Modeling round goby growth in Lake Michigan and Lake Huron with multi-model inference

Abstract

Although the round goby Neogobius melanostomus has become established throughout the Laurentian Great Lakes, a multi-model inference (MMI) approach toward characterizing round goby growth in the Laurentian Great Lakes has yet to applied using otolith-derived data. Further, spatial variation in round goby growth among lakes has yet to be investigated. For each sex, growth of round gobies at three locations of Lake Michigan and four locations of Lake Huron was investigated using MMI, based on information theory, with three candidate growth models. These three growth models included the von Bertalanffy model, the Gompertz model, and the logistic model. The von Bertalanffy model was most often selected (13 out of 14 cases) as the ‘best’ model among all candidate models, followed by the logistic model. None of the best models were strongly supported as a ‘clear winner’. At least one additional model was supported by the data in each of the 14 cases, indicating that there is a substantial degree of uncertainty in model selection. When model selection uncertainty was ignored, standard errors of growth parameters were underestimated in 8 of the 14 cases. Overall, round gobies in Lake Michigan attained larger sizes at age and grew faster than in Lake Huron. Based on multi-model inference, our study provided a robust assessment of round goby growth, which will be essential in better managing sport fisheries in both lakes.