Beacham et al. : Population structure of Oncorhynchus keta across the Pacific Rim 
245 
local refuges may also have been present in the Kam- 
chatka region (Varnavskaya et al., 1994), and in British 
Columbia, on the Queen Charlotte Islands and perhaps 
on coastal islands in the central coast region (Warner 
et al., 1982; Wood, 1995). Seeb and Crane (1999) indi- 
cated that existing populations from the Alaska Penin- 
sula south to Washington may have derived primarily 
from the southern refuge, whereas Asian and western 
Alaskan populations may have derived from a northern 
refuge. Microsatellite variation can be used to examine 
relationships between existing Pacific Rim population 
structure and proposed patterns of dispersal from gla- 
cial refuges. 
In the current study, we evaluated chum salmon dis- 
persal pathways from glacial refugia after glacial re- 
treat. In addition, we examined regional differentiation 
in allelic frequencies and levels of allelic diversity to 
evaluate whether local enhancement activities have had 
any effect on genetic diversity or population structure. 
These objectives were accomplished by analyzing varia- 
tion at 14 microsatellite loci to evaluate relationships 
among Pacific Rim populations of chum salmon. The 
distribution of genetic diversity among regions, popula- 
tions, and sampling years was estimated in the study. 
Materials and methods 
More than 53,000 chum salmon from 381 populations 
from Korea, Japan, Russia, Alaska, Canada, and Wash- 
ington were analyed from 59 geographic regions (Table 
1, Fig. 1), with the specific populations and sample 
sizes outlined by Beacham et al. 1 Tissue samples were 
collected from mature chum salmon, preserved in 95% 
ethanol, and analyzed at the Molecular Genetics Labo- 
ratory at the Pacific Biological Station (Fisheries and 
Oceans Canada, Nanaimo, BC). DNA was extracted 
from the tissue samples using a variety of methods, 
including a chelex resin protocol outlined by Small et 
al. (1998), a Qiagen 96-well Dneasy® procedure (Mis- 
sissauga, Ontario), or a Promega Wizard SV96 Genomic 
DNA Purification system (Promega, Madison, WI). Once 
DNA was extracted, surveys of variation at 14 micro- 
satellite loci were conducted: Ots3 (Banks et al., 1999), 
Oke3 (Buchholz et al., 2001), Oki2 (Smith et al., 1998), 
OkilOO (Beacham et al., 2008a), Omml070 (Rexroad et 
al., 2001), OmylOll (Spies et al., 2005), OnelOl, Onel02, 
Onel04, Onelll, and OtielM (Olsen et al., 2000), Otsl03 
(Nelson and Beacham, 1999), Ssa419 (Cairney et al., 
2000), and OtsG68 (Williamson et al., 2002). 
In general, polymerase chain reaction (PCR) DNA 
amplifications were conducted using DNA Engine Cycler 
Tetrad2 (BioRad, Hercules, CA) in 6,uL volumes consist- 
ing of 0.15 units of Taq polymerase, 1 pL of extracted 
1 Beacham, T. D., J. R. Candy, S. Urawa, S. Sato, N. V. Var- 
navskaya, K. D. Le, and M. Wetklo. 2008. Microsatellite 
stock identification of chum salmon on a Pacific Rim basis 
and a comparison with single nucleotide polymorphisms 
(SNPs). Manuscript in review. 
DNA, lx PCR Hotstar buffer (Qiagen, Mississauga, 
Ontario, Canada), 60 pM each nucleotide, 0.40 pM of 
each primer, and deionized water. The thermal cycling 
profile involved one cycle of 15 minutes at 95°C, fol- 
lowed by 30-40 cycles of 20 seconds at 94°C, 30 to 60 
seconds at 47-65°C and 30 to 60 seconds at 68-72°C 
(depending on the locus). Specific PCR conditions for a 
particular locus could vary from this general summary 
as outlined by Beacham et al. (in press). PCR fragments 
were initially size fractionated in denaturing polyacryl- 
amide gels using an ABI 377 automated DNA sequencer 
(Applied Biosystems, Foster City, CA), and genotypes 
were scored by Genotyper 2.5 software (Applied Bio- 
systems, Foster City, CA) using an internal lane sizing 
standard. Later in the study, microsatellites were size 
fractionated in an ABI 3730 capillary DNA sequencer 
(Applied Biosystems, Foster City, CA), and genotypes 
were scored by GeneMapper software 3.0 (Applied Bio- 
systems, Foster City, CA) using an internal lane sizing 
standard. Allele identification between the two sequenc- 
ers were standardized by analyzing approximately 600 
individuals on both platforms and converting the sizing 
in the gel-based data set to match that obtained from 
the capillary-based set. 
Data analysis 
All annual samples available for a location were com- 
bined to estimate population allele frequencies, as was 
recommended by Waples (1990). Each population at each 
locus was tested for departure from Hardy-Weinberg 
equilibrium by using the computer software Genetic 
Data Analysis (GDA) (Univ. of Connecticut, Storrs, CT). 
Critical significance levels for simultaneous tests were 
evaluated using sequential Bonferroni adjustment (Rice 
1989). Weir and Cockerham’s (1984) F ST estimates for 
each locus over all populations were calculated with 
FSTAT version 2. 9. 3. 2 (Goudet, 1995). The significance 
of the multilocus F ST value over all samples was deter- 
mined by jackknifing the F gT value over loci. The 59 
geographic regions outlined in Table 1 were combined 
into 15 larger regional groups as outlined in Table 3 
in order to display mean pairwise F ST values between 
regions, but the two additional continental reporting 
groups (Asia, North America) incorporated in Table 3 
were not used in the analysis of regional F ST variation. 
Cavalli-Sforza and Edwards (CSE) (1967) chord dis- 
tance was used to estimate genetic distances among all 
populations. An unrooted neighbor-joining tree based 
upon CSE was generated using NJPLOT (Perriere and 
Gouy, 1996). Bootstrap support (by sampling loci) for 
the major nodes in the tree was evaluated with the 
CONSENSE program in PHYLIP software, based upon 
1000 replicate trees (Felsenstein, 1993). FSTAT was 
used to measure the “allelic richness” (allelic diver- 
sity standardized to a sample size of 911 fish) for each 
regional group of populations evaluated. The distribu- 
tion of genetic variation in chum salmon was evaluated 
among regions, among populations within regions, and 
among sampling years within populations. In order to 
