Animal Social Networks

Abstract

Social network analysis (SNA) is a powerful tool that allows researchers to understand and quantify the structure and dynamics of animal societies. It is particularly useful for studying the transmission of disease, information and culture in groups and within populations and the evolution of cooperation, but is likely to be applicable for almost any subject that involves animals interacting with one another. Social networks can be potentially constructed using any association or interaction as long as a significant proportion of the focal group/population is individually identifiable. Once networks are constructed summary metrics can be extracted and used to answer the questions of interest. Care must be taken to make sure that the metrics used are appropriate for the hypotheses under test. The continuous advances in technology mean that SNA is becoming more and more widespread and increasingly relevant to understanding animal societies.

Key Concepts:

  • All animal societies can potentially be studied using social network analysis.

  • Social networks incorporate all the relationships among individuals within a group at the same time, rather than focussing on relationships between pairs of individuals, so are more realistic than traditional approaches.

  • Social network analysis has provided important insights into a wide‚Äźrange of areas, from the structure of complex societies to the transmission of information and disease within groups and the evolution of cooperation.

  • It is possible to incorporate spatial and temporal information when analysing networks of social relationships.

  • Social network analysis can also be useful in conservation, for example, management of endangered species, as well as theoretical studies.

  • There are many ways of building networks and numerous network metrics available for describing nodes and networks, making social network analysis valuable for many research questions.

  • Advances in technology are making it easier to apply social network analysis to animal societies.

Keywords: social networks; disease transmission; group dynamics; social behaviour; conservation biology; social evolution; wildlife management; population ecology

Figure 1.

Toy sociogram (visualisation of network) showing nodes (filled circles) and edges (connecting lines) between three clusters of individuals within a group. Width of line is used to represent the strength of the edge, colour represents sex. The blue node connecting all three main clusters has the potential to be a ‘super‐spreader’ of information or disease through the network as a whole due to its high betweenness.

Figure 2.

Toy matrices showing data for SNA. (a) Binary symmetric matrix of an undirected relationship (e.g. association – nodes were either seen together (1) or not (0)); (b) half‐weight symmetric matrix of association (AA and CC were seen together in 37% of observations); (c) asymmetric matrix of a directed behaviour (e.g. aggression) and (d) asymmetric matrix showing movement of individuals between sites.

close

References

Aplin LM, Farine DR, Morand‐Ferron J and Sheldon BC (2012) Social networks predict patch discovery in a wild population of songbirds. Proceedings of the Royal Society B: Biological Sciences 279: 4199–4205.

Bastian M, Heymann S and Jacomy M (2009) Gephi: an Open Source Software for Exploring and Manipulating Networks. San Jose, California: International AAAI conference on weblogs and social media.

Blonder B and Dornhaus A (2011) Time‐ordered networks reveal limitations to information flow in ant colonies. (R Planque, (ed.)). PLoS One 6: e20298.

Borgatti SP, Everett MG and Freeman LC (2002) Ucinet for Windows: Software for Social Network Analysis. Harvard, MA: Analytic Technologies.

Butts CT (2013) sna: Tools for Social Network Analysis. R package version 2.3‐1. http://CRAN.R‐project.org/package=sna

Christley RM, Pinchbeck GL, Bowers RG et al. (2005) Infection in social networks: using network analysis to identify high‐risk individuals. American Journal of Epidemiology 162: 1024–1031.

Clay CA, Lehmer EM, Previtali A, St Jeor S and Dearing MD (2009) Contact heterogeneity in deer mice: implications for Sin Nombre virus transmission. Proceedings of the Royal Society B: Biological Sciences 276: 1305–1312.

Croft DP, Edenbrow M, Darden SK et al. (2011a) Effect of gyrodactylid ectoparasites on host behaviour and social network structure in guppies Poecilia reticulata. Behavioral Ecology and Sociobiology 65: 2219–2227.

Croft DP, James R and Krause J (2008) Exploring Animal Social Networks. Oxford. Princeton University Press.

Croft DP, James R, Thomas POR et al. (2005) Social structure and co‐operative interactions in a wild population of guppies (Poecilia reticulata). Behavioral Ecology and Sociobiology 59: 644–650.

Croft DP, Madden JR, Franks DW and James R (2011b) Hypothesis testing in animal social networks. Trends in Ecology & Evolution 26: 502–507.

Cross PC, Creech TG, Ebinger MR et al. (2012) Wildlife contact analysis: emerging methods, questions, and challenges. Behavioral Ecology and Sociobiology 66: 1437–1447.

Cross P, Lloyd‐Smith J, Bowers J et al. (2004) Integrating association data and disease dynamics in a social ungulate: bovine tuberculosis in African buffalo in the Kruger National Park. Annales Zoologici Fennici 41: 879–892.

Csardi G and Nepusz T (2006) The igraph software package for complex network research. InterJournal Complex Systems: 1695. http://igraph.sf.net

Davis S, Klassovskiy N, Ageyev V et al. (2007) Plague metapopulation dynamics in a natural reservoir: the burrow system as the unit of study. Epidemiology and Infection 135: 740–748.

Drewe JA, Madden JR and Pearce GP (2009) The social network structure of a wild meerkat population: 1. Inter‐group interactions. Behavioral Ecology and Sociobiology 63: 1295–1306.

Drewe JA, Weber N, Carter SP et al. (2012) Performance of proximity loggers in recording intra‐ and inter‐species interactions: a laboratory and field‐based validation study. PLoS One 7: e39068.

Expert P, Evans TS, Blondel VD and Lambiotte R (2011) Uncovering space‐independent communities in spatial networks. Proceedings of the National Academy of Sciences of the USA 108: 7663–7668.

Ferrari MJ, Perkins SE, Pomeroy LW and Bjørnstad ON (2011) Pathogens, social networks, and the paradox of transmission scaling. Interdisciplinary Perspectives on Infectious Diseases 2011: 267049.

Fewell JH (2003) Social insect networks. Science 301: 1867–1870.

Franks DW, Ruxton GD and James R (2010) Sampling animal association networks with the gambit of the group. Behavioral Ecology and Sociobiology 64: 493–503.

Fraser MW and Hawkins JD (1984) The social networks of opioid abusers. The International Journal of the Addictions 19(8): 903–917.

Godfrey SS, Bull CM, James R and Murray K (2009) Network structure and parasite transmission in a group living lizard, the gidgee skink, Egernia stokesii. Behavioral Ecology and Sociobiology 63: 1045–1056.

Haddadi H, King AJ, Wills AP et al. (2011) Determining association networks in social animals: choosing spatial–temporal criteria and sampling rates. Behavioral Ecology and Sociobiology 65: 1659–1668.

Hamede RK, Bashford J, McCallum H and Jones M (2009) Contact networks in a wild Tasmanian devil (Sarcophilus harrisii) population: using social network analysis to reveal seasonal variability in social behaviour and its implications for transmission of devil facial tumour disease. Ecology Letters 12: 1147–1157.

Harrison XA, Tregenza T, Inger R et al. (2010) Cultural inheritance drives site fidelity and migratory connectivity in a long‐distance migrant. Molecular Ecology 19: 5484–5496.

Henzi SP, Lusseau D, Weingrill T, Schaik CP and Barrett L (2009) Cyclicity in the structure of female baboon social networks. Behavioral Ecology and Sociobiology 63: 1015–1021.

James R, Croft DP and Krause J (2009) Potential banana skins in animal social network analysis. Behavioral Ecology and Sociobiology 63: 989–997.

Jeanson R (2012) Long‐term dynamics in proximity networks in ants. Animal Behaviour 83: 915–923.

Kao RR, Green DM, Johnson J and Kiss IZ (2007) Disease dynamics over very different time‐scales: foot‐and‐mouth disease and scrapie on the network of livestock movements in the UK. Journal of the Royal Society Interface 4: 907–916.

Keeling MJ and Eames KTD (2005) Networks and epidemic models. Journal of the Royal Society Interface 2: 295–307.

Klovdahl AS, Potterat JJ, Woodhouse DE et al. (1994) Social networks and infectious disease: the Colorado Springs Study. Social Science & Medicine 38: 79–88.

Leu ST, Bashford J, Kappeler PM and Bull CM (2010) Association networks reveal social organization in the sleepy lizard. Animal Behaviour 79: 217–225.

Lusseau D, Schneider K, Boisseau OJ et al. (2003) The bottlenose dolphin community of Doubtful Sound features a large proportion of long‐lasting associations. Behavioral Ecology and Sociobiology 54: 396–405.

Madden JR, Drewe JA, Pearce GP and Clutton‐Brock TH (2009) The social network structure of a wild meerkat population: 2 Intragroup interactions. Behavioral Ecology and Sociobiology 64: 81–95.

Madden JR, Drewe JA, Pearce GP and Clutton‐Brock TH (2011) The social network structure of a wild meerkat population: 3. Position of individuals within networks. Behavioral Ecology and Sociobiology 65: 1857–1871.

Marsh MK, McLeod SR, Hutchings MR and White PCL (2011) Use of proximity loggers and network analysis to quantify social interactions in free‐ranging wild rabbit populations. Wildlife Research 38: 1.

McDonald RA, Delahay RJ, Carter SP, Smith GC and Cheeseman CL (2008) Perturbing implications of wildlife ecology for disease control. Trends in Ecology & Evolution 23: 53–56.

Newman M and Park J (2003) Why social networks are different from other types of networks. Physical Review E 68: 1–8.

Opsahl T (2009) Structure and Evolution of Weighted Networks, pp. 104–122. London, UK: University of London (Queen Mary College). http://toreopsahl.com/publications/thesis/; http://toreopsahl.com/tnet/

Opsahl T, Colizza V, Panzarasa P and Ramasco JJ (2008) Prominence and control: the weighted rich‐club effect. Physical Review Letters 101: 4.

Otterstatter MC and Thomson JD (2007) Contact networks and transmission of an intestinal pathogen in bumble bee (Bombus impatiens) colonies. Oecologia 154: 411–421.

Perkins SE, Cagnacci F, Stradiotto A, Arnoldi D and Hudson PJ (2009) Comparison of social networks derived from ecological data: implications for inferring infectious disease dynamics. Journal of Animal Ecology 78: 1015–1022.

Pike TW, Samanta M, Lindström J and Royle NJ (2008) Behavioural phenotype affects social interactions in an animal network. Proceedings of the Royal Society B: Biological Sciences 275: 2515–2520.

Pinter‐Wollman N, Wollman R, Guetz A, Holmes S and Gordon DM (2011) The effect of individual variation on the structure and function of interaction networks in harvester ants. Journal of the Royal Society Interface 8: 1562–1573.

Porphyre T, McKenzie J and Stevenson MA (2011) Contact patterns as a risk factor for bovine tuberculosis infection in a free‐living adult brushtail possum Trichosurus vulpecula population. Preventive Veterinary Medicine 100: 221–230.

Riordan P, Delahay RJ, Cheeseman C, Johnson PJ and Macdonald DW (2011) Culling‐induced changes in badger (Meles meles) behaviour, social organisation and the epidemiology of bovine tuberculosis. PLoS One 6: e28904.

Royle NJ, Pike TW, Heeb P, Richner H and Kölliker M (2012) Offspring social network structure predicts fitness in families. Proceedings of the Royal Society B: Biological Sciences 279: 4914–4922.

Sih A, Hanser SF and McHugh KA (2009) Social network theory: new insights and issues for behavioral ecologists. Behavioral Ecology and Sociobiology 63: 975–988.

Snijders TAB, van de Bunt GG and Steglich CEG (2010) Introduction to stochastic actor‐based models for network dynamics. Social Networks 32: 44–60.

Snijders TAB, Pattison PE, Robins GL and Handcock MS (2006) New Specifications for exponential random graph models (RM Stolzenberg, ed.). Sociological Methodology 36: 99–153.

Sterne JA and Smith DG (2001) Sifting the evidence – what's wrong with significance tests? British Medical Journal 322: 226–231.

Vicente J, Delahay RJ, Walker NJ and Cheeseman CL (2007) Social organization and movement influence the incidence of bovine tuberculosis in an undisturbed high‐density badger Meles meles population. Journal of Animal Ecology 76: 348–360.

Weihing J, White PCL and Clout MN (2005) Contact rates between possums revealed by proximity data loggers. Journal of Applied Ecology 42: 595–604.

Whitehead H (2007) Selection of models of lagged identification rates and lagged association rates using AIC and QAIC. Communications in Statistics – Simulation and Computation 36: 1233–1246.

Whitehead H (2008) Analyzing Animal Societies: Quantitative Methods for Vertebrate Social Analysis. Chicago: University of Chicago Press.

Whitehead H (2009) SOCPROG programs: analyzing animal social structures. Behavioral Ecology and Sociobiology 63: 765–778.

Whitehead H and Dufault S (1999) Techniques for analyzing vertebrate social structure using identified individuals: review and recommendations. (PJB Slater, JS Rosenblatt, CT Snowdon and TJ Roper (eds)). Advances in the Study of Behavior 28: 33–74.

Zohdy S, Kemp AD, Durden LA, Wright PC and Jernvall J (2012) Mapping the social network: tracking lice in a wild primate (Microcebus rufus) population to infer social contacts and vector potential. BMC Ecology 12: 4.

Further Reading

Bascompte J (2007) Networks in ecology. Basic and Applied Ecology 8: 485–490.

Handcock MS, Hunter DR, Butts CT, Goodreau SM and Morris M (2003) Statnet: Software Tools for the Statistical Modeling of Network Data. http://statnetproject.org

Krause J, Croft DP and James R (2007) Social network theory in the behavioural sciences: potential applications. Behavioral Ecology and Sociobiology 62: 15–27.

Krause J, Lusseau D and James R (2009) Animal social networks: an introduction. Behavioral Ecology and Sociobiology 63: 967–973.

Wey T, Blumstein DT, Shen W and Jordán F (2008) Social network analysis of animal behaviour: a promising tool for the study of sociality. Animal Behaviour 75: 333–344.

Contact Editor close
Submit a note to the editor about this article by filling in the form below.

* Required Field

How to Cite close
Downing, Beatrice C, and Royle, Nick J(Jun 2013) Animal Social Networks. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0024661]