A variety of methods that include a simulation analysis, coestimates of migration and divergence times, and estimates of minimum ages of populations sampled up and down the North American Pacific coast all strongly revealed a history of range persistence in X. atropurpureus and extreme range contraction and expansion from a southern refugium in X. mucosus.
Genetic variability in a given gene is the result of a complex interplay of evolutionary forces such as selection, mutation rate and recombination, and demographic history of the populations including changes in population size, migration and divergence [ 1].
To further test migration rate and divergence time between neighboring populations in Far Eastern Russia and Hokkaido, an isolation-with-migration model was applied using software IMa2 [ 42, 43].
MDIV was utilized to produce effective migration rate and divergence time estimates among neighbouring Breeding Stocks (A, B, C and X).
To determine whether resulting estimates of effective population size, migration rate, and divergence time were significantly different from one another, we used a probability assessment method [40] based on comparisons of randomly drawn values from resulting marginal distributions.
In order to estimate effective migration rates and divergence times between neighbouring breeding stocks in a stepping-stone fashion (Stock A vs. Stock B, Stock B vs. Stock C, Stock C vs. Stock X) we used a maximum-likelihood framework based on coalescence theory, implemented in the programs MDIV and MIGRATE 2.0.3 [69], [69].
In this approach, the model consists of two populations that diverge from an ancestral population and exchange migrants, and the values of these parameters (migration rate and divergence time) are inferred.
The present study is, to our knowledge, the first example that clearly suggests that distinct intraspecific ecotypes of an amphidromous fish species (as determined from morphological, reproductive, behavioural, and genetic evidence) may be formed as a consequence of loss of migration and subsequent divergence.
Demographic parameters, including effective population size (θ for 4Neμ), migration rate (m), and divergence time (t) were estimated, assuming an Isolation-with-Migration (IM) model, using the IMa2 program [ 58, 59].
Coalescent simulations, implemented in IMa2 (Hey, 2010; Nielsen and Wakeley, 2001), were used to generate neutral estimates of migration (2Nm), effective population size, and divergence times (tμ TMRCA).
Migration rate (m), effective population size, and divergence time between populations were estimated using the coalescent-based isolation-with-migration model implemented in IMa [ 75].
