Fisheries Stock Assessment Models Research Articles

Using cross validation model selection to determine the shape of nonparametric selectivity curves in fisheries stock assessment models

We used hold-out cross validation model selection to determine the most appropriate form of the selectivity curve, using a nonparametric approach to represent selectivity. The cross-validation method is based on setting aside a portion of the catch-at-age (or catch-at-length) data to use as a test data set. The remaining catch-at-age data, along with other data (e.g. relative indices of abundance) are used to estimate the parameters of the stock assessment model, including the selectivity parameters. These parameter estimates are then used to predict the catch at age for the test data set. The selectivity model that produces the closest predictions to the test data set is chosen as the selectivity model to use in the assessment. The selectivity model we use is nonparametric, based on estimating an individual selectivity parameter for each age and then applying smoothness penalties to constrain how much the selectivity can change from age to age. The smoothness penalties we consider are the first, second, and third differences, a length-based penalty, and a monotonic penalty. The penalties are applied on the logarithm of selectivity to avoid scale-related problems and improve stability. The method was applied to the assessment of bigeye tuna in the eastern Pacific Ocean. We found that the estimated management quantities were relatively robust within the set of smoothness penalties that gave low cross-validations scores. We also found that poor choices for the smoothness penalties could give very different results. Poor choices include both under-smoothing (e.g. no penalties) and over-smoothing (penalties that are too large). The most influential factor was the inclusion of a monotonic penalty.

Do albacore exert top-down pressure on northern anchovy? Estimating anchovy mortality as a result of predation by juvenile north pacific albacore in the California current system

Quantifying the mortality of marine fishes is important for understanding spawner–recruit relationships, predicting year-class strength, and improving fishery stock assessment models. There is increasing evidence that pelagic predators can exert a top-down influence on prey, especially during critical early life-history stages. The objective of this study was to quantify predation by North Pacific albacore on Northern anchovy in the California current system (CCS). I estimated the abundance of juvenile albacore in the CCS from 1966–2005 using stock assessment models and spatially explicit catch-per-unit-effort time series. Anchovy abundance (1966–93), both recruitment and total biomass, was obtained from a stock assessment model. Annual rates of anchovy consumption by albacore were calculated using diet studies of albacore in the CCS, an age-structured bioenergetics model, and regional estimates of albacore abundance. The range of estimates was large: albacore may remove from less than 1% to over 17% of anchovy pre-recruitment biomass annually. Relationships between predation and recruitment biomass were consistent with expectations from top-down effects, but further study is required. This is the first attempt to quantify a specific source of mortality on anchovy recruits and to demonstrate potential top-down effects of predation on anchovy.

A Simulation‐Based Method to Determine Model Misspecification: Examples Using Natural Mortality and Population Dynamics Models

AbstractRecent developments in the models used in wildlife and fisheries science have allowed the inclusion of a wider range of data than previously. However, the diagnostics of such complex models have not kept pace. We describe a new diagnostic technique based on simulation analysis. Model misspecification was identified through simulation methods that created a distribution of likely parameter values for a model that was correctly specified. If the actual estimate of that parameter is outside the bounds of the simulated distribution, then the model is probably misspecified. We tested the reliability of the new diagnostic by introducing known‐model misspecification into complex fisheries stock assessment models. We then compared the results from this new diagnostic with those of a more tradition fisheries diagnostic, namely, retrospective analysis. The simulation‐based diagnostic was shown to identify misspecification affecting the estimated dynamics more reliably than retrospective analysis.Received December 6, 2010; accepted March 27, 2011

Multispecies estimation of Bayesian priors for catchability trends and density dependence in the US Gulf of Mexico

Fishery-dependent catch-per-unit-effort (CPUE) derived indices of stock abundance are commonly used in fishery stock assessment models and may be significantly biased due to changes in catchability over time. Factors causing time-varying catchability include density-dependent habitat selection and technology improvements such as global positioning systems. In this study, we develop a novel multispecies method to estimate Bayesian priors for catchability functional parameters. This method uses the deviance information criterion to select a parsimonious functional model for catchability among 10 hierarchical and measurement error models. The parsimonious model is then applied to multispecies data, while excluding one species at a time, to develop Bayesian priors that can be used for each excluded species. We use this method to estimate catchability trends and density dependence for seven stocks and four gears in the Gulf of Mexico by comparing CPUE-derived index data with abundance estimates from virtual population analysis calibrated with fishery-independent indices. Catchability density dependence estimates mean that CPUE indices are hyperstable, implying that stock rebuilding in the Gulf may be progressing faster than previously estimated. This method for estimating Bayesian priors can provide a parsimonious method to compensate for time-varying catchability and uses multispecies fishery data in a novel manner.

Dealing with missing covariate data in fishery stock assessment models

Covariates are now commonly used in fisheries stock assessment models to provide additional information about model parameters, but their use can be complicated by missing values. A wide range of covariates have been used (e.g. environment, disease, predation, food, pollutants) to model different processes (e.g. recruitment, natural mortality, growth, catchability). Several approaches are available to deal with missing covariate values. We illustrate a likelihood based approach to deal with missing covariate data when including covariates into fisheries stock assessment models. The method treats the missing covariate values as parameters from a random effects distribution. The parameters of the random effects distribution are estimated based on the observed values of the covariate. The true likelihood is implemented by integrating across the missing value random effect and, in our stock assessment example, a random effect for unexplained variation in recruitment using Laplace approximation. Simulation analysis is used to test the performance of the method and compare it to alternative approaches: (1) ignoring the covariate altogether, (2) ignoring the years with missing covariate values, (3) substituting the missing values with the mean of the observed values, and (4) estimating the missing values as free parameters. We apply the simulation analysis to a linear regression and a statistical catch-at-age stock assessment model. The simulation analysis results indicate that the random effects method for dealing with missing covariate data works moderately well, but it does not provide a substantial benefit over other less complex methods.

Length and Weight Relationships for 31 Species of Fishes Caught by Trawl Off the Arabian Sea Coast of Oman

Length and weight measurement for 31f ish species encountered in the Arabian Sea , o f f t h e Oma n Coast, were collected by demersal trawling during March 2007 and March 2008. A total of 3,261 specimens were measured for total length, or fork length, where appropriate, and green weight. Several commonly caught commercial species undergo onboard processing (dressing) prior to packaging, and dressed weight to green weight regressions and conversion factors were calculated for 12 of these species. The relationships obtained in this study were compared with those of other studies for the same fish species. These data are fundamental to understanding the biological parameters of fishes, and can be applied to fisheries stock assessment and management models.

Population viability analysis based on combining Bayesian, integrated, and hierarchical analyses

Abstract Several methods used in fisheries stock assessment models that can be applied to population viability analysis are presented. (1) Integrated analysis allows the use of all information on a particular population, and ensures that all model assumptions and parameter are consistent throughout the analysis, that uncertainty is propagated throughout the analysis, and that the correlation among parameters is preserved. (2) Bayesian analysis allows for the inclusion of prior information, and is a convenient way to represent uncertainty. (3) Random-effects models based on hierarchical modeling allow information to be shared among parameter estimates and allow the separation of process error from estimation error. (4) Non-parametric representation of parameters allows for a more flexible relationship among the parameters. (5) Robust likelihood functions provide an automatic method to reduce the influence of outliers when the data sets are large. These methods are applied to artificial data sets provided by the Extinction Risk Working Group of the National Center for Ecological Analysis and Synthesis (NCEAS) using AD Model Builder software (Otter Research™).

Use of Conventional Fishery Models to Assess Entrainment and Impingement of Three Lake Michigan Fish Species

Abstract Two conventional fishery stock assessment models, the surplus-production model and the dynamic-pool model, were applied to assess the impacts of water withdrawals by electricity-generating plants, industries, and municipalities on the standing stocks and yields of alewife Alosa pseudoharengus, rainbow smelt Osmerus mordax, and yellow perch Perca flavescens in Lake Michigan. Impingement and entrainment estimates were based on data collected at 15 power plants. The surplus-production model was fitted to the three populations with catch and effort data from the commercial fisheries. Dynamic-pool model parameters were estimated from published data. The numbers entrained and impinged are large, but the proportions of the standing stocks impinged and the proportions of the eggs and larvae entrained are small. The reductions in biomass of the stocks and in maximum sustainable yields are larger than the proportions impinged. The reductions in biomass, based on 1975 data and an assumed full water withdraw...