S C OT T A . PAV E Y E T A L . covariate. Body length (MOH) accounts for some variation in egg mass, so the incorporation of body length in the ANCOVA allowed us to perform residuals analysis to look at the effect of the habitat type on egg mass. TIME SINCE DIVERGENCE AND MIGRATION Isolation with migration analysis We performed an analysis using the program isolation with mi- gration (IM) (Hey and Nielsen 2004) on a microsatellite DNA database from Pavey et al. (2007). We performed initial pilot runs of the program with large priors to make sure that the pos- terior probability area was contained within the priors. We then fine-tuned the priors to “zoom in” to show the detail of the pos- terior distributions while still encompassing the whole for each parameter in the model. After initial pilot runs of the program, we executed three long runs with 18 heated chains for ∼10,000,000 steps. The command line for these runs was: −q1 5 −m1 50 −m2 50 −t 1 −b 72.0 −l 24.0 −u 5 −p 4567 −n 18 −k 100 −fg −g1 0.6 −g2 0.95 −e 24.0. The first four commands set the priors for all of the parameters. The “−b” command sets the program burn in for 72 h. The “−l” command tells the program to make and output file every 24 h. The “−u” command sets the generation time of 5 years. The “−p” command sets the output options. The “−n” command sets the number of chains to 18. The “−k” com- mand sets the number of swap attempts per step to 100. The “−fg” command sets the heating scheme to geometric. The “−g1” and “−g2” specify the degree of chain heating. The “−e” command creates a checkpoint file every 24 h. We report high point and av- erage posterior probability estimates from all loci for time since divergence onset, migration rate in each direction, and average date of migration events. Finally, we report effective number of migrants for each population. Because all parameters estimated in the IM model are in units of mutation, we need to estimate mutation rate to convert the parameter estimates into demographic units. Experimental work with other tetranucleotide microsatellites has demonstrated that mutation rate is often larger than the commonly used de- fault mutation rate of 1 × 10−4 (Weber and Wong 1993; Ellegren 1995; Leopoldino and Pena 2003). We estimated mutation rate for each locus using two different methods. First, with the ex- ception of One105, our markers are highly polymorphic, so we expect a larger than average mutation rate. We assigned the con- servative mutation rate of 1 × 10−4 to our one moderately poly- morphic marker, One105, and used the mutation rate scalar es- timates obtained from running the IM program to estimate the mutation rates of the other loci. Second, mutation rates in tetranu- cleotide microsatellites are shown to vary with length of repeat unit (Leopoldino and Pena 2003). We calculated a regression equation using the data from Leopoldino and Pena (2003) which was obtained from comparing observed mutation rate with geo- 1 7 7 6 EVOLUTION JUNE 2010 metric mean of number of repeats. We converted estimated pa- rameters into demographic units with the method yielding the more conservative (slower) global mutation rate, which is the ge- ometric mean of the mutation rates scaled from the model output for all loci. IM assumptions The IM program has several assumptions about the input data. Perfect tetranucleotide microsatellites have been shown to ex- hibit mutations that are well described by the stepwise mutation model (Shiver et al. 1993; Leopoldino and Pena 2003), which is the model used in IM (Hey and Nielsen 2006). One marker from this database, One110, did not meet the requirement of the IM program of a perfect repeat and was excluded. We included the five microsatellites that followed a perfect tetranucleotide repeat pattern from this database of 268 individuals. Another assumption about the input data is that the markers should not be physically linked. We tested for linkage disequilibrium and found no sig- nificant linkage disequilibrium in any of these markers (Pavey et al. 2007). A third assumption about the input data is that the markers are not under selection. This assumption is more dif- ficult to explicitly test. One potential indication of selection is deviations from Hardy–Weinberg equilibrium (Conner and Hartl 2004), which is not present with these data (Pavey et al. 2007) or with similar data on these same markers in another study in- volving sockeye salmon in southwest Alaska (Olsen et al. 2004). Another potential indication of selection is outlier loci, or one or two loci being primarily responsible for the measured genetic differences. This was not indicated with these markers in an- other study (Olsen et al. 2004). To determine whether this was the case in our dataset, we used the program WHICHLOCI to determine the relative contribution of each marker to the mea- sured genetic divergence. Because we ran IM without the One110 locus (see above) we ran WHICHLOCI both including and ex- cluding One110. Also, we sequentially dropped each locus to see if this substantially affected the FST between these populations. We preformed this analysis in GENEPOP (Raymond and Rousset 1995). Another assumption of IM is that the two analyzed popula- tions are more closely related to each other than they are to other populations. (Hey and Nielsen 2006). The basic phylogenetic unit of lake-type sockeye salmon is the nursery lake (Wood 1995; Beacham et al. 2006). When glaciers recede and expose new lake habitats, sockeye colonize. Divergence also occurs among habi- tats within the nursery lake, but genetic differences are generally much smaller within lakes than between lakes. This is the situation at Aniakchak (Pavey et al. 2007). The FST between the beach and outlet populations within Surprise Lake was smaller than the FST between either of these populations and any other population out- side of Surprise Lake. These relationships are further illustrated