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.     

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.     

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).     

Habitat structure helps guide the emergence of colony-level personality in social spiders

Studying the environmental factors that guide the emergence of collective behaviors is instrumental to understanding the ecology and evolution of animal societies. Although recent work has provided insights into the demographic factors that influence inter-colony variation in collective behavior (i.e., colony-level personality or collective personality), relatively few studies have investigated how the physical environment (e.g., habitat structure) affects colony-level personality. Here, we study the emergence of collective personality in prey capture behavior in the social spider, Stegodyphus dumicola. We measured collective prey capture behavior four times over 36 days in a classic repeated measures design. We used four different artificial habitat (web support) structures in three different treatments: habitat structure was either (1) fixed and undisturbed, (2) disturbed with a complete removal of webbing between each measurement, or (3) disturbed with changes of habitat structure between each measurement. Our results revealed that repeatability in colony-level personality was retained as long as habitat structure was not altered. However, the repeatability of colony-level personality declined precipitously when groups were forced to build their webs on novel habitat structures. Furthermore, habitat structure affected collective capture behavior, that is, latency to attack and the number of attackers differed among colonies on different habitat structures. Collectively, our data demonstrate that habitat structure is instrumental in shaping both the mean and repeatability of the collective behavior of colonies and may influence overall foraging success.     

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.     

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).     

Validation of an automated data collection method for quantifying social networks in collective behaviours

The social network of preferences among group members can affect the distribution and consequences of collective behaviours. However, the behavioural contexts and taxa in which social network structure has been described are still limited because such studies require extensive data. Here, we highlight the use of an automated passive integrated transponder (PIT)-tag monitoring system for social network analyses and do so in a novel context—nestling provisioning in an avian cooperative breeder, for which direct observation of social behaviours is difficult. First, we used observers and cameras to arrive at a suitable metric of nest visit synchrony in the PIT-tag data. Second, we validated the use of this metric for social network analyses using internal nest video cameras. Third, we used hierarchical regression models with ‘sociality’ parameter to investigate structure of networks collected from multiple groups. Use of PIT tags led to nest visitation duration and frequency being obtained with a high degree of accuracy for all group members, except for the breeding female for whom accurate estimations required the use of a video camera due to her high variability in visitation time. The PIT-tag dataset uncovered significant variability in social network structure. Our results highlight the importance of combining complementary observation methods when conducting social network analyses of wild animals. Our methods can also be generalised to multiple contexts in social systems wherever repeated encounters with other individuals in closed space have ecological implications.     

Group structure in locust migratory bands

Locust swarms are spectacular and damaging manifestations of animal collective movement. Here, we capture fundamental features of locust mass movement in the field, including a strongly non-linear relationship between collective alignment and density known only from earlier theoretical models and laboratory experiments. Migratory bands had a distinct structure, with a single high-density peak at the front, where collective alignment was high, followed by an exponential decay in density. As predicted by theory, alignment decreased with decreasing density, and fluctuations of movement direction became large until order amongst group members at the back of the band was totally lost. Remarkably, we found that the coordinated movement of migratory bands, which can be several kilometres wide and contain many millions of individuals, results from interactions occurring at a scale of 13.5 cm or less. Our results indicate that locust band structure and dynamics differ markedly from what is known (or assumed) about other large moving groups such as fish schools or bird flocks, yet they still conform to key general predictions made by collective movement models that explain how billions of individuals can align using local interactions.     

Shark personalities? Repeatability of social network traits in a widely distributed predatory fish

Interest in animal personalities has generated a burgeoning literature on repeatability in individual traits such as boldness or exploration through time or across different contexts. Yet, repeatability can be influenced by the interactive social strategies of individuals, for example, consistent inter-individual variation in aggression is well documented. Previous work has largely focused on the social aspects of repeatability in animal behaviour by testing individuals in dyadic pairings. Under natural conditions, individuals interact in a heterogeneous polyadic network. However, the extent to which there is repeatability of social traits at this higher order network level remains unknown. Here, we provide the first empirical evidence of consistent and repeatable animal social networks. Using a model species of shark, a taxonomic group in which repeatability in behaviour has yet to be described, we repeatedly quantified the social networks of ten independent shark groups across different habitats, testing repeatability in individual network position under changing environments. To understand better the mechanisms behind repeatable social behaviour, we also explored the coupling between individual preferences for specific group sizes and social network position. We quantify repeatability in sharks by demonstrating that despite changes in aggregation measured at the group level, the social network position of individuals is consistent across treatments. Group size preferences were found to influence the social network position of individuals in small groups but less so for larger groups suggesting network structure, and thus, repeatability was driven by social preference over aggregation tendency.     

Social attributes and associated performance measures in marmots: bigger male bullies and weakly affiliating females have higher annual reproductive success

Studying the structure of social interactions is fundamental in behavioral ecology as social behavior often influences fitness and thus natural selection. However, social structure is often complex, and determining the most appropriate measures of variation in social behavior among individuals can be difficult. Social network analysis generates numerous, but often correlated, measures of individual connectedness derived from a network of interactions. We used measures of individual connectedness in networks of affiliative and agonistic interactions in yellow-bellied marmots, Marmota flaviventris, to first determine how variance was structured among network measures. Principal component analysis reduced our set of network measures to four “social attributes” (unweighted connectedness, affiliation strength, victimization, and bullying), which revealed differences between patterns of affiliative and agonistic interactions. We then used these extracted social attributes to examine the relationship between an individual’s social attributes and several performance measures: annual reproductive success, parasite infection, and basal stress. In male marmots, bullying was positively associated with annual reproductive success, while in females, affiliation strength was negatively associated with annual reproductive success. No other social attributes were significantly associated with any performance measures. Our study highlights the utility of considering multiple dimensions when measuring the structure and functional consequences of social behavior.     

Social network analysis and valid Markov chain Monte Carlo tests of null models

Analyses of animal social networks derived from group-based associations often rely on randomisation methods developed in ecology (Manly, Ecology 76:1109–1115, 1995) and made available to the animal behaviour community through implementation of a pair-wise swapping algorithm by Bejder et al. (Anim Behav 56:719–725, 1998). We report a correctable flaw in this method and point the reader to a wider literature on the subject of null models in the ecology literature. We illustrate the importance of correcting the method using a toy network and use it to make a preliminary analysis of a network of associations among eagle rays.

Nonparametric mean estimation using partially ordered sets

In ranked-set sampling (RSS), the ranker must give a complete ranking of the units in each set. In this paper, we consider a modification of RSS that allows the ranker to declare ties. Our sampling method is simply to break the ties at random so that we obtain a standard ranked-set sample, but also to record the tie structure for use in estimation. We propose several different nonparametric mean estimators that incorporate the tie information, and we show that the best of these estimators is substantially more efficient than estimators that ignore the ties. As part of our comparison of estimators, we develop new results about models for ties in rankings. We also show that there are settings where, to achieve more efficient estimation, ties should be declared not just when the ranker is actually unsure about how units rank, but also when the ranker is sure about the ranking, but believes that the units are close.     

Estimating the extent and structure of trade in horticultural orchids via social media

The wildlife trade is a lucrative industry involving thousands of animal and plant species. The increasing use of the internet for both legal and illegal wildlife trade is well documented, but there is evidence that trade may be emerging on new online technologies such as social media. Using the orchid trade as a case study, we conducted the first systematic survey of wildlife trade on an international social‐media website. We focused on themed forums (groups), where people with similar interests can interact by uploading images or text (posts) that are visible to other group members. We used social‐network analysis to examine the ties between 150 of these orchid‐themed groups to determine the structure of the network. We found 4 communities of closely linked groups based around shared language. Most trade occurred in a community that consisted of English‐speaking and Southeast Asian groups. In addition to the network analysis, we randomly sampled 30 groups from the whole network to assess the prevalence of trade in cultivated and wild plants. Of 55,805 posts recorded over 12 weeks, 8.9% contained plants for sale, and 22–46% of these posts pertained to wild‐collected orchids. Although total numbers of posts about trade were relatively small, the large proportion of posts advertising wild orchids for sale supports calls for better monitoring of social media for trade in wild‐collected plants.     

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.     

Using circuit theory to model connectivity in ecology, evolution, and conservation

Connectivity among populations and habitats is important for a wide range of ecological processes. Understanding, preserving, and restoring connectivity in complex landscapes requires connectivity models and metrics that are reliable, efficient, and process based. We introduce a new class of ecological connectivity models based in electrical circuit theory. Although they have been applied in other disciplines, circuit-theoretic connectivity models are new to ecology. They offer distinct advantages over common analytic connectivity models, including a theoretical basis in random walk theory and an ability to evaluate contributions of multiple dispersal pathways. Resistance, current, and voltage calculated across graphs or raster grids can be related to ecological processes (such as individual movement and gene flow) that occur across large population networks or landscapes. Efficient algorithms can quickly solve networks with millions of nodes, or landscapes with millions of raster cells. Here we review basic circuit theory, discuss relationships between circuit and random walk theories, and describe applications in ecology, evolution, and conservation. We provide examples of how circuit models can be used to predict movement patterns and fates of random walkers in complex landscapes and to identify important habitat patches and movement corridors for conservation planning.     

Network position: a key component in the characterization of social personality types

In recent years, animal social interactions have received much attention in terms of personality research (e.g. aggressive or cooperative interactions). However, other components of social behaviour such as those describing the intensity, frequency, directedness and individual repeatability of interactions in animal groups have largely been neglected. Network analysis offers a valuable opportunity to characterize individual consistency of traits in labile social groups and therein provide novel insights to personality research in ways previously not possible using traditional techniques. Should individual network positions be consistently different between individuals under changing conditions, they might reflect expressions of an individual's personality. Here, we discuss a conceptual framework for using network analyses to infer the presence of individual differences and present a statistical test based on randomization techniques for testing the consistency of network positions in individuals. The statistical tools presented are useful because if particular individuals consistently occupy key positions in social networks, then this is also likely to have consequences for their fitness as well as for that of others in the population. These consequences may be particularly significant since individual network position has been shown to be important for the transmission of diseases, socially learnt information and genetic material between individuals and populations.     

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.