Richard James

Exploring Animal Social Networks

Preface vii Chapter 1: Introduction to Social Networks 1 Chapter 2: Data Collection 19 Chapter 3: Visual Exploration 42 Chapter 4: Node-Based Measures 64 Chapter 5: Statistical Tests of Node-Based Measures 88 Chapter 6: Searching for Substructures 117 Chapter 7: Comparing Networks 141 Chapter 8: Conclusions 163 Glossary of Frequently Used Terms 173 References 175 Index 187

Social network theory in the behavioural sciences: potential applications

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.

Social networks in the guppy (Poecilia reticulata)

Social network theory is used to elicit details of the social structure of a population of free–ranging guppies, Poecilia reticulata. They were found to have a complex and highly structured social network, which exhibited characteristics consistent with the ‘small world’ phenomenon. Stable partner associations between individuals were observed, a finding that fulfils the basic prerequisite for the evolution of reciprocal altruism. The findings are discussed in relation to the ecology and evolution of the wild population, highlighting the potential application of network theory to social associations in animals.

Assortative interactions and social networks in fish

The mechanisms underpinning the structure of social networks in multiple fish populations were investigated. To our knowledge this is the first study to provide replication of social networks and therefore probably the first that allows general conclusions to be drawn. The social networks were all found to have a non-random structure and exhibited ‘social cliquishness’. A number of factors were observed to contribute to this structuring. Firstly, social network structure was influenced by body length and shoaling tendency, with individuals interacting more frequently with conspecifics of similar body length and shoaling tendency. Secondly, individuals with many social contacts were found to interact with each other more often than with other conspecifics, a phenomenon known as a ‘positive degree correlation’. Finally, repeated interactions between pairs of individuals occurred within the networks more often than expected by random interactions. The observed network structures will have ecological and evolutionary implications. For example, the occurrence of positive degree correlations suggests the possibility that pathogens and information (that are socially transmitted) could spread very fast within the populations. Furthermore, the occurrence of repeated interactions between pairs of individuals fulfils an important pre-requisite for the evolution of reciprocal altruism.

Hypothesis testing in animal social networks

Behavioural ecologists are increasingly using social network analysis to describe the social organisation of animal populations and to test hypotheses. However, the statistical analysis of network data presents a number of challenges. In particular the non-independent nature of the data violates the assumptions of many common statistical approaches. In our opinion there is currently confusion and uncertainty amongst behavioural ecologists concerning the potential pitfalls when hypotheses testing using social network data. Here we review what we consider to be key considerations associated with the analysis of animal social networks and provide a practical guide to the use of null models based on randomisation to control for structure and non-independence in the data.

Animal social networks: an introduction

Network analysis has a long history in the mathematical and social sciences and the aim of this introduction is to provide a brief overview of the potential that it holds for the study of animal behaviour. One of the most attractive features of the network paradigm is that it provides a single conceptual framework with which we can study the social organisation of animals at all levels (individual, dyad, group, population) and for all types of interaction (aggressive, cooperative, sexual etc.). Graphical tools allow a visual inspection of networks which often helps inspire ideas for testable hypotheses. Network analysis itself provides a multitude of novel statistical tools that can be used to characterise social patterns in animal populations. Among the important insights that networks have facilitated is that indirect social connections matter. Interactions between individuals generate a social environment at the population level which in turn selects for behavioural strategies at the individual level. A social network is often a perfect means by which to represent heterogeneous relationships in a population. Probing the biological drivers for these heterogeneities, often as a function of time, forms the basis of many of the current uses of network analysis in the behavioural sciences. This special issue on social networks brings together a diverse group of practitioners whose study systems range from social insects over reptiles to birds, cetaceans, ungulates and primates in order to illustrate the wide-ranging applications of network analysis.

Consensus Decision Making by Fish

Decisions reached through consensus are often more accurate, because they efficiently utilize the diverse information possessed by group members [1-3]. A trust in consensus decision making underlies many of our democratic political and judicial institutions [4], as well as the design of web tools such as Google, Wikipedia, and prediction markets [5, 6]. In theory, consensus for the option favored by the majority of group members will lead to improved decision-making accuracy as group size increases [2, 4]. Although group-living animals are known to utilize social information [7-10], little is known about whether or not decision accuracy increases with group size. In order to reach consensus, group members must be able to integrate the disparate information they possess. Positive feedback, resulting from copying others, can spread information quickly through the group, but it can also result in all individuals making the same, possibly incorrect, choice [8, 11, 12]. On the other hand, if individuals never copy each other, their decision making remains independent and they fail to benefit from information exchange [4]. Here, we show how small groups of sticklebacks (Gasterosteus aculeatus) reach consensus when choosing which of two replica fish to follow. As group size increases, the fish make more accurate decisions, becoming better at discriminating subtle phenotypic differences of the replicas. A simple quorum rule proves sufficient to explain our observations, suggesting that animals can make accurate decisions without the need for complicated comparison of the information they possess. Furthermore, although submission to peers can lead to occasional cascades of incorrect decisions, these can be explained as a byproduct of what is usually accurate consensus decision making.

Social structure and co-operative interactions in a wild population of guppies ( Poecilia reticulata )

In contrast to the substantial number of theoretical papers that have examined the mechanisms by which cooperation may evolve, very few studies have investigated patterns of co-operation in natural animal populations. In the current study, we use a novel approach, social network analysis, to investigate the structure of co-operative interactions in the context of predator inspection in a wild population of guppies (Poecilia reticulata). Female guppies showed social preferences for stable partners, fulfilling a key assumption made by models of reciprocity. In the laboratory, wild female guppies disproportionately engaged in predator inspection with others with whom they had strong social associations. Furthermore, pairs of fish that frequently engaged in predator inspection did so in a particularly co-operative way, potentially reducing costs associated with predator inspection. Taken together, these results provide evidence for assortative interactions forming the basis of co-operation during predator inspection in a natural fish population. The occurrence of highly interconnected social networks between stable partners suggests the existence of co-operation networks in free-ranging populations of the guppy.

Behavioural trait assortment in a social network: patterns and implications

The social fine structure of a population plays a central role in ecological and evolutionary processes. Whilst many studies have investigated how morphological traits such as size affect social structure of populations, comparatively little is known about the influence of behaviours such as boldness and shyness. Using information on social interactions in a wild population of Trinidadian guppies (Poecilia reticulata), we construct a social network. For each individual in the network, we quantify its behavioural phenotype using two measures of boldness, predator inspection tendency, a repeatable and reliably measured behaviour well studied in the context of co-operation, and shoaling tendency. We observe striking heterogeneity in contact patterns, with strong ties being positively assorted and weak ties negatively assorted by our measured behavioural traits. Moreover, shy fish had more network connections than bold fish and these were on average stronger. In other words, social fine structure is strongly influenced by behavioural trait. We assert that such structure will have implications for the outcome of selection on behavioural traits and we speculate that the observed positive assortment may act as an amplifier of selection contributing to the maintenance of co-operation during predator inspection.

Network structure and parasite transmission in a group living lizard, the gidgee skink, Egernia stokesii

Gidgee skinks (Egernia stokesii) form large social aggregations in rocky outcrops across the Flinders Ranges in South Australia. Group members share refuges (rock crevices), which may promote parasite transmission. We measured connectivity of individuals in networks constructed from patterns of common crevice use and observed patterns of parasitism by three blood parasites (Hemolivia, Schellackia and Plasmodium) and an ectoparasitic tick (Amblyomma vikirri). Data came from a 1-year mark-recapture study of four populations. Transmission networks were constructed to represent possible transmission pathways among lizards. Two lizards that used the same refuge within an estimated transmission period were considered connected in the transmission network. An edge was placed between them, directed towards the individual that occupied the crevice last. Social networks, a sub-set of same-day only associations, were small and highly fragmented compared with transmission networks, suggesting that non-synchronous crevice use leads to more transmission opportunities than direct social association. In transmission networks, lizards infested by ticks were connected to more other tick-infested lizards than uninfected lizards. Lizards infected by ticks and carrying multiple blood parasite infections were in more connected positions in the network than lizards without ticks or with one or no blood parasites. Our findings suggest higher levels of network connectivity may increase the risk of becoming infected or that parasites influence lizard behaviour and consequently their position in the network.