Fitting Surplus Production Models: Comparing Methods and Measuring Uncertainty

1993 ◽  
Vol 50 (12) ◽  
pp. 2597-2607 ◽  
Author(s):  
Tom Polacheck ◽  
Ray Hilborn ◽  
Andre E. Punt
Keyword(s):  
Maximum Sustainable Yield ◽  
Parameter Estimates ◽  
Error Estimator ◽  
Sustainable Yield ◽  
Observation Error ◽  
Error Estimators ◽  
Averaging Methods ◽  
Surplus Production ◽  
Production Models ◽  
Process Error

Three approaches are commonly used to fit surplus production models to observed data: effort-averaging methods; process-error estimators; and observation-error estimators. We compare these approaches using real and simulated data sets, and conclude that they yield substantially different interpretations of productivity. Effort-averaging methods assume the stock is in equilibrium relative to the recent effort; this assumption is rarely satisfied and usually leads to overestimation of potential yield and optimum effort. Effort-averaging methods will almost always produce what appears to be "reasonable" estimates of maximum sustainable yield and optimum effort, and the r2 statistic used to evaluate the goodness of fit can provide an unrealistic illusion of confidence about the parameter estimates obtained. Process-error estimators produce much less reliable estimates than observation-error estimators. The observation-error estimator provides the lowest estimates of maximum sustainable yield and optimum effort and is the least biased and the most precise (shown in Monte-Carlo trials). We suggest that observation-error estimators be used when fitting surplus production models, that effort-averaging methods be abandoned, and that process-error estimators should only be applied if simulation studies and practical experience suggest that they will be superior to observation-error estimators.

scholarly journals Maximum Sustainable Yield for the Passur river fishery of Bangladesh by using Surplus-production model

2021 ◽  
Vol 48 (2) ◽  
pp. 301-312
Author(s):  
Muhammad Abdur Rouf ◽  
Sheik Istiak Md Shahriar ◽  
Md Hafizur Rahman ◽  
Md Mehedi Hasan ◽  
Al Hasan Antu ◽  
...  
Keyword(s):  
Fishing Effort ◽  
Mean Value ◽  
Maximum Sustainable Yield ◽  
Production Model ◽  
Error Estimator ◽  
Sustainable Yield ◽  
Observation Error ◽  
Total Allowable Catch ◽  
Surplus Production ◽  
Surplus Production Model

Maximum sustainable yield (MSY), fishing effort (fmsy) and total allowable catch (TAC) of major fishery in the Passur River, Bangladesh were estimated using surplus production model (Schaefer and Fox model) with observation-error estimator based on four years (2011-2014) catch and effort data. Fox model was especially highlighted in this study; the estimated value of MSY was 4.61 kg with corresponding fmsy of 13.51 units (200m2SBN/day). Moreover, the mean value of MSY and TAC with 95% confidence interval in stochastic method was 4.53 kg and 4.08 kg respectively with the 13.22 units of fishing effort (fmsy). The overall results provide clear evidence that the fishery of the Passur River is being overexploited in the months from December to March. Sustainable exploitation of this stock can be assured through reducing present fishing effort. In addition, TAC might be incorporated along with several existing fisheries management measures to ensure the compensation of this stock towards long term sustainability. Bangladesh J. Zool. 48(2): 301-312, 2020

scholarly journals Comment on “Impacts of historical warming on marine fisheries production”

Science ◽  
2019 ◽  
Vol 365 (6454) ◽  
pp. eaax5721 ◽  
Author(s):  
Cody Szuwalski
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Maximum Sustainable Yield ◽  
Structured Data ◽  
Sea Surface ◽  
Sustainable Yield ◽  
Surplus Production ◽  
Production Models ◽  
Fitting Production ◽  
Age Structured

Free et al. (Reports, 1 March 2019, p. 979) linked sea surface temperature (SST) to surplus production and estimated a 4% decline in maximum sustainable yield (MSY) since 1930. Changes in MSY are expected when fitting production models to age-structured data, so attributing observed changes to SST is problematic. Analyses of recruitment (a metric of productivity in the same database) showed increases in global productivity.

Extending production models to include process error in the population dynamics

2003 ◽  
Vol 60 (10) ◽  
pp. 1217-1228 ◽  
Author(s):  
Andre E Punt
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Population Dynamics ◽  
Total Error ◽  
Error Estimator ◽  
Observation Error ◽  
Production Models ◽  
Fitting Production ◽  
Process Error ◽  
Relative Errors

Four methods for fitting production models, including three that account for the effects of error in the population dynamics equation (process error) and when indexing the population (observation error), are evaluated by means of Monte Carlo simulation. An estimator that represents the distributions of biomass explicitly and integrates over the unknown process errors numerically (the NISS estimator) performs best of the four estimators considered, never being the worst estimator and often being the best in terms of the medians of the absolute values of the relative errors. The total-error approach outperforms the observation-error estimator conventionally used to fit dynamic production models, and the performance of a Kalman filter based estimator is particularly poor. Although the NISS estimator is the best-performing estimator considered, its estimates of quantities of management interest are severely biased and highly imprecise for some of the scenarios considered.

Parameter estimation in modelling the dynamics of fish stock biomass: are currently used observation-error estimators reliable?

1998 ◽  
Vol 55 (3) ◽  
pp. 749-760 ◽  
Author(s):  
Y Chen ◽  
N Andrew
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Likelihood Method ◽  
Fish Stock ◽  
Error Estimator ◽  
Observation Error ◽  
Estimation Errors ◽  
Abundance Index ◽  
Error Estimators ◽  
Production Models

Production models are used in fisheries when only a time series of catch and abundance indices are available. Observation-error estimators are commonly used to fit the models to the data with a least squares type of objective function. An assumption associated with observation-error estimators is that errors occur only in the observed abundance index but not in the dynamics of stock and observed catch. This assumption is usually unrealistic. Because the least squares methods tend to be sensitive to error assumptions, results derived from these methods may be unreliable. In this study, we propose a robust observation-error estimator. We evaluate the performance of this method, together with the commonly used maximum likelihood method, under different error assumptions. When there was only observation error in the abundance index, maximum likelihood tended to perform better. However, with both observation and process errors, maximum likelihood yielded much larger estimation errors compared with the proposed method. This study suggests that the proposed method is robust to error assumptions. Because the magnitude and types of error cannot often be specified with confidence, the proposed method offers a potentially useful addition to methods used to fit production models to abundance index and catch data.

Estimation of maximum sustainable yield of Gelidium robustum seaweed fishery in Mexico

2005 ◽  
Vol 85 (4) ◽  
pp. 775-778 ◽  
Author(s):  
M. Casas-Valdez ◽  
D. Lluch-Belda ◽  
S. Ortega-García ◽  
S. Hernández-Vázquez ◽  
E. Serviere-Zaragoza ◽  
...  
Keyword(s):  
Dynamic Model ◽  
West Coast ◽  
Baja California ◽  
Maximum Sustainable Yield ◽  
Sustainable Yield ◽  
Red Seaweed ◽  
The West ◽  
Surplus Production ◽  
Production Models ◽  
Gelidium Robustum

Surplus production models were used to assess the fishery condition of red seaweed Gelidium robustum off the west coast of the Baja California Peninsula from 1985 to 1997. The maximum sustainable yield and optimum effort estimated by the Schaefer model were 705 tn and 457 teams, while the Fox model estimated 670 tn and 510 teams. The determination coefficients were r2=0·62 for the Fox and r2=0·58 for the Schaefer model. These results suggest that the resource is not overexploited. Fitting the data to Hilborn & Walters' dynamic model was not satisfactory.

Bayesian and Related Approaches to Fitting Surplus Production Models

1994 ◽  
Vol 51 (8) ◽  
pp. 1823-1831 ◽  
Author(s):  
John M. Hoenig ◽  
William G. Warren ◽  
Max Stocker
Keyword(s):  
Prior Distribution ◽  
Empirical Bayes ◽  
Fishing Effort ◽  
Maximum Sustainable Yield ◽  
Bayes Estimator ◽  
Production Model ◽  
Sustainable Yield ◽  
Cancer Magister ◽  
Surplus Production ◽  
Production Models

The Schaefer surplus production model relates equilibrium yield to fishing effort and can be fitted using just information on catch and fishing effort. Sometimes, the fitted model predicts a maximum sustainable yield (height of the parabola) that is clearly unrealistic. In this case, one may wish to use prior information on maximum sustainable yield either to constrain the height of the parabola or to provide a prior distribution for Bayesian estimation. To construct a Bayes estimator, one would generally specify a noninformative prior on the residual error variance and, possibly, on the width of the parabola; the prior distribution for height could be obtained by examining fisheries for similar stocks or species on a per unit area basis. Another possibility is to use an empirical Bayes estimator when data from several fisheries (e.g., individual lakes) are available for several years. The methodology is illustrated on catch and effort data for big-eye tuna (Thunnus obesus) and Dungeness crab (Cancer magister). The approach can be extended to other fishery models, including nonequilibrium production models. The prior distribution parameters can be allowed to depend on covariates.

scholarly journals Fisheries Reference Point and Stock Status of Croaker Fishery (Sciaenidae) Exploited from the Bay of Bengal, Bangladesh

2022 ◽  
Vol 10 (1) ◽  
pp. 63
Author(s):  
Partho Protim Barman ◽  
Md. Mostafa Shamsuzzaman ◽  
Petra Schneider ◽  
Mohammad Mojibul Hoque Mozumder ◽  
Qun Liu
Keyword(s):  
Bay Of Bengal ◽  
Maximum Sustainable Yield ◽  
Reference Points ◽  
Production Model ◽  
Fishing Pressure ◽  
Surplus Production ◽  
Stock Status ◽  
Management Measures ◽  
Production Models ◽  
The Monte Carlo Method

This research evaluated fisheries reference points and stock status to assess the sustainability of the croaker fishery (Sciaenidae) from the Bay of Bengal (BoB), Bangladesh. Sixteen years (2001–2016) of catch-effort data were analyzed using two surplus production models (Schaefer and Fox), the Monte Carlo method (CMSY) and the Bayesian state-space Schaefer surplus production model (BSM) method. This research applies a Stock–Production Model Incorporating Covariates (ASPIC) software package to run the Schaefer and Fox model. The maximum sustainable yield (MSY) produced by all models ranged from 33,900 to 35,900 metric tons (mt), which is very close to last year’s catch (33,768 mt in 2016). The estimated B > BMSY and F < FMSY indicated the safe biomass and fishing status. The calculated F/FMSY was 0.89, 0.87, and 0.81, and B/BMSY was 1.05, 1.07, and 1.14 for Fox, Schaefer, and BSM, respectively, indicating the fully exploited status of croaker stock in the BoB, Bangladesh. The representation of the Kobe phase plot suggested that the exploitation of croaker stock started from the yellow (unsustainable) quadrant in 2001 and gradually moved to the green (sustainable) quadrant in 2016 because of the reduction in fishing efforts and safe fishing pressure after 2012. Thus, this research suggests that the current fishing pressure needs to be maintained so that the yearly catch does not exceed the MSY limit of croaker. Additionally, specific management measures should implement to guarantee croaker and other fisheries from the BoB.

scholarly journals Determination of The Status of Utilization and Level of Sepat (Trichogaster sp) in Public Waters of Mainland Banjar Regency

2019 ◽  
Vol 5 (1) ◽  
pp. 11-17
Author(s):  
Irhamsyah Irhamsyah ◽  
Novita Sari ◽  
Iriansyah Iriansyah
Keyword(s):  
Maximum Sustainable Yield ◽  
Catch Rate ◽  
Survey Method ◽  
Utilization Rate ◽  
Sustainable Yield ◽  
Effort Level ◽  
Surplus Production ◽  
The Status

 The fishing of Sepat (Trichogaster sp) in freshwaters of Banjar Regency with a solid intensity that has lasted long enough. This study aims: (1) Knowing the model of surplus production that can be used. (2) Knowing the optimum effort Sepat  (3) Knowing maximum sustainable yield. (4) Knowing the utilization level of Sepat. (5) Knowing the effort level of Sepat. The method that used in this research is survey method and collecting data. Data is analyzed by the Schaefer’s model and Fox’s model. The result of this research: (1) The best model is the Schaefer model with R2 and validation value. (2) The catch rate of Sepat is 45630 trip per year. (3) Maximum sustainable yield of Sepat is 45,466 ton per year. (4) The utilization rate of Sepat (Trichogaster sp) is 33%  which shows there has been no more catch under fishing (5) Effort level of Sepat  is 16 %.

Using demographic methods to construct Bayesian priors for the intrinsic rate of increase in the Schaefer model and implications for stock rebuilding

2001 ◽  
Vol 58 (9) ◽  
pp. 1871-1890 ◽  
Author(s):  
M K McAllister ◽  
E K Pikitch ◽  
E A Babcock
Keyword(s):  
Intrinsic Rate ◽  
Maximum Sustainable Yield ◽  
Production Model ◽  
Intrinsic Rate Of Increase ◽  
Point Estimate ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Sustainable Yield ◽  
Rate Of Increase ◽  
Demographic Methods

Even though Bayesian methods can provide statistically rigorous assessments of the biological status of fisheries resources, uninformative data (e.g., declining catch rate series with little variation in fishing effort) can produce highly imprecise parameter estimates. This can be counteracted with the use of informative Bayesian prior distributions (priors) for model parameters. We develop priors for the intrinsic rate of increase (r) in the Schaefer surplus production model using demographic methods and illustrate the utility of this with an application to large coastal sharks in the Atlantic. In 1996, a U.S. stock assessment obtained a point estimate for r of 0.26. For such long-lived and low-fecund organisms, this could potentially be too high. Yet it was used to predict that within about 10 years, a 50% reduction in the 1995 catch level should result in >50% chance of increasing the population to the abundance required to produce maximum sustainable yield. In contrast, a Bayesian assessment that used demographic analysis to construct a prior for r with a median of 0.07 and coefficient of variation (CV) of 0.7 indicated that within 30 years, this policy would have only a very small chance of increasing the population to maximum sustainable yield.

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