ArticleMolecular ecology2009
Statistical hypothesis testing in intraspecific phylogeography: nested clade phylogeographical analysis vs. approximate Bayesian computation.
Article in Molecular ecology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed.
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- An efficient pipeline to generate data for studies in plastid population genomics and phylogeography.Applications in plant sciences · 2017Article
- Effects of the population pedigree on genetic signatures of historical demographic events.Proceedings of the National Academy of Sciences of the United States of America · 2016Article
- Supersize me: how whole-genome sequencing and big data are transforming epidemiology.Trends in microbiology · 2014Review
- Biological races in humans.Studies in history and philosophy of biological and biomedical sciences · 2013Article
- Approximate Bayesian computation.PLoS computational biology · 2013Article
- Molecular insights into the historic demography of bowhead whales: understanding the evolutionary basis of contemporary management practices.Ecology and evolution · 2012Article
- Different processes lead to similar patterns: a test of codivergence and the role of sea level and climate changes in shaping a southern temperate freshwater assemblage.BMC evolutionary biology · 2011Article
- Lack of confidence in approximate Bayesian computation model choice.Proceedings of the National Academy of Sciences of the United States of America · 2011Article
- Conservation genetics and phylogeography of endangered and endemic shrub Tetraena mongolica (Zygophyllaceae) in Inner Mongolia, China.BMC genetics · 2011Article
- Three roads diverged? Routes to phylogeographic inference.Trends in ecology & evolution · 2010Article
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- Incoherent phylogeographic inference.Proceedings of the National Academy of Sciences of the United States of America · 2010Article
- Correcting approximate Bayesian computation.Trends in ecology & evolution · 2010Article
- Coherent and incoherent inference in phylogeography and human evolution.Proceedings of the National Academy of Sciences of the United States of America · 2010Article
- Inference of population history by coupling exploratory and model-driven phylogeographic analyses.International journal of molecular sciences · 2010Review
- Coalescent-based, maximum likelihood inference in phylogeography.Molecular ecology · 2010Article
- In defence of model-based inference in phylogeography.Molecular ecology · 2010Article
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Abstract
Nested clade phylogeographical analysis (NCPA) and approximate Bayesian computation (ABC) have been used to test phylogeographical hypotheses. Multilocus NCPA tests null hypotheses, whereas ABC discriminates among a finite set of alternatives. The interpretive criteria of NCPA are explicit and allow complex models to be built from simple components. The interpretive criteria of ABC are ad hoc and require the specification of a complete phylogeographical model. The conclusions from ABC are often influenced by implicit assumptions arising from the many parameters needed to specify a complex model. These complex models confound many assumptions so that biological interpretations are difficult. Sampling error is accounted for in NCPA, but ABC ignores important sources of sampling error that creates pseudo-statistical power. NCPA generates the full sampling distribution of its statistics, but ABC only yields local probabilities, which in turn make it impossible to distinguish between a good fitting model, a non-informative model, and an over-determined model. Both NCPA and ABC use approximations, but convergences of the approximations used in NCPA are well defined whereas those in ABC are not. NCPA can analyse a large number of locations, but ABC cannot. Finally, the dimensionality of tested hypothesis is known in NCPA, but not for ABC. As a consequence, the 'probabilities' generated by ABC are not true probabilities and are statistically non-interpretable. Accordingly, ABC should not be used for hypothesis testing, but simulation approaches are valuable when used in conjunction with NCPA or other methods that do not rely on highly parameterized models.
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