Social transmission in networks: global efficiency peaks with intermediate levels of modularity

Revisiting the Modularity-Disease transmission Link: Uncovering the importance of intra-modular structure

Studies have shown that the internal structure of modules is hardly important for the spread of epidemics. However, most of these studies have assumed that intra-module connectivity and inter-module connectivity do not affect each other. In reality, changes in the internal structure of modules may affect inter-module links and thus change the modularity of the entire network. Therefore, we have developed a theoretical network model with adjustable modularity to investigate the impact of this situation on disease transmission. Our findings indicate that the intra-module structure plays a crucial role in disease outbreaks. Changes in intra-module structure lead to significant numerical changes in peak prevalence and duration of disease. This implies that the potential impact of changes in exposure patterns within modules should also be considered when investigating the exact impact of modular social networks on disease burden.

The role of social attraction and social avoidance in shaping modular networks

How interactions between individuals contribute to the emergence of complex societies is a major question in behavioural ecology. Nonetheless, little remains known about the type of immediate social structure (i.e. social network) that emerges from relationships that maximize beneficial interactions (e.g. social attraction towards informed individuals) and minimize costly relationships (e.g. social avoidance of infected group mates). We developed an agent-based model where individuals vary in the degree to which individuals signal benefits versus costs to others and, on this basis, choose with whom to interact depending on simple rules of social attraction (e.g. access to the highest benefits) and social avoidance (e.g. avoiding the highest costs). Our main findings demonstrate that the accumulation of individual decisions to avoid interactions with highly costly individuals, but that are to some extent homogeneously beneficial, leads to more modular networks. On the contrary, individuals favouring interactions with highly beneficial individuals, but that are to some extent homogeneously costly, lead to less modular networks. Interestingly, statistical models also indicate that when individuals have multiple potentially beneficial partners to interact with, and no interaction cost exists, this also leads to more modular networks. Yet, the degree of modularity is contingent upon the variability in benefit levels held by individuals. We discuss the emergence of modularity in the systems and their consequences for understanding social trade-offs.

Comparative approaches in social network ecology

Social systems vary enormously across the animal kingdom, with important implications for ecological and evolutionary processes such as infectious disease dynamics, anti-predator defence, and the evolution of cooperation. Comparing social network structures between species offers a promising route to help disentangle the ecological and evolutionary processes that shape this diversity. Comparative analyses of networks like these are challenging and have been used relatively little in ecology, but are becoming increasingly feasible as the number of empirical datasets expands. Here, we provide an overview of multispecies comparative social network studies in ecology and evolution. We identify a range of advancements that these studies have made and key challenges that they face, and we use these to guide methodological and empirical suggestions for future research. Overall, we hope to motivate wider publication and analysis of open social network datasets in animal ecology.

Social learning mechanisms shape transmission pathways through replicate local social networks of wild birds

The emergence and spread of novel behaviours via social learning can lead to rapid population-level changes whereby the social connections between individuals shape information flow. However, behaviours can spread via different mechanisms and little is known about how information flow depends on the underlying learning rule individuals employ. Here, comparing four different learning mechanisms, we simulated behavioural spread on replicate empirical social networks of wild great tits and explored the relationship between individual sociality and the order of behavioural acquisition. Our results reveal that, for learning rules dependent on the sum and strength of social connections to informed individuals, social connectivity was related to the order of acquisition, with individuals with increased social connectivity and reduced social clustering adopting new behaviours faster. However, when behavioural adoption depends on the ratio of an individuals' social connections to informed versus uninformed individuals, social connectivity was not related to the order of acquisition. Finally, we show how specific learning mechanisms may limit behavioural spread within networks. These findings have important implications for understanding whether and how behaviours are likely to spread across social systems, the relationship between individuals' sociality and behavioural acquisition, and therefore for the costs and benefits of sociality.

Socioconnectomics: Connectomics Should Be Extended to Societies to Better Understand Evolutionary Processes

Connectomics, which is the network study of connectomes or maps of the nervous system of an organism, should be applied and expanded to human and animal societies, resulting in the birth of the domain of socioconnectomics compared to neuroconnectomics. This new network study framework would open up new perspectives in evolutionary biology and add new elements to theories, such as the social and cultural brain hypotheses. Answering questions about network topology, specialization, and their connections with functionality at one level (i.e., neural or societal) may help in understanding the evolutionary trajectories of these patterns at the other level. Expanding connectomics to societies should be done in comparison and combination with multilevel network studies and the possibility of multiorganization selection processes. The study of neuroconnectomes and socioconnectomes in animals, from simpler to more advanced ones, could lead to a better understanding of social network evolution and the feedback between social complexity and brain complexity.

Social information-mediated population dynamics in non-grouping prey

Abstract

Inadvertent social information (ISI) use, i.e., the exploitation of social cues including the presence and behaviour of others, has been predicted to mediate population-level processes even in the absence of cohesive grouping. However, we know little about how such effects may arise when the prey population lacks social structure beyond the spatiotemporal autocorrelation originating from the random movement of individuals. In this study, we built an individual-based model where predator avoidance behaviour could spread among randomly moving prey through the network of nearby observers. We qualitatively assessed how ISI use may affect prey population size when cue detection was associated with different probabilities and fitness costs, and characterised the structural properties of the emerging detection networks that would provide pathways for information spread in prey. We found that ISI use was among the most influential model parameters affecting prey abundance and increased equilibrium population sizes in most examined scenarios. Moreover, it could substantially contribute to population survival under high predation pressure, but this effect strongly depended on the level of predator detection ability. When prey exploited social cues in the presence of high predation risk, the observed detection networks consisted of a large number of connected components with small sizes and small ego networks; this resulted in efficient information spread among connected individuals in the detection networks. Our study provides hypothetical mechanisms about how temporary local densities may allow information diffusion about predation threats among conspecifics and facilitate population stability and persistence in non-grouping animals.

Significance statement

The exploitation of inadvertently produced social cues may not only modify individual behaviour but also fundamentally influence population dynamics and species interactions. Using an individual-based model, we investigated how the detection and spread of adaptive antipredator behaviour may cascade to changes in the demographic performance of randomly moving (i.e., non-grouping) prey. We found that social information use contributed to population stability and persistence by reducing predation-related per capita mortality and raising equilibrium population sizes when predator detection ability reached a sufficient level. We also showed that temporary detection networks had structural properties that allowed efficient information spread among prey under high predation pressure. Our work represents a general modelling approach that could be adapted to specific predator-prey systems and scrutinise how temporary local densities allow dynamic information diffusion about predation threats and facilitate population stability in non-grouping animals.

Impact of joint interactions with humans and social interactions with conspecifics on the risk of zooanthroponotic outbreaks among wildlife populations

Pandemics caused by pathogens that originate in wildlife highlight the importance of understanding the behavioral ecology of disease outbreaks at human–wildlife interfaces. Specifically, the relative effects of human–wildlife and wildlife-wildlife interactions on disease outbreaks among wildlife populations in urban and peri-urban environments remain unclear. We used social network analysis and epidemiological Susceptible-Infected-Recovered models to simulate zooanthroponotic outbreaks, through wild animals’ joint propensities to co-interact with humans, and their social grooming of conspecifics. On 10 groups of macaques (Macaca spp.) in peri-urban environments in Asia, we collected behavioral data using event sampling of human–macaque interactions within the same time and space, and focal sampling of macaques’ social interactions with conspecifics and overall anthropogenic exposure. Model-predicted outbreak sizes were related to structural features of macaques’ networks. For all three species, and for both anthropogenic (co-interactions) and social (grooming) contexts, outbreak sizes were positively correlated to the network centrality of first-infected macaques. Across host species and contexts, the above effects were stronger through macaques’ human co-interaction networks than through their grooming networks, particularly for rhesus and bonnet macaques. Long-tailed macaques appeared to show intraspecific variation in these effects. Our findings suggest that among wildlife in anthropogenically-impacted environments, the structure of their aggregations around anthropogenic factors makes them more vulnerable to zooanthroponotic outbreaks than their social structure. The global features of these networks that influence disease outbreaks, and their underlying socio-ecological covariates, need further investigation. Animals that consistently interact with both humans and their conspecifics are important targets for disease control.

Reconstructing social networks of Late Glacial and Holocene hunter-gatherers to understand cultural evolution

Culture is increasingly being framed as a driver of human phenotypes and behaviour. Yet very little is known about variations in the patterns of past social interactions between humans in cultural evolution. The archaeological record, combined with modern evolutionary and analytical approaches, provides a unique opportunity to investigate broad-scale patterns of cultural change. Prompted by evidence that a population's social connectivity influences cultural variability, in this article, we revisit traditional approaches used to infer cultural evolutionary processes from the archaeological data. We then propose that frameworks considering multi-scalar interactions (from individuals to populations) over time and space have the potential to advance knowledge in cultural evolutionary theory. We describe how social network analysis can be applied to analyse diachronic structural changes and test cultural transmission hypotheses using the archaeological record (here specifically from the Marine Isotope Stage 3 ca 57-29 ka onwards). We argue that the reconstruction of prehistoric networks offers a timely opportunity to test the interplay between social connectivity and culture and ultimately helps to disentangle evolutionary mechanisms in the archaeological record. This article is part of a discussion meeting issue 'The emergence of collective knowledge and cumulative culture in animals, humans and machines'.

Group size and modularity interact to shape the spread of infection and information through animal societies

Social interactions between animals can provide many benefits, including the ability to gain useful environmental information through social learning. However, these social contacts can also facilitate the transmission of infectious diseases through a population. Animals engaging in social interactions therefore face a trade-off between the potential informational benefits and the risk of acquiring disease. Theoretical models have suggested that modular social networks, associated with the formation of groups or sub-groups, can slow spread of infection by trapping it within particular groups. However, these social structures will not necessarily impact the spread of information in the same way if its transmission follows a “complex contagion”, e.g. through individuals disproportionally copying the majority (conformist learning). Here we use simulation models to demonstrate that modular networks can promote the spread of information relative to the spread of infection, but only when the network is fragmented and group sizes are small. We show that the difference in transmission between information and disease is maximised for more well-connected social networks when the likelihood of transmission is intermediate. Our results have important implications for understanding the selective pressures operating on the social structure of animal societies, revealing that highly fragmented networks such as those formed in fission–fusion social groups and multilevel societies can be effective in modulating the infection-information trade-off for individuals within them.

Antivax movement and epidemic spreading in the era of social networks: Nonmonotonic effects, bistability, and network segregation

In this work, we address a multicoupled dynamics on complex networks with tunable structural segregation. Specifically, we work on a networked epidemic spreading under a vaccination campaign with agents in favor and against the vaccine. Our results show that such coupled dynamics exhibits a myriad of phenomena such as nonequilibrium transitions accompanied by bistability. Besides we observe the emergence of an intermediate optimal segregation level where the community structure enhances negative opinions over vaccination but counterintuitively hinders-rather than favoring-the global disease spreading. Thus our results hint vaccination campaigns should avoid policies that end up segregating excessively antivaccine groups so that they effectively work as echo chambers in which individuals look to confirmation without jeopardizing the safety of the whole population.

Improving pandemic mitigation policies across communities through coupled dynamics of risk perception and infection

Capturing the coupled dynamics between individual behavioural decisions that affect disease transmission and the epidemiology of outbreaks is critical to pandemic mitigation strategy. We develop a multiplex network approach to model how adherence to health-protective behaviours that impact COVID-19 spread are shaped by perceived risks and resulting community norms. We focus on three synergistic dynamics governing individual behavioural choices: (i) social construction of concern, (ii) awareness of disease incidence, and (iii) reassurance by lack of disease. We show why policies enacted early or broadly can cause communities to become reassured and therefore unwilling to maintain or adopt actions. Public health policies for which success relies on collective action should therefore exploit the behaviourally receptive phase; the period between the generation of sufficient concern to foster adoption of novel actions and the relaxation of adherence driven by reassurance fostered by avoidance of negative outcomes over time.

Hierarchical networks of food exchange in the black garden ant Lasius niger

In most eusocial insects, the division of labor results in relatively few individuals foraging for the entire colony. Thus, the survival of the colony depends on its efficiency in meeting the nutritional needs of all its members. Here, we characterize the network topology of a eusocial insect to understand the role and centrality of each caste in this network during the process of food dissemination. We constructed trophallaxis networks from 34 food-exchange experiments in black garden ants (Lasius niger). We tested the influence of brood and colony size on (i) global indices at the network level (i.e., efficiency, resilience, centralization, and modularity) and (ii) individual values (i.e., degree, strength, betweenness, and the clustering coefficient). Network resilience, the ratio between global efficiency and centralization, was stable with colony size but increased in the presence of broods, presumably in response to the nutritional needs of larvae. Individual metrics highlighted the major role of foragers in food dissemination. In addition, a hierarchical clustering analysis suggested that some domestics acted as intermediaries between foragers and other domestics. Networks appeared to be hierarchical rather than random or centralized exclusively around foragers. Finally, our results suggested that networks emerging from social insect interactions can improve group performance and thus colony fitness.

New dimensions for animal communication networks: space and time

Communication is a social process and usually occurs in a network of signalers and receivers. While social network analysis has received enormous recent attention from animal behaviorists, there have been relatively few attempts to apply these techniques to communication networks. Communication networks have the potential to offer novel insights into social network studies, and yet are especially challenging subjects, largely because of their unique spatiotemporal characteristics. Namely, signals propagate through the environment, often dissociating from the body of the signaler, to influence receiver behavior. The speed of signal propagation and the signal's active space will affect the congruence of communication networks and other types of social network; in extreme cases the signal may persist and only first be detected long after the signaler has left the area. Other signals move more rapidly and over greater distances than the signaler could possibly move to reach receivers. We discuss the spatial and temporal consequences of signaling in networks and highlight the distinction between the physical location of the signaler and the spread of influence of its signals, the effects of signal modality and receiver sensitivity on communication network properties, the potential for feedbacks between network layers, and approaches to analyzing spatial and temporal change in communication networks in conjunction with other network layers.

A multilevel analytical framework for studying cultural evolution in prehistoric hunter-gatherer societies

Over the past decade, a major debate has taken place on the underpinnings of cultural changes in human societies. A growing array of evidence in behavioural and evolutionary biology has revealed that social connectivity among populations and within them affects, and is affected by, culture. Yet the interplay between prehistoric hunter-gatherer social structure and cultural transmission has typically been overlooked. Interestingly, the archaeological record contains large data sets, allowing us to track cultural changes over thousands of years: they thus offer a unique opportunity to shed light on long-term cultural transmission processes. In this review, we demonstrate how well-developed methods for social structure analysis can increase our understanding of the selective pressures underlying cumulative culture. We propose a multilevel analytical framework that considers finer aspects of the complex social structure in which regional groups of prehistoric hunter-gatherers were embedded. We put forward predictions of cultural transmission based on local- and global-level network metrics of small-scale societies and their potential effects on cumulative culture. By bridging the gaps between network science, palaeodemography and cultural evolution, we draw attention to the use of the archaeological record to depict patterns of social interactions and transmission variability. We argue that this new framework will contribute to improving our understanding of social interaction patterns, as well as the contexts in which cultural changes occur. Ultimately, this may provide insights into the evolution of human behaviour.

Primate Infectious Disease Ecology: Insights and Future Directions at the Human-Macaque Interface

Global population expansion has increased interactions and conflicts between humans and nonhuman primates over shared ecological space and resources. Such ecological overlap, along with our shared evolutionary histories, makes human-nonhuman primate interfaces hot spots for the acquisition and transmission of parasites. In this chapter, we bring to light the importance of human-macaque interfaces in particular as hot spots for infectious disease ecological and epidemiological assessments. We first outline the significance and broader objectives behind research related to the subfield of primate infectious disease ecology and epidemiology. We then reveal how members of the genus Macaca, being among the most socioecologically flexible and invasive of all primate taxa, live under varying degrees of overlap with humans in anthropogenic landscapes. Thus, human-macaque interfaces may favor the bidirectional exchange of parasites. We then review studies that have isolated various types of parasites at human-macaque interfaces, using information from the Global Mammal Parasite Database (GMPD: http://www.mammalparasites.org/). Finally, we elaborate on avenues through which the implementation of both novel conceptual frameworks (e.g., Coupled Systems, One Health) and quantitative network-based approaches (e.g., social and bipartite networks, agent-based modeling) may potentially address some of the critical gaps in our current knowledge of infectious disease ecology at human-primate interfaces.

A Bibliometric Analysis of the Health Field Regarding Social Networks and Young People

Social networks have historically been used to share information and support regarding health-related topics, and this usage has increased with the rise of online social media. Young people are high users of social media, both as passive listeners and as active contributors. This study aimed to map the trends in publications focused on social networks, health, and young people over the last 40 years. Scopus and the program VOSviewer were used to map the frequency of the publications, keywords, and clusters of researchers active in the field internationally. A structured keyword search using the Scopus database yielded 11,966 publications. The results reveal a long history of research on social networks, health, and young people. Research articles were the most common type of publication (68%), most of which described quantitative studies (82%). The main discipline represented in this literature was medicine, with 6062 documents. North American researchers dominate the field, both as authors and partners in international research collaborations. The present article adds to the literature by elucidating the growing importance of social networks in health research as a topic of study. This may help to inform future investments in public health research and surveillance using these novel data sources.