Perching but not foraging networks predict the spread of novel foraging skills in starlings

Mixed-species groups and the question of dominance in the social ecosystem

Dominance interactions and hierarchies are of long-standing interest in the field of animal behaviour. Currently, dominance hierarchies are viewed as complex social structures formed by repeated interactions between individuals. Most studies on this phenomenon come from single-species groups. However, animals are constantly surrounded by and interact with individuals of other species. Behaviour and social interactions of individuals can be shaped by the presence or behaviour of other species in their social ecosystem, which has important implications for social behaviour in groups. Given how ubiquitous mixed-species animal groups are, deeper study of the relationships between mixed-species group (MSG) structure and dominance will be key to understanding constraints on individual behaviour and decision making. Here we call for more research into dominance interactions among individuals in MSGs. Greater understanding of the dynamics of dominance relationships among individuals in MSGs, whose size and composition can change considerably over shorter and longer term time frames, will be crucial to understanding their structure and functioning. This article is part of the theme issue 'Mixed-species groups and aggregations: shaping ecological and behavioural patterns and processes'.

Social situations differ in their contribution to population-level social structure in griffon vultures

Social relationships among animals emerge from interactions in multiple ecological and social situations. However, we seldom ask how each situation contributes to the global structure of a population, and whether different situations contribute different information about social relationships and the position of individuals within the social fabric. Griffon vultures (Gyps fulvus) interact socially in multiple situations, including communal roosting, joint flights, and co-feeding. These social interactions can influence population-level outcomes, such as disease transmission and information sharing that determine survival and response to changes. We examined the unique contribution of each social and ecological situation to the social structure of the population and individuals' positions within the overall social network using high-resolution GPS tracking. We found that the number of individuals each vulture interacted with (degree) was best predicted by diurnal interactions-both during flights and on the ground (such as when feeding). However, the strength of social bonds, that is, the number of interactions an individual had (strength), was best predicted by interactions on the ground-both during the day (e.g., while feeding) and at night (e.g., while roosting) but not by interactions while flying. Thus, social situations differ in their impact on the relationships that individuals form. By incorporating the ecological situations in which social interactions occur we gain a more complete view of how social relationships are formed and which situations are important for different types of interactions.

Learning strategies and long-term memory in Asian short-clawed otters (Aonyx cinereus)

Social learning, where information is acquired from others, is taxonomically widespread. There is growing evidence that animals selectively employ 'social learning strategies', which determine e.g. when to copy others instead of learning asocially and whom to copy. Furthermore, once animals have acquired new information, e.g. regarding profitable resources, it is beneficial for them to commit it to long-term memory (LTM), especially if it allows access to profitable resources in the future. Research into social learning strategies and LTM has covered a wide range of taxa. However, otters (subfamily Lutrinae), popular in zoos due to their social nature and playfulness, remained neglected until a recent study provided evidence of social learning in captive smooth-coated otters (Lutrogale perspicillata), but not in Asian short-clawed otters (Aonyx cinereus). We investigated Asian short-clawed otters' learning strategies and LTM performance in a foraging context. We presented novel extractive foraging tasks twice to captive family groups and used network-based diffusion analysis to provide evidence of a capacity for social learning and LTM in this species. A major cause of wild Asian short-clawed otter declines is prey scarcity. Furthering our understanding of how they learn about and remember novel food sources could inform key conservation strategies.

Detecting and quantifying social transmission using network-based diffusion analysis

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.

How to make methodological decisions when inferring social networks

Social network analyses allow studying the processes underlying the associations between individuals and the consequences of those associations. Constructing and analyzing social networks can be challenging, especially when designing new studies as researchers are confronted with decisions about how to collect data and construct networks, and the answers are not always straightforward. The current lack of guidance on building a social network for a new study system might lead researchers to try several different methods and risk generating false results arising from multiple hypotheses testing. Here, we suggest an approach for making decisions when starting social network research in a new study system that avoids the pitfall of multiple hypotheses testing. We argue that best edge definition for a network is a decision that can be made using a priori knowledge about the species and that is independent from the hypotheses that the network will ultimately be used to evaluate. We illustrate this approach with a study conducted on a colonial cooperatively breeding bird, the sociable weaver. We first identified two ways of collecting data using different numbers of feeders and three ways to define associations among birds. We then evaluated which combination of data collection and association definition maximized (a) the assortment of individuals into previously known "breeding groups" (birds that contribute toward the same nest and maintain cohesion when foraging) and (b) socially differentiated relationships (more strong and weak relationships than expected by chance). This evaluation of different methods based on a priori knowledge of the study species can be implemented in a diverse array of study systems and makes the case for using existing, biologically meaningful knowledge about a system to help navigate the myriad of methodological decisions about data collection and network inference.

Choosing a sensible cut-off point: assessing the impact of uncertainty in a social network on the performance of NBDA

Network-based diffusion analysis (NBDA) has become a widely used tool to detect and quantify social learning in animal populations. NBDA infers social learning if the spread of a novel behavior follows the social network and hence relies on appropriate information on individuals’ network connections. Most studies on animal populations, however, lack a complete record of all associations, which creates uncertainty in the social network. To reduce this uncertainty, researchers often use a certain threshold of sightings for the inclusion of animals (which is often arbitrarily chosen), as observational error decreases with increasing numbers of observations. Dropping individuals with only few sightings, however, can lead to information loss in the network if connecting individuals are removed. Hence, there is a trade-off between including as many individuals as possible and having reliable data. We here provide a tool in R that assesses the sensitivity of NBDA to error in the social network given a certain threshold for the inclusion of individuals. It simulates a social learning process through a population and then tests the power of NBDA to reliably detect social learning after introducing observational error into the social network, which is repeated for different thresholds. Our tool can help researchers using NBDA to select a threshold, specific to their data set, that maximizes power to reliably quantify social learning in their study population.

Stress hormones, social associations and song learning in zebra finches

The use of information provided by others is a common short-cut adopted to inform decision-making. However, instead of indiscriminately copying others, animals are often selective in what, when and whom they copy. How do they decide which 'social learning strategy' to use? Previous research indicates that stress hormone exposure in early life may be important: while juvenile zebra finches copied their parents' behaviour when solving novel foraging tasks, those exposed to elevated levels of corticosterone (CORT) during development copied only unrelated adults. Here, we tested whether this switch in social learning strategy generalizes to vocal learning. In zebra finches, juvenile males often copy their father's song; would CORT-treated juveniles in free-flying aviaries switch to copying songs of other males? We found that CORT-treated juveniles copied their father's song less accurately as compared to control juveniles. We hypothesized that this could be due to having weaker social foraging associations with their fathers, and found that sons that spent less time foraging with their fathers produced less similar songs. Our findings are in line with a novel hypothesis linking early-life stress and social learning: early-life CORT exposure may affect social learning indirectly as a result of the way it shapes social affiliations.This article is part of the theme issue 'Causes and consequences of individual differences in cognitive abilities'.

The conceptual foundations of network-based diffusion analysis: choosing networks and interpreting results

Network-based diffusion analysis (NBDA) is a statistical technique for detecting the social transmission of behavioural innovations in groups of animals, including humans. The strength of social transmission is inferred from the extent to which the diffusion (spread) of the innovation follows a social network. NBDA can have two goals: (a) to establish whether social transmission is occurring and how strong its effects are; and/or (b) to establish the typical pathways of information transfer. The technique has been used in a range of taxa, including primates, cetaceans, birds and fish, using a range of different types of network. Here I investigate the conceptual underpinnings of NBDA, in order to establish the meaning of results using different networks. I develop a model of the social transmission process where each individual observation of the target behaviour affects the rate at which the observer learns that behaviour. I then establish how NBDAs using different networks relate to this underlying process, and thus how we can interpret the results of each. My analysis shows that a different network or networks are appropriate depending on the specific goal or goals of the study, and establishes how the parameter estimates yielded from an NBDA can be interpreted for different networks.This article is part of the themed issue 'Process and pattern in innovations from cells to societies'.

Consistency of fish-shoal social network structure under laboratory conditions

We investigated the consistency of association network structure for groups of sticklebacks Gasterosteus aculeatus. Each group was observed twice and we varied the duration between observations and the size of the experimental arena that they were observed in. At the dyad level, we found positive correlations between dyad interaction frequencies across observations. At the group level we found variation in four network metrics between observations, but only in treatments where the duration between observations was short. Specifically, fish formed more and smaller groups in the second observation in this treatment. Fish were also organized into more subunits in the larger arenas. Finally, we saw positive correlations between some group network metrics across observations suggesting relative consistency at the group level. There are several processes that might drive these interaction patterns. Our findings have implications for experimental design and the comparison and integration of findings of experiments from different studies carried out under different conditions.

Analysing animal social network dynamics: the potential of stochastic actor-oriented models

Animals are embedded in dynamically changing networks of relationships with conspecifics. These dynamic networks are fundamental aspects of their environment, creating selection on behaviours and other traits. However, most social network‐based approaches in ecology are constrained to considering networks as static, despite several calls for such analyses to become more dynamic.

There are a number of statistical analyses developed in the social sciences that are increasingly being applied to animal networks, of which stochastic actor‐oriented models (SAOMs) are a principal example. SAOMs are a class of individual‐based models designed to model transitions in networks between discrete time points, as influenced by network structure and covariates. It is not clear, however, how useful such techniques are to ecologists, and whether they are suited to animal social networks.

We review the recent applications of SAOMs to animal networks, outlining findings and assessing the strengths and weaknesses of SAOMs when applied to animal rather than human networks. We go on to highlight the types of ecological and evolutionary processes that SAOMs can be used to study.

SAOMs can include effects and covariates for individuals, dyads and populations, which can be constant or variable. This allows for the examination of a wide range of questions of interest to ecologists. However, high‐resolution data are required, meaning SAOMs will not be useable in all study systems. It remains unclear how robust SAOMs are to missing data and uncertainty around social relationships.

Ultimately, we encourage the careful application of SAOMs in appropriate systems, with dynamic network analyses likely to prove highly informative. Researchers can then extend the basic method to tackle a range of existing questions in ecology and explore novel lines of questioning.

Wild cricket social networks show stability across generations

BACKGROUND

A central part of an animal's environment is its interactions with conspecifics. There has been growing interest in the potential to capture these interactions in the form of a social network. Such networks can then be used to examine how relationships among individuals affect ecological and evolutionary processes. However, in the context of selection and evolution, the utility of this approach relies on social network structures persisting across generations. This is an assumption that has been difficult to test because networks spanning multiple generations have not been available. We constructed social networks for six annual generations over a period of eight years for a wild population of the cricket Gryllus campestris.

RESULTS

Through the use of exponential random graph models (ERGMs), we found that the networks in any given year were able to predict the structure of networks in other years for some network characteristics. The capacity of a network model of any given year to predict the networks of other years did not depend on how far apart those other years were in time. Instead, the capacity of a network model to predict the structure of a network in another year depended on the similarity in population size between those years.

CONCLUSIONS

Our results indicate that cricket social network structure resists the turnover of individuals and is stable across generations. This would allow evolutionary processes that rely on network structure to take place. The influence of network size may indicate that scaling up findings on social behaviour from small populations to larger ones will be difficult. Our study also illustrates the utility of ERGMs for comparing networks, a task for which an effective approach has been elusive.

Sequential phenotypic constraints on social information use in wild baboons

Social information allows the rapid dissemination of novel information among individuals. However, an individual's ability to use information is likely to be dependent on phenotypic constraints operating at three successive steps: acquisition, application, and exploitation. We tested this novel framework by quantifying the sequential process of social information use with experimental food patches in wild baboons (Papio ursinus). We identified phenotypic constraints at each step of the information use sequence: peripheral individuals in the proximity network were less likely to acquire and apply social information, while subordinate females were less likely to exploit it successfully. Social bonds and personality also played a limiting role along the sequence. As a result of these constraints, the average individual only acquired and exploited social information on.

Bayesian Model Selection with Network Based Diffusion Analysis

A number of recent studies have used Network Based Diffusion Analysis (NBDA) to detect the role of social transmission in the spread of a novel behavior through a population. In this paper we present a unified framework for performing NBDA in a Bayesian setting, and demonstrate how the Watanabe Akaike Information Criteria (WAIC) can be used for model selection. We present a specific example of applying this method to Time to Acquisition Diffusion Analysis (TADA). To examine the robustness of this technique, we performed a large scale simulation study and found that NBDA using WAIC could recover the correct model of social transmission under a wide range of cases, including under the presence of random effects, individual level variables, and alternative models of social transmission. This work suggests that NBDA is an effective and widely applicable tool for uncovering whether social transmission underpins the spread of a novel behavior, and may still provide accurate results even when key model assumptions are relaxed.

Seeding an arbitrary convention in capuchin monkeys: the effect of social context

The study of social learning in non-human animals has advanced beyond attempts to determine which animals are capable of learning socially to investigations of the factors that influence transmission. Capuchin monkeys (Sapajus sp.) are adept social learners of various behaviours including extractive foraging techniques and social customs. Here, we conducted an open diffusion experiment to determine whether capuchins would learn an arbitrary convention from a knowledgeable demonstrator. In addition, we investigated whether rank, sex and social context affected acquisition and expression of the behaviour. Participation in the experiment was strongly influenced by dominance rank in the group setting. However, when tested individually, the majority of individuals participated and faithfully copied the convention that was seeded into their group. Our findings demonstrate that capuchins can acquire an arbitrary convention via social learning, but that social context must be carefully considered in studies of social learning.

Early-Life Stress Triggers Juvenile Zebra Finches to Switch Social Learning Strategies

Stress during early life can cause disease and cognitive impairment in humans and non-humans alike [1]. However, stress and other environmental factors can also program developmental pathways [2, 3]. We investigate whether differential exposure to developmental stress can drive divergent social learning strategies [4, 5] between siblings. In many species, juveniles acquire essential foraging skills by copying others: they can copy peers (horizontal social learning), learn from their parents (vertical social learning), or learn from other adults (oblique social learning) [6]. However, whether juveniles’ learning strategies are condition dependent largely remains a mystery. We found that juvenile zebra finches living in flocks socially learned novel foraging skills exclusively from adults. By experimentally manipulating developmental stress, we further show that social learning targets are phenotypically plastic. While control juveniles learned foraging skills from their parents, their siblings, exposed as nestlings to experimentally elevated stress hormone levels, learned exclusively from unrelated adults. Thus, early-life conditions triggered individuals to switch strategies from vertical to oblique social learning. This switch could arise from stress-induced differences in developmental rate, cognitive and physical state, or the use of stress as an environmental cue. Acquisition of alternative social learning strategies may impact juveniles’ fit to their environment and ultimately change their developmental trajectories.

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Juvenile zebra finches learn foraging skills from their parents

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Stress hormone exposure triggers juveniles to learn from unrelated adults instead

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Stress may be a cue juveniles use to inform their behavioral strategies

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Switching social learning strategy may alter developmental trajectories adaptively

Phenotypic assortment in wild primate networks: implications for the dissemination of information

Individuals' access to social information can depend on their social network. Homophily-a preference to associate with similar phenotypes-may cause assortment within social networks that could preclude information transfer from individuals who generate information to those who would benefit from acquiring it. Thus, understanding phenotypic assortment may lead to a greater understanding of the factors that could limit the transfer of information between individuals. We tested whether there was assortment in wild baboon (Papio ursinus) networks, using data collected from two troops over 6 years for six phenotypic traits-boldness, age, dominance rank, sex and the propensity to generate/exploit information-using two methods for defining a connection between individuals-time spent in proximity and grooming. Our analysis indicated that assortment was more common in grooming than proximity networks. In general, there was homophily for boldness, age, rank and the propensity to both generate and exploit information, but heterophily for sex. However, there was considerable variability both between troops and years. The patterns of homophily we observed for these phenotypes may impede information transfer between them. However, the inconsistency in the strength of assortment between troops and years suggests that the limitations to information flow may be quite variable.