Pacific mackerel are a productive small pelagic species inhabiting the Northeast Pacific Ocean, characterized by highly variable and infrequent recruitment success and associated stock abundance in any given year based primarily on oceanographic conditions and less so, on direct fishing pressure. In this context, determination of appropriate selectivity assumptions and estimators to use in formal fish stock assessments is not straightforward and demands further scrutiny, given both outside and inside the model, plausible scenarios exist for using age or length data in concert with age- or length-based selectivity. The current stock assessment model was simplified by omitting/pooling particular data sources and fixing parameters to produce two baseline models that included either age-composition or length-composition data. Each baseline model was evaluated in terms of age and length selectivity parameterization. A parametric bootstrap procedure within the Stock Synthesis modeling platform was used to produce four simulated data sets for examining the quality (precision and bias) of derived management statistics of interest (current spawning biomass, MSY, stock depletion, etc.). The benefits of this approach for conducting future sensitivity analysis and diagnostic examinations surrounding the ongoing stock assessment are discussed in this presentation.
Selectivity is one of the most influential components of integrated stock assessment models. The choice among alternative selectivity types is often subjective and can produce very different assessment results and related impacts on management quantities. Integrated stock assessment models have typically relied on parametric selectivity functions that can be straightforward to implement and interpret, but may lack enough flexibility to fit the age or size composition data. Although non-parametric functional forms, such as cubic splines, allow for more flexibility, they have not been used as extensively. Good practices for using cubic spline selectivity are not available and their tradeoffs compared to other functional forms are little known. Here we use a simulation approach to develop and test a general method to implement cubic spline selectivity using the Stock Synthesis assessment model. The method takes into account the range, number and location of knots, as well as their slopes used to define the cubic splines. The approach is tested on multiple, single species stock assessments to include a wide range of life histories, data availability, and selectivity shapes. The pros and cons of using cubic spline selectivity relative to alternative parametric functional forms are discussed, along with good practices and alternative implementation methods.
Management of marine resources depends on assessment of stock status in relation to established reference points. The efficacy of fishery stock assessments in estimating historical abundance patterns and providing the basis for applying harvest strategies depends on factors such as species-specific life-history traits, characteristics of the fishery, and the quality and quantity of available data. Statistical catch-at-age (or catch-at-length) models have become an established tool for assessing the status of fish stocks worldwide. Stock Synthesis (SS) is a statistical catch-at-age analysis population modeling framework increasingly used in stock assessments. SS can use several data sources for parameter estimation. A simulation-estimation process is used to evaluate the performance (bias and precision) of SS in terms of estimating standard metrics used in fishery management, conditioned upon fishery input data and life-history traits. Three main questions are addressed: 1) How well can management metrics be estimated for different life-history types (e.g., demersal, long-lived pelagic, and short-lived pelagic) when the same information (in terms of quantity and quality of data) are used?, 2) How does the frequency and duration of length- and age- composition data (and conditional age-at-length data) affect the bias or precision of estimates of management quantities for different life-history types?, and 3) How does catch history affect the estimation of management metrics for different life-history patterns?
Natural mortality (M) is typically assumed to be constant across time, sex, and age in fishery stock assessment models. However, M is rarely constant in reality as a result of the combined impacts of predation, environmental factors, and physiological trade-offs. Although one can acknowledge the potential importance of modelling heterogeneity in M, methods to estimate even an age- and time-invariant M within age-structured assessment models rely on informative length- and age-composition data, which are not always available. Misspecification of M can lead to bias in quantities estimated by stock assessment models, potentially resulting in misspecification of fishery reference points and catch limits, with the magnitude of bias likely dependent on life history and fishing history. Monte Carlo simulation is used to evaluate the ability of statistical catch-at-age (SCAA) models to estimate spawning stock biomass, stock status, and fishery reference points when the true M is age-specific or age-invariant, but time-varying. Stock assessment methods included SCAA models with (1) an age-invariant pre-specified M, (2) an age-invariant estimated M, and (3) age-specific estimated M. Simulations were conducted for three hypothetical fish stocks under two historical fishing scenarios. Stock Synthesis was used to generate the data and estimate management quantities. Bias and variance is evaluated for spawning stock biomass, depletion, and estimated parameters and the "minmax" approach is used to identify a "best" way to deal with M when it is thought to vary over time or by age (i.e. identify the stock assessment configuration for the assessment which is least wrong given no information about the true characteristics of M).
Retrospective patterns are systematic changes in estimates of population size, or other assessment model-derived quantities, that occur as additional years of data are added to an assessment. These patterns are an insidious problem in stock assessment, and can lead to severe errors when providing management advice. However, the cause of these patterns is not fully understood. A few studies have shown that retrospective patterns can arise from model miss-specification, particularly when data are non-stationary but this is ignored when assessments are conducted, and that the inclusion of time-varying selectivity can help to eliminate, or at least reduce, their incidence. We use simulations to explore which factors may lead to retrospective patterns in statistical catch-at-age stock assessment models. Specifically, we test how several biological and modeling factors can induce retrospective patterns for various life histories. We explore the potential effects of catch patterns, as well as model misspecification from time-varying biological parameters, time-varying selectivity and catchability, and their interactions. In cases where retrospective patterns were observed, we evaluate the appropriateness of including time-varying selectivity in the assessment as a means to correct them.