Exploring animal social networks
Darren P Croft, Richard James &
Princeton University Press; 1 edition
(July 1, 2008)
SOCIAL NETWORK ANALYSIS WEIGHED AGAINST THE TRADITIONAL ANALYTICAL METHODS IN BEHAVIOURAL SCIENCES
Some time ago, my supervisor posed a question that many people who study behaviour have wondered about: what are the new insights that social network analysis (SNA) can provide that are not possible with traditional analytical means in behavioural sciences? If you have also wondered the same and want to know how SNA can contribute to studying behaviour, ecology and evolution, Exploring Animal Social Networks, by Darren Croft, Richard James and Jens Krause, provides a lucid account. The book describes why SNA is a powerful tool in describing social structures across different levels of organisation, from the individual to the population.
Academia, like governmental intelligence agencies, has, in the last decade, paid great attention to the growing networks of people connected through microblogging and social networking sites, with a focus on understanding who is connected to whom and the nature of these connections in the network. Network analysis has provided an analytical framework for studying such a complex and large body of interactions. Historically, SNA has been widely used in the social sciences to understand complex human interactions with statistical physicists contributing a great deal in developing the methods of SNA. Croft et al give a brief account of these historical developments in the opening chapter of the book. Assuming no prior knowledge on the part of the reader, the authors anticipate the questions that a reader is most likely to have—What is a network? Why use network analysis? How is SNA different from other statistical methods? While providing answers to these questions, the authors keep the readers interested with numerous examples from research that cut across taxa, from primates to social insects—à la the hugely popular textbook “An Introduction to Behavioural Ecology” by John Krebs and Nicholas Davies.
How does one collect data for SNA? How can one extract information on interactions from existing datasets using network analysis? The authors devote on data collection, which deals with a wide range of questions from arranging data to representing relational data to designing sampling protocols for collecting data. This chapter engages in the fundamental questions of defining associations, either based on proximity or space use, or based on interactions.
How do we visualise interactions or customise networks based on our biological questions? In chapters 3-6, the authors carry out a thorough quantitative exploration of different properties and types of social networks (but with numerous real and made-up examples, it is never boring or scary!). These chapters are very useful for researchers and students who want to learn the nitty-gritty of network analysis. For example, explains different components and parameters in a network (like centrality measures) that are important for understanding biological interactions. Based on the network and their parameters, there are statistical tests, like randomisation, which help in comparing the observed network with a randomised network that provides a null hypothesis. Further to this, the authors also explain how to filter out the not-so-significant interactions in a network and focus on the core interactions.
In the animal world, heterogeneity is ubiquitous, with individuals in groups often differing from each other in their phenotypes (morphology or behaviour). In chapter 6, the authors discuss how this heterogeneity can be understood using SNA, looking at the finer substructures of the network. This is particularly relevant for those who want to study the role of individuals, thereby formulating testable hypotheses. Furthermore, long-term studies commonly deal with data on individuals over time and under different ecological conditions. The chapter on comparing networks deals at length with both the methods and biological relevance of comparing networks separated in time. This contributes to understanding not only the role of ecological or individual variations in animal societies, but also provides insights into evolution of social organisations in animals.
Throughout the book, authors briefly discuss software like SOCPROG and UCINET useful for network analyses, along with the visualisation package NETDRAW. Though these are either freeware or shareware, these require platforms like Windows or MATLAB, which are proprietary themselves. However, this shortcoming of dependency in software running on proprietary platforms can be discounted for two reasons—first, this book is not meant to be a software guide to SNA and predominantly deals with the concepts and questions to understand interactions in animal world using SNA; second, many of the packages, especially those in R-statistical package (like tnet, sna or igraph) widely used now, have been developed after the publication of the book (2008).
Network analysis is, today, an important tool for researchers from a wide spectrum of fields in biology which includes conservation biologists, community ecologists, epidemiologists and behavioural ecologists. Across this spectrum, the greatest interest in SNA has been in its ability to link individual behaviour and population-level phenomena. This book is clearly the first such effort in the context of animal societies.
Subhankar Chakraborty studies animal behaviour and conservation genetics. mail.subhankarc@ gmail.com.