Selected Publications

Wind connectivity has been identified as a key factor driving many biological processes. Existing software available for managing wind data are often overly complex for studying many ecological processes and cannot be incorporated into a broad framework. Here we present rWind, an R langauge package to download and manage surface wind data from the Global Forecasting System and to compute wind connectivity between locations. Data obtained with rWind can be used in a general framework for analysis of biological processes to develop hypotheses about the role of wind in driving ecological and evolutionary patterns.
In Ecography, 2018.

In 1973, the statistician Francis Anscombe used a clever set of bivariate datasets (now known as Anscombe’s quartet) to illustrate the importance of graphing data as a component of statistical analyses. In his example, each of the four datasets yielded identical regression coefficients and model fits, and yet when visualized revealed strikingly different patterns of covariation between x and y. Phylogenetic comparative methods (the set of methodologies that use phylogenies, often combined with phenotypic trait data, to make inferences about evolution) are statistical methods too; yet visualizing the data and phylogeny in a sensible way that would permit us to detect unexpected patterns or unanticipated deviations from model assumptions is not a routine component of phylogenetic comparative analyses. Here, we use a quartet of phylogenetic datasets to illustrate that the same estimated parameters and model fits can be obtained from data that were generated using markedly different procedures—including pure Brownian motion evolution and randomly selected data uncorrelated with the tree. Just as in the case of Anscombe’s quartet, when graphed the differences between the four datasets are quickly revealed. The intent of this article is to help build the general case that phylogenetic comparative methods are statistical methods and consequently that graphing or visualization should invariably be included as an essential step in our standard data analytical pipelines. Phylogenies are complex data structures and thus visualizing data on trees in a meaningful and useful way is a challenging endeavour. We recommend that the development of graphical methods for simultaneously visualizing data and tree should continue to be an important goal in phylogenetic comparative biology.
In Methods in Ecology and Evolution, 2018.

After more than fifteen years of existence, the R package ape has continuously grown its contents, and has been used by a growing community of users. The release of version 5.0 has marked a leap towards a modern software for evolutionary analyses. Efforts have been put to improve efficiency, flexibility, support for ‘big data’ (R’s long vectors), ease of use and quality check before a new release. These changes will hopefully make ape a useful software for the study of biodiversity and evolution in a context of increasing data quantity.
In Bioinformatics, 2018.

The fields of phylogenetic tree and network inference have dramatically advanced in the past decade, but independently with few attempts to bridge them. Here we provide a framework, implemented in the phangorn library in R, to transfer information between trees and networks. This includes: (i) identifying and labelling equivalent tree branches and network edges, (ii) transferring tree branch support to network edges, and (iii) mapping bipartition support from a sample of trees (e.g. from bootstrapping or Bayesian inference) onto network edges. The ability to readily combine tree and network information should lead to more comprehensive evolutionary comparisons and inferences.
In Methods in Ecology and Evolution, 2017.

Media coverage

Creators of computer programs that underpin experiments don’t always get their due — so the website Depsy is trying to track the impact of research code.
In Nature, 2016.

Recent Publications

More Publications

Graphs in phylogenetic comparative analysis: Anscombe's quartet revisited

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Comparison of the Serum Tumor Markers S100 and Melanoma-inhibitory Activity (MIA) in the Monitoring of Patients with Metastatic Melanoma Receiving Vaccination Immunotherapy with Dendritic Cells

View Journal Article

Intertwining phylogenetic trees and networks

View Journal Article

Recent Posts

If data are not sequence alignment an phyDat object then there are generic functions as.phyDat() in phangorn to transform a matrices and data.frames into phyDat objects. For example you can read in your data with read.table() or read.csv(), but you might need to transpose your data. For matrices as.phyDat() assumes that the entries each row belongs to one individual (taxa), but for data.frame each column. For binary data you can transform these with a command like (depending how you coded them):


As you may have seen a version of ape and phangorn have been released. ape jumped to version 4.0 and you can see the changes here. Some of the nicest new features are that many useful functions for transforming phylo objects have been made generic. This includes functions like is.rooted, unroot, reorder is.binary, is.ultrametric or di2multi and these now work also on multiPhylo objects. In practice this means that instead of typing


phangorn is a package for R. To use it, you first need to download and install R. RStudio provides a nice user interface for R. You can install the latest release version from CRAN install.packages("phangorn") or install the latest development version using the install_github function in the devtools package from github. library(devtools) install_github("KlausVigo/phangorn") For devtools to work on windows you need addionally to have installed Rtools and on mac you need Xcode to compile some C code.




Phylogenetic analysis in R