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.

Comparison of the performance of age-structured models with few survey indices

2018 ◽  
Vol 75 (6) ◽  
pp. 2016-2024
Author(s):  
Hiroshi Okamura ◽  
Yuuho Yamashita ◽  
Momoko Ichinokawa ◽  
Shota Nishijima
Keyword(s):  
Stock Assessment ◽  
Population Analysis ◽  
Assessment Model ◽  
Sufficient Information ◽  
Abundance Estimates ◽  
Virtual Population ◽  
Stock Biomass ◽  
Abundance Indices ◽  
Spawning Stock ◽  
Age Structured

Abstract Age-structured models have played an important role in fisheries stock assessment. Although virtual population analysis (VPA) was once the most widely used stock assessment model for when catch-at-age information is available, (hierarchical) statistical catch-at-age analysis (SCAA) is about to take that position. However, the estimation performance of different age-structured models has not been evaluated sufficiently, especially in cases where there are few available abundance indices. We examined the performance of VPA and SCAA using simulation data in which only the abundance indices of spawning stock biomass and recruitment were available. The simulation demonstrated that VPA with the ridge penalty selected by minimizing retrospective bias provided near-unbiased abundance estimates without catch-at-age error and moderately biased estimates with catch-at-age error, whereas SCAA with random-walk selectivity suffered from problems in estimating parameters and population states. Without sufficient information on abundance trends, naïvely using SCAA with many random effects should be done cautiously, and comparing results from various age-structured models via simulation tests will be informative in selecting an appropriate stock assessment model.

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.

CCAMLR’s precautionary approach to management focusing on Ross Sea toothfish fishery

2015 ◽  
Vol 27 (4) ◽  
pp. 333-340 ◽  
Author(s):  
Stuart Hanchet ◽  
Keith Sainsbury ◽  
Doug Butterworth ◽  
Chris Darby ◽  
Viacheslav Bizikov ◽  
...  
Keyword(s):  
Fisheries Management ◽  
Management System ◽  
Ross Sea ◽  
Stock Assessment ◽  
Precautionary Approach ◽  
Adaptive Feedback ◽  
Stock Biomass ◽  
Stock Assessments ◽  
Antarctic Marine ◽  
Spawning Stock

AbstractSeveral recent papers have criticized the scientific robustness of the fisheries management system used by the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR), including that for Ross Sea toothfish. Here we present a response from the wider CCAMLR community to address concerns and to correct some apparent misconceptions about how CCAMLR acts to promote conservation whilst allowing safe exploitation in all of its fisheries. A key aspect of CCAMLR’s approach is its adaptive feedback nature; regular monitoring and analysis allows for adjustments to be made, as necessary, to provide a robust management system despite the statistical uncertainties inherent in any single assessment. Within the Ross Sea, application of CCAMLR’s precautionary approach has allowed the toothfish fishery to develop in a steady fashion with an associated accumulation of data and greater scientific understanding. Regular stock assessments of the fishery have been carried out since 2005, and the 2013 stock assessment estimated current spawning stock biomass to be at 75% of the pre-exploitation level. There will always be additional uncertainties which need to be addressed, but where information is lacking the CCAMLR approach to management ensures exploitation rates are at a level commensurate with a precautionary approach.

Performance of a fisheries catch-at-age model (Stock Synthesis) in data-limited situations

2011 ◽  
Vol 62 (8) ◽  
pp. 927 ◽  
Author(s):  
Chantell R. Wetzel ◽  
André E. Punt
Keyword(s):  
Stock Assessment ◽  
Estimation Method ◽  
Assessment Method ◽  
Assessment Model ◽  
Limited Data ◽  
Composition Data ◽  
Spawning Stock ◽  
Age Model ◽  
Age Structured ◽  
The Impact

Limited data are a common challenge posed to fisheries stock assessment. A simulation framework was applied to examine the impact of limited data and data type on the performance of a widely used catch-at-age stock-assessment method (Stock Synthesis). The estimation method provided negatively biased estimates of current spawning-stock biomass (SSB) relative to the unfished level (final depletion) when only recent survey indices were available. Estimation of quantities of management interest (unfished SSB, virgin recruitment, target fishing mortality and final depletion) improved substantially even when only minimal-length-composition data from the survey were available. However, the estimates of some quantities (final depletion and unfished SSB) remained biased (either positively or negatively) even in the scenarios with the most data (length compositions, age compositions and survey indices). The probability of overestimating yield at the target SSB relative to the true such yield was ~50%, a risk-neutral result, for all the scenarios that included length-composition data. Our results highlight the importance of length-composition data for the performance of an age-structured assessment model, and are encouraging for the assessment of data-limited stocks.

A reanalysis of North Sea plaice spawning-stock biomass using the annual egg production method

2009 ◽  
Vol 66 (9) ◽  
pp. 1999-2011 ◽  
Author(s):  
Cindy J. G. van Damme ◽  
Loes J. Bolle ◽  
Clive J. Fox ◽  
Petter Fossum ◽  
Gerd Kraus ◽  
...  
Keyword(s):  
North Sea ◽  
Egg Production ◽  
Stock Assessment ◽  
Population Analysis ◽  
Spatial Dynamics ◽  
Production Method ◽  
Southern Bight ◽  
The North ◽  
Stock Biomass ◽  
Spawning Stock

Abstract van Damme, C. J. G., Bolle, L. J., Fox, C. J., Fossum, P., Kraus, G., Munk, P., Rohlf, N., Witthames, P. R., and Dickey-Collas, M. 2009. A reanalysis of North Sea plaice spawning-stock biomass using the annual egg production method. – ICES Journal of Marine Science, 66: 1999–2011. Uncertainty about the quality of current virtual population analysis-based stock assessment for North Sea plaice (Pleuronectes platessa) has led to various abundance indices. We compared biomass estimates from the annual egg production (AEP) method with current stock assessments based on catch-at-age to validate the current and historical perception of exploitation. The AEP method was also used to investigate the dynamics of the spatial components of plaice in the North Sea. We corrected for fecundity down-regulation and changes in sex ratio. Estimates from both methods were similar in trend and absolute biomass. On the Dogger Bank, there was a dramatic decline in biomass from 1948 and 1950 to 2004, and in the Southern Bight, the stock appeared to increase from 1987 and 1988 to 2004, although not reaching the historically high levels of 1948 or 1950. The timing of spawning of North Sea plaice does not appear to have changed throughout the period of high exploitation. We conclude that the AEP method is a useful way to hindcast the spatial dynamics of heavily exploited flatfish stocks.

Accounting for marine reserves using spatial stock assessments

2015 ◽  
Vol 72 (2) ◽  
pp. 262-280 ◽  
Author(s):  
Carey R. McGilliard ◽  
André E. Punt ◽  
Richard D. Methot ◽  
Ray Hilborn
Keyword(s):  
Spatial Heterogeneity ◽  
Stock Assessment ◽  
Marine Reserves ◽  
Movement Patterns ◽  
Catch Rate ◽  
Fish Stock ◽  
Rate Data ◽  
Stock Assessments ◽  
Age Structured ◽  
Movement Parameters

Some fish stock assessments are conducted in regions that contain no-take marine reserves (NTMRs). NTMRs are expected to lead to spatial heterogeneity in fish biomass by allowing a buildup of biomass inside their borders while fishing pressure occurs outside. Stock assessments do not typically account for spatial heterogeneity caused by NTMRs, which may lead to biased estimates of biomass. Simulation modeling is used to analyze the ability of several stock assessment configurations to estimate current biomass after the implementation of a single, large NTMR. Age-structured spatial operating models with three patterns of ontogenetic movement are used to represent the “true” population dynamics. Results show that assessing populations as a single stock with use of fishery catch-rate data and without accounting for the NTMR results in severe underestimation of biomass for two of the movement patterns. Omitting fishery catch-rate data or allowing time-varying dome-shaped selectivity after NTMR implementation leads to improved estimates of current biomass, but severe bias in estimated trends in biomass over time. Performing separate assessments for fished areas and NTMRs leads to improved estimation performance in the absence of movement among assessment areas, but can severely overestimate biomass otherwise. Performing a spatial assessment with estimation of movement parameters among areas was found to be the best way to assess a species, even when movement patterns were unknown. However, future work should explore the performance of spatial assessments when catchability varies among areas.

scholarly journals Development of a stock–recruit model for simulating stock dynamics for uncertain situations: the example of Northeast Atlantic mackerel (Scomber scombrus)

2011 ◽  
Vol 68 (5) ◽  
pp. 848-859 ◽  
Author(s):  
E. John Simmonds ◽  
Andrew Campbell ◽  
Dankert Skagen ◽  
Beatriz A. Roel ◽  
Ciaran Kelly
Keyword(s):  
Stock Assessment ◽  
Management Plan ◽  
Maximum Sustainable Yield ◽  
Fish Stock ◽  
Northeast Atlantic ◽  
Atlantic Mackerel ◽  
Scomber Scombrus ◽  
Stock Biomass ◽  
Spawning Stock ◽  
Functional Forms

Abstract Simmonds, E. J., Campbell, A., Skagen, D., Roel, B. A., and Kelly, C. 2011. Development of a stock–recruit model for simulating stock dynamics for uncertain situations: the example of Northeast Atlantic mackerel (Scomber scombrus). – ICES Journal of Marine Science, 68: 848–859. The assumption of a relationship between recruitment and a spawning stock is the cornerstone of the precautionary approach and may constrain the use of a maximum sustainable yield (MSY) target for fisheries management, because the failure to include such a relationship suggests that providing a measure of stock protection is unnecessary. The implications of fitting different functional forms and stochastic distributions to stock-and-recruit data are investigated. The importance of these considerations is shown by taking a practical example from management: the management plan for Northeast Atlantic mackerel (Scomber scombrus), a fish stock with an average annual catch of 600 000 t. The historical range of spawning-stock biomass is narrow, and historical data from a stock assessment explain only a small proportion of the recruitment variability. We investigate how best to reflect the uncertainty in the stock–recruit relationship. Selecting a single model based on simple statistical criteria can have major consequences for advice and is problematic. Selecting a distribution of models with derived probabilities gives a more complete perception of uncertainty in dynamics. Differences in functional form, distribution of deviations, and variability of coefficients are allowed. The approach appropriately incorporates uncertainty in the stock–recruit relationship for FMSY estimation.

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.

Sensitivities and Variances of Virtual Population Analysis As Applied to the Mackerel, Scomber japonicus

1988 ◽  
Vol 45 (3) ◽  
pp. 539-547 ◽  
Author(s):  
Michael H. Prager ◽  
Alec D. MacCall
Keyword(s):  
Stock Assessment ◽  
Population Analysis ◽  
Error Variance ◽  
Quantitative Model ◽  
Fish Stock ◽  
Scomber Japonicus ◽  
Virtual Population Analysis ◽  
Virtual Population ◽  
Coefficients Of Variation ◽  
Fish Stock Assessment

Virtual population analysis (VPA) is widely used in fish stock assessment. However, VPA results are generally presented as point estimates, without error variance. Using numerical methods, we estimated the total variance of historical (1929–65) biomass estimates of mackerel, Scomber japonicus, off southern California. In the years before 1940, coefficients of variation (CV's) approached 100%; later, when weights at age and the age structure of the catch were better known, the CV's were about 25%. Most of the variability derives from uncertainties in estimates of natural mortality (M) and of weights at age. We also developed dimensionless coefficients (sensitivities) to examine the effects of errors in the inputs on the VPA biomass estimates. The largest sensitivities were to M and the total catch and varied substantially from year to year. As expected, sensitivity to M decreased with increasing exploitation, and sensitivity to catch increased with increasing exploitation. Using such sensitivities, one could estimate the error in a biomass estimate for a past year when M (or any other input) was thought to be unusually high or low. Thus, retrospective corrections can be made. Also, such sensitivities form an analytic tool for examining the properties of VPA, or any quantitative model.

scholarly journals Systmod II: Approaching a real dynamic computer model for fish stock assessment and development of fishery strategies

2016 ◽  
Author(s):  
Kristin Hamre ◽  
Steinar Moen ◽  
Johannes Hamre
Keyword(s):  
Stock Assessment ◽  
Growth Function ◽  
Field Studies ◽  
Fish Growth ◽  
Fish Stock ◽  
Initial State ◽  
Fish Stocks ◽  
Spawning Stock ◽  
Fish Stock Assessment ◽  
In The Beginning

Simulating development of fish stocks may be as complex as calculation of the development of the atmosphere, which is treated in meteorology as an initial value problem in physics. This approach was first proposed by Abbe and Bjerknes in the beginning of the 20 th century and today huge systems of differential equations are used to predict the weather. A similar approach to fisheries biology and ecology requires a real dynamic population model, which calculates the development of fish stocks from an initial state with equations that are independent of time. Here we present Systmod II, which uses a length-based growth function with a parameter for environmental variation and length-based data structure. The model uses monthly time steps to integrate population growth by moving fish to higher length groups as they grow. Since fish growth and maturity correlate more with length than with age, this gives comprehensive and clear results. The model was validated for Norwegian Spring-Spawning herring, using observed data from ICES working groups, and correlations (R2) between simulated and observed stock (total stock, spawning stock and catchable stock, numbers and biomass) were above 0.93. At present, the model makes reliable predictions on the short term (3 year for herring). For long term forecasts, better predictions of recruitment are needed . Since length is the main variable of the growth function, the state of the fish stock, including variability in length per yearclass, can be measured in situ, using hydro-acoustic trawl surveys. Data for modelling of many of the relations are still lacking, but can be filled in from future field studies.

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