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In order to verify these results we used the approximate Bayesian computation (ABC) approach [38] to test whether a closed single-population-bottleneck model (no gene flow), an open population-bottleneck model (gene flow following the period of minimum population size) or a constant-population-size model provided the best fit for our data.
In the 'gene flow at bottleneck' model, gene flow occurred only in the bottleneck period.
In the 'gene flow after bottleneck' model, gene flow occurred after the end of bottleneck period.
We used graph theory to model gene flow within the resulting network of genetic connectivity among fishers in order to relate system- and node-level biological characteristics to landscape quality.
We applied circuit theory to model gene flow across a spatially heterogeneous landscape and determine the impact of isolation-by-distance (IBD) and different IBR scenarios on observed patterns of genetic differentiation (McRae 2006; e.g. McRae and Beier 2007).
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Modelling gene flow across natural landscapes is a current challenge of population genetics.
Previous studies modeled gene flow as a function of distance from the nearest transgenic source field.
Most models on introgression from genetically modified (GM) plants have focused on small spatial scales, modelling gene flow from a field containing GM plants into a single adjacent population of a wild relative.
Progress has also been made by combining niche models and coalescent models [29] and modelling gene flow as random walks in heterogeneous landscapes [30].
Four models ('no gene flow' model, 'continuous gene flow' model, 'gene flow at bottleneck' model and 'gene flow after bottleneck' model) were assumed.
The coalescent simulation was performed using ms software [ 50] and the four models ('no gene flow' model, 'continuous gene flow' model, 'gene flow at bottleneck' model and 'gene flow after bottleneck' model) were introduced.
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