Simultaneous Analysis of Genetic and Length Data to Estimate Changes in Year-Class Strength of Lake Michigan Lake Sturgeon

Estimating temporal changes in year-class strength of populations of long-lived fish species can be difficult but also very important for management purposes.  We expanded Bayesian-based genetic stock identification methods to include age or length (along with information on growth rates) data as a means for estimating changes in year-class strength.  To decrease the number of estimated parameters, changes in year-class strength are modeled as a function of spawning population and year class effects.  Through stochastic simulations, we found that the proposed method was fairly robust to aging error and uncertainty in length-age relationships, and that accurate estimates could be obtained from realistic sample sizes.  We demonstrate our proposed method using genetic and length data for Lake Sturgeon (Acipenser fulvescens) from Lake Michigan.  Mixture fishery data were obtained from Green Bay, with spawning population data coming from 5 Lake Michigan tributaries (Fox, Manistee, Menomonee, Muskegon, and Peshtigo rivers).  We believe our proposed method holds great promise for sturgeon and other long-lived species for which it is reasonable to expect fairly consistent changes in year-class strength over time.