Gaunitz, C., Fages, A., Hanghøj, K., Albrechtsen, A., Khan, N., Schubert, M., et al. (2018). Ancient genomes revisit the ancestry of domestic and Przewalski's horses. Science, 360(6384), 111–114.
Abstract: The Eneolithic Botai culture of the Central Asian steppes provides the earliest archaeological evidence for horse husbandry, ~5,500 ya, but the exact nature of early horse domestication remains controversial. We generated 42 ancient horse genomes, including 20 from Botai. Compared to 46 published ancient and modern horse genomes, our data indicate that Przewalski's horses are the feral descendants of horses herded at Botai and not truly wild horses. All domestic horses dated from ~4,000 ya to present only show ~2.7% of Botai-related ancestry. This indicates that a massive genomic turnover underpins the expansion of the horse stock that gave rise to modern domesticates, which coincides with large-scale human population expansions during the Early Bronze Age.
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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., James, R., Franks, D. W., & Croft, D. P. (2015). Animal Social Networks. Oxford: Oxford University Press.
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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
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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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James, R., Croft, D., & Krause, J. (2009). Potential banana skins in animal social network analysis. Behav. Ecol. Sociobiol., 63(7), 989-997.
Abstract: Social network analysis is an increasingly popular tool for the study of the fine-scale and global social structure of animals. It has attracted particular attention by those attempting to unravel social structure in fission–fusion populations. It is clear that the social network approach offers some exciting opportunities for gaining new insights into social systems. However, some of the practices which are currently being used in the animal social networks literature are at worst questionable and at best over-enthusiastic. We highlight some of the areas of method, analysis and interpretation in which greater care may be needed in order to ensure that the biology we extract from our networks is robust. In particular, we suggest that more attention should be given to whether relational data are representative, the potential effect of observational errors and the choice and use of statistical tests. The importance of replication and manipulation must not be forgotten, and the interpretation of results requires care.
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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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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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