Candy et at: Dividing population genetic distance data by partitioning optimization 
55 
Besides the history of transferred populations, other 
factors may determine genetic stock structure. Time 
of return to spawning grounds may provide a nat- 
ural barrier to gene flow, preventing geographically 
superimposed populations from becoming genetically 
similar (Hendry and Day, 2005). Founder effects may 
play a role in shaping population structure, especially 
after recent colonization (Ramstad et ah, 2004). Al- 
though multiyear sampling should address this prob- 
lem, sampling error could be indistinguishable from 
allelic frequencies that are changed by some perturbing 
force. Indeed, small effective population size, where few 
related individuals are breeding, will hasten genetic 
drift (Waples, 1990). As a consequence of our inability 
to understand all mechanisms controlling gene flow, 
Waples (1991) warns against drawing inferences based 
on physical characteristics of the habitat without sup- 
porting biological information that links habitat differ- 
ences to adaptations. 
Little genetic variation with respect to population 
differentiation appears to have occurred in Robertson 
Creek over 23-30 years. Assuming that a majority of 
Robertson Creek fish return as four-year-olds (Healey, 
1991), these years represent six to eight generations of 
Chinook salmon. The stability of microsatellite mark- 
ers has been reported elsewhere for Atlantic salmon 
( Salmo salar) over a time frame of three to five gen- 
erations (Tessier and Bernatchez, 1999). Furthermore, 
the genetic variation between populations with mic- 
rosatellite markers was found to be 19 times greater 
than the interannual variation for sockeye salmon 
( Oncorhynchus nerka; Beacham et al., 2006b). 
Microsatellites provide highly stable, reliable ge- 
netic markers for comparisons of genetic variation 
across the range of a species and are thus becoming 
an important tool for the management and conserva- 
tion of genetic diversity of Pacific salmon species. Al- 
though genetic characters detected with these mark- 
ers are neutral with respect to natural selection, it is 
likely that they are indicators of local adaptation in 
other encoding parts of the genome (Waples, 1991). 
Fine-scale grouping of genetically similar popula- 
tions allows managers to make informed harvest and 
enhancement decisions. As was evident with Chi- 
nook salmon from the west coast of Vancouver Island, 
strictly geographically based assumptions regarding 
the level of genetic relatedness between populations 
can be incorrect. 
Acknowledgements 
The computer program PORGS, using the cost function 
(Roth et al., 2003) and RGS (Knuth, 2005; Algorithm 
7.2.1.5H, modified for r blocks) is available from the 
contact author or for downloading from http://www.pac. 
dfo-mpo.gc.ca/sci/mgl/data_e.htm. We thank the staff 
at Robertson, Nitinat, and Conuma hatcheries, and 
R. Dunlop of the Nuu-chah-nulth Tribal Fisheries, for 
providing tissue samples for this analysis. We thank 
staff of the Molecular Genetics Laboratory (M. Wetklo, 
K. Jonsen, and J. Supernault) for laboratory work. 
We also thank the contributions of three anonymous 
reviewers who helped provide focus and clarity to the 
methods portion of this article. 
Literature cited 
Beacham, T. D., K. L. Jonsen, J. Supernault, M. Wetklo, L. Deng, 
and N. Varnavskaya. 
2006a. Pacific Rim population structure of Chinook salmon 
as determined from microsatellite variation. Trans. 
Am. Fish. Soc. 135:1604-1621. 
Beacham, T. D., B. McIntosh, C. MacConnachie, K. M. Miller, 
R. E. Withler, and N. Varnavskaya. 
2006b. Pacific Rim population structure of sockeye salmon 
as determined from microsatellite analysis. Trans. 
Am. Fish. Soc. 135:174-187. 
Buhmann, J. M. 
2002. Data clustering and learning. In Handbook of 
brain theory and neural networks, 2 nd ed. (M. A. Arbib, 
ed.), p. 308-312. MIT Press, Cambridge, MA. 
Cameron, P. J. 
1994. Combinatronics, Topics, Techniques, and Algo- 
rithms, 355 p. Cambridge Univ. Press, Cambridge, UK. 
Candy, J. R., and T. D. Beacham. 
2000. Patterns of homing and straying in southern British 
Columbia coded-wire tagged chinook salmon ( Oncorhyn- 
chus tshawytscha) populations. Fish. Res. 4:41-56. 
Cavalli-Sforza, L. L., and A. W. F. Edwards. 
1967. Phylogenetic analysis: models and estimation 
procedures. Evolution 21:550-570. 
Corander, J., P. Waldmann, and M. J. Sillanpaa. 
2003. Bayesian analysis of genetic differentiation between 
populations. Genetics 163:367-374. 
Cross, C. L., L. Lapi, and E. A. Perry. 
1991. Production of Chinook and Coho salmon from Brit- 
ish Columbia hatcheries, 1971 through 1989. Can. 
Tech. Rep. Fish. Aquat. Sci. 1816, 48 p. 
Felsenstein, J. 
1985. Confidence limits on phylogenies: an approach 
using the bootstrap. Evol. 39:783-791. 
1989. PHYLIP — Phylogeny inference package, vers. 
3.2. Cladistics 5:164-166. 
Goudet, J. 
1995. FSTAT, a program to calculate F-statistics, vers, 
1.2. J. Hered. 86:485-486. 
Harden Jones, F. R. 
1968. Fish migration, 134 p. St. Martin’s Press, New 
York, NY. 
Healey, M. C. 
1991. Life history of Chinook salmon (Oncorhynchus 
tshawytscha). In Pacific salmon life history (C. Groot, 
and L. Margolis, eds.), p. 313-393. Univ. British Colum- 
bia Press, Vancouver, B.C. 
Hendry, A. P, and T. Day. 
2005. Population structure attributable to reproductive 
time: isolation by time and adaptation by time. Mol. 
Ecol . 14:901-916. 
Hofmann, T., and J. M. Buhmann. 
1997. Pairwise data clustering by deterministic an- 
nealing. IEEE Trans. Pattern Anal. Mach. Intel. 
18:1-37. 
