Heterochronous DNA data consist of sequences of different ages. Such data are often used in epidemiology and ancient DNA subdisciplines to study demographic changes over time (Drummond et al. 2003). In epidemiology, they can provide substantial insights into the spread and evolution of infectious diseases and therefore help to control possible future outbreaks (Pybus & Rambaut 2009). In ancient DNA research, DNA extracted from samples up to hundreds of thousands of years old is often used to investigate demographic changes in mammal populations in response to climate and habitat change or the spread of modern humans (e.g. Hadly et al. 2004; Shapiro et al. 2004; Campos et al. 2010; Prost et al. 2010).
The inferences drawn from such data when analysed with the powerful tools of the field [such as Markov-chain Monte Carlo sampling procedures–based temporal approaches (Drummond et al. 2005) and Approximate Bayesian Computation (ABC; Beaumont, Zhang & Balding 2002)] lend themselves well to graphical expression (such as skyline plots and joint posterior distributions). However, depicting the raw data itself is problematic. The familiar haplotype network is a two-dimensional, intuitively appealing summary of genetic diversity within a single group, in which the size of each node represents the frequency of a haplotype, and the length of (or number of tick-marks on) the links represents the amount of genetic divergence (Posada & Crandall 2001). To display information from more than one group, researchers must resort to replacing the nodes of the haplotype network with pie charts. The results are generally difficult to interpret; as one prominent graphic designer has said, ‘the only worse design than a pie chart is several of them’. (Tufte 2001, p. 178). A more elegant and accessible way to explore temporal coherence is through the use of a three-dimensional figure where networks from each sampled timepoint are arranged in distinct levels and haplogroups shared between are connected by vertical columns. The first example of such a design was recently published in Prost et al. (2010). In their study, two-dimensional networks were constructed using TCS software (Clement, Posada & Crandall 2000) and subsequently combined into a three-dimensional structure by hand using standard graphical tools. However, constructing three-dimensional networks by hand is difficult and time-consuming. Here, we present an R script to automatically produce three-dimensional statistical parsimony networks, substantially alleviating both problems.