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Quantitative Resource Assessment LLC

San Diego, CA, United States
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Punt A.E.,University of Washington | Punt A.E.,CSIRO | Huang T.,University of Washington | Maunder M.N.,Quantitative Resource Assessment LLC | Maunder M.N.,Inter American Tropical Tuna Commission
ICES Journal of Marine Science | Year: 2013

Punt, A. E., Huang, T., and Maunder, M. N. 2013. Review of integrated size-structured models for stock assessment of hard-to-age crustacean and mollusc species. - ICES Journal of Marine Science, 70:16-33.Crustaceans and molluscs such as crabs, rock lobsters, prawns, abalone, and oysters constitute large and valuable fisheries. However, assessments of these species are hampered because they cannot be production aged, in contrast to many teleosts. The major data sources for these species, in addition to catch and abundance index data, are the size compositions of the catches and of any fishery-independent indices. Assessments of such species have been conducted using age-based methods of stock assessment, as well as surplus production models. However, size-structured methods are now preferred because they can make full use of size-composition data, are able to integrate multiple sources of data, and produce the types of outputs which are needed for management purposes. An advantage of size-based models over age-based models is that all processes can be size-based, and these processes can modify the (unmodelled) size-at-age distribution. We review these methods, highlighting the choices that need to be made when developing integrated size-structured stock assessments, the data sources which are typically available and how they are used for parameter estimation, and contrast a number of such assessments worldwide. © 2012 International Council for the Exploration of the Sea.

Hyun S.-Y.,University of Massachusetts Dartmouth | Maunder M.N.,Quantitative Resource Assessment LLC | Maunder M.N.,Inter American Tropical Tuna Commission | Rothschild B.J.,University of Massachusetts Dartmouth
ICES Journal of Marine Science | Year: 2014

Many fish stock assessments use a survey index and assume a stochastic error in the index on which a likelihood function of associated parameters is built and optimized for the parameter estimation. The purpose of this paper is to evaluate the assumption that the standard deviation for the difference in the log-transformed index is approximately equal to the coefficient of variation of the index, and also to examine the homo- and heteroscedasticity of the errors. The traditional practice is to assume a common variance of the index errors over time for estimation convenience. However, if additional information is available about year-to-year variability in the errors, such as year-to-year coefficient of variation, then we suggest that the heteroscedasticity assumption should be considered. We examined five methods with the assumption of a multiplicative error in the survey index and two methods with that of an additive error in the index: M1, homoscedasticity in the multiplicative error model; M2, heteroscedasticity in the multiplicative error model; M3, M2 with approximate weighting and an additional parameter for scaling variance; M4-M5, pragmatic practices; M6, homoscedasticity in the additive error model; M7, heteroscedasticity in the additive error model. M1-M2 and M6-M7 are strictly based on statistical theories, whereas M3-M5 are not. Heteroscedasticity methods M2, M3, and M7 consistently outperformed the other methods. However, we select M2 as the best method. M3 requires one more parameter than M2. M7 has problems arising from the use of the raw scale as opposed to the logarithm transformation. Furthermore, the fitted survey index in M7 can be negative although its domain is positive. © 2014 © International Council for the Exploration of the Sea 2014. All rights reserved.

Maunder M.N.,Quantitative Resource Assessment LLC | Maunder M.N.,Inter American Tropical Tuna Commission | Deriso R.B.,University of California at San Diego | Deriso R.B.,Inter American Tropical Tuna Commission
Fisheries Research | Year: 2010

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. © 2009 Elsevier B.V. All rights reserved.

The stock-recruitment relationship is one of the most uncertain processes of fish population dynamics, and is highly influential with respect to fisheries management advice. The stock recruitment relationship has a direct impact on reference points commonly used in contemporary fisheries management. Simulation analysis has shown that the steepness of the Beverton-Holt stock-recruitment relationship is difficult to estimate for most fish stocks, which has led to the use of proxy reference points. Proxy maximum sustainable yield reference points based on spawning biomass-per-recruit, which are commonly used when the stock-recruitment relationship is uncertain, are a linear function of steepness. Risk in terms of lost yield is generally lower when steepness is underestimated compared to when steepness is overestimated because the yield curve is flat when steepness is high (close to one: recruitment is independent of stock size), indicating that using a lower value of steepness might be appropriate. Simulation analysis based on data for summer flounder in the US mid-Atlantic indicates that steepness can be estimated from the data. Steepness is estimated to be close to one and a high steepness is supported by estimates for related species and from life history theory. Current target (F 35%) and threshold (F 40%) spawning biomass-per-recruit reference points used for summer flounder imply steepness values of 0.73 and 0.66, respectively, for the Beverton-Holt stock-recruitment relationship. © 2012 Elsevier B.V.

Maunder M.N.,Quantitative Resource Assessment LLC | Maunder M.N.,Inter American Tropical Tuna Commission | Deriso R.B.,Inter American Tropical Tuna Commission
Canadian Journal of Fisheries and Aquatic Sciences | Year: 2011

Multiple factors acting on different life stages influence population dynamics and complicate the assessment and management of populations. To provide appropriate management advice, the data should be used to determine which factors are important and what life stages they impact. It is also important to consider density dependence because it can modify the impact of some factors. We develop a state-space multistage life cycle model that allows for density dependence and environmental factors to impact different life stages. Models are ranked using a two-covariates-at-a-time stepwise procedure based on AICc model averaging to reduce the possibility of excluding factors that are detectable in combination, but not alone. Impact analysis is used to evaluate the impact of factors on the population. The framework is illustrated by application to delta smelt (Hyposmesus transpacificus), a threatened species that is potentially impacted by multiple anthropogenic factors. Our results indicate that density dependence and a few key factors impact the delta smelt population. Temperature, prey, and predators dominated the factors supported by the data and operated on different life stages. The included factors explain the recent declines in delta smelt abundance and may provide insight into the cause of the pelagic species decline in the San Francisco Estuary.

Maunder M.N.,Quantitative Resource Assessment LLC | Maunder M.N.,Inter American Tropical Tuna Commission | Deriso R.B.,Inter American Tropical Tuna Commission | Hanson C.H.,and Hanson Inc.
Fisheries Research | Year: 2015

Factors impacting the survival of individuals between two life stages have traditionally been evaluated using log-linear regression of the ratio of abundance estimates for the two stages. These analyses require simplifying assumptions that may impact the results of hypothesis tests and subsequent conclusions about the factors impacting survival. Modern statistical methods can reduce the dependence of analyses on these simplifying assumptions. State-space models and the related concept of random effects allow the modeling of both process and observation error. Nonlinear models and associated estimation techniques allow for flexibility in the system model, including density dependence, and in error structure. Population dynamics models link information from one stage to the next and over multiple time periods and automatically accommodate missing observations. We investigate the impact of observation error, density dependence, population dynamics, and data for multiple stages on hypothesis testing using data for longfin smelt in the San Francisco Bay-Delta. © 2014 The Authors.

Catch-at-age (or catch-at-length) data are one of the major components of most modern statistical stock assessment methods. Catch-at-age data provide, among other things, information about gear selectivity and recruitment strength. Catch-at-age data can also have a large influence on the estimates of fishing mortality, absolute abundance, and trends in abundance. The multinomial distribution describes the theoretical sampling process that is used to collect catch-at-age data, but only under the assumption of random sampling. Sampling designs generally employed to collect fishery-related data lead to age-composition estimates that depart from the strict theoretical multinomial probability distribution. Lack of independence can, for example, be due to size- or age-specific schooling or aggregating, causing positive correlations among individuals, and overdispersion. An additional cause of inadequacy of the multinomial assumption is model misspecification. Therefore, the effective sample size that should be used in an assessment model can be much smaller than the actual sample size. This can cause inappropriate weighting among data sets and negatively biased estimates of uncertainty. I use simulation analysis to evaluate five methods to estimate the effective sample size for catch-at-age data: (1) iterative multinomial likelihood; (2) normal approximation, using binomial variance; (3) lognormal likelihood with variance proportional to the inverse of the proportion; (4) Dirichlet likelihood; and (5) a multivariate normal approximation. The results show that all five methods perform similarly, but do not reduce estimation error relative to using the actual sample size unless the effective sample size is about one fifth of the actual sample size. All but (4) produced positively biased estimates of the effective sample size. If the effective sample size is not known within half an order of magnitude, I recommend using the lognormal likelihood with variance proportional to the inverse of the proportion and a regression against the actual sample size. This method is less computationally intense than (1), more robust than (2) and (4), and produces the least biased estimates of effective sample size, except for (4). Unlike (1), it can be included in Bayesian analysis. © 2011 Elsevier B.V.

Maunder M.N.,Quantitative Resource Assessment LLC | Wong R.A.,002 Bayside Dr.
Fisheries Research | Year: 2011

We investigate several approaches to estimate natural mortality (M) for summer flounder Paralichthys dentatus. Historically, a value of 0.2y-1 has been used for all ages and both males and females in stock assessments. Recently, M has been increased to 0.3y-1 for males. A range of estimates of M are available from the different approaches. Methods based on maximum age are not appropriate due to exploitation history and the sampling design used to collect age data and estimates of M from relationships with life history parameters are too imprecise to be useful in the stock assessments. Estimates of M from other species are variable and many are unreliable. Modeling higher M for young individuals may be appropriate given the substantial number of 0-year old individuals caught in the fisheries and surveys. Methods based on M being inversely proportional to size may provide a useful age-structured M for young individuals, but the absolute level still needs to be estimated to scale the age-based curve. Simulation analysis suggests that, given the model assumptions and the type of data available, the stock assessment model is able to estimate both female and male M with moderate precision, but with some bias depending on the true values of M. The estimates of M from the assessment were 0.29 (0.23-0.34)y-1 and 0.54 (0.49-0.59)y-1 for females and males, respectively, which are considerably higher than the values used in the current assessment. However, these estimates are sensitive to model assumptions. The estimates of M for males are consistently higher than those for females. A well designed and implemented tagging program where the tagging data are integrated into the stock assessment may be the best approach to produce reliable estimates of M for summer flounder. In the meantime, estimating sex-specific M within the stock assessment model appears to be the most appropriate approach. © 2011 Elsevier B.V.

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