Growth Models for Fisheries: The Effect of Unbalanced Sampling Error On Model Selection, Parameter Estimation, and Biological Predictions

摘要: ABSTRACT Field studies that attempt to estimate the mean growth rates of individuals in a population usually yield unbalanced data and various forms sampling error. If these are poor representation population, then estimates may also be unrepresentative population. We present an assessment performance uncertainty 4 models—the von Bertalanffy, Gompertz, inverse logistic, Schnute models—fitted with scenarios The each model was determined by comparing highest likelihoods outcomes known case outcomes. A Monte Carlo simulation framework used generate consisting 8 typical error common tag-recapture data. Each evaluated according 2 metrics: rate (i.e., metric for uncertainty) prediction accuracy biological predictions such as age). Results indicate inadequate size range ...