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. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau and R. James).     

Multiple reproductive strategies in a tropical hover wasp

Reproductive skew theory has been an important component of efforts to design a unifying theory of social evolution, as it aims to explain patterns of reproductive partitioning in animal societies as a function of relatedness, group productivity, fighting ability and ecological constraints on independent reproduction. However, empirical tests of the theory have often provided ambiguous or non-conclusive results, assumptions behind alternative models have rarely been tested, and theoretical elaborations have shown the limitations of the reproductive skew approach. Here we analyse a relatively large sample of colonies of the Stenogastrine wasp Parischnogaster mellyi with a powerful set of DNA microsatellite markers. We show that various apparently stable forms of social organisation co-exist in a single population, and that sharing of reproduction between related and unrelated egg-laying females occurs in some of the nests. Present reproductive skew theory appears to be at best partly sufficient to account for the observed complexity of social organisation. The observed patterns of colony composition and reproductive sharing are weakly consistent with the hypothesis of reproductive transactions, while they can more parsimoniously be explained by the life-history characteristics of the species.Communicated by R.F.A. Moritz     

A foundation for developing a methodology for social network sampling

Researchers are increasingly turning to network theory to understand the social nature of animal populations. We present a computational framework that is the first step in a series of works that will allow us to develop a quantitative methodology of social network sampling to aid ecologists in their social network data collection. To develop our methodology, we need to be able to generate networks from which to sample. Ideally, we need to perform a systematic study of sampling protocols on different known network structures, as network structure might affect the robustness of any particular sampling methodology. Thus, we present a computational tool for generating network structures that have user-defined distributions for network properties and for key measures of interest to ecologists. The user defines the values of these measures and the tool will generate appropriate network randomizations with those properties. This tool will be used as a framework for developing a sampling methodology, although we do not present a full methodology here. We describe the method used by the tool, demonstrate its effectiveness, and discuss how the tool can now be utilized. We provide a proof-of-concept example (using the assortativity measure) of how such networks can be used, along with a simulated egocentric sampling regime, to test the level of equivalence of the sampled network to the actual network. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau and R. James).     

Social networks and models for collective motion in animals

The theory of collective motion and the study of animal social networks have, each individually, received much attention. Currently, most models of collective motion do not consider social network structure. The implications for considering collective motion and social networks together are likely to be important. Social networks could determine how populations move in, split up into and form separate groups (social networks affecting collective motion). Conversely, collective movement could change the structure of social networks by creating social ties that did not exist previously and maintaining existing ties (collective motion affecting social networks). Thus, there is a need to combine the two areas of research and examine the relationship between network structure and collective motion. Here, we review different modelling approaches that combine social network structures and collective motion. Although many of these models have not been developed with ecology in mind, they present a current context in which a biologically relevant theory can be developed. We argue that future models in ecology should take inspiration from empirical observations and consider different mechanisms of how social preferences could be expressed in collectively moving animal groups.     

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.     

Potential banana skins in animal social network analysis

Social network analysis is an increasingly popular tool for the study of the fine-scale and global social structure of animals. It has attracted particular attention by those attempting to unravel social structure in fission–fusion populations. It is clear that the social network approach offers some exciting opportunities for gaining new insights into social systems. However, some of the practices which are currently being used in the animal social networks literature are at worst questionable and at best over-enthusiastic. We highlight some of the areas of method, analysis and interpretation in which greater care may be needed in order to ensure that the biology we extract from our networks is robust. In particular, we suggest that more attention should be given to whether relational data are representative, the potential effect of observational errors and the choice and use of statistical tests. The importance of replication and manipulation must not be forgotten, and the interpretation of results requires care. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau and R. James).     

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.     

Structure of the social network and its influence on transmission dynamics in a honeybee colony

Infectious processes in a social group are driven by a network of contacts that is generally structured by the organization arising from behavioral and spatial heterogeneities within the group. Although theoretical models of transmission dynamics have placed an overwhelming emphasis on the importance of understanding the network structure in a social group, empirical data regarding such contact structures are rare. In this paper, I analyze the network structure and the correlated transmission dynamics within a honeybee colony as determined by food transfer interactions and the changes produced in it by an experimental manipulation. The study demonstrates that widespread transmission in the colony is correlated to a lower clustering coefficient and higher robustness of the social network. I also show that the social network in the colony is determined by the spatial distribution of various age classes, and the resulting organizational structure provides some amount of immunity to the young individuals. The results of this study demonstrates how, using the honeybee colony as a model system, concepts in network theory can be combined with those in behavioral ecology to gain a better understanding of social transmission processes, especially those related to disease dynamics.     

The concepts of emergent and collective properties in individual-based models—Summary and outlook of the Bornhöved case studies

Ecology requires the conceptual and technical ability to analyse complex and dynamic systems consisting of a high and variable number of components and relations. These components are part of a variable interaction structure in a spatially heterogeneous context. The components of ecological interaction networks can give rise to self-organised, and scale-dependent interaction patterns and processes, which are the underlying causes of the overall ecological systems states.The individual-based modelling approach provides a widely applicable simulation framework based on a ‘hierarchy theory’ view of ecological systems.Here, we summarise and generalise the theoretical implications of the modelling studies presented in this volume in the field of terrestrial and aquatic, animal and plant ecology. The case studies cover a representative profile of processes related to ecological applications, such as food web interactions, population dynamics, dispersal, energy physiology, nutrient allocation and mutual impact of morphological and physiological development. The generic approach applied in this context allows a hierarchical representation of ecological systems and their components. Model results are obtained as self-organised structural relation networks and as aggregated quantitative states. In order to address different model characteristics we distinguish collective and emergent properties. Collective properties are those that are attributed equally to different organisation levels of the system. Emergent properties result from the activities of lower level entities on a higher organisation level, while not being present on the lower level. They can be subdivided into aggregational and connective properties. Emergent properties that are aggregational are those which emerge as a result of an aggregation procedure by an observer on the higher level which does not make sense or is not applicable on lower levels. Emergent properties that are connective, however, are based on an interaction network of lower level entities, which brings about the specific system characteristic.This classification of model results will allow to generalise the achievements and potential of the individual-based modelling approach in ecology.     

Social network theory: new insights and issues for behavioral ecologists

Until recently, few studies have used social network theory (SNT) and metrics to examine how social network structure (SNS) might influence social behavior and social dynamics in non-human animals. Here, we present an overview of why and how the social network approach might be useful for behavioral ecology. We first note four important aspects of SNS that are commonly observed, but relatively rarely quantified: (1) that within a social group, differences among individuals in their social experiences and connections affect individual and group outcomes; (2) that indirect connections can be important (e.g., partners of your partners matter); (3) that individuals differ in their importance in the social network (some can be considered keystone individuals); and (4) that social network traits often carry over across contexts (e.g., SN position in male–male competition can influence later male mating success). We then discuss how these four points, and the social network approach in general, can yield new insights and questions for a broad range of issues in behavioral ecology including: mate choice, alternative mating tactics, male–male competition, cooperation, reciprocal altruism, eavesdropping, kin selection, dominance hierarchies, social learning, information flow, social foraging, and cooperative antipredator behavior. Finally, we suggest future directions including: (1) integrating behavioral syndromes and SNT; (2) comparing space use and SNS; (3) adaptive partner choice and SNS; (4) the dynamics and stability (or instability) of social networks, and (5) group selection shaping SNS. This contribution is part of the special issue “Social networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau and R. James).     

The social network structure of a wild meerkat population: 2. Intragroup interactions

Knowledge of the structure of networks of social interactions is important for understanding the evolution of cooperation, transmission of disease, and patterns of social learning, yet little is known of how environmental, ecological, or behavioural factors relate to such structures within groups. We observed grooming, dominance, and foraging competition interactions in eight groups of wild meerkats (Suricata suricatta) and constructed interaction networks for each behaviour. We investigated relationships between networks for different social interactions and explored how group attributes (size and sex ratio), individual attributes (tenure of dominants), and ecological factors (ectoparasite load) are related to variation in network structure. Network structures varied within a group according to interaction type. Further, network structure varied predictably with group attributes, individual attributes, and ecological factors. Networks became less dense as group size increased suggesting that individuals were limited in their number of partners. Groups with more established dominant females were more egalitarian in their grooming and foraging competition interactions, but more despotic in their dominance interactions. The distribution of individuals receiving grooming became more skewed at higher parasite loads, but more equitable at low parasite loads. We conclude that the pattern of interactions between members of meerkat groups is not consistent between groups but instead depends on general attributes of the group, the influence of specific individuals within the group, and ecological factors acting on group members. We suggest that the variation observed in interaction patterns between members of meerkat groups may have fitness consequences both for individual group members and the group itself.     

Selection on social traits in greater spear-nosed bats, Phyllostomus hastatus

Many studies assume that selection molds social traits and have investigated the manner in which this occurs, yet very few studies have measured the strength of selection on social traits in natural populations. In this paper, I report results of phenotypic selection analyses on two social traits – the size of social groups and the frequency of group foraging – in Phyllostomus hastatus, the greater spear-nosed bat. I found significant positive directional selection on individual group foraging frequency, but no directional selection on individuals in different-sized social groups. These results have implications for the structure of social groups, cooperative behavior among group mates, and maternal investment strategies. I argue that combining studies of natural selection on wild populations with experiments to identify the agents and mechanisms of selection can do much to increase our understanding of social evolution.     

Females go where the food is: does the socio-ecological model explain variation in social organisation of solitary foragers?

The socio-ecological model (SEM) links ecological factors with characteristics of social systems and allows predictions about the relationships between resource distribution, type of competition and social organisation. It has been mainly applied to group-living species but ought to explain variation in social organisation of solitary species as well. The aim of this study was to test basic predictions of the SEM in two solitary primates, which differ in two characteristics of female association patterns: (1) spatial ranging and (2) sleeping associations. Beginning in August 2002, we regularly (re-)captured and marked individuals of sympatric populations of Madame Berthe's and grey mouse lemurs (Microcebus berthae, Microcebus murinus) in Kirindy Forest (Madagascar). We recorded data on spatial patterns, feeding and social behaviour by means of direct observation of radio-collared females. The major food sources of M. berthae occurred in small dispersed patches leading to strong within-group scramble competition and over-dispersed females with a low potential for female associations. In contrast, M. murinus additionally used patchily distributed, high-quality (large) resources facilitating within-group contest competition. The combined influence of less strong within-group scramble and contest as well as between-group contest over non-food resources allowed females of this species to cluster in space. Additionally, we experimentally manipulated the spatial distribution of food sources and found that females adjusted their spatial patterns to food resource distribution. Thus, our results support basic predictions of the SEM and demonstrated that it can also explain variation in social organisation of solitary foragers.     

Simulation of information propagation in real-life primate networks: longevity, fecundity, fidelity

In many vertebrate species, we find temporally stable traditions of socially learned behaviors. The social structure of animal populations is highly diverse and it has been proposed that differences in the social organization influence the patterns of information propagation. Here, we provide results of a simulation study of information propagation on real-life social networks of 70 primate groups comprising 30 different species. We found that models that include the social structure of a group differ significantly from those that assume random associations of individuals. Information spreads slower in the structured groups than in the well-mixed groups. While we found only a minor effect on the path lengths of the transmission chains, robustness against information extinction was strongly influenced by the group structure. Interestingly, robustness against information loss was not correlated with propagation speed but could be predicted reasonably well by relative strength assortativity—a structural network metric. In those groups where highly pro-social individuals preferentially interact with other pro-social individuals, information was more likely to be lost. Our results show that incorporating group structure in any social propagation model significantly alters predictions for spreading patterns, speed, and robustness of information.     

Structure and resilience of the social network in an insect colony as a function of colony size

Social interactions are critical to the organization of worker activities in insect colonies and their consequent ecological success. The structure of this interaction network is therefore crucial to our understanding of colony organization and functioning. In this paper, I study the properties of the interaction network in the colonies of the social wasp Ropalidia marginata. I find that the network is characterized by a uniform connectivity among individuals with increasing heterogeneity as colonies become larger. Important network parameters are found to be correlated with colony size and I investigate how this is reflected in the organization of work in colonies of different sizes. Finally, I test the resilience of these interaction networks by experimental removal of individuals from the colony and discuss the structural properties of the network that are related to resilience in a social network. This contribution is part of the special issue “Social Networks: new perspectives” (Guest Editors: J. Krause, D. Lusseau, and R. James).     

Social network properties within a fish assemblage invaded by non-native sunbleak Leucaspius delineatus

Network theory is commonly used to reveal social interactions and the organisation of interconnected nodes—but has not yet been applied to animal invasions. Non-native species invasions are now considered one of the foremost threats to natural ecosystems and biodiversity. This is the first attempt to assess social network properties within a freshwater fish assemblage invaded by a non-native fish species. We show that invasive sunbleak Leucaspius delineatus is socially more strongly interconnected with native species than the native species with each other. The social networks also reveal characteristics of a ‘small world’ such as low clustering coefficients C and short path lengths L. The findings may indicate potential traits of successful invaders and the implication for the spread of pathogens between individuals within a group of animals that contain a non-native invasive species. The success of establishment and subsequent invasion may be highlighted not only by the capacity of the new species to adapt to the new environment, but also in its capacity to penetrate the social circle of the native community.     

Determining association networks in social animals: choosing spatial–temporal criteria and sampling rates

Social Network Analysis has become an important methodological tool for advancing our understanding of human and animal group behaviour. However, researchers tend to rely on arbitrary distance and time measures when defining ‘contacts’ or ‘associations’ between individuals based on preliminary observation. Otherwise, criteria are chosen on the basis of the communication range of sensor devices (e.g. bluetooth communication ranges) or the sampling frequencies of collection devices (e.g. Global Positioning System devices). Thus, researchers lack an established protocol for determining both relevant association distances and minimum sampling rates required to accurately represent the network structure under investigation. In this paper, we demonstrate how researchers can use experimental and statistical methods to establish spatial and temporal association patterns and thus correctly characterise social networks in both time and space. To do this, we first perform a mixing experiment with Merino sheep (Ovis aries) and use a community detection algorithm that allows us to identify the spatial and temporal distance at which we can best identify clusters of previously familiar sheep. This turns out to be within 2–3 m of each other for at least 3 min. We then calculate the network graph entropy rate—a measure of ease of spreading of information (e.g. a disease) in a network—to determine the minimum sampling rate required to capture the variability observed in our sheep networks during distinct activity phases. Our results indicate the need for sampling intervals of less than a minute apart. The tools that we employ are versatile and could be applied to a wide range of species and social network datasets, thus allowing an increase in both the accuracy and efficiency of data collection when exploring spatial association patterns in gregarious species.     

The social network structure of a wild meerkat population: 1. Inter-group interactions

Groups of individuals frequently interact with each other, but typically analysis of such interactions is restricted to isolated dyads. Social network analysis (SNA) provides a method of analysing polyadic interactions and is used to analyse interactions between individuals. We use a population of 12 groups (ca. 250 animals) of wild meerkats (Suricata suricatta) to test whether SNA can also be used to describe and elucidate patterns of inter-group interactions. Using data collected over 24 months, we constructed two sets of networks, based on direct encounters between groups and instances of roving males visiting other groups. We analysed replicated networks of each type of interaction to investigate similarities between networks of different social interactions as well as testing their stability over time. The two network types were similar to each other when derived from long-term data, but showed significant differences in structure over shorter timescales where they varied according to seasonal and ecological conditions. Networks for both types of inter-group interaction constructed from data collected over 3 months reliably described long-term (12- and 24-month) patterns of interactions between groups, indicating a stable social structure despite variation in group sizes and sex ratios over time. The centrality of each meerkat group in roving interactions networks was unaffected by the sex ratio of its members, indicating that male meerkats preferentially visit geographically close groups rather than those containing most females. Indeed, the strongest predictors of network structure were spatial factors, suggesting that, in contrast to analyses of intra-group interactions, analyses of inter-group interactions using SNA must take spatial factors into account.