Summary 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.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.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.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.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.

In human societies, cultural norms arise when behaviours are transmitted with high-fidelity social learning through social networks1. However a paucity of experimental studies has meant that there is no comparable understanding of the process by which socially transmitted behaviours may spread and persist in animal populations2,3. Here, we introduce alternative novel foraging techniques into replicated wild sub-populations of great tits (Parus major), and employ automated tracking to map the diffusion, establishment and long-term persistence of seeded behaviours. We further use social network analysis to examine social factors influencing diffusion dynamics. From just two trained birds in each sub-population, information spread rapidly through social network ties to reach an average of 75% of individuals, with 508 knowledgeable individuals performing 58,975 solutions. Sub-populations were heavily biased towards the technique originally introduced, resulting in established local arbitrary traditions that were stable over two generations, despite high population turnover. Finally, we demonstrate a strong effect of social conformity, with individuals disproportionately adopting the most frequent local variant when first learning, but then also continuing to favour social over personal information by matching their technique to the majority variant. Cultural conformity is thought to be a key factor in the evolution of complex culture in humans4-7. In providing the first experimental demonstration of conformity in a wild non-primate, and of cultural norms in foraging techniques in any wild animal, our results suggest a much wider evolutionary occurrence of such apparently complex cultural behaviour.

Conflicts of interest about where to go and what to do are a primary challenge of group living. However, it remains unclear how consensus is achieved in stable groups with stratified social relationships. Tracking wild baboons with high-resolution GPS and analyzing their movements relative to one another reveals that a process of shared decision-making governs baboon movement. Rather than preferentially following dominant individuals, baboons are more likely to follow when multiple initiators agree. When conflicts arise over the direction of movement, baboons choose one direction over the other when the angle between them is large, but compromise if not. These results are consistent with models of collective motion, suggesting that democratic collective action emerging from simple rules is widespread, even in complex, socially-stratified societies.

Summary Null models are an important component of the social network analysis toolbox. However, their use in hypothesis testing is still not widespread. Furthermore, several different approaches for constructing null models exist, each with their relative strengths and weaknesses, and often testing different hypotheses.In this study, I highlight why null models are important for robust hypothesis testing in studies of animal social networks. Using simulated data containing a known observation bias, I test how different statistical tests and null models perform if such a bias was unknown.I show that permutations of the raw observational (or ‘pre‐network’) data consistently account for underlying structure in the generated social network, and thus can reduce both type I and type II error rates. However, permutations of pre‐network data remain relatively uncommon in animal social network analysis because they are challenging to implement for certain data types, particularly those from focal follows and GPS tracking.I explain simple routines that can easily be implemented across different types of data, and supply R code that applies each type of null model to the same simulated dataset. The R code can easily be modified to test hypotheses with empirical data. Widespread use of pre‐network data permutation methods will benefit researchers by facilitating robust hypothesis testing.

Social environments have an important effect on a range of ecological processes, and form a crucial component of selection. However, little is known of the link between personality, social behaviour and population structure. We combine a well-understood personality trait with large-scale social networks in wild songbirds, and show that personality underpins multiple aspects of social organisation. First, we demonstrate a relationship between network centrality and personality with 'proactive' (fast-exploring) individuals associating weakly with greater numbers of conspecifics and moving between flocks. Second, temporal stability of associations relates to personality: 'reactive' (slow-exploring) birds form synergistically stable relationships. Finally, we show that personality influences social structure, with males non-randomly distributed across groups. These results provide strong evidence that songbirds follow alternative social strategies related to personality. This has implications not only for the causes of social network structure but also for the strength and direction of selection on personality in natural populations.

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Treating women who have autoantibodies and recurrent fetal loss with prednisone and aspirin is not effective in promoting live birth, and it increases the risk of prematurity.

Many animal social structures are organized hierarchically, with some individuals monopolizing resources. Dominance hierarchies have received great attention from behavioural and evolutionary ecologists. There are many methods for inferring hierarchies from social interactions. Yet, there are no clear guidelines about how many observed dominance interactions (i.e. sampling effort) are necessary for inferring reliable dominance hierarchies, nor are there any established tools for quantifying their uncertainty. We simulate interactions (winners and losers) in scenarios of varying steepness (the probability that a dominant defeats a subordinate based on their difference in rank). Using these data, we (1) quantify how the number of interactions recorded and the steepness of the hierarchy affect the performance of five methods for inferring hierarchies, (2) propose an amendment that improves the performance of a popular method, and (3) suggest two easy procedures to measure uncertainty and steepness in the inferred hierarchy. We find that the ratio of interactions to individuals required to infer reliable hierarchies is surprisingly low, but depends on the steepness of the hierarchy and the method used. We show that David's score and our novel randomized Elo-rating are the best methods when hierarchies are not extremely steep, where the original Elo-rating, the I&SI and the recently described ADAGIO perform less well. In addition, we show that two simple methods can be used to estimate uncertainty at the individual and group level, and that the randomized Elo-rating repeatability provides researchers with a standardized measure valid for comparing the steepness of different hierarchies. We provide several worked examples to guide researchers interested in studying dominance hierarchies. Methods for inferring dominance hierarchies are relatively robust. We recommend that a ratio of observed interactions to individuals of at least 10 (for steep hierarchies), and ideally 20 serves as a good benchmark. Our simple procedures for estimating uncertainty in the observed data will facilitate evaluating whether sufficient data have been collected, while plotting the shape of the hierarchy will provide new insights into the social structure of the study organism.