Trends in Ecology & Evolution
ReviewThe evolutionary and ecological consequences of animal social networks: emerging issues
Section snippets
Population social structure
The social structure of animal groups and populations has been a long-standing topic in biological research 1, 2. Approximately 10 years ago, social network analysis entered behavioural biology 3, 4, 5 and provided a new way of investigating animal social structure (see Box 1 for a brief introduction to animal social networks). Social network analysis (see Glossary) has promoted an understanding of the social fine structure of groups, communities, and entire populations, thereby generating new
Evolutionary dynamics
Two important aspects of SNS are the network of fitness-determining interactions (aka ‘interaction network’) and the structure of dispersal of offspring (aka ‘replacement network’) [19]. A growing body of recent theoretical research suggests that the fine structure of these networks can have substantial consequences for evolutionary dynamics and evolutionary outcomes.
First, genetic drift is predicted to depend on SNS 20, 21. In particular, when comparing regular-, to small world-, to random-,
Social evolution
A key factor in social evolution is the social environment that individuals are confronted with. Here, we discuss two main ways by which SNS affects the evolution of social behaviour via the social environment.
First, SNS affects the evolution of cooperation. Although selfish individuals are favoured in well-mixed populations, cooperation can become evolutionary competitive when SNS promotes the clustering of strategy types [6]. This is because such clustering makes cooperators more likely to
Coevolution
We have seen that SNS can profoundly influence evolutionary dynamics and social evolution. Thus, it should come as no surprise that SNS can affect coevolutionary processes in which two or more species affect each other's evolution. Here, we discuss the role of SNS in host–pathogen interactions, the evolution of restraint, and mutualism.
In host–pathogen interactions, the SNS of the host can affect virulence and transmissibility of pathogens, and host resistance. Theory predicts that sparse host
Population stability
A key issue in community ecology is how the structure of species networks affects the stability, resilience, and robustness of these networks (e.g., [57]). By contrast, comparatively few studies have investigated how SNS affects the stability of populations. We here discuss two main ways that link SNS with population stability.
First, SNS is predicted to mediate whether and to what extent a population fragments in response to the removal of individuals [58]. In networks where all individuals
Dispersal
Identifying the key factors that drive dispersal has been a long-standing topic in ecological research. Several factors have been identified, including kin competition, inbreeding avoidance, resource competition, and parasitism 67, 68. However, the role of SNS on dispersal has rarely been studied. Here, we discuss three ways in which SNS can affect adaptive dispersal decisions via the above-mentioned factors.
First, optimal dispersal decisions are affected by the relatedness of an individual
Invasion
A key question in invasion ecology is which factors determine whether an invasion will be successful [78]. Up to now, the role of SNS for invasion success has received surprisingly little attention. Here, we discuss two ways in which SNS can affect invasion success.
First, SNS affects flow processes within populations 11, 12, which can affect invasion success in two ways: by affecting the information available to individuals, and by affecting the evolutionary response of a population.
Population social structure: a fundamental biological dimension
Much previous research on animal social networks focussed on a limited number of contexts, such as information and disease transmission and cooperation. Here, we have reviewed the recently accumulating evidence that population social structure affects a broader range of ecological and evolutionary processes (Table 1) and we hope that our work inspires further research into these and related areas. Next to the issues discussed above, promising topics for future research include the consistency
Acknowledgements
The authors thank Paul Craze, David McDonald, and Dan Blumstein for insightful comments and constructive criticism. The authors also thank Stefan Krause, Sander van Doorn, and Franjo Weissing for advice. Funding is acknowledged from a Rubicon fellowship (R.H.J.M.K.), the Alexander von Humboldt Foundation (A.D.M.W.), the Leverhulme Trust (D.P.C.) and the B-Types research project (SAW-2013-IGB-2) funded through the Leibniz Competition (M.W. and J.K.).
Glossary
- Average path length
- the average shortest distance between all possible pairs of nodes in a network. Low values mean that, on average, few connections are needed to connect two nodes, implying high transmission efficiency through a network.
- Clustering coefficient
- a measure of how tightly nodes are clustered in a network. This can apply to a node (local clustering coefficient) or a network (global clustering coefficient). The local clustering coefficient quantifies the extent to which the immediate
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These authors contributed equally to this article.