Beacham et al: Population structure of Oncorhynchus gorbuscha in British Columbia and Washington, determined with microsatellites 
253 
at these loci largely compensates for size homoplasy 
(Estoup et al., 2002), and therefore the pattern of popu- 
lation structure identified was unlikely to be obscured 
or distorted by size homoplasy. 
The distribution of genetic variance indicated that 
differences in allele frequencies between the brood- 
lines were about three times greater than differences 
for all regional and population sources of variation, 
but that regional differences in allele frequencies were 
only about 1.4 times larger than differences among 
populations within regions. Previous analyses of an- 
nual variation in allele frequencies within populations 
have indicated that this source of variation can be as 
large as differences among populations within regions 
(Golovanov et al., 2009). Although not specifically il- 
lustrated in the current study because of limitations of 
the software to facilitate a four-level nested analysis 
of variance, similar results (not shown) were observed 
in the current study. The larger importance of annual 
variation within populations relative to population dif- 
ferentiation compared with other Pacific salmon species 
likely reflected the reduced population differentiation 
observed in pink salmon relative to that observed in 
other salmon species (Beacham et al., 2009, 2011). 
The current study indicates that the largest deter- 
minant of population structure of pink salmon in Brit- 
ish Columbia and Washington was year of spawning 
(odd or even), with a distinct separation of the two 
broodlines. With the odd-year broodline, regional dif- 
ferentiation was stronger in the southern portion of the 
distribution of populations, with the greatest population 
differentiation within a region observed in Washing- 
ton. The Hood Canal hatchery population (also known 
as Hoodsport hatchery 47°23'37"N, 123°08'54"W) was 
the most distinct, even though this population was 
derived from adults returning to the Dungeness River 
and Dosewallips River in 1953 (Hard et al., 1996). The 
distinctiveness of this population was reflected in allelic 
frequency differentiation. For example, the frequency 
of the Onel02 3U allele was 0.54 in the Hood Canal 
hatchery population, 0.34 in the Gray Wolf River popu- 
lation, and <0.20 in all other Washington populations. 
Additionally, the frequency of Onel09 133 was 0.33 in the 
Hood Canal hatchery population, but <0.20 in all other 
Washington populations. All populations from drainages 
entering Hood Canal (Hood Canal hatchery, Dosewal- 
lips River, Duckabush River, Hamma Hamma River) or 
the Strait of Juan de Fuca (Dungeness River, Gray Wolf 
River) were distinct from those on the eastern side of 
Puget Sound (Snohomish River, Stillaguamish River, 
Skagit River, Green River, Puyallup River). Genetic 
separation of Strait of Juan de Fuca populations and 
Hood Canal populations from those in eastern Puget 
Sound was initially described by Shaklee et al. (1991) in 
an analysis of allozyme variation. However, as described 
by Shaklee et al. (1991), the Nooksack River population, 
located in the northeastern section of Puget Sound and 
nearest to the border with Canada, clustered with Hood 
Canal and Strait of Juan de Fuca populations, rather 
than with geographically closer populations on the east 
side of Puget Sound. Similar results were observed in 
the current study. Shaklee et al. (1991) suggested that 
the genetic similarity of the Nooksack River population 
to that of Hood Canal populations was a consequence of 
a 1977 transfer of fertilized eggs from the Hood Canal 
hatchery to a tributary of the Nooksack River and a 
reduction of the native population due to habitat degra- 
dation. As Shaklee et al. (1991) outlined, this enhance- 
ment effort may have caused a genetic change in the 
characteristics of this population that has persisted 
over time (Hard et al., 1996). 
Fraser River populations were separate from those 
in southern British Columbia (east coast of Vancouver 
Island, south coast mainland) and Washington, confirm- 
ing the results from the previous analysis of allozyme 
variation reported by Beacham et al. (1988) and Shak- 
lee et al. (1991). In the Fraser River drainage, some 
separation was observed between populations spawning 
upstream from the Fraser River canyon (southern limit 
approximately 175 km upstream from the mouth) from 
those spawning downstream of the canyon. Genetic 
separation between upriver and downriver populations 
had also been had been outlined previously by Beacham 
et al. (1988) and Shaklee et al. (1991). Similar genetic 
separation between upper drainage and lower drain- 
age populations has been observed in coho salmon (O. 
kisutch) (Beacham et al., 2011) and reflects geographic 
separation between the two groups of populations. 
In northern British Columbia, odd-year broodline 
populations in the Skeena River drainage were separate 
from those farther south in the central coastal region of 
British Columbia and from those farther north on the 
northern coastal region of British Columbia in a simi- 
lar pattern to that outlined by Beacham et al. (1988). 
Similar differentiation was also observed in the even- 
year broodline, with Skeena River drainage populations 
distinct from other populations in northern British 
Columbia. Some differentiation was observed in the 
current study between even-year broodline pink salmon 
populations from the Queen Charlotte Islands and other 
regions in northern British Columbia (central coast, 
Skeena River, north coast) ( F ST =0.004-0.006); differen- 
tiation of populations from the Queen Charlotte Islands 
had also been observed by Beacham et al. in 1988. 
Studies of population structure in Pacific salmon are 
a useful initial step in developing and applying genetic 
variation to the problem of estimating stock composition 
in mixed-stock salmon fisheries. The key to successful 
application of genetic variation to estimation of stock 
composition centers around whether or not there is a 
regional basis to population structure. This is a key 
consideration because a regionally based population 
structure is generally required for genetic stock identi- 
fication estimation, with the assumption that the por- 
tion of the mixed-stock sample derived from unsampled 
populations is allocated to sampled populations from the 
same region. With this assumption, the cost and com- 
plexity of developing a baseline for stock composition 
analysis is reduced, and refinements in estimated stock 
compositions are possible as the baseline is enhanced 
