The automation and evaluation of nested clade phylogeographic analysis.
Nested clade phylogeographic analysis (NCPA) is a popular method for reconstructing the demographic history of spatially distributed populations from genetic data. Although some parts of the analysis are automated, there is no unique and widely followed algorithm for doing this in its entirety, beginning with the data, and ending with the inferences drawn from the data. This article describes a method that automates NCPA, thereby providing a framework for replicating analyses in an objective way. To do so, a number of decisions need to be made so that the automated implementation is representative of previous analyses. We review how the NCPA procedure has evolved since its inception and conclude that there is scope for some variability in the manual application of NCPA. We apply the automated software to three published datasets previously analyzed manually and replicate many details of the manual analyses, suggesting that the current algorithm is representative of how a typical user will perform NCPA. We simulate a large number of replicate datasets for geographically distributed, but entirely random-mating, populations. These are then analyzed using the automated NCPA algorithm. Results indicate that NCPA tends to give a high frequency of false positives. In our simulations we observe that 14% of the clades give a conclusive inference that a demographic event has occurred, and that 75% of the datasets have at least one clade that gives such an inference. This is mainly due to the generation of multiple statistics per clade, of which only one is required to be significant to apply the inference key. We survey the inferences that have been made in recent publications and show that the most commonly inferred processes (restricted gene flow with isolation by distance and contiguous range expansion) are those that are commonly inferred in our simulations. However, published datasets typically yield a richer set of inferences with NCPA than obtained in our random-mating simulations, and further testing of NCPA with models of structured populations is necessary to examine its accuracy.
Panchal
Mahesh
M
School of Biological Sciences, University of Reading, Whiteknights, Reading, UK. m.panchal@rdg.ac.uk
Beaumont
Mark A
MA
D000465:Algorithms; D000818:Animals; D002965:Classification / Q000379:methods; D001517:Coleoptera / Q000145:classification / Q000235:genetics; D003198:Computer Simulation; D004272:DNA, Mitochondrial / Q000737:chemistry; D005843:Geography; D008116:Lizards / Q000145:classification / Q000235:genetics; D010802:Phylogeny; D017422:Sequence Analysis, DNA; D012984:Software
D004272:DNA, Mitochondrial