Those who fail to learn from history are condemned to repeat it: A perspective on current stock assessment good practices and the consequences of not following them

Abstract

The ideal stock assessment would be able to estimate all of the key parameters related to population processes within a framework that assigns appropriate weight to the data, fits the data adequately, and captures all sources of uncertainty related to estimation, including model uncertainty, process uncertainty, and observation uncertainty. The aim of good practice guidelines is to avoid the pitfalls of earlier analysis methods, and consequently provide assessments that reflect objective scientific information on which management decisions can be based. This paper outlines a framework for the component of a stock assessment related to fitting population dynamics models to monitoring data to support decision making, which follows from what would be considered good (but not necessarily best) practice in the field. The paper identifies current good and best practices related to selecting a model structure, parameterizing growth, recruitment, natural mortality and the stock-recruitment relationship, as well as how to select among model configurations based on diagnostics and weight data and priors within assessments based on the existing literature, including past Center for the Advancement of Population Assessment Methodology (CAPAM) workshop reports and the results of simulation studies that explored the performances of different ways to configure stock assessments.