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Lusseau, D., Whitehead, H., & Gero, S. (2008). Incorporating uncertainty into the study of animal social networks. Anim. Behav., 75(5), 1809–1815.
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Whitehead, H. (2009). SOCPROG programs: analysing animal social structures. Behav. Ecol. Sociobiol., 63(5), 765–778.
Abstract: Abstract SOCPROG is a set of programs which analyses data on animal associations. Data usually come from observations of the social behaviour of individually identifiable animals. Associations among animals, sampling periods, restrictions on the data and association indices can be defined very flexibly. SOCPROG can analyse data sets including 1,000 or more individuals. Association matrices are displayed using sociograms, principal coordinates analysis, multidimensional scaling and cluster analyses. Permutation tests, Mantel and related tests and matrix correlation methods examine hypotheses about preferred associations among individuals and classes of individual. Weighted network statistics are calculated and can be tested against null hypotheses. Temporal analyses include displays of lagged association rates (rates of reassociation following an association). Models can be fitted to lagged association rates. Multiple association measures, including measures produced by other programs such as genetic or range use data, may be analysed using Mantel tests and principal components analysis. SOCPROG also performs mark-recapture population analyses and movement analyses. SOCPROG is written in the programming language MATLAB and may be downloaded free from the World Wide Web.
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Whitehead, H., & Dufault, S. (1999). Techniques for Analyzing Vertebrate Social Structure Using Identified Individuals: Review and Recommendations. In Charles T. Snowden and Timothy J. Roper J. S. R. Peter J.B. Slater (Ed.), (Vol. Volume 28, pp. 33–74). Academic Press.
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Connor, R. C., Mann, J., Tyack, P. L., & Whitehead, H. (1998). Social evolution in toothed whales. Trends. Ecol. Evol, 13(6), 228–232.
Abstract: Two contrasting results emerge from comparisons of the social systems of several odontocetes with terrestrial mammals. Researchers have identified remarkable convergence in prominent features of the social systems of odontocetes such as the sperm whale and bottlenose dolphin with a few well-known terrestrial mammals such as the elephant and chimpanzee. In contrast, studies on killer whales and Baird's beaked whale reveal novel social solutions to aquatic living. The combination of convergent and novel features in odontocete social systems promise a more general understanding of the ecological determinants of social systems in both terrestrial and aquatic habitats, as well as the relationship between relative brain size and social evolution.
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Whitehead, H. (2008). Precision and power in the analysis of social structure using associations. Anim. Behav., 75(3), 1093–1099.
Abstract: I develop guidelines for assessing the precision and power of statistical techniques that are frequently used to study nonhuman social systems using observed dyadic associations. Association indexes estimate the proportion of time that two individuals are associated. Binomial approximation and nonparametric bootstrap methods produce similar estimates of the precision of association indexes. For a mid-range (0.4-0.9) association index to have a standard error of less than 0.1 requires about 15 observations of the pair associated, and for it to be less than 0.05, this rises to 50 observations. The coefficient of variation among dyads of the proportion of time that pairs of individuals are actually associated describes social differentiation (S), and this may be estimated from association data using maximum likelihood. With a poorly differentiated population (S~0.2), a data set needs about five observed associations per dyad to achieve a correlation between true and estimated association indexes of r=~0.4. It requires about 10 times as much data to achieve a representation with r=~0.8. Permutation tests usually reject the null hypothesis that individuals have no preferred associates when S2H>5, where H is the mean number of observed associations per individual. Thus most situations require substantial numbers of observations of associations to give useful portrayals of social systems, and sparse association data inform only when social differentiation is high.
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