Should surplus production models be fishery description tools rather than biological models?

1995 ◽  
Vol 8 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Francis Laloë
Keyword(s):  
Biological Models ◽  
Surplus Production ◽  
Production Models

A Robust Method for Parameter Estimation from Catch and Effort Data

1989 ◽  
Vol 46 (1) ◽  
pp. 137-144 ◽  
Author(s):  
D. Ludwig ◽  
C. J. Walters
Keyword(s):  
Parameter Estimation ◽  
Robust Estimation ◽  
Robust Method ◽  
Amount Of Information ◽  
Surplus Production ◽  
Production Models ◽  
Composite Estimation ◽  
Estimation Scheme

The problem of robust estimation of optimal effort levels from surplus production models is considered. A variety of models are used to generate data, for the purpose of testing estimation schemes. The result of an estimation is an estimate of the optimal effort. These efforts are compared using the expected discounted value of a deterministic stock, which corresponds to the model used to generate the data. Such a criterion takes into account not only the loss due to bias in the estimated optimal effort, but also the loss due to the variance of the estimator. Estimation is difficult if there is a lack of informative variation in effort levels or stock sizes. In such cases, the estimation scheme which maximizes the criterion described above sacrifices realism in the representation of the stock-production relationship in order to reduce the variance of the estimate of optimal effort. We present a composite estimation scheme which performs acceptably in all the cases we have examined, and whose performance degrades slowly as the amount of information in the data decreases.

scholarly journals Reference points and management strategies: lessons from quantum mechanics

2006 ◽  
Vol 63 (1) ◽  
pp. 4-11 ◽  
Author(s):  
Jon T. Schnute ◽  
Rowan Haigh
Keyword(s):  
Quantum Mechanics ◽  
Management Strategies ◽  
Practical Importance ◽  
Reference Points ◽  
Surplus Production ◽  
Management Algorithm ◽  
Production Models ◽  
Conceptual Role ◽  
Common Framework ◽  
Mathematical Algorithms

Abstract Fisheries management often relies heavily on precautionary reference points estimated from complex statistical models. An alternative approach uses management strategies defined by mathematical algorithms that calculate controls, like catch quotas, directly from the observed data. We combine these two distinct paradigms into a common framework using arguments from the historical development of quantum mechanics. In fisheries, as in physics, the core of the argument lies in the technical details. We illustrate the process of designing a management algorithm similar to one actually used by the International Whaling Commission. Reference points and surplus production models play a conceptual role in defining management strategies, even if marine populations do not obey such simplistic rules. Physicists have encountered similar problems in formulating quantum theory, where mathematical objects with seemingly unrealistic properties generate results of great practical importance.

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.

Surplus Production Models

2008 ◽  
pp. 105-126 ◽  
Author(s):  
Jon T. Schnute ◽  
Laura J. Richards
Keyword(s):  
Surplus Production ◽  
Production Models

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.

A performance evaluation of surplus production models with time-varying intrinsic growth in dynamic ecosystems

2019 ◽  
Vol 76 (12) ◽  
pp. 2245-2255 ◽  
Author(s):  
Geneviève M. Nesslage ◽  
Michael J. Wilberg
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Survey Data ◽  
Atlantic Menhaden ◽  
Data Sets ◽  
Time Varying ◽  
Intrinsic Growth Rate ◽  
Surplus Production ◽  
Production Models ◽  
Exploitation Rate

We conducted a simulation study to evaluate performance of surplus production models (SPMs) with a time-varying intrinsic growth rate (SPMTVr) for stocks with predation-driven changes in productivity. Data sets were simulated using an age-structured, linked, predator–prey model of Atlantic menhaden (Brevoortia tyrannus), a forage fish native to the Northwest Atlantic, and Atlantic striped bass (Morone saxatilis), its primary predator, with differing time series of fishing mortality on both predator and prey. Simulations generated test data sets for Atlantic menhaden SPMs that included either a static or time-varying intrinsic growth rate parameter. The SPMTVr largely produced more accurate, less variable estimates of exploitation rate and biomass than models with static intrinsic growth. We also applied SPMTVr to empirical Atlantic menhaden catch and survey data for 1964–2016. The SPMTVr fit the survey data well, estimated an intrinsic growth rate time series that mirrored long-term juvenile survey trends, and produced biomass and exploitation rate trends that mirrored a statistical catch-at-age model. The SPMTVr estimated dynamic, maximum sustainable yield (MSY)-based reference points that reflected changing stock productivity.

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