Abstract
We propose a twostep procedure for estimating multiple migration rates in an approximate Bayesian computation (ABC) framework, accounting for global nuisance parameters. The approach is not limited to migration, but generally of interest for inference problems with multiple parameters and a modular structure (e.g. independent sets of demes or loci). We condition on a known, but complex demographic model of a spatially subdivided population, motivated by the reintroduction of Alpine ibex (Capra ibex) into Switzerland. In the first step, the global parameters ancestral mutation rate and male mating skew have been estimated for the whole population in Aeschbacher et al. (Genetics 2012; 192: 1027). In the second step, we estimate in this study the migration rates independently for clusters of demes putatively connected by migration. For large clusters (many migration rates), ABC faces the problem of too many summary statistics. We therefore assess by simulation if estimation per pair of demes is a valid alternative. We find that the tradeoff between reduced dimensionality for the pairwise estimation on the one hand and lower accuracy due to the assumption of pairwise independence on the other depends on the number of migration rates to be inferred: the accuracy of the pairwise approach increases with the number of parameters, relative to the joint estimation approach. To distinguish between low and zero migration, we perform ABCtype model comparison between a model with migration and one without. Applying the approach to microsatellite data from Alpine ibex, we find no evidence for substantial gene flow via migration, except for one pair of demes in one direction.
Original language  English 

Pages (fromto)  9871002 
Number of pages  16 
Journal  Molecular Ecology 
Volume  22 
Issue number  4 
DOIs  
Publication status  Published  Feb 2013 
Keywords
 FINITE MIXTURES
 ALPINE IBEX
 GENE FLOW
 LIKELIHOOD
 migration rates
 divide and conquer
 POPULATION SUBDIVISION
 CAPRAIBEXIBEX
 Alpine ibex
 MODEL
 PAIRWISE DIFFERENCES
 approximate Bayesian computation
 CHAIN MONTECARLO
 INFECTIOUS KERATOCONJUNCTIVITIS
 pairwise estimation
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Data from: Approximate Bayesian computation for modular inference problems with many parameters: the example of migration rates
Aeschbacher, S. (Contributor), Futschik, A. (Contributor) & Beaumont, M. A. (Contributor), Dryad, 14 Nov 2012
DOI: 10.5061/dryad.274b1, http://datadryad.org/stash/dataset/doi:10.5061/dryad.274b1
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