Incorporating temporal variation in the growth of red abalone (Haliotis rufescens) using hierarchical Bayesian growth models

Abstract

Many marine species exhibit temporal variation in individual growth. Yearly variation in growth has been identified for red abalone ( Haliotis rufescens ) in southern California, USA, but has not been previously incorporated into growth models. In this study, Bayesian hierarchical models were developed to describe variability in growth rates for the Johnsons Lee red abalone population. Although the Bayesian hierarchical modeling estimates are close to estimates of the nonhierarchical highly parameterized model that assigns an estimate of parameters to each data period when the sample sizes are high, the hyperparameters in the hierarchical model are more useful in incorporating the temporal variability into the stock assessment. By ignoring temporal variability, confidence intervals of the estimates of growth can be unrealistically narrow, possibly leading to bias when these models are used for developing biological reference points such as F0.1, Fmax, or Fx%. The use of a Bayesian hierarchical approach is generally suggested for future growth modeling and for per-recruitment models that include growth when determining precautionary management decisions.