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Fishery Bulletin 112(2-3) 
contribute to PFMC fisheries, and therefore it was un- 
likely that large numbers of fish would originate from 
any single unrepresented population, let alone a highly 
divergent one; and 3) our approach is more appropriate 
for identification of fish whose genotypes are aberrant 
because of genotyping complications or sample contam- 
ination. Regardless of which method is used, all GSI 
estimation should include some analysis to identify fish 
that are either from populations not included in the 
baseline or that have aberrant genotypes for another 
reason. 
GSI is highly dependent on source populations be- 
ing genetically differentiated enough from one another 
for discrimination. In situations where hatchery brood- 
stock transfers, supplementation, or other processes in- 
crease straying and gene flow between fish populations, 
genetic differentiation decreases and it can become 
more difficult to use GSI. Such is the case in the Cen- 
tral Valley of California, where average Fgx between 
populations in the fall-run reporting unit was 0.006 
and in the spring-run reporting unit was 0.013. In the 
dendrogram (Fig. 1), this region was characterized by 
extremely short branch lengths, small internodal dif- 
ferences, and weak bootstrap support. Extensive stray- 
ing of hatchery salmon due to off-site juvenile releases 
(California Hatchery Scientific Review Group 9 ) and 
water operations (Fisher, 1994) has eliminated his- 
torical differentiation between populations of fall-run 
Chinook Salmon (Williamson and May, 2005). Intro- 
gression between fall-run and spring-run fish at the 
Feather River Hatchery, and likely elsewhere within 
the basin, has reduced differentiation between these 2 
phenotypes, with mean F gx of 0.025 between fall-run 
and naturally spawning spring-run populations. 
Sampling of different stocks for baseline construc- 
tion in the presence of high stray rates is not entirely 
straightforward, particularly when populations are 
largely sympatric and not visually distinguishable. 
For example, there is clearly a single fish from the 
Central Valley fall-run reporting unit that was sam- 
pled as a winter-run fish in our baseline. These types 
of occurrences are almost inevitable given the high 
degree of disturbance and hatchery supplementation 
over much of the range of Chinook Salmon. One ap- 
proach is to move fish with discrepant genotypes from 
the baseline populations in which they were sampled 
to the ones to which they are assigned with GSI (e.g., 
Banks et al., 2000). However, such a procedure can 
introduce an upward bias in the predicted accuracy of 
the baseline, if, in fact, the removed fish actually do 
belong to the populations from which they were sam- 
pled and simply have unlikely genotypes at the genet- 
ic markers used for baseline construction. We chose to 
9 California Hatchery Scientific Review Group. 2012. Cali- 
fornia Hatchery Review Report, 102 p. Prepared for the 
U.S. Fish and Wildlife Service and Pacific States Marine 
Faisheries Commission. [Available from http://swfsc.noaa. 
gov/publications/FED/01067.pdf and (appendices) http://ca- 
hatcheryreview.com/reports.] 
be conservative by both 1) accepting a slightly lower 
rate of predicted resolution obtained by not removing 
miscategorized fish and 2) avoiding an upward bias 
in predicted GSI accuracy if the fish removed are not 
miscategorized. 
Implications for management 
Accurately estimating the proportion of fish from dif- 
ferent populations in mixed-stock ocean fisheries has 
important applications for harvest management and 
conservation. Stocks that comingle in ocean fisheries 
can vary widely in productivity and abundance. With- 
out precise information on their ocean distribution, as 
can be provided by GSI, managers have few options 
for protection of depressed or at-risk stocks from fish- 
ery impacts other than that of shutting down or cur- 
tailing fisheries over broad areas, as is currently done 
(Beacham et al., 2008). For example, in 2008 and 2009, 
the largest closures on record of fisheries in Califor- 
nia and Oregon were enacted to protect the severely 
reduced Central Valley fall-run stock (Lindley et al., 
2009). The economic effects of fishery closures are 
substantial, resulting in millions of dollars of lost in- 
come for fishermen, coastal communities, and retailers 
(Michael 10 ). 
Management of Chinook Salmon in California, Or- 
egon, and Washington and in fisheries managed by 
the Pacific Salmon Commission depends heavily on 
information generated by an elaborate CWT program 
(Hankin et al., 2005). Tiny wire tags are mechanically 
implanted into the heads of juvenile fish, with each tag 
bearing a code that identifies the release group and 
source hatchery (or stock) of that fish. Tagging of natu- 
rally spawned juvenile fish has generally proven un- 
successful (Beacham et al., 1996), and, for that reason, 
tagged hatchery stocks are used as proxies to estimate 
fishery impacts for groups of natural stocks. Aside from 
the largely unvalidated assumption that such proxies 
accurately reflect fishery impacts on associated natu- 
ral stocks (Hankin et al., 2005), the physical effects 
of tagging fish and removing their nerve-rich adipose 
fin (Buckland-Nicks et al., 2012) as an associated ex- 
ternal mark can increase disease transmission (Elliott 
and Pascho, 2001), interfere with homing (Morrison 
and Zajac, 1987; Habicht et al., 1998) and swimming- 
ability (Reimchen and Temple, 2004) and may affect 
size-at-return for adult salmon (Vander Haegen et al., 
2005). Moreover, extremely low recovery rates mean 
that CWT data are often quite limited and great un- 
certainty is frequently associated with the estimates 
derived from them (Hankin et al., 2005). 
GSI has been advanced as an alternative to CWTs 
in fishery management for several decades. Our direct 
10 Michael, J. 2010. Employment impacts of California salm- 
on fishery closures in 2008 and 2009. Business Forecasting 
Center, Univ. of the Pacific, Stockton, CA. [Available from 
http://forecast.pacific.edu/BFC%20salmon%20jobs.pdf.] 
