Multilayer network analysis : new opportunities and challenges for studying animal social systems

M.J.H. is supported by a European Research Council H2020 grant (#638873) awarded to Ellouise Leadbeater. M.J.S is funded by the University of Exeter.Peer reviewedPublisher PD

Understanding animal social structure: exponential random graph models in animal behaviour research

M.J.S. is funded by a NERC grant NE/M004546/1. D.N.F. is funded by the Natural Sciences and Engineering Research Council of Canada. We thank Jared Wilson-Aggarwal for helpful discussions and two anonymous referees for constructive comments that improved the article.Peer reviewedPublisher PD

The use of multilayer network analysis in animal behaviour

Network analysis has driven key developments in research on animal behaviour by providing quantitative methods to study the social structures of animal groups and populations. A recent formalism, known as \emph{multilayer network analysis}, has advanced the study of multifaceted networked systems in many disciplines. It offers novel ways to study and quantify animal behaviour as connected 'layers' of interactions. In this article, we review common questions in animal behaviour that can be studied using a multilayer approach, and we link these questions to specific analyses. We outline the types of behavioural data and questions that may be suitable to study using multilayer network analysis. We detail several multilayer methods, which can provide new insights into questions about animal sociality at individual, group, population, and evolutionary levels of organisation. We give examples for how to implement multilayer methods to demonstrate how taking a multilayer approach can alter inferences about social structure and the positions of individuals within such a structure. Finally, we discuss caveats to undertaking multilayer network analysis in the study of animal social networks, and we call attention to methodological challenges for the application of these approaches. Our aim is to instigate the study of new questions about animal sociality using the new toolbox of multilayer network analysis.Comment: Thoroughly revised; title changed slightl

Cosmic Evolution of Black Holes and Spheroids. II: Scaling Relations at z=0.36

We combine Hubble Space Telescope images of a sample of 20 Seyfert galaxies at z=0.36 with spectroscopic information from the Keck Telescope to determine the black hole mass - spheroid luminosity relation (M-L), the Fundamental Plane (FP) of the host galaxies and the M-sigma relation. Assuming pure luminosity evolution, we find that the host spheroids had smaller luminosity and stellar velocity dispersion than today for a fixed M. The offsets correspond to Delta log L_B,0=0.40+-0.11+-0.15 (Delta log M = 0.51+-0.14+-0.19) and Delta log sigma = 0.13+-0.03+-0.05 (Delta log M = 0.54+-0.12+-0.21), respectively for the M-L and M-sigma relation. A detailed analysis of known systematic errors and selection effects shows that they cannot account for the observed offset. The data are inconsistent with pure luminosity evolution and the existence of universal and tight scaling relations. To obey the three local scaling relations by z=0 the distant spheroids have to grow their stellar mass by approximately 60% (\Delta log M_sph=0.20+-0.14) in the next 4 billion years. The measured evolution can be expressed as M/ M_sph ~ (1+z)^{1.5+-1.0}. Based on the disturbed morphologies of a fraction of the sample (6/20) we suggest collisional mergers with disk-dominated systems as evolutionary mechanism.Comment: 17 pages, 10 figures; accepted for publication in the Astrophysical Journa

Diversity in Valuing Social Contact and Risk Tolerance Lead to the Emergence of Homophily in Populations Facing Infectious Threats

How self-organization leads to the emergence of structure in social populations remains a fascinating and open question in the study of complex systems. One frequently observed structure that emerges again and again across systems is that of self-similar community, i.e., homophily. We use a game theoretic perspective to explore a case in which individuals choose affiliation partnerships based on only two factors: the value they place on having social contacts, and their risk tolerance for exposure to threat derived from social contact (e.g., infectious disease, threatening ideas, etc.). We show how diversity along just these two influences are sufficient to cause the emergence of self-organizing homophily in the population. We further consider a case in which extrinsic social factors influence the desire to maintain particular social ties, and show the robustness of emergent homophilic patterns to these additional influences. These results demonstrate how observable population-level homophily may arise out of individual behaviors that balance the value of social contacts against the potential risks associated with those contacts. We present and discuss these results in the context of outbreaks of infectious disease in human populations. Complementing the standard narrative about how social division alters epidemiological risk, we here show how epidemiological risk may deepen social divisions in human populations.Comment: 17 pages, 4 figure

Comparative approaches in social network ecology

Abstract 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

Multilayer and multiplex networks: an introduction to their use in veterinary epidemiology

This is the final version. Available from Frontiers Media via the DOI in this record.Contact network analysis has become a vital tool for conceptualizing the spread of pathogens in animal populations and is particularly useful for understanding the implications of heterogeneity in contact patterns for transmission. However, the transmission of most pathogens cannot be simplified to a single mode of transmission and, thus, a single definition of contact. In addition, host-pathogen interactions occur in a community context, with many pathogens infecting multiple host species and most hosts being infected by multiple pathogens. Multilayer networks provide a formal framework for researching host-pathogen systems in which multiple types of transmission-relevant interactions, defined as network layers, can be analyzed jointly. Here, we provide an overview of multilayer network analysis and review applications of this novel method to epidemiological research questions. We then demonstrate the use of this technique to analyze heterogeneity in direct and indirect contact patterns amongst swine farms in the United States. When contact among nodes can be defined in multiple ways, a multilayer approach can advance our ability to use networks in epidemiological research by providing an improved approach for defining epidemiologically relevant groups of interacting nodes and changing the way we identify epidemiologically important individuals such as superspreaders.Biotechnology and Biological Sciences Research Council (BBSRC)NIFA-NSF-NIH Ecology and Evolution of Infectious Disease awardAgriculture and Food Research InitiativeSwine Health Information Center (SHIC)University of MinnesotaUniversity of Exete

Characterization of potential superspreader farms for bovine tuberculosis:A review

Background: Variation in host attributes that influence their contact rates and infectiousness can lead some individuals to make disproportionate contributions to the spread of infections. Understanding the roles of such ‘superspreaders’ can be crucial in deciding where to direct disease surveillance and controls to greatest effect. In the epidemiology of bovine tuberculosis (bTB) in Great Britain, it has been suggested that a minority of cattle farms or herds might make disproportionate contributions to the spread of Mycobacterium bovis, and hence might be considered ‘superspreader farms’.Objectives and Methods: We review the literature to identify the characteristics of farms that have the potential to contribute to exceptional values in the three main components of the farm reproductive number - Rf: contact rate, infectiousness and duration of infectiousness, and therefore might characterize potential superspreader farms for bovine tuberculosis in Great Britain.Results: Farms exhibit marked heterogeneity in contact rates arising from between-farm trading of cattle. A minority of farms act as trading hubs that greatly augment connections within cattle trading networks. Herd infectiousness might be increased by high within-herd transmission or the presence of supershedding individuals, or infectiousness might be prolonged due to undetected infections or by repeated local transmission, via wildlife or fomites.Conclusions: Targeting control methods on putative superspreader farms might yield disproportionate benefits in controlling endemic bovine tuberculosis in Great Britain. However, real-time identification of any such farms, and integration of controls with industry practices, present analytical, operational and policy challenges.<br/

Common datastream permutations of animal social network data are not appropriate for hypothesis testing using regression models

1. Social network methods have become a key tool for describing, modelling and testing hypotheses about the social structures of animals. However, due to the non-independence of network data and the presence of confounds, specialised statistical techniques are often needed to test hypotheses in these networks. Datastream permutations, originally developed to test the null hypothesis of random social structure, have become a popular tool for testing a wide array of null hypotheses in animal social networks. In particular, they have been used to test whether exogenous factors are related to network structure by interfacing these permutations with regression models.2. Here, we show that these datastream permutations typically do not represent the null hypothesis of interest to researchers interfacing animal social network analysis with regression modelling, and use simulations to demonstrate the potential pitfalls of using this methodology.3. Our simulations show that, if used to indicate whether a relationship exists between network structure and a covariate, datastream permutations can result in extremely high type I error rates, in some cases approaching 50%. In the same set of simulations, traditional node-label permutations produced appropriate type I error rates (~5%).4. Our analysis shows that datastream permutations do not represent the appropriate null hypothesis for these analyses. We suggest that potential alternatives to this procedure may be found in regarding the problems of non-independence of network data and unreliability of observations separately. If biases introduced during data collection can be corrected, either prior to model fitting or within the model itself, node-label permutations then serve as a useful test for interfacing animal social network analysis with regression modelling

Social structure contains epidemics and regulates individual roles in disease transmission in a group-living mammal

Population structure is critical to infectious disease transmission. As a result, theoretical and empirical contact network models of infectious disease spread are increasingly providing valuable insights into wildlife epidemiology. Analyzing an exceptionally detailed dataset on contact structure within a high‐density population of European badgers Meles meles, we show that a modular contact network produced by spatially structured stable social groups, lead to smaller epidemics, particularly for infections with intermediate transmissibility. The key advance is that we identify considerable variation among individuals in their role in disease spread, with these new insights made possible by the detail in the badger dataset. Furthermore, the important impacts on epidemiology are found even though the modularity of the Badger network is much lower than the threshold that previous work suggested was necessary. These findings reveal the importance of stable social group structure for dis‐ease dynamics with important management implications for socially structured populations