The good practices of practicable alchemy in the stock assessment continuum: Fundamentals and principles of analytical methods to support science-based fisheries management under data and resource limitations

Abstract

It is the exceptionally rare case one can directly and with little uncertainty measure fish absolute abundance through many stock generations in all areas of a stock’s range. Instead, we often seek “gold standard” stock assessments—models that use catch, abundance indices and biological compositions to produce precise and unbiased indicators of stock status for management use. Unfortunately, data and resource limitations affect our ability to collect all the desired information and apply methods with low uncertainty in the results. To confront this challenge of poorly informative data and low resource situations, a host of analytical approaches have been developed to engage the power of fisheries science to inform management decisions despite limitations. These methods are numerous and often challenging to understand and navigate, despite being simplified (though not simple) approaches. It is important to understand where these methods come from, how they can be used, and how to evaluate them. Often they are presented as alchemically providing golden outputs despite heavy assumptions and impure inputs. Here I aim to provide both scientific context of and guidance in organizing and applying so-called data and resource limited stock assessments. I offer a list of best practices by presenting fundamental principles of modelling and highlighting leading edge tools for organizing and conducting analyses under a variety of constraining conditions, offering a conceptualization of stock assessment expressing the interconnectedness of each method and how those can be largely unified under a common modeling framework. The concept of a stock assessment continuum is described, along with discrete examples in the form of a decision tree outlining the major modelling groups for a large variety of data availability scenarios. The basic approach to applied fisheries science and management is presented as interpreting uncertain model outputs (i.e, indicators) using reference points that can then be linked to management decisions via control rules that should express risk tolerance to meeting management objectives in light of uncertain outcomes. The role of simulation testing of management procedures is highlighted in order to evaluate robustness to uncertainty. While more and better data should be a focus of any management system, there is no excuse to wait for golden outputs. We have the tools and theory ready to help direct management of data and resource limited stocks now.