BELL, M.A., LLOYD, G.T. 2015. strap: an R package for plotting phylogenies against stratigraphy and assessing their stratigraphic congruence. Palaeontology 58, 2, 379–389.
Mark A. Bell and Graeme T. Lloyd- Mark A. Bell - Department of Earth Sciences, University College London, London, UK (email: mark.bell521@gmail.com)
- Graeme T. Lloyd - Department of Earth Sciences, University of Oxford, Oxford, UK (email: graemetlloyd@gmail.com)
- Graeme T. Lloyd - Department of Biological Sciences, Faculty of Science, Macquarie University, Sydney, Australia
- Issue published online: 5 MAR 2015
- Article first published online: 18 DEC 2014
- Manuscript Accepted: 9 NOV 2014
- Manuscript Received: 8 AUG 2014
strap (Stratigraphic Tree Analysis for Palaeontology) is a new package for the freely available statistical programming language R designed to perform three main tasks: (1) to time-scale phylogenies of fossil taxa; (2) to plot those time-scaled trees against stratigraphy; and (3) to assess congruence between phylogenies and stratigraphy. Time-scaling is performed with the DatePhylo function, with three approaches offered. Plotting trees against a choice of five different geological time scaless is possible using the geoscalePhylo function. Finally, the function StratPhyloCongruence calculates stratigraphic congruence measures for one or more input phylogenies, with no taxon limit. All three major congruence measures are offered: Stratigraphic Consistency Index (SCI), Manhattan Stratigraphic Measure (MSM*) and the gap excess ratio (GER; including GERt and GER*), as well as the pseudocongruence measure, the Relative Completeness Index (RCI). Each measure has an accompanying significance test that works by comparing the input trees against a user-defined number of randomly generated topologies with the same taxon set and age ranges. Additional options for generating these random topologies allow the user to fix the outgroup or retain the input tree shape to make fairer comparisons. A tutorial that assumes no prior knowledge of R showcases all three functions using two different example data sets.
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