Abstract
Mixed stock analysis (MSA) estimates the relative contributions of distinct populations in a mixture of organisms. Increasingly, MSA is used to judge the presence or absence of specific populations in specific mixture samples. This is commonly done by inspecting the bootstrap confidence interval of the contribution of interest. This method has a number of statistical deficiencies, including almost zero power to detect small contributions even if the population has perfect identifiability. We introduce a more powerful method based on the likelihood ratio test and compare both methods in a simulation demonstration using a 17 population baseline of sockeye salmon, Oncorhynchus nerka, from the Kenai River, Alaska, watershed. Power to detect a nonzero contribution will vary with the population(s) identifiability relative to the rest of the baseline, the contribution size, mixture sample size, and analysis method. The demonstration shows that the likelihood ratio method is always more powerful than the bootstrap method, the two methods only being equal when both display 100% power. Power declines for both methods as contribution declines, but it declines faster and goes to zero for the bootstrap method. Power declines quickly for both methods as population identifiability declines, though the likelihood ratio test is able to capitalize on the presence of ¡®perfect identification¡¯ characteristics, such as private alleles. Given the baseline-specific nature of detection power, researchers are encouraged to conduct a priori power analyses similar to the current demonstration when planning their applications.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Aitchison, J.A. 1992. On criteria for measures of compositional difference. Math. Geol. 24: 365-379.
Banks, M.A. & W. Eichert. 2000. WHICHRUN (Version 3.2) a computer program for population assignment of individuals based on multilocus genotype data. J. Hered. 91: 87-89.
Begg, G.A., K.D. Friedland & J.B. Pearce. 1999. Stock identification - its role in stock assessment and fisheries management. Fish. Res. 43: 1-3.
Cornuet, J.M., S. Piry, G. Luikart, A. Estoup & M. Solignac. 1999. New methods employing multilocus genotypes to select or exclude populations as origins of individuals. Genetics 153: 1989-2000.
Davison, A.C. & D.V. Hinkley. 1997. Bootstrap Methods and their Application, Cambridge University Press, Cambridge, UK. 582 pp.
Debevec, E.M., R.B. Gates,M.Masuda, J.J. Pella, J.H. Reynolds & L.W. Seeb. 2000. SPAM (version 3.2): Statistics Program for Analyzing Mixtures. J. Hered. 91: 509-511.
Fournier, D.A., T.D. Beacham, B.E. Ridell & C.A. Busack. 1984. Estimating stock composition in mixed stock fisheries using morphometric, meristic, and electrophoretic characteristics. Can. J. Fish. Aquat. Sci. 41: 400-408.
Hoenig, J.M. & D.M. Heisey. 2001. The abuse of power: The pervasive fallacy of power calculations of data analysis. Amer. Stat. 55: 19-25.
Ihssen, P.E., H.E. Booke, J.M. Casselman, J.M. McGlade, N.R. Payne & F.M. Utter. 1981. Stock identification: Materials and methods. Can. J. Fish. Aquat. Sci. 38: 1838-1855.
Lunneborg, C.E. 2000. Data Analysis by Resampling: Concepts and Applications, Duxbury Press, Pacific Grove, CA, U.S.A. 568 pp.
Marshall, S., D. Bernard, R. Conrad, B. Cross, D. McBride, A. McGregor, S. McPherson, G. Oliver, S. Sharr & B. Van Allen. 1987. Application of scale patterns analysis to the management of Alaska¡¯s sockeye salmon (Onchorhynchus nerka) fisheries. pp. 207-326. In: H.D. Smith, L. Margolis & C.C. Wood (ed.) Sockeye Salmon (Oncorhynchus nerka) Population Biology and Future Management, Canadian Special Pub. on Fish. and Aquat. Sci. 96.
Millar, R.B. 1987. Maximum likelihood estimation of mixed stock fishery composition. Can. J. Fish. Aquat. Sci. 44: 583-590.
Milner, G.B., D.J. Teel, F.M. Utter & C.L. Burley. 1981. Columbia River stock identification study: Validation of genetic method. Final report of research (FY80) financed by Bonneville Power Administration Contract DE-A179-80BP18488, National Marine Fisheries Service, Northwest and Alaska Fisheries Center, Seattle, WA. 35 pp.
Moles, A. & K. Jensen. 2000. Prevalence of the sockeye salmon brain parasite Myxobolus arcticus in selected Alaska streams. Alaska Fish. Res. Bull. 6: 85-93.
Pearce, J.M., B.J. Pierson, S.L. Talbot, D.V. Derksen,D.Kraege & K.T. Scribner. 2000. A genetic evaluation of morphology used to identify harvested Canada geese. J. Wild. Manag. 64: 863-874.
Pella, J.J. & M. Masuda. 2001. Bayesian methods for stockmixture analysis from genetic characters. Fish. Bull. 99: 151-167.
Pella, J.J.&G.B. Milner. 1987. Useofgenetic marksinstock composition analysis. pp. 247-276. In: N. Ryman & F. Utter (ed.) Population Genetics and Fishery Management, Washington Sea Grant Program, Seattle, WA.
Redner, R.A. & H.F. Walker. 1984. Mixture densities, maximum likelihood and the EM algorithm. Soc. Indust. Appl. Math. Rev. 26: 195-239.
Reynolds, J.H. 2001. SPAM version 3.5: User’s guide addendum. Addendum to special publication No. 15, Alaska Dept. of Fish and Game, Commercial Fisheries Division, Gene Conservation Lab, 333 Raspberry Rd., Anchorage, AK, 99518-1599 (available:http://www.cf.adfg.state.ak.us/geninfo/research/genetics/Software/SpamPage.htm).
Reynolds, J.H. & W.D. Templin. 2003. Testing component contributions in finite discrete mixtures: detecting specific populations in mixed stock fisheries. pp. 2873-2878. In: Proceedings of the Joint Statistical Meetings, New York, New York, Aug. 11-15, 2002. Am. Stat. Assoc.,Alexandria,VA.
Ruzzante, D.E., C.T. Taggart, S. Lang & D. Cook. 2000. Mixed-stock analysis of Atlantic cod near the Gulf of St. Lawrence based on microsatellite DNA. Ecol. Appl. 10: 1090-1109.
Seeb, L.W. & P.A. Crane. 1999. Allozymes and mitochondrial NDA discriminate Asian and North American populations of chum salmon in mixed-stock fisheries along the south coast of the Alaska Peninsula. Trans. Amer. Fish. Soc. 128: 88-103.
Seeb, L.W., C. Habicht, W.D. Templin, K.E. Tarbox, R.Z. Davis, L.K. Brannian & J.E. Seeb 2000. Genetic diversity of sockeye salmon of Cook Inlet, Alaska, and its application to management of populations affected by the Exxon Valdez Oil Spill. Trans. Amer. Fish. Soc. 129: 1223-1249.
Shaklee, J.B., F.W. Allendorf, D.C. Morizot & G.S. Whitt. 1990. Gene nomenclature for protein-coding loci in fish. Trans. Amer. Fish. Soc. 119: 2-15.
Shaklee, J.B., T.D. Beacham, L. Seeb & B.A. White. 1999. Managing fisheries using genetic data: Case studies from four species of Pacific salmon. Fish. Res. 43: 45-78.
Smouse, P.E., R.S. Waples & J.A. Tworek. 1990. A genetic mixture analysis for use with incomplete source population data. Can. J. Fish. Aquat. Sci. 47: 620-634.
Stuart, A., J.K. Ord & S. Arnold. 1999. Kendall¡¯s Advanced Theory of Statistics Vol 2A: Classical Inference and the Linear Model, 6th edition, Oxford University Press, New York, NY, U.S.A. 885 pp.
Urawa, S., K. Nagasawa, L. Margolis & A. Moles. 1998. Stock identification of chinook salmon (Onchorhynchus tshawytscha) in the North Pacific Ocean and Bering Sea by parasite tags. Nor. Pacific Anadro. Fish. Com. Bull.1: 199-204.
Weir, B.S. 1996. Genetic data analysis II, Sinauer Associates, Sunderland, MA, U.S.A. 445 pp.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2004 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Reynolds, J.H., Templin, W.D. (2004). Detecting specific populations in mixtures. In: Gharrett, A.J., et al. Genetics of Subpolar Fish and Invertebrates. Developments in environmental biology of fishes, vol 23. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0983-6_19
Download citation
DOI: https://doi.org/10.1007/978-94-007-0983-6_19
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-3759-4
Online ISBN: 978-94-007-0983-6
eBook Packages: Springer Book Archive