Introduction

The role of ecological models in fisheries science is primarily for stock assessment, and stock assessment is about making quantitative predictions about population change in response to alternative management choices. A stock assessment model is actually a collection of several submodels that deal with specific components of the entire system, and the level of complexity of each of these submodels can range from simple with very few unknown parameters to very complex with thousands of unknown parameters. Regardless of the level of complexity among competing models, there are three basic objectives that we hope to obtain in fisheries stock assessment:

1. Stock status: to specifically assess the current level of exploitation (the fraction of the total population that is being removed each year) and the current abundance relative to some management target.

2. Stock productivity: to specifically assess the shape of the underlying production function and the level of exploitation deemed sustainable. Also, to determine which harvest policies should be used to ensure sustainability.

3. Stock reconstruction: to specifically assess how the components of population change (recruitment, mortality, net migration) have varied over time, and whether or not these variations are related to fishing and/or environmental changes.

A typical modern-day stock assessment usually begins with the third objective in order to examine the first two objectives.

The basic structure for any assessment model requires at least five key components (Figure 1), and each of these components are linked such that a simple change in the data or assumption about the model structure could ultimately redefine the management objective. Overall, there are two key parameters of interest in fisheries stock assessment models: (1) a parameter that defines the overall population scale (i.e., how large is the population), and (2) a parameter that defines the underlying production function (i.e., the intrinsic rate of growth or how resilient the population is to disturbance). The interplay between these two parameters ultimately defines the suitable range of alternative harvest policies.

The essential components of a fisheries stock assessment model outlined in Figure 1 will form the basic outline for this article. We will begin with a description of the types of data that are frequently encountered and used in fisheries stock assessment. Then we provide a few examples of the types of population dynamics models and error structures that are used to make inference about components of population change over time. Following this, we will discuss how the population models are used to generate predicted observations in order to proceed with the next step of the assessment - comparing

Figure 1 The essential components of a fisheries stock assessment model. There are at least five essential components in a fisheries stock assessment model, and modern fisheries stock assessment models integrate all of this information into a single framework that is ultimately used for providing management advice.

Figure 1 The essential components of a fisheries stock assessment model. There are at least five essential components in a fisheries stock assessment model, and modern fisheries stock assessment models integrate all of this information into a single framework that is ultimately used for providing management advice.

predicted and observed values using a variety of statistical approaches. Lastly, we discuss how this information is used to formulate yield recommendations and management advice.

Before proceeding with a detailed description of the various components of a modern fisheries stock assessment model, it is worth mentioning that many of the components or submodels of a modern assessment have now been integrated into a single modeling framework. By analogy, this evolution of fisheries stock assessment is similar to the evolution of a modern bread maker, where all of the ingredients for a loaf of bread are simply tossed into a machine and four hours later the final product is produced. This has both advantages and disadvantages; uncertainty in each of the components (e.g., uncertainty in estimation of natural mortality) is carried forward into the management advice and is advantageous because a more precautionary or conservative strategy may be warranted due to additional uncertainty. A disadvantage is that such model integration may also create structural confounding that obfuscates the underlying production function, which ultimately gives rise to correct policy advice. Returning to the bread maker, this would be equivalent to the addition of unwanted ingredients that result in a brick of flour rather than a loaf of bread.

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