ABC as a flexible framework to estimate demography over space and time: some cons, many pros.
The analysis of genetic variation to estimate demographic and historical parameters and to quantitatively compare alternative scenarios recently gained a powerful and flexible approach: the Approximate Bayesian Computation (ABC). The likelihood functions does not need to be theoretically specified, but posterior distributions can be approximated by simulation even assuming very complex population models including both natural and human-induced processes. Prior information can be easily incorporated and the quality of the results can be analysed with rather limited additional effort. ABC is not a statistical analysis per se, but rather a statistical framework and any specific application is a sort of hybrid between a simulation and a data-analysis study. Complete software packages performing the necessary steps under a set of models and for specific genetic markers are already available, but the flexibility of the method is better exploited combining different programs. Many questions relevant in ecology can be addressed using ABC, but adequate amount of time should be dedicated to decide among alternative options and to evaluate the results. In this paper we will describe and critically comment on the different steps of an ABC analysis, analyse some of the published applications of ABC and provide user guidelines.
Bertorelle
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Department of Biology and Evolution, University of Ferrara, Via Borsari 46, 44100 Ferrara, Italy. ggb@unife.it
Benazzo
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Mona
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D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D005828:Genetics, Population / Q000379:methods*; D015233:Models, Statistical; D011157:Population Dynamics; D012984:Software