Beacham et al.: Population structure of Oncorhynchus gorbuscha in British Columbia and Washington, determined with microsatellites 
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group of populations (regional F ST values ranging from 
0.006 to 0.010). The least differentiation was observed 
between populations from the northern and central 
coastal regions of British Columbia (F ST = 0.001). In 
the even-year broodline, the largest regional genetic 
differentiation was observed between populations from 
the east coast of Vancouver Island and those from the 
Queen Charlotte Islands (F ST = 0.007), whereas the least 
differentiation between regional groups of populations 
was observed between the northern and central coastal 
regions of British Columbia (F sr =0.001) (Table 6). 
Two distinct lineages of pink salmon were observed 
in the cluster analysis, and they were clearly based 
on whether pink salmon spawned in odd-numbered or 
even-numbered years. All odd-year populations clus- 
tered together with 100% bootstrap support, as did 
all even-year populations (Fig. 2). Within the odd-year 
broodline, a Washington group of populations was well 
supported (Washington populations clustered together 
in 83% of dendrograms evaluated). Within Washing- 
ton, further geographic subdivision was observed, with 
populations from the Strait of Juan de Fuca (Gray 
Wolf River, Dungeness River) clustering with the Hood 
Canal hatchery population in 100% of dendrograms 
evaluated. The Hood Canal hatchery population was 
the most genetically distinct population included in 
the survey (Fig. 2). The three remaining populations 
from Hood Canal (Dosewallips River, Hamma Hamma 
River, and Duckabush River), in addition to the Gray 
Wolf River, Dungeness River, and Hood Canal hatchery 
populations, were well separated from other popula- 
tions in Washington, clustering together in 100% of 
dendrograms evaluated. Pink salmon populations from 
the Fraser River in southern British Columbia were 
a well-defined geographic cluster — all 15 populations 
clustered together in 98% of dendrograms evaluated. 
Furthermore, populations in the upper portion of the 
drainage were separated from those populations in the 
lower portion of the drainage, with upper populations 
clustering together in 98% of dendrograms evaluated. 
Populations from the central portion of the east coast 
of Vancouver Island (Quinsam River, Puntledge River, 
Oyster River, Big Qualicum River, and Nanaimo River) 
clustered together in 100% of dendrograms evaluated, 
as did 98% of populations from the northern portion 
of the east coast of Vancouver Island (Keogh River, 
Quatse River, Cluxewe River). Populations from the 
northern portion of the South Coast of British Colum- 
bia (Kakweiken River, Lull Creek, Ahta Creek, Heydon 
River, Glendale River) constituted a well-defined group 
(96% of dendrograms evaluated). Those in the southern 
portion of the south coast (Cheakamus River, Ashlu 
River, Mamquam River, Squamish River, Indian River) 
were not well supported, but displayed some affinity 
to each other in the cluster analysis (Table 6). All 10 
odd-year populations sampled from the Skeena River 
formed a distinct regional group (50% bootstrap sup- 
port). Populations sampled from the central coast and 
north coast regions of British Columbia did not cluster 
into distinct geographic units. Although some sepa- 
ration was observed, genetic differentiation between 
populations in the two regions was limited, and this 
was reflected in the lack of consistency in population 
clustering in the dendrograms evaluated. 
Cluster analysis of the populations sampled in the 
even-year broodline revealed a general lack of consisten- 
cy in geographically based clustering of the populations. 
The only exception was observed for the Skeena River 
drainage, where all seven populations sampled clus- 
tered together in 97% of dendrograms evaluated. There 
was some evidence for a weak association for 19 of 21 
populations from the Queen Charlotte Islands, but the 
cluster was not well supported (22% bootstrap support). 
As with the odd-year broodline, populations sampled 
from the central coast and north coast regions of Brit- 
ish Columbia did not cluster into distinct geographic 
units (Fig. 2), which again reflected the overall lack of 
genetic differentiation (F sr =0.001) between populations 
in the two regions. 
Discussion 
In the current study of microsatellite variation in pink 
salmon, approximately 46,500 individuals were surveyed 
from 146 odd-year and 116 even-year populations, 16 
microsatellites were analyzed encompassing 812 alleles, 
and 12-85 alleles were identified per locus. Sample size 
ranged from 18 to 755 individuals per population, with at 
least 100 individuals sampled in 127 of the 146 odd-year 
populations, and 92 of the 116 even-year populations. 
Only six odd-year and seven even-year populations had 
fewer than 40 individuals surveyed. With a range in the 
number of individuals sampled per population, sampling 
errors may have influenced the estimated allele frequen- 
cies within populations, particularly for populations with 
fewer than 40 individuals sampled. If sampling errors 
are large in estimation of allele frequencies, there is a 
potential for these errors to obscure genetic relationships 
among related populations. Kalinowski (2005) reported 
that loci with larger numbers of alleles produced esti- 
mates of genetic distance with lower coefficients of varia- 
tion than loci with fewer numbers of alleles, without 
requiring larger sample sizes from each population. 
Given the results from the cluster analysis, variation in 
the number of individuals sampled per population likely 
did not result in misidentification of genetic relationships 
among populations. 
Inferences from the genetic relationships of popula- 
tions surveyed in our study were dependent upon ac- 
curate determination of population allele frequencies. 
Microsatellite alleles differ in size, but alleles of the 
same size at a locus in geographically separate popula- 
tions may not have the same origin as a result of size 
homoplasy. Convergent mutations in different lineages 
may produce alleles of the same size, with the result 
that there may be greater differentiation among lin- 
eages than revealed by analysis of size variation alone. 
However, with over 800 alleles observed across all loci 
in the study, the large amount of variation present 
