Comparison of the frequentist properties of Bayes and the maximum likelihood estimators in an age-structured fish stock assessment model

2002 ◽  
Vol 59 (1) ◽  
pp. 136-143 ◽  
Author(s):  
Anders Nielsen ◽  
Peter Lewy
Keyword(s):  
Maximum Likelihood ◽  
Stock Assessment ◽  
Process Variations ◽  
Bayes Estimator ◽  
Assessment Model ◽  
Fish Stock ◽  
The North ◽  
The North Sea ◽  
Age Structured ◽  
Fish Stock Assessment

A simulation study was carried out for a separable fish stock assessment model including commercial and survey catch-at-age and effort data. All catches are considered stochastic variables subject to sampling and process variations. The results showed that the Bayes estimator of spawning biomass is a useful but slightly biased estimator for which the frequentist variance can be estimated by the posterior variance. Comparisons further show that the Bayes estimator is better than the maximum likelihood in the sense that it is less biased and, surprisingly, has a much lesser variance. The catch simulations were based on the North Sea plaice (Pleuronectes platessa) stock and fishery data.

Detecting and correcting underreported catches in fish stock assessment: trial of a new method

2010 ◽  
Vol 67 (8) ◽  
pp. 1247-1261 ◽  
Author(s):  
Nicolas Bousquet ◽  
Noel Cadigan ◽  
Thierry Duchesne ◽  
Louis-Paul Rivest
Keyword(s):  
Gadus Morhua ◽  
Stock Assessment ◽  
Population Analysis ◽  
Assessment Model ◽  
Fish Stock ◽  
Stock Biomass ◽  
Stock Assessments ◽  
Spawning Stock ◽  
Age Structured ◽  
Fish Stock Assessment

Landings from fisheries are often underreported, that is, the true landings are greater than those reported. Despite this bias, reported landings are widely used in fish stock assessments, and this might lead to overoptimistic exploitation strategies. We construct a statistical stock assessment model that accounts for underreported landings using the theory of censoring with sequential population analysis (SPA). The new model is developed and implemented specifically for the cod stock ( Gadus morhua ) from the southern Gulf of St. Lawrence (Canada). This stock is known to have unreported overfishing during 1985–1992. We show in simulations that for this stock, the new censored model can correctly detect the problematic landings. These corrections are nearly insensitive to subjective boundaries placed on real catches and robust to modifications imposed in the simulation of landings. However, when surveys are too noisy, the new SPA for censored catches can result in increased uncertainty in parameters used for management such as spawning stock biomass and age-structured stock size.

A recipe for Bayesian network driven stock assessment

2004 ◽  
Vol 61 (9) ◽  
pp. 1647-1657 ◽  
Author(s):  
T R Hammond
Keyword(s):  
Bayesian Network ◽  
Stock Assessment ◽  
Assessment Model ◽  
Fish Stock ◽  
Posterior Density ◽  
Discrete Random Variables ◽  
Analytic Integration ◽  
Network Propagation ◽  
A New Technique ◽  
Fish Stock Assessment

Markov Chain Monte Carlo (MCMC), the most widely used algorithm in Bayesian statistics, can fail to converge. Although convergence is tested by various diagnostics, these can only reveal failure, never success. To avoid these difficulties, this paper suggests a recipe for using Bayesian network propagation (BNP) to compute posterior results for fish stock assessment. Bayesian networks employ discrete random variables and specify relationships between them with conditional probability tables. Therefore, the recipe uses a new technique called "fuzzy discretization" to convert a continuous Bayesian model into a discrete Bayesian network. The technique is illustrated on a Schaefer assessment model by showing how model equations can be converted to probability tables. Posterior density estimates for carrying capacity (K) from both MCMC and BNP were compared with exact results (obtained by analytic integration and grid search) under three scenarios. BNP outperformed MCMC (as implemented in WinBUGS) in all scenarios, though MCMC diagnostics previously deemed sufficient reported no problems. Tightening the grid resolution of discrete numeric variables over regions of high posterior probability greatly improved BNP performance, so a grid selection heuristic is included in the recipe. In summary, this recipe may provide an effective alternative to MCMC for similar Bayesian problems.

Fleksibest—an age–length structured fish stock assessment model

2002 ◽  
Vol 55 (1-3) ◽  
pp. 87-101 ◽  
Author(s):  
Kristin Guldbrandsen Frøysa ◽  
Bjarte Bogstad ◽  
Dankert W. Skagen
Keyword(s):  
Stock Assessment ◽  
Assessment Model ◽  
Fish Stock ◽  
Fish Stock Assessment

scholarly journals Accounting for correlated observations in an age-based state-space stock assessment model

2016 ◽  
Vol 73 (7) ◽  
pp. 1788-1797 ◽  
Author(s):  
Casper W. Berg ◽  
Anders Nielsen
Keyword(s):  
State Space ◽  
Stock Assessment ◽  
Age Groups ◽  
Assessment Model ◽  
Fish Stock ◽  
Confidence Bounds ◽  
Independence Assumption ◽  
Fish Stocks ◽  
Fish Stock Assessment ◽  
Improved Model

Abstract Fish stock assessment models often rely on size- or age-specific observations that are assumed to be statistically independent of each other. In reality, these observations are not raw observations, but rather they are estimates from a catch-standardization model or similar summary statistics based on observations from many fishing hauls and subsamples of the size and age composition of the data. Although aggregation mitigates the strong intra-haul correlation between sizes/ages that is usually found in haul-by-haul data, violations of the independence assumption can have a large impact on the results and specifically on reported confidence bounds. A state-space assessment model that allows for correlations between age groups within years in the observation model for catches and surveys is presented and applied to data on several North Sea fish stocks using various correlation structures. In all cases the independence assumption is rejected. Less fluctuating estimates of the fishing mortality is obtained due to a reduced process error. The improved model does not suffer from correlated residuals unlike the independent model, and the variance of forecasts is decreased.

scholarly journals Modelling stochastic fish stock dynamics using Markov Chain Monte Carlo

2003 ◽  
Vol 60 (4) ◽  
pp. 743-752 ◽  
Author(s):  
P. Lewy ◽  
A. Nielsen
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Sampling Error ◽  
Stock Assessment ◽  
Fish Stock ◽  
Sources Of Information ◽  
Process Error ◽  
Age Structured ◽  
Fish Stock Assessment

Abstract A new age-structured stock dynamics approach including stochastic survival and recruitment processes is developed and implemented. The model is able to analyse detailed sources of information used in standard age-based fish stock assessment such as catch-at-age and effort data from commercial fleets and research surveys. The stock numbers are treated as unobserved variables subject to process errors while the catches are observed variables subject to both sampling and process errors. Results obtained for North Sea plaice using Markov Chain Monte Carlo methods indicate that the process error by far accounts for most of the variation compared to sampling error. Comparison with results from a simpler separable model indicates that the new model provides more precise estimates with fewer parameters.

scholarly journals Comprehensive discard reconstruction and abundance estimation using flexible selectivity functions

2009 ◽  
Vol 66 (4) ◽  
pp. 763-771 ◽  
Author(s):  
G. Aarts ◽  
J. J. Poos
Keyword(s):  
Stock Assessment ◽  
Sustainable Use ◽  
Abundance Estimation ◽  
Commercial Fishing ◽  
The North ◽  
Minimum Landing Size ◽  
The North Sea ◽  
Age Model ◽  
Age Structured ◽  
Precise Time

Abstract Aarts, G., and Poos, J. J. 2009. Comprehensive discard reconstruction and abundance estimation using flexible selectivity functions. – ICES Journal of Marine Science, 66: 763–771. The additional mortality caused by discarding may hamper the sustainable use of marine resources, especially if it is not accounted for in stock assessment and fisheries management. Generally, long and precise time‐series on age-structured landings exist, but historical discard estimates are often lacking or imprecise. The flatfish fishery in the North Sea is a mixed fishery targeting mainly sole and plaice. Owing to the gear characteristics and a minimum landing size for these species, considerable discarding occurs, especially for juvenile plaice. Discard samples collected by on-board observers are available since 1999 from a limited number of commercial fishing trips. Here, we develop a statistical catch-at-age model with flexible selectivity functions to reconstruct historical discards and estimate stock abundance. We do not rely on simple predefined selectivity ogives, but use spline smoothers to capture the unknown non-linear selectivity and discard patterns, and allow these to vary in time. The model is fitted to the age-structured landings, discards, and survey data, the most appropriate model is selected, and estimates of uncertainty are obtained.

scholarly journals Stock assessment and projections incorporating discard estimates in some years: an application to the hake stock in ICES Divisions VIIIc and IXa

2010 ◽  
Vol 67 (6) ◽  
pp. 1185-1197 ◽  
Author(s):  
C. Fernández ◽  
S. Cerviño ◽  
N. Pérez ◽  
E. Jardim
Keyword(s):  
Time Series ◽  
Stock Assessment ◽  
Reference Points ◽  
Assessment Model ◽  
Marine Science ◽  
Fishing Mortality ◽  
Change Over Time ◽  
Age Structured ◽  
Available Information ◽  
Over Time

Abstract Fernández, C., Cerviño, S., Pérez, N., and Jardim, E. 2010. Stock assessment and projections incorporating discard estimates in some years: an application to the hake stock in ICES Divisions VIIIc and IXa. – ICES Journal of Marine Science, 67: 1185–1197. A Bayesian age-structured stock assessment model is developed to take into account available information on discards and to handle gaps in the time-series of discard estimates. The model incorporates mortality attributable to discarding, and appropriate assumptions about how this mortality may change over time are made. The result is a stock assessment that accounts for information on discards while, at the same time, producing a complete time-series of discard estimates. The method is applied to the hake stock in ICES Divisions VIIIc and IXa, for which the available data indicate that some 60% of the individuals caught are discarded. The stock is fished by Spain and Portugal, and for each country, there are discard estimates for recent years only. Moreover, the years for which Portuguese estimates are available are only a subset of those with Spanish estimates. Two runs of the model are performed; one assuming zero discards and another incorporating discards. When discards are incorporated, estimated recruitment and fishing mortality for young (discarded) ages increase, resulting in lower values of the biological reference points Fmax and F0.1 and, generally, more optimistic future stock trajectories under F-reduction scenarios.

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