Comparisons of likelihood and machine learning methods of individual classification

Journal of Heredity

Volumn 93, Issue 4, 2002, Pages 260-269

  Guinand, B ^a,d   Topchy, A ^b   Page, K S ^a,e   Burnham Curtis, M K ^c,f   Punch, W F ^b   Scribner, Kim T ^a  

^a MICHIGAN STATE UNIVERSITY   (United States)

^b Michigan State University   (United States)

^c U S GEOLOGICAL SURVEY   (United States)

^d CNRS   (France)

^e MINNESOTA DEPARTMENT OF NATURAL RESOURCES   (United States)

^f US Fish and Wildlife National Forensics Laboratory   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ALLELE; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; CLUSTER ANALYSIS; COMPARATIVE STUDY; DECISION THEORY; GENE LOCUS; GENETIC VARIABILITY; LEARNING; MACHINE; POPULATION DIFFERENTIATION; POPULATION GENETICS; PRIORITY JOURNAL; SIMULATION; STATISTICAL MODEL; TROUT;

SALMONIDAE; SALVELINUS; SALVELINUS NAMAYCUSH;

EID: 0036661271     PISSN: 00221503     EISSN: None     Source Type: Journal    
DOI: 10.1093/jhered/93.4.260     Document Type: Article

References (99)

1. Almudevar A, 2000. Exact confidence regions for species assignment based on DNA markers. Can J Stat 28:81-95.
2. Aurelle D, 1999. Contacts Secondaires Naturels et Artificiels Chez la Truite Commune (Salmo trutta, L.) des Pyrénées Occidentales Françaises: Utilisation de Marqueurs Microsatellites Pour la Distinction de Taxons Faiblement Différenciés (PhD dissertation). France: University Montpellier II.
3. Aurelle D, Lek S, Giraudel JL, and Berrebi P, 1999. Microsatellites and artificial neural networks: tools for the discrimination between natural and hatchery brown trout (Salmo trutta, L.) in Atlantic populations. Ecol Model 120:313-324.
4. Beacham TD, Pollard S, and Le KD, 1999. Population structure and stock identification of steelhead in southern British Columbia, Washington, and the Columbia River based on microsatellite DNA variation. Trans Am Fish Soc 128:1068-1084.
5. Beaumont MA, Barratt EM, Gotelli D, Kitchener AC, Daniels MJ, Pritchard JK, and Bruford MW, 2001. Genetic diversity and introgression in the Scottish wildcat. Mol Ecol 10:319-336.
6. Bell JF, 1996. Application of classification trees to habitat preference of upland birds. J Appl Stat 23:349-359
7. Bell JF, 1999. Tree-based methods. In: Machine Learning Methods for Ecological Applications (Fielding AH, ed). Boston: Kluwer Academic.
8. Bernatchez L and Duchesne P, 2000. Individual-based genotype analysis in studies of parentage and population assignment: how many loci, how many alleles? Can J Fish Aquat Sci 57:1-12.
9. Boddy L and Morris CW, 1999. Artificial neural networks for pattern recognition. In: Machine Learning Methods for Ecological Applications (Fielding AH, ed). Boston: Kluwer Academic.
10. Boddy L, Morris CW. Wilkins MF, Al-Haddad L, Tarran GA, Jonker RR, and Burkill PH, 2000. Identification of 72 phytoplankton species by radial basis function neural networks analysis of flow cytometry data. Mar Ecol Prog Ser 195:47-59.
11. Bogdanowitcz SM, Mastro VC, Prasher DC, and Harrison RG, 1997. Microsatellite DNA variation among Asian and North American gypsy moths (Lepidoptera: Lymantridae). Ann Entomol Soc Am 90:768-775.
12. Bowcock AM, Ruiz Linares A, Tomfohrde E, Minch E, Kidd JR, and Cavalli-Sforza R, 1994. High resolution of human evolutionary trees with polymorphic microsatellites. Nature 368:455-457.
13. Congiu L, Dupanloup I, Patarnello T, Fontana F, Rossi R, Arlati G, and Zane L, 2001. Identification of interspecific hybrids by amplified fragment length polymorphism: The case of sturgeon. Mol Ecol 10:2355-2359.
14. Cornuet JM, Aulagnier S, Lek S, Franck P, and Solignac M, 1996. Classifying individuals among infra-specific taxa using microsatellite data and neural networks. C R Acad Sci Paris Life Sci 319:1167-1177.
15. Cornuet JM, Piry S, Luikart G, Estoup A, and Solignac M, 1999. New methods employing multilocus genotypes to select or exclude populations as origins of individuals. Genetics 153:1989-2000.
16. Curtis D, North BV, and Sham PC, 2001. Use of an artificial neural network to detect association between a disease and multiple marker genotypes. Ann Hum Genet 65:95-107.
17. Davies N, Villablanca FX, and Roderick GK, 1999. Bioinvasions of the medfly Ceratitis capitata: Source estimation using DNA sequences at multiple intron loci. Genetics 153:351-360.
18. Dasarathy BV, 1991. Nearest neighbor (NN) norms: NN pattern classification techniques. Washington, DC: IEEE Computer Society.
19. Dawson KJ and Belkhir K, 2001. A Bayesian approach to the identification of panmictic populations and the assignment of individuals. Genet Res 78:59-77.
20. Devijver PA and Kittler J, 1982. Pattern recognition: a statistical approach. Englewood Cliffs, NJ: Prentice-Hall International.
21. Douglas MR and Brunner PC, 2002. Biodiversity of central alpine Coregonus (Salmoniformes): Impact of one-hundred years of management. Ecol Appl 12:154-172.
22. Duda RO, Hart PE, and Stork DG, 2000. Pattern classification, 2nd ed. New York: John Wiley & Sons.
23. Duin RPW, 1996. A note on comparing classifiers. Pattern Recogn 17:529-536.
24. Efron B, 1983. Estimating the error rate of a prediction rule: Improvement on cross-validation. J Am Stat Assoc 78:103-130.
25. Eldridge MDB, Kinnear JE, and Onus ML, 2001. Source population of dispersing rock-wallabies (Petrogale lateralis) identified by assignment tests on multilocus genotypic data. Mol Ecol 10:2867-2876.
26. Estoup A and Angers B, 1998. Microsatellites and minisatellites for molecular ecology: Theoretical and empirical considerations. In: Advances in Molecular Ecology (Carvalho GR, ed). Amsterdam: IOS Press.
27. Evett IW and Weir BS, 1998. Interpreting DNA evidence. Boston: Sinauer.
28. Fielding AH, ed. 1999. Machine Learning Methods for Ecological Applications. Boston: Kluwer Academic.
29. Fielding AH and Bell JF, 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38-49.
30. Fritzner NG, Hansen MM, Madsen SS, and Kristiansen K, 2001. Use of microsatellite markers for identification of indigenous brown trout in a geographical region heavily influenced by stocked domesticated trout. J Fish Biol 58:1197-1210.
31. Galbusera P, Lens L, Schenck T, Waiyaki E, and Matthysen E, 2000. Genetic variability and gene flow in the globally, critically-endangered taita brush. Conserv Genet 1:45-55.
32. Giraudel JL, Aurelle D, Lek S, and Berrebi P, 2000. Application of the self-organizing map method to microsatellite data: How to detect genetic structure in brown trout (Salmo trutta) populations. In: Artificial Neuronal Networks: Application to Ecology and Evolution (Lek S and Guégan JF, eds). Berlin: Springer-Verlag; 187-202.
33. Grigull J, Alexandrova R, and Paterson AD, 2001. Clustering of pedigrees using marker allele frequencies: Impact on linkage analysis. Genet Epidemiol 21(suppl 1):61-66.
34. Haig SM, Gratto-Trevor CL, Mullins TD, and Colwell MA, 1997. Population identification of western hemisphere shorebirds throughout the annual cycle. Mol Ecol 6:412-427.
35. Hansen MM, Kenchington E, and Nielsen EE, 2001a. Assigning individual fish to populations using microsatellite DNA markers. Fish Fish Ser 2:93-112.
36. Hansen MM, Nielsen EE, and Mensberg KLD, 1997. The problem of sampling families rather than populations: Relatedness among individuals in samples of juvenile brown trout Salmo trutta L. Mol Ecol 6:469-474.
37. Hansen MM, Ruzzante DE, Nielsen EE, and Mensberg KLD, 2000. Microsatellite and mitochondrial DNA polymorphism reveals life-history dependent interbreeding between hatchery and wild brown trout (Salmo trutta L.). Mol Ecol 9:583-594.
38. Hansen MM, Ruzzante DE, Nielsen EE, and Mensberg KLD, 2001b. Brown trout (Salmo trutta) stocking impact assessment using microsatellite DNA markers. Ecol Appl 11:148-160.
39. Huang S-P and Weir BS, 2001. Estimating the total number of alleles using sample coverage method. Genetics 159:1365-1373.
40. Huberty CJ, 1994. Applied discriminant analysis. New York: Wiley Interscience.
41. Jain AK, Duin RPW, and Mao J, 2000. Statistical pattern recognition: A review. IEEE Trans Patt Anal Mach Intell 22:4-37.
42. Jarne P and Lagoda PJL, 1996. Microsatellites, from molecules to populations and back. Trends Ecol Evol 11:424-429.
43. Karul C, Soyupak S, Cilesiz AF, Akbay N, and Germen E, 2000. Case studies on the use of neural networks in eutrophication modeling. Ecol Model 134:145-152.
44. Karystinos GN and Pados DA, 2000. On overfitting, generalization, and randomly expanded training sets. IEEE Trans Neur Network 11:1050-1057.
45. King TL, Kalinowski ST, Schill WB, Spidle AP, and Lubinski BA, 2001. Population structure of Atlantic salmon (Salmo salar L.): A range-wide perspective from microsatellite DNA variation. Mol Ecol 10:807-821.
46. Knutsen H, Knutsen JA, and Jorde PE, 2001. Genetic evidence for mixed origin of recolonized sea trout populations. Heredity 87:207-214.
47. Koskinen MT, Piironen J, and Primmer CR, 2001. Interpopulation genetic divergence in European grayling (Thymallus thymallus, Salmonidae) at a microgeographic scale: Implications for conservation. Conserv Genet 2:133-143.
48. Kyle CJ and Strobeck C, 2001. Genetic structure of North American wolverine (Gulo gulo) populations. Mol Ecol 10:337-347.
49. Lek S and Guégan JF, eds. 2000. Artificial Neuronal Networks: Application to Ecology and Evolution. Berlin: Springer-Verlag.
50. Leung PS and Tran LT, 2000. Predicting shrimp disease occurrence: Artificial neural networks vs. logistic regression. Aquaculture 187:35-49.
51. Manel S, Dias JM, Buckton ST, and Ormerod SJ, 1999. Alternative methods for predicting species distribution: An illustration with Himalayan river birds. J Appl Ecol 36:734-747.
52. Marshall AR, Blankenship HL, and Connor WP, 2000. Genetic characterization of naturally spawned Snake River fall-run chinook salmon. Trans Am Fish Soc 129:680-698.
53. Martinez JL, Dumas J, Beall E, and Garcia-Vazquez E, 2001. Assessing introgression of foreign strains in wild Atlantic salmon populations: Variation in microsatellites assessed in historic scale collections. Freshwater Biol 46:835-844.
54. Mitchell TM, 1997. Machine Learning. Boston: McGraw-Hill.
55. Müller J, 2000. Mitochondrial DNA variation and the evolutionary history of cryptic Gammarus fossarum types. Mol Phylogenet Evol 15:260-268.
56. National Research Council, 1996. The Evaluation of Forensic DNA Evidence. Washington, DC: National Academy Press.
57. Neraas LP and Spruell P, 2001. Fragmentation of riverine systems: The genetic effects of dams on bull trout (Salvelinus confluentus) in the Clark Fork River system. Mol Ecol 10:1153-1164.
58. Nielsen EE, Hansen MM, and Bach LA, 2001a. Looking for a needle in a haystack: Discovery of indigenous Atlantic salmon (Salmo salar L.) in stocked populations. Conserv Genet 2:219-232.
59. Nielsen EE, Hansen MM, Schmidt C, Meldrup D, and Gronkjaer P, 2001b. Population of origin of Atlantic cod. Nature 413:272.
60. Olsen JB, Bentzen P, Banks MA, Shaklee JB, and Young S, 2000. Microsatellites reveal population identity of individual pink salmon to allow supportive breeding of a population at risk of extinction. Trans Am Fish Soc 129:232-242.
61. Paetkau D, Calvert W, Stirling I, and Strobeck C, 1995. Microsatellite analysis of population structure in Canadian polar bears. Mol Ecol 4:347-354.
62. Page KS, 2001 Genetic Diversity and Interrelationships of Wild and Hatchery Lake Trout in the Upper Great Lakes: Inferences for Broodstock Management and Development of Restoration Strategies (MSc thesis). East Lansing: Michigan State University.
63. Pella JJ and Masuda M, 2001. Bayesian methods for analysis of stock mixtures from genetic characters. Fish Bull 99:151-167.
64. Pella JJ and Milner GB, 1987. Use of genetic marks in stock composition analysis. In: Population Genetics and Fishery Management (Ryman N and Utter F, eds). Seattle: University of Washington Press; 247-276.
65. Pettersson JCE, Hansen MM, and Bohlin T, 2001. Does dispersal from landlocked trout explain the coexistence of resident and migratory trout females in a small stream? J Fish Biol 58:487-495.
66. Polzhien RO, Hamr J, Mallory FF, and Strobeck C, 2000. Microsatellite analysis of North American wapiti (Cervus elaphus) populations. Mol Ecol 9:1561-1576.
67. Pope LC, Estoup A, and Moritz C, 2000. Phylogeography and population structure of an ecotonal marsupial, Bettongia tropica, determined using mtDNA and microsatellites. Mol Ecol 9:2040-2053.
68. Potvin C and Bernatchez L, 2001. Lacustrine spatial distribution of landlocked Atlantic salmon populations assessed across generations by multilocus individual assignment and mixed-stock analysis. Mol Ecol 10:2375-2388.
69. Primmer CR, Aho T, Piironen J, Estoup A, Cornuet JM, and Ranta E, 1999. Microsatellite analysis of hatchery stocks and natural populations of Arctic charr, Salvelinus alpinus, from the Nordic region: Implications for conservation. Hereditas 130:277-289.
70. Primmer CR, Koskinen MT, and Piironen J, 2000. The one that did not get away: Individual assignment using microsatellite data detects a case of fishing competition fraud. Proc R Soc Lond B 267:1699-1704.
71. Pritchard JK, Stephens M, and Donnelly P, 2000. Inference of population structure using multilocus genotype data. Genetics 155:945-959.
72. Quinlain JR, 1993. C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.
73. Randi E and Lucchini V, 2002. Detecting rare introgression of domestic dog genes into wild wolf (Canis lupus) populations by Bayesian admixture analyses of microsatellite variation. Conserv Genet 3:31-45.
74. Randi E, Pierpaoli M, Beaumont M, Ragni B, and Sforzi A, 2001. Genetic identification of wild and domestic cats (Felis sylvestris) and their hybrids using Bayesian clustering methods. Mol Biol Evol 18:1679-1693.
75. Rannala B and Mountain JL, 1997. Detecting immigration using multilocus genotypes. Proc Natl Acad Sci USA 94:9197-9202.
76. Raymer ML, Punch WS III, Venkataraman S, Sanschagrin PC, Goodman ED, and Kuhn LA, 1997. Predicting conserved water-mediated and polar ligand interactions in proteins using a K-nearest neighbor genetic algorithm. J Mol Biol 265:445-464.
77. Riordan P, 1998. Unsupervised recognition of individual tigers and snow leopards from their footprints. Anim Conserv 1:253-262.
78. Ripley BD, 1996. Pattern Recognition and Neural Networks. Cambridge: Cambridge University Press.
79. Roeder AD, Marshall RK, Mitchelson AJ, Visagathilagar T, Ritchie PA, Love DR, Pakai TJ, McPartlan HC, Murray ND, Robinson KR, Kerry KR, and Lambert DM, 2001. Gene flow on the ice: Genetic differentiation among Adélie penguin colonies around Antarctica. Mol Ecol 10:1645-1656.
80. Roques S, Duchesne P, and Bernatchez L, 1999. Potential of microsatellites for individual assignment: The North Atlantic redfish (genus Sebastes) species complex as a case study. Mol Ecol 8:1703-1717.
81. Rosenberg NA, Burke T, Feldman MW. Friedlin P, Groenen MA, Hillel J, Maki-Tanila A, Tixier-Boichard M, Vignal A, Wimmers K, and Weigend S, 2001. Empirical evaluation of genetic clustering methods using multilocus genotypes from 20 chicken breeds. Genetics 159:699-713.
82. Salzberg SL, 1997. On comparing classifiers: Pitfalls to avoid and a recommended approach. Data Min Knowl Disc 1:317-328.
83. Schmidt CA, 1999. Variation and congruence of microsatellite markers for Peromyscus leucopus. J Mammal 80:522-529.
84. Schulte-Hostedde Al, Gibbs HL, and Millar JS, 2001. Microgeographic genetic structure in the yellow-pine chipmunk (Tamias amoenus). Mol Ecol 10:1625-1631.
85. Sefc KM, Lopes MS, Lefort L, Botta R, Roubelakis-Angelakis K, Ibanez J, Pejic I, Wagner H, Glössl J, and Steinkellner H, 2000. Microsatellite variability in grapevine cultivars from different European regions and evaluation of assignment testing to assess the geographic origin of cultivars. Theor Appl Genet 100:498-505.
86. Semagn K, Bjornstadt A, Stedje B, and Bekele E, 2000. Comparison of multivariate methods for the analysis of genetic resources and adaptation in Phytolacca didecandra using RAPD. Theor Appl Genet 101:1145-1154.
87. Smouse PE and Chevillon C, 1998. Analytical aspects of population-specific DNA fingerprinting for individuals. J Hered 89:143-150.
88. Smouse PE, Spielman RS, and Park MH, 1982. Multiple-locus allocation of individuals to groups as a function of the genetic variation within and differences among human populations. Am Nat 119:445-463.
89. Spidle AP, Schill WB, Lubinski BA, and King TL, 2001. Fine-scale population structure in Atlantic salmon from Maine's Penobscot River drainage. Conserv Genet 2:11-24.
90. Taylor EB, Beacham TD, and Kaeriyama M, 1994. Population structure and identification of North Pacific Ocean chum salmon (Oncorhynchus keta) revealed by an analysis of minisatellite DNA variation. Can J Fish Aquat Sci 51:1430-1442.
91. Taylor EB, Kuiper A, Troffe PM, Hoysak DJ, and Pollard S, 2000. Variation in developmental biology and microsatellite DNA in reproductive ecotypes of kokanee, Oncorhynchus nerka: Implications for declining populations in a large British Columbia lake. Conserv Genet 1:213-249.
92. Thorrold SR, Latkoczy C, Swart PK, and Jones CM, 2001. Natal homing in a marine fish metapopulation. Science 291:297-299.
93. Tsutsui ND, Suarez AV, Holway DA, and Case TJ, 2001. Relationships among native and introduced populations of the Argentine ant (Linepithema humile) and the source of introduced populations. Mol Ecol 10:2051-2161.
94. Vázquez-Domínguez E, Paetkau D, Tucker N, Hinten G, and Moritz C, 2001. Resolution of natural groups using iterative assignment tests: An example from two species of Australian rats (Rattus). Mol Ecol 10:2069-2078.
95. Waser PM and Strobeck C, 1998. Genetic signatures of interpopulation dispersal. Trends Ecol Evol 13:43-44.
96. Whitler RE, Beacham TD, Watkins RF, and Stevens TA, 1994. Identification of farm-reared and native chinook salmon (Oncorhynchus tshawytscha) on the west coast of Vancouver Island, British Columbia, including the nuclear DNA probe B2-2. Can J Fish Aquat Sci 51(suppl 1):267-276.
97. Wiens JJ, 1999. Polymorphisms in systematics and comparative biology. Ann Rev Ecol Syst 30:327-362.
98. Wright S, 1965. The interpretation of population structure by F-statistics with special regards to system of mating. Evolution 19:395-420.
99. Zeisset I and Beebee TJC, 2001. Determination of biogeographical range: An application of molecular phylogeography to European pool frog Rana lessonae. Proc R Soc Lond B 268:933-938.