Congruification: support for time scaling large phylogenetic trees


  • Jonathan M. Eastman,

    Corresponding author
    1. Institute for Bioinformatics and Evolutionary Studies (IBEST), University of Idaho, Moscow, Russia
    • Department of Biological Sciences, University of Idaho, Moscow, Russia
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  • Luke J. Harmon,

    1. Department of Biological Sciences, University of Idaho, Moscow, Russia
    2. Institute for Bioinformatics and Evolutionary Studies (IBEST), University of Idaho, Moscow, Russia
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  • David C. Tank

    1. Institute for Bioinformatics and Evolutionary Studies (IBEST), University of Idaho, Moscow, Russia
    2. College of Natural Resources & Stillinger Herbarium, University of Idaho, Moscow, Russia
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Correspondence author. E-mail:


  1. Approaches for efficient statistical estimation of large phylogenies are now available (Bioinformatics, 2006, 22, 2688), and yet we lack adequate tools for synthesizing information from previous analyses into large timetrees. Here, we present a cross-platform r tool that integrates with tree of life efforts by mapping divergence times from an existing timetree (a ‘reference’) to another uncalibrated phylogeny (a ‘target’) that samples from the same lineage. Leveraging existing methods for rate-smoothing phylograms, this tool enables the rapid generation of very large timetrees where direct estimation of the timing of lineage diversification is either impracticable or impossible.
  2. The primary output of the tool is to return divergence times for nodes resolved as concordant between the reference and target. Given the computed set of secondary calibrations, post hoc tree transformation can be accomplished using existing resources that assume either a strict or relaxed evolutionary clock.
  3. Our software is provided open source in the geiger package ( and is thoroughly demonstrated in the Supporting Information.


With the continued development of supertree, supermatrix and megaphylogeny approaches (Smith, Beaulieu & Donoghue 2009), phylogeny estimates of large size are becoming increasingly possible (e.g. Stamatakis 2006; Smith & Donoghue 2008; Smith et al. 2011; Hinchliff & Roalson 2013). While these methods enable estimation of phylogenetic structure across a broad swath of biodiversity, lagging in development are explicit model-based methods for the coestimation of topology and divergence times for very large trees (here defined as involving thousands of taxa) and data matrices with large amounts of missing data (i.e. supermatrices). Direct inference of the temporal component of evolution is often missing in these large-scale analyses, where it is possible only to estimate branch lengths in terms of the product rt, where r is molecular evolutionary rate and t is elapsed time. Moreover, it is often difficult to synthesize branch length information from trees estimated for the same lineage, but where the sets of sampled taxa are inconsistent. This issue is especially glaring where trees sample at different levels of a systematic hierarchy (e.g. some are species level, others are exemplar phylogenies of families, genera, etc.).

We use an example from amphibian phylogenetics to illustrate certain difficulties faced by empiricists attempting to estimate credible timetrees and to a possible resolution. A large amphibian phylogeny was published by Pyron & Wiens (2011), for which the underlying data set samples over 2800 species. The phylogeny estimate is clearly unprecedented in sampling density for amphibian molecular phylogenetics, yet the tree is also unscaled to absolute time. This lack of temporal information is limiting in many ways. For instance, one cannot calculate or compare absolute diversification rates of amphibian traits or lineages. For the same lineage, Roelants et al. (2007) estimated a timetree (i.e. a phylogenetic tree with branch lengths calibrated to units of time elapsed) with relatively sparse sampling across the vast majority of extant amphibian families. It would seem desirable to use information contained within the Roelants et al. (2007) tree to scale the larger, more densely sampled tree of Pyron & Wiens (2011) to absolute time. This would be an overly arduous task without a tool we present and dub ‘congruification’. In the Supporting Information, we illustrate our solution and demonstrate congruification in scaling the Pyron & Wiens (2011) tree to time.


Estimating phylogeny for which branch lengths are in units of time is currently a very difficult computational problem for large trees and sparse data matrices. Some of the most sophisticated methods available for coestimation of topology and time-scaled branch lengths (e.g. beast, Drummond et al. 2012; mrbayes, Ronquist & Huelsenbeck 2003) are simply not practical for data sets with thousands of taxa, yet there is tremendous demand for such timetrees. We assume that reasonable time scalings of a phylogram can be achieved by exploiting information contained in existing time-calibrated trees that sample from the same lineage.

We introduce ‘congruification’ as a novel tool for automating the resolution of all possible secondary calibrations of a tree, requiring at minimum two phylogenetic trees: a reference (scaled in units of absolute time) and a target (yet unscaled to time, but whose branch lengths are in units of the expected number of character-state changes). Unless the target and reference correspond perfectly in the particular taxa sampled, congruification also requires an expert-curated linkage table that links tip labels found in the reference to those found in the target (Fig. 1). This allows concordant nodes to be resolved even where ‘levels’ of sampling differ between the reference (e.g. family level) and target (e.g. species level). Indeed, if we are willing to assert the monophyly of lineages exemplified in the reference tree, we can time-scale a target even where there is no direct overlap in the sampled tips of the two trees. In order to exploit the reference to the fullest extent, the provided linkage table should be consistent with shared ancestry among tips of both trees (e.g. if the linkage table contains taxonomic names, these taxa should be monophyletic).

Figure 1.

Illustration of the time scaling, using a target tree (panel a) and a reference tree (panel b); panel c shows a ‘congruified’ target tree. Tip labels in the middle panel represent exemplars for 12 lineages (e.g. genera). Tip labels in upper- and lower-most panels represent samples (e.g. species) from within many (but in this case not all) of the lineages shown in b. Circles denote three nodes (of several others) that are concordant between the target and reference. All members of H, I, J, K and L are subtended by the light grey circle in all trees. Note that despite the incompletely overlapping sets of tips between the reference and target (i.e. species from lineage Z and lineage G occur in only the reference and target, respectively), concordant splits can still be identified (white circle). Furthermore, despite nonmonophyly of lineage C in the target (panels a and c), the dark grey circle demarcates a concordant split between reference and target where all members of the B and C lineages are subtended. Panel c shows the ultrametricized target, informed by node heights for concordant splits from the reference. The look-up table associated with this instance of congruification might appear as follows: image


The following describes the algorithm used to congruify a target tree. The algorithm is available from the R-package, geiger.

  1. Congruification maps leave from the target tree to individual samples in the reference, based on a linkage table. The linkage table used in Fig. 1 would map samples ‘J1’ and ‘J2’ from the target (Fig. 1a) to the representative tip ‘J’ in the reference (Fig. 1b), making the implicit assumption that lineage ‘J’ is monophyletic. A linkage table (see Fig. 1 caption) is unnecessary if all tips occurring in the target also occur in the reference, and mappings are thereby one to one.
  2. The algorithm determines the set of tips (TI) that are sampled in the target and are present as mapped tips in the reference. Tips that occur in only the target or reference are excluded from TI. The subset of TI that descends from each node in the tree is used to erect a binary ‘membership’ vector at that node, the entries of which denote the presence or absence of each species in TI among the node's descendants. A membership vector is constructed for each node in the target and the reference. Only exactly matching vectors are accepted as identifying concordant nodes between the target and reference. If nonunique membership vectors exist in a given tree (i.e. multiple nodes have an identical array of descendant tips found in TI), precedence is given to the most tipward node for each set of nonunique vectors.
  3. Temporal information is mapped from reference to target using the resolved set of concordant nodes. Divergence times of nodes in the reference are extracted to serve as calibration points for the concordant nodes in the target. A node in the target that does not have a perfect match in the reference is left uncalibrated.
  4. The target is scaled to absolute time using a separate software using the set of calibrations extracted from the reference. The set of calibrations from concordant nodes between the trees is returned to the user in tabular format. This output can be easily manipulated for time scaling using any of several existing algorithms: these include r8s (Sanderson 2002), pathd8 (Britton et al. 2007), and treePL (Smith & O'Meara 2012).


In the Supporting Information, we demonstrate a complete example of congruification using the time-calibrated Roelants et al. (2007) reference and the uncalibrated Pyron & Wiens (2011) target amphibian trees. Though similar in phylogenetic breadth, these trees differ markedly in the density of sampling (152 vs. 2871 taxa sampled, respectively). We use several accessory functions to resample the reference as an exemplar tree, and we use the NCBI taxonomy database to provide a linkage table between samples in the target and reference. Final time scaling of the target is achieved using the recently introduced tool for rate smoothing, treePL (Smith & O'Meara 2012).


We note that congruification makes no attempt at reconciliation of topological discordance between the reference and target. Indeed, the tool only serves to scale the target to be as temporally consistent with the reference as possible (Fig. 1). While this implementation provides an automated means of resolving secondary calibrations, certain aspects of congruification are less than ideal. Graur & Martin (2004) raise the issue of extra-primary or indirect calibration, wherein molecular-based estimates of divergence time are used in secondary analyses as a calibration point (i.e. treating the divergence date as an error-free estimator of the unknown parameter value). The use of congruification is not far removed from such a practice. In cases where direct use of independent calibrations (e.g. from the fossil record) is currently impracticable, we argue that congruification nevertheless has the ability to provide reasonable estimates of divergence timing in the target. Much as Graur & Martin (2004) caution, the uncertainty associated with any estimator must not only be acknowledged but should be represented in every post hoc analysis. We thus strongly recommend time scaling the target using many independent samples in proportion to a target distribution (e.g. a posterior distribution of trees) for both the reference and target. We caution that the presence of rogue taxa or erroneous placement of taxa in either the reference or target will prevent a full exploitation of dates in the reference. Certainly, the greater the topological consistency between reference and target, the greater the amount of temporal information that can be mapped from reference to target and exploited for time scaling.

To maximize correspondence between the target and reference, it may be preferable to prune the reference tree to leave a single representative for broader monophyletic lineages. Tree correspondence will be maximal when all the tips in the reference can be matched either to the set of tips present in the target or to groups defined by the look-up table. Pruning the reference to exemplars will be especially desirable where sampling differs extensively between the two trees (e.g. the same genus is sampled in both the target and reference but the species sampled are nonoverlapping). We provide a function, exemplar.phylo(), that automates this procedure if the reference is supplied with a taxonomy for the sampled lineages. The default behaviour of this function is to use the NCBI taxonomic database. As a simple extension of the congruification algorithm, we provide a convenient tool – nodelabel.phylo() – by which to label internal nodes of phylogenies to correspond with groups defined by a given taxonomic resource. Several functions related to manipulation and use of taxonomic data, including those just described, are demonstrated in the Supporting Information.


By integrating vetted and time-scaled inferences of phylogeny, careful systematic treatments and newly generated estimates of phylogeny, congruification will allow users to rapidly generate large timetrees, a central facet in the study of pattern and process in the diversification of life.


We thank M.E. Alfaro, J.W. Beaulieu, J.W. Brown, B.C. O'Meara, M.W. Pennell, K. Roelants, G.J. Slater, S.A. Smith and A.E. Zanne for support provided during development of this project. Two anonymous reviewers, G. Thomas and E. Paradis, provided constructive critiques of the manuscript in its earlier form. Financial support to LJH was provided by NSF DEB 0919499 and DEB 1208912; and the NESCent working group, Tempo and mode of plant trait evolution (NSF #EF- 0905606), provided support for JME and DCT.