Factors Affecting the Recruitment of Lake Whitefish in Two Areas of Northern Lake Michigan

Abstract

Stock-recruitment and integrated recruitment models incorporating biotic and abiotic factors were developed for lake whitefish populations in northern Green Bay and the North Shore areas of Lake Michigan. Abundance and recruitment indices were calculated for the 1961-1985 year classes based on lake whitefish catch and effort data from the commercial fishery in each area. Previous research indicates that spawning stock abundance, winter ice cover, and spring temperatures are important in determining the egg and larval abundance and survival of lake whitefish. Therefore, spawning stock abundance, ice cover, winter wind velocity, and spring water and air temperature variables were used as model inputs in regression modeling. The biotic/abiotic recruitment model for northern Green Bay hindcasted lake whitefish recruitment as a function of spawning stock abundance and the number of days that ice cover exceeded 40% during egg incubation. This regression model (R 2 = 0.62) demonstrated improved hindcasting ability of historic recruitment when compared to the Beverton-Holt (R 2 = 0.37) or the Ricker (R 2 = 0.33) stock-recruitment models for the 1961-1985 cohorts. The biotic/abiotic recruitment model for the North Shore hindcasted lake whitefish recruitment as a function of average air temperature in May after larval emergence, the number of days that ice concentration exceeded 70% during egg incubation, and spawning stock abundance. The regression model (R 2 = 0.57) also demonstrated improved hindcasting ability of historical recruitment when compared with Beverton-Holt (R 2 = 0.09) or the Ricker (R 2 = 0.13) stock-recruitment models. Results of this study indicate that biotic/abiotic recruitment models were more successful in hindcasting recruitment than solely biologically based stock-recruitment relationships. Consideration of significant abiotic variables will be useful in the management of lake whitefish stocks in the Great Lakes by improving forecasts of recruitment.