2008
DOI: 10.1139/f08-049
|View full text |Cite
|
Sign up to set email alerts
|

An improved method for predicting the accuracy of genetic stock identification

Abstract: 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 … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

1
279
0
1

Year Published

2010
2010
2020
2020

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 221 publications
(281 citation statements)
references
References 27 publications
1
279
0
1
Order By: Relevance
“…Sacramento and Chipps Island trawls are considered two high-priority locations for genetic monitoring of SRWRC for four reasons: (1) Although this monitoring review focuses primarily on advances for SRWRC, the resolution of the current genetic baseline provides high-precision identification of both ESA-listed salmon runs in the Central Valley (i.e., SRWRC and wild spring-run Chinook from Deer, Mill, and Butte creeks; Banks et al 2000;Seeb et al 2007;Anderson et al 2008;Banks et al 2014;Clemento et al 2014). However, there are challenges in distinguishing the late-fall run from fall run, and in identifying wild Feather River spring-run Chinook Salmon, because of introgression with fall-run Chinook Salmon (Clemento et al 2014).…”
Section: Discussionmentioning
confidence: 99%
“…Sacramento and Chipps Island trawls are considered two high-priority locations for genetic monitoring of SRWRC for four reasons: (1) Although this monitoring review focuses primarily on advances for SRWRC, the resolution of the current genetic baseline provides high-precision identification of both ESA-listed salmon runs in the Central Valley (i.e., SRWRC and wild spring-run Chinook from Deer, Mill, and Butte creeks; Banks et al 2000;Seeb et al 2007;Anderson et al 2008;Banks et al 2014;Clemento et al 2014). However, there are challenges in distinguishing the late-fall run from fall run, and in identifying wild Feather River spring-run Chinook Salmon, because of introgression with fall-run Chinook Salmon (Clemento et al 2014).…”
Section: Discussionmentioning
confidence: 99%
“…To determine whether juveniles could be assigned to their population of origin, mixture analysis was performed by means of the conditional maximum likelihood approach of ONCOR (Anderson et al 2008). Confidence intervals (CIs) of mixture proportions were determined through 10 000 bootstraps.…”
Section: Methodsmentioning
confidence: 99%
“…The accuracy of the genetic stock identification was assessed through self-assignment of adult individuals to their respective populations by using the leave-one-out cross-validation test and through 100% fishery simulations of 50 individuals and 1000 simulations. These methods provide less biased predictions of accuracy than conventional parametric bootstrapping procedures (Anderson et al 2008) and probably give more realistic estimates of the power of assignments. …”
mentioning
confidence: 99%
“…This deficiency limits the number of alleles represented in the simulated offspring and may even underestimate homoplasy, a common problem encountered with genetic stock identification of monitored fisheries (e.g. Anderson et al, 2008).…”
Section: Genetic Characterization Of Hybridsmentioning
confidence: 99%