Damien R. Farine

Social networks predict patch discovery in a wild population of songbirds

Animals use social information in a wide variety of contexts. Its extensive use by individuals to locate food patches has been documented in a number of species, and various mechanisms of discovery have been identified. However, less is known about whether individuals differ in their access to, and use of, social information to find food. We measured the social network of a wild population of three sympatric tit species (family Paridae) and then recorded individual discovery of novel food patches. By using recently developed methods for network-based diffusion analysis, we show that order of arrival at new food patches was predicted by social associations. Models based only on group searching did not explain this relationship. Furthermore, network position was correlated with likelihood of patch discovery, with central individuals more likely to locate and use novel foraging patches than those with limited social connections. These results demonstrate the utility of social network analysis as a method to investigate social information use, and suggest that the greater probability of receiving social information about new foraging patches confers a benefit on more socially connected individuals.

Social network analysis of mixed-species flocks: exploring the structure and evolution of interspecific social behaviour

Mixed-species social aggregations are common across taxa. There are two, nonexclusive, hypotheses typically proposed to explain the formation of social groups: increased predator vigilance and greater foraging efficiency. In mixed-species groups, these hypotheses are typically tested with species-level summary measures such as flocking propensity, the assignment of species-level roles, mean body size, and foraging and habitat characteristics. Literature syntheses make it clear that while these hypotheses are important, much about mixed-species groups remains unexplained. We suggest that we can substantially increase our understanding of the evolution and ecology of mixed-species social groups in terms of both traditional and novel hypotheses by shifting the analytical focus to bottom-up approaches common in intraspecific investigations of sociality. Bottom-up approaches to analyses of social structure treat pairwise interactions as the fundamental unit of analysis and social structure as an emergent property rather than relying on a priori assignments of species as units of association. The construction of social networks from pairwise interaction rates allows us to assess the factors that promote group formation on the basis of individuals, a more appropriate level of selection, rather than species groups. We illustrate this approach with data from mixed-species foraging assemblies in tits (Paridae), finding significant effects of dominance on social behaviour within species. This new focus allows us to address questions about active associations among heterospecifics, the role of individuals within mixed-species societies, and the role of environments, which will collectively provide a richer description of the evolution and function of mixed-species societies.

Feeder use predicts both acquisition and transmission of a contagious pathogen in a North American songbird

Individual heterogeneity can influence the dynamics of infectious diseases in wildlife and humans alike. Thus, recent work has sought to identify behavioural characteristics that contribute disproportionately to individual variation in pathogen acquisition (super-receiving) or transmission (super-spreading). However, it remains unknown whether the same behaviours enhance both acquisition and transmission, a scenario likely to result in explosive epidemics. Here, we examined this possibility in an ecologically relevant host–pathogen system: house finches and their bacterial pathogen, Mycoplasma gallisepticum, which causes severe conjunctivitis. We examined behaviours likely to influence disease acquisition (feeder use, aggression, social network affiliations) in an observational field study, finding that the time an individual spends on bird feeders best predicted the risk of conjunctivitis. To test whether this behaviour also influences the likelihood of transmitting M. gallisepticum, we experimentally inoculated individuals based on feeding behaviour and tracked epidemics within captive flocks. As predicted, transmission was fastest when birds that spent the most time on feeders initiated the epidemic. Our results suggest that the same behaviour underlies both pathogen acquisition and transmission in this system and potentially others. Identifying individuals that exhibit such behaviours is critical for disease management.

Developmental stress predicts social network position

The quantity and quality of social relationships, as captured by social network analysis, can have major fitness consequences. Various studies have shown that individual differences in social behaviour can be due to variation in exposure to developmental stress. However, whether these developmental differences translate to consistent differences in social network position is not known. We experimentally increased levels of the avian stress hormone corticosterone (CORT) in nestling zebra finches in a fully balanced design. Upon reaching nutritional independence, we released chicks and their families into two free-flying rooms, where we measured daily social networks over five weeks using passive integrated transponder tags. Developmental stress had a significant effect on social behaviour: despite having similar foraging patterns, CORT chicks had weaker associations to their parents than control chicks. Instead, CORT chicks foraged with a greater number of flock mates and were less choosy with whom they foraged, resulting in more central network positions. These findings highlight the importance of taking developmental history into account to understand the drivers of social organization in gregarious species.

Proximity as a proxy for interactions: issues of scale in social network analysis

Social network analysis (SNA) is a widely used framework for quantifying structure patterns and social processes in animal populations. At its simplest, SNA requires only a set of observations made on multiple pairs of individuals to provide access to powerful statistical measures of group or population-level properties. This flexibility has allowed researchers to use SNA to gain insight into a range of different biological systems and address a multitude of hypotheses. A major step when designing new studies, or when retrospectively applying SNA to existing data, is to decide how social ties are defined. This fundamental unit provides the edges, or links, between the nodes (individuals) in the network. The definition of an edge is likely to be determined by two primary considerations: (1) the edge definition must be relevant to the question or hypothesis being addressed, and (2) whether it is possible to observe the study subjects interacting or not (issues that are inherent to the species of interest, e.g. because of individuals spending most of their time in high forest canopies or underwater). Thus, most network studies employ taxon-specific edge definitions

Inferring social structure from temporal data

Social network analysis has become a popular tool for characterising the social structure of populations. Animal social networks can be built either by observing individuals and defining links based on the occurrence of specific types of social interactions, or by linking individuals based on observations of physical proximity or group membership, given a certain behavioural activity. The latter approaches of discovering network structure require splitting the temporal observation stream into discrete events given an appropriate time resolution parameter. This process poses several non-trivial problems which have not received adequate attention so far. Here, using data from a study of passive integrated transponder (PIT)-tagged great tits Parus major, we discuss these problems, demonstrate how the choice of the extraction method and the temporal resolution parameter influence the appearance and properties of the retrieved network and suggest a modus operandi that minimises observer bias due to arbitrary parameter choice. Our results have important implications for all studies of social networks where associations are based on spatio-temporal proximity, and more generally for all studies where we seek to uncover the relationships amongst a population of individuals that are observed through a temporal data stream of appearance records.

The role of social and ecological processes in structuring animal populations: a case study from automated tracking of wild birds

Both social and ecological factors influence population process and structure, with resultant consequences for phenotypic selection on individuals. Understanding the scale and relative contribution of these two factors is thus a central aim in evolutionary ecology. In this study, we develop a framework using null models to identify the social and spatial patterns that contribute to phenotypic structure in a wild population of songbirds. We used automated technologies to track 1053 individuals that formed 73 737 groups from which we inferred a social network. Our framework identified that both social and spatial drivers contributed to assortment in the network. In particular, groups had a more even sex ratio than expected and exhibited a consistent age structure that suggested local association preferences, such as preferential attachment or avoidance. By contrast, recent immigrants were spatially partitioned from locally born individuals, suggesting differential dispersal strategies by phenotype. Our results highlight how different scales of social decision-making, ranging from post-natal dispersal settlement to fission–fusion dynamics, can interact to drive phenotypic structure in animal populations.

Social organisation of thornbill-dominated mixed-species flocks using social network analysis

Mixed-species associations are a widespread phenomenon, comprising interacting heterospecific individuals which gain predator, foraging or social benefits. Avian flocks have traditionally been classified as monolithic species units, with species-wide functional roles, such as nuclear, active, passive, or follower. It has also been suggested that flocks are mutualistic interactions, where niches of participating species converge. However the species-level perspective has limited previous studies, because both interactions and benefits occur at the level of the individual. Social network analysis provides a set of tools for quantitative assessment of individual participation. We used mark-resighting methods to develop networks of nodes (colour-marked individuals) and edges (their interactions within flocks). We found that variation in flock participation across individuals within species, especially in the buff-rumped thornbill, encompassed virtually the entire range of variation across all individuals in the entire set of species. For example, female, but not male, buff-rumped thornbills had high network betweenness, indicating that they interact with multiple flocks, likely as part of a female-specific dispersal strategy. Finally, we provide new evidence that mixed-species flocking is mutualistic, by quantifying an active shift in individual foraging niches towards those of their individual associates, with implications for trade-off between costs and benefits to individuals derived from participating in mixed-species flocks. This study is, to our knowledge, the first instance of a heterospecific social network built on pairwise interactions.

Environment modulates population social structure: Experimental evidence from replicated social networks of wild lizards

Social structure is a fundamental component of a population that drives ecological and evolutionary processes ranging from parasite transmission to sexual selection. Nevertheless, we have much to learn about factors that explain variation in social structure. We used advances in biologging and social network analysis to experimentally test how the local habitat, and specifically habitat complexity, modulates social structure at different levels in wild populations. Sleepy lizards, Tiliqua rugosa, establish nonrandom social networks that are characterized by avoidance of some neighbours and frequent interactions with one opposite-sex individual. Using synchronous GPS locations of all adult lizards, we constructed social networks based on spatial proximity of individuals. We increased habitat structural complexity in two study populations by adding 100 short fences across the landscape. We then compared the resulting movement behaviour and social structure between these populations and two unmanipulated populations. Social connectivity (network density) and social stability, measured at weekly intervals, were greater in populations with increased habitat structural complexity. The level of agonistic interaction (quantified as scale damage) was also higher, indicating a fitness cost of greater social connectivity. However, some network parameters were unaffected by increased complexity, including disassortative mixing by sex, and at the individual level, social differentiation among associates (coefficient of variation of edge weights) and maximal interaction frequencies (maximal edge weight). This suggests divergent effects of changed ecological conditions on individual association behaviour compared to the resulting social structure of the population. Our results contrast with those from studies of more gregarious species, in which higher structural complexity in the environment relaxed the social connectivity. This shows that the response to altered ecological conditions can differ fundamentally between species or between populations, and we suggest that it depends on their tendency for gregarious behaviour.

A guide to null models for animal social network analysis

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.