDA R package. Based on the data users provide, STbayes automatically generates multi-network, multi-diffusion models that allow for covariates that may influence transmission and varying (random) effects. Using simulated data, we demonstrate that this model can accurately differentiate the relative contribution of individual and social learning in the spread of information through networked populations. We illustrate how incorporating upstream uncertainty about the relationships between individuals can improve model fit. Our framework can be used to infer complex transmission rules, and we describe a numerically stable parametrization of frequency-dependent transmission. Finally, we introduce support for dynamic transmission weights and a 'high-resolution' data mode, which allows users to make use of fine-scale data collected by contemporary automated tracking methods. These extensions increase the set of contexts that this type of model may be used for.

Collective behaviour by eusocial insect colonies is typically achieved through multiple communication networks that produce complex behaviour at the group level but often appear to provide redundant or even competing information. A classic example occurs in honeybee (Apis mellifera) colonies, where both the dance communication system and robust scent-based mechanisms contribute to the allocation of a colony's workforce by regulating the flow of experienced foragers among known food sources. Here we analysed social connectivity patterns during the reactivation of experienced foragers to familiar feeding sites to show that these social information pathways are not simply multiple means to achieve the same end but intersect to play complementary roles in guiding forager behaviour. Using artificial feeding stations, we mimicked a natural scenario in which two forager groups were simultaneously collecting from distinct patches containing different flowering species. We then observed the reactivation of these groups at their familiar feeding sites after interrupting their foraging. Social network analysis revealed that temporarily unemployed individuals interacted more often and for longer with foragers that advertised a familiar versus unfamiliar foraging site. Due to such resource-based assortative mixing, network-based diffusion analysis estimated that reactivation events primarily resulted from interactions among bees that had been trained to the same feeding station and less so from different-feeder interactions. Both scent- and dance-based interactions strongly contributed to reactivation decisions. However, each bout of dance-following had an especially strong effect on a follower's likelihood of reactivation, particularly when dances indicated locations familiar to followers. Our findings illustrate how honeybee foragers can alter their social connectivity in ways that are likely to enhance collective outcomes by enabling foragers to rapidly access up-to-date information about familiar foraging sites. In addition, our results highlight how reliance on multiple communication mechanisms enables social insect workers to utilise flexible information-use strategies that are robust to variation in the availability of social information.

Abstract This chapter considers ‘social learning’, broadly defined as learning that is facilitated by observation of, or interaction with, another individual (or its products). Of most interest is social learning that results in ‘social transmission’: the observer adopting behaviour patterns matching those of the demonstrator. It looks at a number of different social learning processes, from simple processes like local enhancement to cognitively complex processes like imitation. Social learning is not always adaptive. Individuals need to employ strategies on when, who, and what is beneficial to copy rather than relying on personal information. A variety of experimental set-ups—ranging from simple diffusion to cross-fostering or translocation studies—in combination with statistical methods such as matrix regressions, network-based diffusion analysis or experience-weighted attraction models provide powerful tools to detect and measure the importance of social learning across species and contexts and identify typical pathways of transmission.

Cultural transmission studies in animals have predominantly focused on identifying between-group variation in tool-use techniques, while immaterial cultures remain understudied despite their potential for highlighting similarities between human and animal culture. Here, using long-term data from two chimpanzee communities, we tested whether one of chimpanzees' most enigmatic social customs-the grooming handclasp-is culturally transmitted by investigating the influence of well-documented human transmission biases on their variational preferences. After identifying differences in style preferences between the communities, we show that older and dominant individuals exert more influence over their partners' handclasp styles. Mothers were equally likely to influence their offspring's preferences as nonkin, indicating that styles are transmitted both vertically and obliquely. Last, individuals gradually converged on the group style, suggesting that conformity guides chimpanzees' handclasp preferences. Our findings show that chimpanzees' social lives are influenced by cultural transmission biases that hitherto were thought to be uniquely human.

Selectively learning from specific types of individuals may be adaptive if demonstrator characteristics can be used to identify more beneficial sources of social information. Such "social learning biases" have been experimentally demonstrated in a number of species, but these experiments generally involve restricted laboratory conditions using a limited number of potential demonstrators and tend to consider only the characteristics of demonstrators rather than the importance of pairwise relationships on information transfer between individuals. In this study, we presented a novel foraging task to a large population of zebra finches (Taeniopygia guttata) housed in a free-flying aviary and used multinetwork Network-Based Diffusion Analysis (NBDA) to establish whether birds learned from individuals they shared particular relationships with. Specifically, we investigated whether task solves followed social learning pathways representing the following relationships between individuals: feeding associations, aggressive interactions, positive associations (e.g. grooming) and mating pairs. We found strong evidence that zebra finches learn from their aggressors, irrespective of the outcome of that aggressive encounter. This has been previously suggested in laboratory-based studies on zebra finches, but never conclusively documented in a freely interacting population. We also found some weaker evidence to suggest that zebra finches learn from their mates-a social learning bias that has previously received little to no attention. However, we found that mates-based learning occurred infrequently and was secondary to aggression-based social learning biases. Our results therefore additionally highlight the importance of including combinations of multiple potential information pathways in social learning analyses to account for secondary learning pathways that may otherwise be missed.

Honey bees famously use waggle dances to communicate foraging locations to nestmates in the hive, thereby recruiting them to those sites. The decision to dance is governed by rules that, when operating collectively, are assumed to direct foragers to the most profitable locations with little input from potential recruits, who are presumed to respond similarly to any dance regardless of its information content. Yet, variation in receiver responses can qualitatively alter collective outcomes. Here, we use network-based diffusion analysis to compare the collective influence of dance information during recruitment to feeders at different distances. We further assess how any such effects might be achieved at the individual level by dance followers either persisting with known sites when novel targets are distant and/or seeking more accurate spatial information to guide long-distance searches. Contrary to predictions, we found little evidence that dance followers' responses depended on target distance over the foraging distances considered here (100-500 m). While dance information was always key to feeder discovery, its importance did not vary with feeder distance, and bees were in fact quicker to abandon previously rewarding sites for distant alternatives. These findings provide empirical support for the longstanding assumption that self-organized foraging by honey bee colonies relies heavily on signal performance rules with limited input from recipients.(c) 2021 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

Although social learning capabilities are taxonomically widespread, demonstrating that freely interacting animals (whether wild or captive) rely on social learning has proved remarkably challenging. Network-based diffusion analysis (NBDA) offers a means for detecting social learning using observational data on freely interacting groups. Its core assumption is that if a target behaviour is socially transmitted, then its spread should follow the connections in a social network that reflects social learning opportunities. Here, we provide a comprehensive guide for using NBDA. We first introduce its underlying mathematical framework and present the types of questions that NBDA can address. We then guide researchers through the process of selecting an appropriate social network for their research question; determining which NBDA variant should be used; and incorporating other variables that may impact asocial and social learning. Finally, we discuss how to interpret an NBDA model's output and provide practical recommendations for model selection. Throughout, we highlight extensions to the basic NBDA framework, including incorporation of dynamic networks to capture changes in social relationships during a diffusion and using a multi-network NBDA to estimate information flow across multiple types of social relationship. Alongside this information, we provide worked examples and tutorials demonstrating how to perform analyses using the newly developed nbda package written in the R programming language.

Animal social network analyses (ASNA) have led to a foundational shift in our understanding of animal sociality that transcends the disciplinary boundaries of genetics, spatial movements, epidemiology, information transmission, evolution, species assemblages and conservation. However, some analytical protocols (i.e., permutation tests) used in ASNA have recently been called into question due to the unacceptable rates of false negatives (type I error) and false positives (type II error) they generate in statistical hypothesis testing. Here, we show that these rates are related to the way in which observation heterogeneity is accounted for in association indices. To solve this issue, we propose a method termed the “global index” (GI) that consists of computing the average of individual associations indices per unit of time. In addition, we developed an “index of interactions” (II) that allows the use of the GI approach for directed behaviours. Our simulations show that GI: 1) returns more reasonable rates of false negatives and positives, with or without observational biases in the collected data, 2) can be applied to both directed and undirected behaviours, 3) can be applied to focal sampling, scan sampling or “gambit of the group” data collection protocols, and 4) can be applied to first- and second-order social network measures. Finally, we provide a method to control for non-social biological confounding factors using linear regression residuals. By providing a reliable approach for a wide range of scenarios, we propose a novel methodology in ASNA with the aim of better understanding social interactions from a mechanistic, ecological and evolutionary perspective.

Honeybees famously use waggle dances to communicate foraging locations to nestmates in the hive, thereby recruiting them to those sites. The decision to dance is governed by rules that, when operating collectively, are assumed to direct foragers to the most profitable locations with little input from potential recruits, who are presumed to respond similarly to any dance regardless of its information content. Yet variation in receiver responses can qualitatively alter collective outcomes. Here, we use network-based diffusion analysis to compare the collective influence of dance information during recruitment to feeders at different distances. We further assess how any such effects might be achieved at the individual level by dance-followers either persisting with known sites when novel targets are distant and/or seeking more accurate spatial information to guide long-distance searches. Contrary to predictions, we found no evidence that dance-followers’ responses depended on target distance. While dance information was always key to feeder discovery, its importance did not vary with feeder distance, and bees were in fact quicker to abandon previously rewarding sites for distant alternatives. These findings provide empirical support for the longstanding assumption that self-organized foraging by honeybee colonies relies heavily on signal performance rules with limited input from recipients.

The structure of a group is critical in determining how a socially learnt behaviour will spread. Predictions from theoretical models indicate that specific parameters of social structure differentially influence social transmission. Modularity describes how the structure of a group or network is divided into distinct subgroups or clusters. Theoretical modelling indicates that the modularity of a network will predict the rate of behavioural spread within a group, with higher modularity slowing the rate of spread and facilitating the establishment of local behavioural variants which can prelude local cultures. Despite prolific modelling approaches, empirical tests via manipulations of group structure remain scarce. We experimentally manipulated the modularity of populations of domestic fowl chicks, Gallus gallus domesticus, to affect the transmission of a novel foraging behaviour. We compared the spread of behaviour in populations with networks of high or low modularity against control populations where social transmission was prevented. We found the foraging behaviour to spread socially between individuals when the social transmission was permitted; however, modularity did not increase the speed of behavioural spread nor lead to the initial establishments of shared behavioural variants. This result suggests that factors in the social transmission process additional to the network structure may influence behavioural spread.

By responding to information gained through observing or interacting with other individuals, fish can learn about important aspects of their environment, including where to forage, how to recognize and avoid predators, and who to mate with. Social learning processes are often closely intertwined with the social environment; whether individuals engage in social learning, who they learn from, and what they learn frequently depend on complex, nonrandom patterns of social interaction. Social network analysis provides a sophisticated toolset for quantifying such elements of social structure. In this chapter, we discuss how integrating social network approaches with investigations into social learning have provided novel and important insights regarding the ways in which fish acquire and use social information in realistic social contexts.

Social transmission of information is taxonomically widespread and could have profound effects on the ecological and evolutionary dynamics of animal communities. Demonstrating this in the wild, however, has been challenging. Here we show by field experiment that social transmission among predators can shape how selection acts on prey defences. Using artificial prey and a novel approach in statistical analyses of social networks, we find that blue tit (Cyanistes caeruleus) and great tit (Parus major) predators learn about prey defences by watching others. This shifts population preferences rapidly to match changes in prey profitability, and reduces predation pressure from naïve predators. Our results may help resolve how costly prey defences are maintained despite influxes of naïve juvenile predators, and suggest that accounting for social transmission is essential if we are to understand coevolutionary processes.

Callitrichidae is a unique primate family not only in terms of the large number of food transfers to infants but also for the prevalence of transfers that are initiated by the adults. It has been hypothesized that, as well as provisioning infants, callitrichid food transfers might function to teach the receiver what food types to eat. If food provisioning has a teaching function, we would expect successful food transfers to be more likely with food types that are novel to the juveniles. We would also expect juveniles to learn about foods from those transfers. We introduced different types of food (some familiar, some novel) to wild groups of golden lion tamarins ( Leontopithecus rosalia ). While novel foods were not more successfully transferred than familiar food in the experiment, transfers were more successful (i.e., the receiver obtained food) when the donor had previous experience with that food. Moreover, we found evidence suggesting that food transfers influenced the future foraging choices of juveniles. Our findings are consistent with the first and third criteria of the functional definition of teaching, which requires that tutors (the adults) modify their behavior in the presence of a naïve individual (a juvenile), and that the naïve individual learns from the modified behavior of the demonstrator. Our findings are also consistent with the provisioning function of food transfer. Social learning seems to play an important role in the development of young tamarins’ foraging preferences.

Van Leeuwen et al. found that two peculiar interactive behaviors (social scratching and groom slapping) transmitted socially through bonobo networks across six European zoos.

Little is known about how multiple social learning strategies interact and how organisms integrate both individual and social information. Here we combine, in a wild primate, an open diffusion experiment with a modeling approach: Network-Based Diffusion Analysis using a dynamic observation network. The vervet monkeys we study were not provided with a trained model; instead they had access to eight foraging boxes that could be opened in either of two ways. We report that individuals socially learn the techniques they observe in others. After having learnt one option, individuals are 31x more likely to subsequently asocially learn the other option than individuals naïve to both options. We discover evidence of a rank transmission bias favoring learning from higher-ranked individuals, with no evidence for age, sex or kin bias. This fine-grained analysis highlights a rank transmission bias in a field experiment mimicking the diffusion of a behavioral innovation.

Cultural behavior, which is transmitted among conspecifics through social learning [1], is found across various taxa [2-6]. Vertical social transmission from parent to offspring [7] is thought to be adaptive because of the parental generation being more skilled than maturing individuals. It is found throughout the animal kingdom, particularly in species with prolonged parental care, e.g., [8, 9]. Social learning can also occur among members of the same generation [4, 10, 11] or between older, non-parental individuals and younger generations [7] via horizontal or oblique transmission, respectively. Extensive work on primate culture has shown that horizontal transmission of foraging behavior is biased toward species with broad cultural repertoires [12] and those with increased levels of social tolerance [13, 14], such as great apes. Vertical social transmission has been established as the primary transmission mechanism of foraging behaviors in the Indo-Pacific bottlenose dolphin (Tursiops aduncus) population of Shark Bay, Western Australia [6, 9, 15, 16]. Here, we investigated the spread of another foraging strategy, "shelling" [17], whereby some dolphins in this population feed on prey trapped inside large marine gastropod shells. Using a multi-network version of "network-based diffusion analysis" (NBDA), we show that shelling behavior spreads primarily through non-vertical social transmission. By statistically accounting for both environmental and genetic influences, our findings thus represent the first evidence of non-vertical transmission of a foraging tactic in toothed whales. This research suggests there are multiple transmission pathways of foraging behaviors in dolphins, highlighting the similarities between cetaceans and great apes in the nature of the transmission of cultural behaviors. VIDEO ABSTRACT.

The spread of socially-learnt behaviours occurs in many animal species, and understanding how behaviours spread can provide novel insights into the causes and consequences of sociality. Within wild populations, behaviour spread is often assumed to occur as a "simple contagion". Yet, emerging evidence suggests behaviours may frequently spread as "complex contagions", and this holds significant ramifications for the modes and extent of transmission. We present a new framework enabling comprehensive examination of behavioural contagions by integrating social-learning strategies into network-based diffusion analyses. We show how our approach allows determination of the relationship between social bonds and behavioural transmission, identification of individual-level transmission rules, and examination of population-level social structure effects. We provide resources that allow general applications across diverse systems, and demonstrate how further study-specific developments can be made. Finally, we outline the new opportunities this framework facilitates, the conceptual contributions to understanding sociality, and its applications across fields.

In shaping how individuals explore their environment and interact with others, personality may mediate both individual and social learning. Yet increasing evidence indicates that personality expression is contingent on social context, suggesting that group personality composition may be key in determining how individuals learn about their environment. Here, we used recovery latency following simulated predator attacks to identify Trinidadian guppies (Poecilia reticulata) that acted in a consistently bold or shy manner. We then employed network-based diffusion analysis to track the spread of a novel foraging behaviour through groups containing different proportions of bold and shy fish. Informed associates promoted learning to a greater extent in bold individuals, but only within groups composed predominately of bold fish. As the proportion of shy fish within groups increased, bold individuals instead emerged as especially effective demonstrators that facilitated learning in others. Individuals were also more likely to learn overall within shy-dominated groups than in bold-dominated ones. We demonstrate that whether and how individuals learn is conditional on group personality composition, indicating that selection may favour traits enabling individuals to better match their behavioural phenotype to their social environment.

The honeybee (Apis mellifera) dance communication system is a marvel of collective behaviour, but the added value it brings to colony foraging efficiency is poorly understood. In temperate environments, preventing communication of foraging locations rarely decreases colony food intake, potentially because simultaneous transmission of olfactory information also plays a major role in foraging. Here, we employ social network analyses that quantify information flow across multiple temporally varying networks (each representing a different interaction type) to evaluate the relative contributions of dance communication and hive-based olfactory information transfer to honeybee recruitment events. We show that virtually all successful recruits to novel locations rely upon dance information rather than olfactory cues that could otherwise guide them to the same resource. Conversely, during reactivation to known sites, dances are relatively less important, as foragers are primarily guided by olfactory information. By disentangling the contributions of multiple information networks, the contexts in which dance communication truly matters amid a complex system full of redundancy can now be identified.