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Approximate Bayesian Computation (approximate + bayesian_computation)
Selected AbstractsThe discordance of diversification: evolution in the tropical-montane frogs of the Eastern Arc Mountains of TanzaniaMOLECULAR ECOLOGY, Issue 18 2010LUCINDA P. LAWSON Abstract Species with similar geographical distribution patterns are often assumed to have a shared biogeographical history, an assumption that can be tested with a combination of molecular, spatial, and environmental data. This study investigates three lineages of Hyperolius frogs with concordant ranges within the Eastern Afromontane Biodiversity Hotspot to determine whether allopatric populations of co-distributed lineages shared a parallel biogeographical response to their shared paleoclimatic histories. The roles of refugial distributions, isolation, and climate cycles in shaping their histories are examined through Hierarchical Approximate Bayesian Computation, comparative phylogeography, and comparisons of current and past geographical distributions using ecological niche models. Results from these analyses show these three lineages to have independent evolutionary histories, which current spatial configurations of sparsely available habitat (montane wetlands) have moulded into convergent geographical ranges. In spite of independent phylogeographical histories, diversification events are temporally concentrated, implying that past vicariant events were significant at the generic level. This mixture of apparently disparate histories is likely due to quantifiably different patterns of expansion and retreat among species in response to past climate cycles. Combining climate modelling and phylogeographical data can reveal unrecognized complexities in the evolution of co-distributed taxa. [source] Statistical hypothesis testing in intraspecific phylogeography: nested clade phylogeographical analysis vs. approximate Bayesian computationMOLECULAR ECOLOGY, Issue 2 2009ALAN R. TEMPLETON 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. [source] THE STATE OF THE FIELD: Combining contemporary and ancient DNA in population genetic and phylogeographical studiesMOLECULAR ECOLOGY RESOURCES, Issue 5 2010MIGUEL NAVASCUÉS Abstract The analysis of ancient DNA in a population genetic or phylogeographical framework is an emerging field, as traditional analytical tools were largely developed for the purpose of analysing data sampled from a single time point. Markov chain Monte Carlo approaches have been successfully developed for the analysis of heterochronous sequence data from closed panmictic populations. However, attributing genetic differences between temporal samples to mutational events between time points requires the consideration of other factors that may also result in genetic differentiation. Geographical effects are an obvious factor for species exhibiting geographical structuring of genetic variation. The departure from a closed panmictic model require researchers to either exploit software developed for the analysis of isochronous data, take advantage of simulation approaches using algorithms developed for heterochronous data, or explore approximate Bayesian computation. Here, we review statistical approaches employed and available software for the joint analysis of ancient and modern DNA, and where appropriate we suggest how these may be further developed. [source] | ||||||||||