Exploring Animal Social Networks available in Paperback, eBook
Exploring Animal Social Networks
- ISBN-10:
- 0691127522
- ISBN-13:
- 9780691127521
- Pub. Date:
- 07/21/2008
- Publisher:
- Princeton University Press
- ISBN-10:
- 0691127522
- ISBN-13:
- 9780691127521
- Pub. Date:
- 07/21/2008
- Publisher:
- Princeton University Press
Exploring Animal Social Networks
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Overview
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.
Product Details
| ISBN-13: | 9780691127521 |
|---|---|
| Publisher: | Princeton University Press |
| Publication date: | 07/21/2008 |
| Edition description: | New Edition |
| Pages: | 208 |
| Product dimensions: | 6.00(w) x 9.10(h) x 0.50(d) |
About the Author
Read an Excerpt
Exploring Animal Social Networks
By Darren P. Croft Richard James Jens Krause Princeton University Press
Copyright © 2008 Princeton University
All right reserved.
ISBN: 978-0-691-12752-1
Chapter One Introduction to Social Networks
Understanding the link between individual behavior and population-level phenomena is a long-standing challenge in ecology and evolutionary biology (Lima and Zollner 1996; Sutherland 1996). Behavior is expressed as a response to intrinsic and extrinsic factors, including an individual's physical and social environment, the latter made up of nonrandom and heterogeneous social interactions (Krause and Ruxton 2002). That is, individuals are part of a network of inter-individual associations that vary in strength, type, and dynamics. The structure of this social network has far-reaching implications for the ecology and evolution of individuals, populations, and species. For example, the social network supports a diverse array of behaviors that will be influenced by its structure, including: finding and choosing a sexual partner, developing and maintaining cooperative relationships, and engaging in foraging and anti-predator behavior. Such behavior is manifested at the population level in the form of, for example, habitat use, disease transmission, information flow, and mating systems, and forms the basis for evolutionary processes including adapting to changing environments, sexual selection, and speciation. Improving our ability to scale up from the individual to the population by establishing why certain patterns of association develop and how inter-individual association patterns affect population-level structure will revolutionize our understanding of the function, evolution, and implications of social organization.
Across the animal kingdom there is immense diversity in social behavior. Social interactions differ in their type (they might be cooperative, antagonistic, or sexual, for example) as well as their frequency and duration; social bonds may last for years or just minutes or seconds. Which type of interaction occurs and with what frequency and duration will depend on factors such as dominance, body size, sex, age, and parasite load of the participating individuals. This raises the question of how we deal with multiple interactions and complex interaction patterns that can arise even if the number of participants is relatively small. Interestingly, sociologists started addressing this question more than sixty years ago when looking at human interaction patterns, and this literature in combination with recent advances in areas such as statistical physics has provided us with a powerful set of tools for the analysis of animal social networks. These tools make it possible to calculate quantitative metrics describing social structure across different scales of organization, from the individual to the population. The aim of this book is to explore some of the techniques of network analysis that might be applied to a study of animal social structure.
1.1 WHAT IS A NETWORK?
The essential elements of a network are "nodes" and "edges." In a graphical representation of a network, each node is represented by a symbol, and every interaction (of whatever sort) between two nodes is represented by a line (edge) drawn between them. In the context of a social network, each node would normally represent an individual animal (though see later in this chapter for some alternate approaches) and each edge would represent some measured social interaction or association. For example, figure 1.1 represents the social network for a population of bottlenose dolphins, Tursiops truncatus, in New Zealand (Lusseau 2003). In figure 1.1 each filled circle (node) represents an individual dolphin and the connections (edges) between them indicate a certain frequency of social contact over a six-year period. This is the type of network we wish to explore in this book. As we will see as our exploration continues, much of the quantitative analysis of animal social networks is performed not on a graphical representation of interactions but on a matrix of values that conveys the same information. Both the graph and the matrix are representations of the same network.
It should not be a surprise to learn that there are many systems, in many walks of life, that can be thought of as a collection of pair-wise connections between objects. Some types of network are very familiar. Probably all of us regularly tap into a telephone network on which we may simply and quickly be connected to pretty much anywhere in the world without giving it much thought. Other technological systems such as electrical power grids (Xu et al. 2004), transport systems (Sen et al. 2003), and the World Wide Web (Tadic 2001) are all quite naturally considered as a network.
Many people have discovered that network theory may provide novel insight into the local and global properties of a system of interconnected objects that is not possible from considering either the interactions between pairs of agents in isolation or from a study of the average properties of the system as a whole. This has lead to researchers studying networks across a range of systems to gain understanding both of their structure and of some of the consequences of this structure. For example, applications of network theory to technological systems include optimizing the efficiency of telephone communication systems and analyzing the vulnerability of power grids to the loss of a power station.
Mathematicians and statistical physicists have made important contributions to the network literature, providing concrete results on the properties of certain large networks with random assignments of edges to nodes (Erdös and Rényi 1959; Bollobás 1985) and unearthing new paradigms for the characterization of the structure of complex networks and some of the processes that might occur on them (for excellent reviews of the world of networks from a physics perspective, see Albert and Barabási 2002; Newman 2003a; Boccaletti et al. 2006).
The networks approach has also been embraced by biologists interested in unraveling the interplay between cell function and the intricate web of interactions between genes, proteins, and other molecules involved in the regulation of cell activity. They are developing a general framework in which the biological functions of a cell can be understood by examining the structure of its interacting components (Kollmann et al. 2005), enabling them to move beyond "parts lists" of a system and to understand how its components interact to produce complex patterns and behaviors (Jasny and Ray 2003). For example, networks have been used to understand how selective forces have acted on the function of metabolic pathways (Rausher, Miller, and Tiffin 1999) and how gene regulatory networks shape patterns of development (von Dassow et al. 2000; MacCarthy, Seymour, and Pomiankowski 2003). A similar approach has been applied at other levels of organization (Proulx Promislow, and Phillips 2005; May 2006). For example, biologists have investigated how cells and organs interact by studying neuronal networks (e.g., Laughlin and Sejnowski 2003), and have considered the structure and stability of ecological systems by plotting trophic interactions between species in the form of a food web (Sole and Montoya 2001; Dunne, Williams, and Martinez 2002). By comparison, relatively few biologists have built and analyzed animal social networks. We will of course be discussing their work throughout the book.
All this wealth of interest from various parties is both a good thing and a potentially bad thing for the budding network analyst who wishes to construct and analyze the structure of an animal social network. On the plus side, there are now many methods and measures that might be brought to bear, and many sources of novel methodology. On the minus side, it must be realized that some of the methods and results derived by one community of researchers do not necessarily translate directly to the analysis of all networks. For example, many of the results obtained by the mathematics and physics community are only applicable for networks with a very large number of nodes. A network such as the internet has so many nodes that its statistical properties may very accurately be approximated by some of the statistical models developed in the physical sciences. Looking for the same properties in a network with a few tens of nodes may, on the other hand, not be so well advised. The type of data that is collected can also have a large effect on how one should go about analyzing a network. Sen et al. (2003) studied the network structure of the Indian railway system. Here the edges represent physical connections (train tracks) between stations, so we can be reasonably confident that the network is an accurate representation of the real system. In contrast, social animals often live in fission-fusion societies (Krause and Ruxton 2002) and their social networks have to be inferred from observations of interactions been individuals or within groups of individuals. This creates a number of methodological issues associated with the sampling effort required to get a representative picture of the "real" network structure. It is essential that we take such factors into consideration when we are exploring and analyzing animal social networks.
1.2 SOCIAL NETWORKS AND RELATED METHODS
Social network theory has its origins in a number of different fields of research on humans. It goes back to the work of psychologists and sociologists in the 1930s who applied elements of mathematical graph theory to human relationships (e.g., Moreno 1934; Lewin 1951), and has mostly been concerned with the scenario in which each node represents a single person and each edge some interaction or relationship between two people. A great deal of progress has been made in the analysis and modeling of human social networks in the past twenty or thirty years, made possible by the advent of readily available and cheap computing power, which enables randomization tests and other simulation techniques to calculate more sophisticated measures of social structure and to bring some much needed statistical rigor to the field. The books by Wasserman and Faust (1994), Scott (2000), and Carrington, Scott, and Wasserman (2005) provide an excellent account, from various angles, of many of the methods that have arisen in the social sciences, and we will refer to these sources frequently throughout the book. In recent times social network theory has also received important impulses from the physics community, which have contributed a number of important theoretical advances such as the small-worlds concept (Watts and Strogatz 1998), algorithms for community detection in networks, and qualitatively new insights into the spread of information through populations (Boccaletti et al. 2006).
Network theory provides a formal framework for the study of complex social relationships. Human social networks have been used to investigate a range of topics. These include the spread of HIV (Potterat et al. 2002), the interconnectedness of company boards of directors (Battiston, Weisbuch, and Bonabeau 2003; Battiston and Catanzaro 2004), and the spread of rumors (Moreno, Nekovee, and Pacheco 2004). In contrast to studies on human social networks, the use of network theory to study the social organization of animal groups or populations is still relatively uncommon (Sade et al. 1988; Connor, Heithaus, and Barre 1999; Fewell 2003; Lusseau 2003; Croft, Krause, and James 2004a; Cross et al. 2004; Flack et al. 2006). Perhaps not surprisingly, some of the earliest applications of ideas developed to study human social networks to other animals came in primatology, though such studies generally did not involve statistical validation of the observed patterns (Sade and Dow 1994). More recent studies (Lusseau 2003; Croft, Krause, and James 2004a) have compared quantitative network measures against null models, or used methods inspired by developments in network theory from the mathematics and physics literature (Lusseau and Newman 2004; Wolf et al. 2007) to relate heterogeneities in animal network structure to the biology of their system. However, despite the vast number of studies in the animal behavior literature that have collected information on interactions or associations between pairs of animals, very few investigations have used a network approach to analyze them. We believe that network theory may offer an exciting method to analyze both new and old data sets, which could provide insights into the structure of animal societies not possible with traditional methods.
At this point it seems appropriate to deal with any nagging doubts in the minds of some readers that this is all something you have seen before, just dressed up in new terminology. Surely, you might be thinking, the matrix of pair-wise interactions is nothing more than an association matrix (Whitehead 1997), and its visualization a sociogram (e.g., Zimen 1982; Sade 1989). Don't we already look for collections of closely associated animals in an association matrix using cluster analysis such as Ward's or unweighted pair group method with arithmetic mean (UPGMA) methods (Whitehead 1999)? Well, the answer is a simple yes. An association matrix and a sociogram are indeed different names for a social network. So what is new, then?
The principal advantage, as we see it, of using the network approach to probe the structure of animal societies is that it allows us to tap into a very wide range of measures and approaches that are, as we have hinted, still being developed in parallel in several disciplines, and to apply these all under the umbrella of a single description of the data and the associated analytic tools. Thus we might learn tricks and methods from all manner of sources that might help us unravel what the important structural elements are in our animal social system, and what biological or other factors might be driving that structure. Of course, the real appeal of any approach that amalgamates many interactions is that in principle we can probe structure on all scales from the individual to the population. We then need robust measures that describe the properties of individuals, communities, and populations; our belief is that there are many methods for achieving this that come under the networks umbrella, that just happen to have been developed in the social or physical sciences. In addition, the analysis and visualization of social connections can often be rolled into a single computer program. Network theory therefore offers an "all in one" package that allows us to move between different levels of social complexity, and to tap into new analytic tools.
As we have already mentioned, the vast majority of social networks employ one node to represent a single individual, and each edge represents some form of interaction or association between two individuals (see figure 1.1). Furthermore, each edge in a social network represents the same type of interaction or association. The animal social networks we will analyse for most of this book fall into this category. Many of the systems we will study in this book are "fission-fusion" societies, in which animals frequently leave or join groups (Krause and Ruxton 2002); examples include species of ungulates, primates, cetaceans, fish, and insects. To investigate the fine-scale structure of social networks in such systems, we need to be able to identify individual animals. However, for some species this is not possible (or too time consuming) due to problems associated with identifying, capturing, or recapturing individuals. In such instances it can be useful to identify categories of individuals and consider interactions between them (rather than the individuals themselves). We will illustrate now how these interactions can be considered as a network.
In a study of a captive wolf pack (Canis lupus), Zimen (1982) made observations on social interactions in a 6-hectare enclosure over a 10-year period (figure 1.2). During this time juveniles matured into adults and the social status of individuals changed; for example the rank of alpha male was occupied by six different animals. The study looked at a number of different behaviors, one of which was termed "following," which involves a dominant individual forcing a subdominant to keep a certain distance.
(Continues...)
Excerpted from Exploring Animal Social Networks by Darren P. Croft Richard James Jens Krause
Copyright © 2008 by Princeton University. Excerpted by permission.
All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.
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
What People are Saying About This
"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"This book introduces ecologists, behaviorists, and others studying social behavior to the methods of network analysis. It is clearly written and accessible to readers whose primary training is in biology, not physics, mathematics, or sociology—the fields in which network techniques have largely been developed. The book is method oriented, so that it can serve as a practical guide to how readers can analyze their own data."—Stephen C. Pratt, Arizona State University"No such book of this kind exists, and because it fills a new niche it will be important. New graphical and analytical techniques are emerging that provide insights into how networks form and function. This book is designed as a primer to introduce students, especially graduate students, to these techniques by using and interpreting examples from animal interactions. For anyone interested in networks, this book will be a useful guide."—Daniel I. Rubenstein, Princeton University
No such book of this kind exists, and because it fills a new niche it will be important. New graphical and analytical techniques are emerging that provide insights into how networks form and function. This book is designed as a primer to introduce students, especially graduate students, to these techniques by using and interpreting examples from animal interactions. For anyone interested in networks, this book will be a useful guide.
— Daniel I. Rubenstein, Princeton University
This book introduces ecologists, behaviorists, and others studying social behavior to the methods of network analysis. It is clearly written and accessible to readers whose primary training is in biology, not physics, mathematics, or sociology—the fields in which network techniques have largely been developed. The book is method oriented, so that it can serve as a practical guide to how readers can analyze their own data.
— Stephen C. Pratt, Arizona State University
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