Comparison of the accuracy and consistency of likelihood‐based estimation routines for genetic stock identification

Summary Genetic stock identification (GSI) frequently is used to assess spawning/breeding population contributions to mixtures of individuals. Although multiple estimation routines are available for conducting GSI, their performance may vary depending on characteristics of assessed source populations and mixtures and of employed genetic markers. We conducted simulations to compare performance of several likelihood-based GSI estimation routines. Estimation routines were implemented in SPAM, ONCOR and AD Model Builder (ADMB). Two ADMB routines were evaluated, one based on conditional maximum likelihood estimation (ADMB-MLE), similar to SPAM and ONCOR, and one based on conditional penalized maximum likelihood estimation (ADMB-PMLE). The simulations examined how performance varied by number of source populations, population divergence levels, number of evaluated loci, and source population and mixture sample sizes. Evaluations included scenarios with many loci with low levels of polymorphism for assessing performance when single nucleotide polymorphism (SNP) markers are incorporated in analyses. Mixture sample size and source population genetic divergence accounted for most of the explained variability in simulation results. Overall, routines based on conditional maximum likelihood estimation (SPAM, ONCOR and ADMB-MLE) had similar levels of accuracy, including scenarios mimicking SNP markers, with SPAM having slightly better accuracy than ONCOR and ADMB-MLE. The accuracy of the ADMB-PMLE routine in many scenarios was noticeably poorer than the other routines, although in some instances accuracy of the ADMB-PMLE estimates approached the other routines with large mixture sample sizes. SPAM, ONCOR and ADMB-MLE also generally had similar levels of performance with respect to consistency, whereas ADMB-PMLE varied widely in consistency due in part to poor accuracy. Because SPAM and ONCOR typically performed better than the ADMB-MLE routine, there appears to be little need for users to program their own likelihood-based estimation routines for standard GSI analyses, although for specialized applications (e.g. modelling contributions as functions of ecological or demographic features), it may be necessary for users to program their own routines. Given the performance of the ADMB-PMLE routine, additional research is needed to determine an appropriate configuration (e.g. penalty, optimization algorithm) for a penalized maximum likelihood GSI estimator.