Decomposed pairwise regression analysis of genetic and geographic distances reveals a metapopulation structure of stream‐dwelling Dolly Varden charr

Abstract

Isolation by distance is usually tested by the correlation of genetic and geographic distances separating all pairwise populations' combinations. However, this method can be significantly biased by only a few highly diverged populations and lose the information of individual population. To detect outlier populations and investigate the relative strengths of gene flow and genetic drift for each population, we propose a decomposed pairwise regression analysis. This analysis was applied to the well-described one-dimensional stepping-stone system of stream-dwelling Dolly Varden charr (Salvelinus malma). When genetic and geographic distances were plotted for all pairs of 17 tributary populations, the correlation was significant but weak (r(2) = 0.184). Seven outlier populations were determined based on the systematic bias of the regression residuals, followed by Akaike's information criteria. The best model, 10 populations included, showed a strong pattern of isolation by distance (r(2) = 0.758), suggesting equilibrium between gene flow and genetic drift in these populations. Each outlier population was also analysed by plotting pairwise genetic and geographic distances against the 10 nonoutlier populations, and categorized into one of the three patterns: strong genetic drift, genetic drift with a limited gene flow and a high level of gene flow. These classifications were generally consistent with a priori predictions for each population (physical barrier, population size, anthropogenic impacts). Combined the genetic analysis with field observations, Dolly Varden in this river appeared to form a mainland-island or source-sink metapopulation structure. The generality of the method will merit many types of spatial genetic analyses.

Inter- and intra-specific genetic divergence of Asian tiger frogs (genus <i>Hoplobatrachus</i>), with special reference to the population structure of <i>H. tigerinus</i> in Bangladesh

“…The Vo population, which we identified as an outlier population in the DPR analysis, may reflect such migration. As mentioned in the original DPR analysis (Koizumi et al, 2006), a population showing a stronger gene flow effect may be the result of long-distance dispersal by water currents from upper regions. This is also applicable to the Vo population because it is located in the middle of the delta area neighboring the Meghna River.…”

“…The DPR of Vo gave lower slope and intersect values, which means lower genetic differentiation even among distant populations (Fig. 7), and is thus recognized as Pattern 4, i.e., gene flow > genetic drift (Koizumi et al, 2006).…”

“…We therefore applied decomposed pairwise regression (DPR) analysis (Koizumi et al, 2006) to determine the characteristics of individual H. tigerinus populations in Bangladesh. In brief, this analysis first detected a putative outlier population, which was not in accordance with the regression lines from the pairwise plot of linearized F ST and geographic distances (for details, see Koizumi et al, 2006). In the original method, true outlier populations are then identified by choosing the best model based on the corrected Akaike information criterion (AIC).…”

Strong gene flow and lack of stable population structure in the face of rapid adaptation to local temperature in a spring-spawning salmonid, the European grayling (Thymallus thymallus)

“…A lack of stability and consistent population structure in equilibrium could therefore indicate adaptive divergence in the face of persistent gene flow in this system. Furthermore, we use the analysis of temporal stability to assess the strength of temporal stochasticity in comparison with fluctuations in gene flow using a decomposed pairwise regression (DPR) analysis (Koizumi et al, 2006) to investigate whether the system is more influenced by drift or gene flow. and VAL); some were sampled only once and others for up to 7 years (see Table 1).…”

“…By using standard regression analysis on all pairwise plots, information on local specialties is lost, that is, subpopulation-specific characters (for example, population size, degree of isolation), which are in turn responsible for the relative strengths of genetic drift and gene flow (Koizumi et al, 2006). This information is important for characterizing complex population systems like metapopulations, which can exhibit complex extinction-recolonization dynamics (Hanski and Gaggiotti, 2004).…”