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Krause, S., Mattner, L., James, R., Guttridge, T., Corcoran, M., Gruber, S., et al. (2009). Social network analysis and valid Markov chain Monte Carlo tests of null models. Behav. Ecol. Sociobiol., 63(7), 1089-1096.
Abstract: Analyses of animal social networks derived from group-based associations often rely on randomisation methods developed in ecology (Manly, Ecology 76:1109–1115, 1995) and made available to the animal behaviour community through implementation of a pair-wise swapping algorithm by Bejder et al. (Anim Behav 56:719–725, 1998). We report a correctable flaw in this method and point the reader to a wider literature on the subject of null models in the ecology literature. We illustrate the importance of correcting the method using a toy network and use it to make a preliminary analysis of a network of associations among eagle rays.
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Krause, J., Lusseau, D., & James, R. (2009). Animal social networks: an introduction. Behav. Ecol. Sociobiol., 63(7), 967-973.
Abstract: Network analysis has a long history in the mathematical and social sciences and the aim of this introduction is to provide a brief overview of the potential that it holds for the study of animal behaviour. One of the most attractive features of the network paradigm is that it provides a single conceptual framework with which we can study the social organisation of animals at all levels (individual, dyad, group, population) and for all types of interaction (aggressive, cooperative, sexual etc.). Graphical tools allow a visual inspection of networks which often helps inspire ideas for testable hypotheses. Network analysis itself provides a multitude of novel statistical tools that can be used to characterise social patterns in animal populations. Among the important insights that networks have facilitated is that indirect social connections matter. Interactions between individuals generate a social environment at the population level which in turn selects for behavioural strategies at the individual level. A social network is often a perfect means by which to represent heterogeneous relationships in a population. Probing the biological drivers for these heterogeneities, often as a function of time, forms the basis of many of the current uses of network analysis in the behavioural sciences. This special issue on social networks brings together a diverse group of practitioners whose study systems range from social insects over reptiles to birds, cetaceans, ungulates and primates in order to illustrate the wide-ranging applications of network analysis.
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Krause, J., Croft, D., & James, R. (2007). Social network theory in the behavioural sciences: potential applications. Behav. Ecol. Sociobiol., 62(1), 15–27.
Abstract: Abstract Social network theory has made major contributions to our understanding of human social organisation but has found relatively little application in the field of animal behaviour. In this review, we identify several broad research areas where the networks approach could greatly enhance our understanding of social patterns and processes in animals. The network theory provides a quantitative framework that can be used to characterise social structure both at the level of the individual and the population. These novel quantitative variables may provide a new tool in addressing key questions in behavioural ecology particularly in relation to the evolution of social organisation and the impact of social structure on evolutionary processes. For example, network measures could be used to compare social networks of different species or populations making full use of the comparative approach. However, the networks approach can in principle go beyond identifying structural patterns and also can help with the understanding of processes within animal populations such as disease transmission and information transfer. Finally, understanding the pattern of interactions in the network (i.e. who is connected to whom) can also shed some light on the evolution of behavioural strategies.
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Couzin, I. D., & Krause, J. (2003). Self-Organization and Collective Behavior in Vertebrates. In Charles T. Snowdon and Timothy J. Roper J. S. R. Peter J. B. Slater (Ed.), Advances in the Study of Behavior (Vol. 32, pp. 1–75). Academic Press.
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Krause, J., Bumann, D., & Todt, D. (1992). Relationship between the position preference and nutritional state of individuals in schools of juvenile roach (Rutilus rutilus). Behav. Ecol. Sociobiol., 30(3), 177–180.
Abstract: Position preferences of well-fed and food-deprived juvenile roach were investigated in schools of 2 and 4 fish in the laboratory. Food-deprived fish appeared significantly more often in the front position than their well-fed conspecifics. For fish at the same hunger level, individuals at the front of the school had the highest feeding rate. These results represent the first evidence for a relationship between the nutritional state of individual fish and their positions in a school and suggest a functional advantage of the preference.
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Guttridge, T. L., Dijk, S., Stamhuis, E. J., Krause, J., Gruber, S. H., & Brown, C. (2013). Social learning in juvenile lemon sharks, Negaprion brevirostris. Animal Cognition, 16(1), 55–64.
Abstract: Social learning is taxonomically widespread and can provide distinct behavioural advantages, such as in finding food or avoiding predators more efficiently. Although extensively studied in bony fishes, no such empirical evidence exists for cartilaginous fishes. Our aim in this study was to experimentally investigate the social learning capabilities of juvenile lemon sharks, Negaprion brevirostris. We designed a novel food task, where sharks were required to enter a start zone and subsequently make physical contact with a target in order to receive a food reward. Naive sharks were then able to interact with and observe (a) pre-trained sharks, that is, ‘demonstrators’, or (b) sharks with no previous experience, that is, ‘sham demonstrators’. On completion, observer sharks were then isolated and tested individually in a similar task. During the exposure phase observers paired with ‘demonstrator’ sharks performed a greater number of task-related behaviours and made significantly more transitions from the start zone to the target, than observers paired with ‘sham demonstrators’. When tested in isolation, observers previously paired with ‘demonstrator’ sharks completed a greater number of trials and made contact with the target significantly more often than observers previously paired with ‘sham demonstrators’. Such experience also tended to result in faster overall task performance. These results indicate that juvenile lemon sharks, like numerous other animals, are capable of using socially derived information to learn about novel features in their environment. The results likely have important implications for behavioural processes, ecotourism and fisheries.
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Croft, D. P., James, R., & Krause, J. (2008). Comparing Networks. In Exploring Animal Social Networks (pp. 141–162). Princeton, NY: Princton University Press.
Abstract: Social network analysis is used widely in the social sciences to study interactions among people, groups, and organizations, yet until now there has been no book that shows behavioral biologists how to apply it to their work on animal populations. Exploring Animal Social Networks provides a practical guide for researchers, undergraduates, and graduate students in ecology, evolutionary biology, animal behavior, and zoology.
Existing methods for studying animal social structure focus either on one animal and its interactions or on the average properties of a whole population. This book enables researchers to probe animal social structure at all levels, from the individual to the population. No prior knowledge of network theory is assumed. The authors give a step-by-step introduction to the different procedures and offer ideas for designing studies, collecting data, and interpreting results. They examine some of today's most sophisticated statistical tools for social network analysis and show how they can be used to study social interactions in animals, including cetaceans, ungulates, primates, insects, and fish. Drawing from an array of techniques, the authors explore how network structures influence individual behavior and how this in turn influences, and is influenced by, behavior at the population level. Throughout, the authors use two software packages--UCINET and NETDRAW--to illustrate how these powerful analytical tools can be applied to different animal social organizations.
Darren P. Croft is lecturer in animal behavior at the University of Wales, Bangor. Richard James is senior lecturer in physics at the University of Bath. Jens Krause is professor of behavioral ecology at the University of Leeds.
Reviews:
“Exploring Animal Social Networks shows behavioral biologists how to apply social network theory to animal populations. In doing so, Croft, James, and Krause illustrate the connections between an animal's individual behaviors and how these, in turn, influence and are influenced by behavior at the population level. . . . Valuable for readers interested in using quantitative analyses to study animal social behaviors.”--Choice
“[T]his volume provides an engaging, accessible, and timely introduction to the use of network theory methods for examining the social behavior of animals.”--Noa Pinter-Wollman, Quarterly Review of Biology
“The book is a useful 'handbook' providing detailed, stepwise procedures sufficient to allow the reader to address a broad range of questions about social interactions. . . . The book includes numerous examples of the kind of research questions one might ask, and, thus, it allows the reader to find the analysis that best fits the data set to be analyzed. Thus, even readers with minimal prior knowledge of social network analysis will be able to apply this approach. And if further assistance is needed, the authors provide numerous references to specific procedures that have been used by others.”--Thomas R. Zentall, PsycCRITIQUES
Endorsements:
“An important and timely addition to the literature. This book should be readily accessible to researchers who are interested in animal social organization but who have little or no experience in conducting network analysis. The book is well-written in an engaging style and contains a good number of examples drawn from a range of taxonomic groups.”--Paul R. Moorcroft, Harvard University
More Endorsements
Table of Contents:
Preface vii
Chapter 1: Introduction to Social Networks 1
Chapter 2: Data Collection 19
Chapter 3: Visual Exploration 42
Chapter 4: Node-Based Measures 64
Chapter 5: Statistical Tests of Node-Based Measures 88
Chapter 6: Searching for Substructures 117
Chapter 7: Comparing Networks 141
Chapter 8: Conclusions 163
Glossary of Frequently Used Terms 173
References 175
Index 187
Subject Area:
* Biological Sciences
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Croft, D. P., James, R., & Krause, J. (Eds.). (2008). Exploring Animal Social Networks. Princton: Princton University Press.
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Krause, J., James, R., Franks, D. W., & Croft, D. P. (2015). Animal Social Networks. Oxford: Oxford University Press.
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