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

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

Evaluation and Value Management in Science

The nature of values has been an ongoing topic of discussion in philosophy, particularly in ethics. However, as the issue of how values should play a role in science has become more prominent, the discussion has not always paid due emphasis on clarifying the nature of values as perhaps it should have been. With the rise of arguments against the value-free ideal there has been an emergence of multiple accounts which aim to explain how values can be used in science while maintaining scientific integrity. I term these complex of approaches “value management.” Value management as a normative idea stems from the notion that somehow values represent some kind of threat to the integrity of science, even if how exactly they present a threat is not clear. What values are, and how they are problematic isn’t always explicit and there is no obvious definition of the term that is shared by philosophers of science. Competing visions of what values should play a role in science, and how they should play a role, may lack justification or be talking past one another if they do not share the same concept of value. To address this, I argue that there needs to be a more precise articulation of what values are, and that once we develop a nuanced account of value, many of the concerns that value management accounts respond to change or disappear. Looking to some historical and modern discussions of values and valuation, I show that there are accounts of value that are compatible with scientific thinking as judgments of practice which emphasize problem-solving and the relationship between events rather than reducing to mere desire. I argue that once we focus on values as verifiable judgments of practice, they are actually a source of scientific integrity and that our attention should focus on how values are experimentally formed through the process of inquiry. My analysis reveals that science is a complex and highly developed form of value judgment and that consideration of what makes value judgments successful is the key to ensuring that science can maintain integrity despite the use of various scientific, ethical, and social factors playing a role in the outcome of a scientific judgment

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

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

Facebook for Geese: The Causes and Consequences of Non-random Social Associations in a Group Forager

The application of social network analysis in animals has facilitated research into dynamic fission-fusion social systems. These have important implications for the evolution of individual social behaviour, and for population-level processes such as information transfer and disease dynamics. This thesis explores the assumptions behind using networks to study animal social systems in projects using individual-marking or biologging. It then applies these methods to study social structure in a study population of a long-distance migrant, the light-bellied brent goose. It provides new insights about the causes and consequences of social structure, and individual social strategies, in a fission-fusion social system in the context of a migratory cycle. We show that social networks have a strong spatial structure, but with additional non-randomness once these spatial constraints have been accounted for. However, individual social associations are seasonally dynamic. These social structures, and their seasonal dynamics, are highly stable between years. Furthermore, non-random associations have important implications for foraging success. Individuals foraging in more familiar flocks are able to spend more time feeding, and less time involved in aggressive interactions or vigilant. This results in social network position influencing the ability of some individuals to gain body condition during spring staging and leave for breeding grounds in better condition. These results highlight the importance of understanding social networks when investigating individual time-budgets in social foragers. They also emphasise the importance of establishing the link between individual status and social network position before drawing any conclusions about the role of social network position in explaining differences in fitness between individuals in fission-fusion social systems.FER

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

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

How social learning shapes the efficacy of preventative health behaviors in an outbreak.

The global pandemic of COVID-19 revealed the dynamic heterogeneity in how individuals respond to infection risks, government orders, and community-specific social norms. Here we demonstrate how both individual observation and social learning are likely to shape behavioral, and therefore epidemiological, dynamics over time. Efforts to delay and reduce infections can compromise their own success, especially when disease risk and social learning interact within sub-populations, as when people observe others who are (a) infected and/or (b) socially distancing to protect themselves from infection. Simulating socially-learning agents who observe effects of a contagious virus, our modelling results are consistent with with 2020 data on mask-wearing in the U.S. and also concur with general observations of cohort induced differences in reactions to public health recommendations. We show how shifting reliance on types of learning affect the course of an outbreak, and could therefore factor into policy-based interventions incorporating age-based cohort differences in response behavior