A Genetic Mixture Analysis for use with Incomplete Source Population Data

1990 ◽  
Vol 47 (3) ◽  
pp. 620-634 ◽  
Author(s):  
Peter E. Smouse ◽  
Robin S. Waples ◽  
Joseph A. Tworek
Keyword(s):  
Oncorhynchus Tshawytscha ◽  
Sampling Error ◽  
Population Data ◽  
Allele Frequencies ◽  
Composition Analysis ◽  
Source Population ◽  
Genetic Management ◽  
Genetic Stock ◽  
Genetic Stock Identification ◽  
Deschutes River

While anadromous salmonids reproduce in fresh water, most harvests occur at sea. Effective genetic management requires knowledge of the stock (source population) composition of the harvest. This is accomplished with genetic stock identification (GSI), which compares the genotypes of harvested fish with those of freshwater stocks, assuming that all candidate stocks are identified and that their allele frequencies are known exactly. We develop methods that: (1) allow for sampling error in allele frequencies of candidate stocks, and (2) evaluate the possibility of unsampled contributing stocks. Composition analysis for chinook salmon (Oncorhynchus tshawytscha) collected for the Bonneville Dam egg bank program in 1980 and 1981 shows that about 10% of both harvests were from the Deschutes River and about 90% from the Hanford Reach area. Contributions from lower Columbia and Snake River stocks or from unidentified sources were limited.

Reply to the comment by Beacham and Withler on “Gene flow increases temporal stability of Chinook salmon (Oncorhynchus tshawytscha) populations in the Upper Fraser River, British Columbia, Canada”Appears in Can. J. Fish. Aquat. Sci. 66: 167–176.

2010 ◽  
Vol 67 (1) ◽  
pp. 206-208 ◽  
Author(s):  
Ryan P. Walter ◽  
J. Mark Shrimpton ◽  
Daniel D. Heath
Keyword(s):  
Temporal Variation ◽  
Chinook Salmon ◽  
Oncorhynchus Tshawytscha ◽  
Sampling Error ◽  
Fraser River ◽  
Genetic Stock ◽  
River Population ◽  
Genetic Population ◽  
Rarefaction Analysis ◽  
Temporal Variance

Beacham and Withler (2010. Can. J. Fish. Aquat. Sci. 67: 202–205) raise concerns about the experimental design and interpretation of data in the analysis of temporal genetic variation of Chinook salmon ( Oncorhynchus tshawytscha ) from the Upper Fraser River, Canada (Walter et al. 2009. Can. J. Fish. Aquat. Sci. 66: 167–176). They note that for the sampled populations, spatial genetic variance should far exceed temporal variance components based on previously published work and suggest that limited sample sizes biased our results by confounding sampling error with temporal variation. Here, we perform a rarefaction analysis by randomly removing up to 50% of the individuals from sample sites, yet the pattern of temporal versus spatial variation is similar to that reported in our original paper. We reiterate that caution should be applied to the interpretation of migration rates estimated from assignment tests, yet the absolute magnitude of our migration estimates was not central to the goals of the original paper. Although Beacham and Withler raise important points on the validation of genetic stock identification analyses, our analyses of temporal variation in genetic population structure in the Upper Fraser River population likely differ due to demographic differences between the timing of sampling of their earlier work versus our analyses.

scholarly journals Managing mixed-stock fisheries: genotyping multi-SNP haplotypes increases power for genetic stock identification

2017 ◽  
Vol 74 (4) ◽  
pp. 429-434 ◽  
Author(s):  
Garrett J. McKinney ◽  
James E. Seeb ◽  
Lisa W. Seeb
Keyword(s):  
Oncorhynchus Tshawytscha ◽  
Mixture Analysis ◽  
Genetic Stock ◽  
Multiple Snps ◽  
Relative Contribution ◽  
Genetic Stock Identification ◽  
Percentage Points ◽  
Haplotype Data ◽  
Increase In Accuracy ◽  
Mixed Stock

A common challenge for fisheries management is resolving the relative contribution of closely related populations where accuracy of genetic assignment may be limited. An overlooked method for increasing assignment accuracy is the use of multi-SNP (single nucleotide polymorphism) haplotypes rather than single-SNP genotypes. Haplotypes increase power for detecting population structure, and loci derived from next-generation sequencing methods often contain multiple SNPs. We evaluated the utility of multi-SNP haplotyping for mixture analysis in western Alaska Chinook salmon (Oncorhynchus tshawytscha). Multi-SNP haplotype data increased the accuracy of mixture analysis for closely related populations by up to seven percentage points relative to single-SNP genotype data for a set of 500 loci; 90% accuracy was achievable with as few as 150 loci with multi-SNP haplotypes but required at least 300 loci with single-SNP genotypes. Individual assignment to reporting groups showed an even greater increase in accuracy of up to 17 percentage points when multi-SNP haplotypes were used. Haplotyping multiple SNPs shows promise to improve the accuracy of assigning unknown fish to population of origin whenever haplotype data are available.

Distinguishing between resident and migrating Atlantic salmon (Salmo salar) stocks by genetic stock composition analysis

1995 ◽  
Vol 52 (4) ◽  
pp. 665-674 ◽  
Author(s):  
Marja-Liisa Koljonen
Keyword(s):  
Atlantic Salmon ◽  
Salmo Salar ◽  
Composition Analysis ◽  
The Baltic Sea ◽  
Genetic Stock ◽  
Atlantic Salmon Salmo Salar ◽  
Genetic Stock Identification ◽  
The Baltic ◽  
Catch Composition ◽  
Mixed Stock

The possibility of using the genetic stock identification (GSI) method to distinguish between individual Atlantic salmon (Salmo salar) stocks and stock groups in Finnish catches was studied. In the Baltic Sea, the Atlantic salmon is a target of a mixed-stock fishery, and information about stock composition would be valuable for the management of the species. The salmon catches on the Finnish west coast consist of two seasonally variable components: a group of northern stocks migrating through the area to the Baltic main basin and the resident Neva salmon. The migratory component includes two endangered wild stocks (Tornionjoki and Simojoki). The allele frequency differences at four polymorphic loci among the stocks allowed reliable catch composition estimates to be made of the migratory and resident components; one stock (Oulujoki) from the northern group could also be identified with reasonable accuracy. Northern migrating stocks accounted for over half the catches at the time of this study. The estimate of natural (nonhatchery) stocks was very low (3% in total).

scholarly journals An improved method for predicting the accuracy of genetic stock identification

2008 ◽  
Vol 65 (7) ◽  
pp. 1475-1486 ◽  
Author(s):  
Eric C. Anderson ◽  
Robin S. Waples ◽  
Steven T. Kalinowski
Keyword(s):  
Conventional Method ◽  
Chinook Salmon ◽  
Cross Validation ◽  
Oncorhynchus Tshawytscha ◽  
Stock Identification ◽  
Improved Method ◽  
Genetic Stock ◽  
Genetic Stock Identification ◽  
Unbiased Estimates ◽  
Leave One Out

Estimating the accuracy of genetic stock identification (GSI) that can be expected given a previously collected baseline requires simulation. The conventional method involves repeatedly simulating mixtures by resampling from the baseline, simulating new baselines by resampling from the baseline, and analyzing the simulated mixtures with the simulated baselines. We show that this overestimates the predicted accuracy of GSI. The bias is profound for closely related populations and increases as more genetic data (loci and (or) alleles) are added to the analysis. We develop a new method based on leave-one-out cross validation and show that it yields essentially unbiased estimates of GSI accuracy. Applying both our method and the conventional method to a coastwide baseline of 166 Chinook salmon ( Oncorhynchus tshawytscha ) populations shows that the conventional method provides severely biased predictions of accuracy for some individual populations. The bias for reporting units (aggregations of closely related populations) is moderate, but still present.

Determining Continent of Origin of Chum Salmon (Oncorhynchus keta) Using Genetic Stock Identification Techniques: Status of Allozyme Baseline in Asia

1994 ◽  
Vol 51 (S1) ◽  
pp. 95-113 ◽  
Author(s):  
Gary A. Winans ◽  
Paul B. Aebersold ◽  
Shigehiko Urawa ◽  
Nataly V. Varnavskaya
Keyword(s):  
Chum Salmon ◽  
Principal Component ◽  
Allele Frequencies ◽  
Oncorhynchus Keta ◽  
Stock Identification ◽  
Genetic Stock ◽  
Data Set ◽  
Chum Salmon Oncorhynchus Keta ◽  
Genetic Stock Identification ◽  
Allozyme Loci

A three-agency program was initiated in 1989 to develop a new multilocus genetic baseline for chum salmon (Oncorhynchus keta) in Japan and Russia for use in stock identification; allele frequencies at 77 allozyme loci are reported in 38 samples covering most of its north–south limits of distribution in Asia. In a 62-locus data set for 17 Japanese and 12 Russian samples, average heterozygosity ranged from 0.066 to 0.087 (mean 0.079) and the average number of P0.95 and P0.99 loci was 14 and 26, respectively. Tests of year-to-year variation in allele frequencies were not significant at five of six locations. For the P0.95 loci, FST values ranged from 0.007 (sMDH-B1*) to 0.154 (mAAT-2*) and averaged 0.038. A clear distinction between Russian and Japanese samples was observed at Nei's D = 0.006, and genetic differentiation generally followed a regional pattern within each country. Principal component analysis of the P0.95 loci revealed a large difference between Japanese and Russian samples. Four loci (sAAT-1,2*, mAAT-2*, LDH-A1*, and PEPLT*) had high loadings on the first two principal components. Analyses of a simulated fishery with 200 fish revealed a high degree of precision in estimating contributions to seven population groups and to country of origin.

Single-nucleotide polymorphisms (SNPs) identified through genotyping-by-sequencing improve genetic stock identification of Chinook salmon (Oncorhynchus tshawytscha) from western Alaska

2014 ◽  
Vol 71 (5) ◽  
pp. 698-708 ◽  
Author(s):  
Wesley A. Larson ◽  
James E. Seeb ◽  
Carita E. Pascal ◽  
William D. Templin ◽  
Lisa W. Seeb
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Chinook Salmon ◽  
Oncorhynchus Tshawytscha ◽  
Sequence Data ◽  
Genotyping By Sequencing ◽  
Stock Identification ◽  
Nucleotide Polymorphisms ◽  
Genetic Stock ◽  
Single Nucleotide ◽  
Genetic Stock Identification

Genetic stock identification (GSI), an important tool for fisheries management that relies upon the ability to differentiate stocks of interest, can be difficult when populations are closely related. Here we genotyped 11 850 single-nucleotide polymorphisms (SNPs) from existing DNA sequence data available in five closely related populations of Chinook salmon (Oncorhynchus tshawytscha) from western Alaska. We then converted a subset of 96 of these SNPs displaying high differentiation into high-throughput genotyping assays. These 96 SNPs (RAD96) and 191 SNPs developed previously (CTC191) were screened in 28 populations from western Alaska. Regional assignment power was evaluated for five different SNP panels, including a panel containing the 96 SNPs with the highest FST across the CTC191 and RAD96 panels (FST96). Assignment tests indicated that SNPs in the RAD96 were more useful for GSI than those in the CTC191 and that increasing the number of reporting groups in western Alaska from one to three was feasible with the FST96. Our approach represents an efficient way to discover SNPs for GSI and should be applicable to other populations and species.

Allele frequencies and population data for 17 Y-STR loci in Paraiba population, Brazil

2014 ◽  
Vol 13 ◽  
pp. e18-e19 ◽  
Author(s):  
Germana Emanuela De Queiroz Rêgo ◽  
Silvana Magna Cavalcante Monte ◽  
Rodrigo Soares De Moura-Neto ◽  
Naila Francis Paulo De Oliveira
Keyword(s):  
Population Data ◽  
Allele Frequencies ◽  
Str Loci

Comprehensive microsatellite baseline for genetic stock identification of Atlantic salmon (Salmo salar L.) in northernmost Europe

2017 ◽  
Vol 74 (8) ◽  
pp. 2159-2169 ◽  
Author(s):  
Mikhail Ozerov ◽  
Juha-Pekka Vähä ◽  
Vidar Wennevik ◽  
Eero Niemelä ◽  
Martin-A. Svenning ◽  
...  
Keyword(s):  
Atlantic Salmon ◽  
Salmo Salar ◽  
Stock Identification ◽  
Genetic Stock ◽  
Atlantic Salmon Salmo Salar ◽  
Genetic Stock Identification

scholarly journals Fast Identification of Urban Sprawl Based on K-Means Clustering with Population Density and Local Spatial Entropy

2018 ◽  
Vol 10 (8) ◽  
pp. 2683 ◽  
Author(s):  
Lingbo Liu ◽  
Zhenghong Peng ◽  
Hao Wu ◽  
Hongzan Jiao ◽  
Yang Yu ◽  
...  
Keyword(s):  
Population Density ◽  
Urban Sprawl ◽  
Population Data ◽  
Entropy Method ◽  
Mobile Phone Data ◽  
Source Population ◽  
Entropy Model ◽  
Spatial Homogeneity ◽  
Open Population ◽  
Spatial Entropy

As urban sprawl is proven to jeopardize the sustainability system of cities, the identification of urban sprawl is essential for urban studies. Compared with previous related studies which tend to utilize more and more complicated variables to recognize urban sprawl while still retaining an element of uncertainty, this paper instead proposes a simplified model to identify urban sprawl patterns. This is a working theory which is based on a diagram interpretation of the classic urban spatial structure patterns of the Chicago School. The method used in our study is K-means clustering with gridded population density and local spatial entropy. The results and comparison with open population data and mobile phone data verify the assumption and furthermore indicate that the accuracy of source population data will limit the precision of output identification. This article concludes that urban sprawl is mainly dominated by population and surrounding unevenness. Moreover, the Floating Catchment Area (FCA) local spatial entropy method presented in this research brings about an integration of Shannon entropy, Tobler’s first law of geography and the Moore neighborhood, improving the spatial homogeneity and locality of Batty’s Spatial Entropy model which can only be used in a general scope.

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