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Somatic growth in fishes often varies through time. Despite this, most stock assessments either fix or estimate a time-invariant growth relationship because estimating time-varying growth parameters can be data intensive and subject to multiple sources of bias. Additionally, estimates of growth are often confounded with estimates of selectivity, particularly if selectivity is also time varying. Incorporating empirical weight-at-age (EWAA) information into assessments is a little-studied alternative that accounts for time-varying growth, but foregoes fixing or estimating growth and length–weight relationships. However, this method requires annual measures of fish weights at each age, and missing values must therefore be interpolated. We used Stock Synthesis in a simulation-testing framework to compare the effect of estimating a single time-invariant growth curve, time-varying growth curves, and incorporating EWAA information on management quantities and parameter estimates. We ran simulations across two fish life histories (hake-like and rockfish-like) and three data cases (data-rich, data-rich with a late-starting survey, and data-moderate). We found that when growth was time invariant, the EWAA approach was unbiased but had twice the median average relative error compared to a model that estimated growth from age and length data. However, for data-rich cases with time-varying growth, the EWAA method resulted in more accurate estimates of spawning stock biomass compared to the approach that estimated time-invariant and time-varying growth parameters, as evidenced by at least a five-fold reduction in range of median relative errors. The magnitude of this effect was greatest for the long-lived, slow-growing life history. For the relatively fast-growing species, estimates from the EWAA method were particularly sensitive to interpolating missing values.

Analysts conducting stock assessments using integrated, age-structured models must discretize length data into a limited number of bins (data bins). Furthermore, some modeling frameworks also allow users to specify a distinct structure for how lengths of fish are represented in the model (model bins). The effect of choices regarding the number and width of these bins on model output is unclear, and these choices are made inconsistently in assessments across regions and species. Here, we used the stock synthesis modeling framework, and the ss3sim stock assessment simulation package, to explore the effects of choices about length discretization on stock assessment performance for three fish life-history types and four data cases. We found that, with all other aspects of a model fixed, increasing the model bin width tended to increase estimates of spawning biomass, but this effect depended on the shape of length-based processes (e.g., growth, maturity, and selectivity). Thus, we suggest analysts using model bins wider than 1 cm explore the effect of this decision on derived management quantities. In the context of estimation, there generally was a predictable tradeoff between estimation accuracy and model run time, with finer model and data bins always improving estimation accuracy and model convergence, but increasing run time. In some cases, wider data bins reduced run time (by up to 50%) with little sacrifice in model estimation performance, particularly those using conditional age-at-length data. This study identifies key aspects to consider when binning length, and provides pertinent information for stock assessment best practice guidelines.

The choice of how to model selectivity differs among approaches to fisheries stock assessment. VPA tends to make only weak assumptions regarding (age-specific) selectivity (asymptotic selectivity and temporal stability of selectivity for the most recent years). In contrast, selectivity is more parametric in “integrated” methods, and can be age-, length-, and age- and length-based. The use of parametric selectivity functions tends to reduce estimation variation because fewer parameters have to be estimated, but incorrect choices for the functional form for selectivity can lead to bias. This paper illustrates effects of poor choices for selectivity on the outcomes of stock assessments, outlines methods for evaluating whether a particular choice for selectivity is appropriate using residual diagnostics, and summarizes current ways to select among alternative functional forms for selectivity. This paper also provides a synthesis of the results of past simulation studies which have explored the ability to correctly parameterize selectivity.

There is widespread recognition that spatial structure is important for fisheries stock assessment, and several efforts have been made to incorporate spatial structure into assessment models. However, most studies exploring the impact of ignoring spatial structure in stock assessments have developed population models with multiple subpopulations, rather than exploring the impact spatial dynamics may have on performance of non-spatially structured assessment methods. Furthermore, the data available for stock assessments usually do not include tagging or other data necessary to estimate movement rates. One solution to this problem is to include several fleets, each with a different selectivity pattern to represent availability, within a spatially-aggregated assessment method. In this study, the impacts of ignoring spatial structure, and the effectiveness of using multiple selectivity patterns as a proxy for spatial structure, are evaluated for the northern subpopulation of Pacific sardine (or California sardine; Sardinops sagax ). A spatially-explicit operating model is used to explore three spatial factors: the existence of size-dependent seasonal migrations across large geographical areas, the influx of another stock into the area of the assessed stock, and the occurrence of recruitment outside the area where it is assumed to occur. Two other factors related to data were evaluated: data availability and data collection design. The assessment model (AM) is based on the 2010 stock assessment for Pacific sardine, implemented in Stock Synthesis, and includes two seasons per year and six fleets, each with a different selectivity pattern. Ignoring spatial structure is found to negatively impact estimation performance, with seasonal movement having the largest impact. The AM compensates for ignoring movement and spatial structure by adjusting the selectivity patterns, but selectivity alone is not able to account for all biases caused by spatial structure.