Do models parameterized with observations from the system predict larval yellow perch (Perca flavescens) growth performance better in Lake Erie?

Growth performance can influence survival during early life. As such, a range of statistical to mechanistic modeling approaches has been used to predict growth performance, with few studies evaluating prediction accuracy. We tested the ability of three models to estimate observed larval yellow perch (Perca flavescens) growth and length in western Lake Erie (USA-Canada). We found that a General Linear Model developed using yellow perch data from western Lake Erie performed best, followed closely by a semi-mechanistic Individual-Based Model (IBM) specific to Lake Erie yellow perch and worst by a General multi-species IBM. We suspect the Statistical Model performed better because, unlike IBMs, it does not require prey availability data, probably poorly represented by zooplankton samples, and because the IBMs are imperfectly parameterized. Our findings indicate that caution should be exercised when using general IBMs given that the models parameterized with observations from the system of interest outperformed the general IBM in providing accurate fish growth and length estimates, pointing to the need for research that can improve existing mechanism-based models of larval growth.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author