scholarly journals Objectives and harvest control rules in the management of the fishery of Norwegian spring-spawning herring

2009 ◽  
Vol 66 (8) ◽  
pp. 1793-1799 ◽  
Author(s):  
Sigurd Tjelmeland ◽  
Ingolf Røttingen
Keyword(s):  
Stock Assessment ◽  
Maximum Sustainable Yield ◽  
Assessment Model ◽  
Main Element ◽  
Management Objectives ◽  
Control Rules ◽  
Harvest Control Rule ◽  
Stock Recruitment ◽  
Harvest Control Rules

Abstract Tjelmeland, S., and Røttingen, I. 2009. Objectives and harvest control rules in the management of the fishery of Norwegian spring-spawning herring. – ICES Journal of Marine Science, 66: 1793–1799. The main element in the management of the Norwegian spring-spawning herring, as implemented by the coastal states, is to conduct the fishery based on a maximum fishing mortality (F) of 0.125. As the appropriateness of this rule (given the stated objectives) has not yet been tested thoroughly, we set out to do this by long-term simulations, in which we applied a range of alternative stock–recruitment relationships. These different relationships are estimated from historical replicates of the stock, as calculated by the herring-stock assessment model SeaStar. During prognostic simulations, a recruitment model is selected probabilistically for each historical replicate based on Akaike weights. We evaluate whether the management objectives are met by applying the present harvest control rule. Results are given for the current assessment option of natural mortality (M = 0.5) in the oldest aggregated age group and for the assessment option used in 2005 and earlier (M = 0.15). These show that perceptions of the long-term yield differ considerably and that the current management is somewhat on the conservative side from the perspective of maximum sustainable yield.

scholarly journals An evaluation of acceptable biological catch (ABC) harvest control rules designed to limit overfishing

2017 ◽  
Vol 74 (7) ◽  
pp. 1028-1040 ◽  
Author(s):  
John Wiedenmann ◽  
Michael Wilberg ◽  
Andrea Sylvia ◽  
Thomas Miller
Keyword(s):  
Fishery Management ◽  
Short Interval ◽  
Assessment Model ◽  
Scientific Uncertainty ◽  
High Yield ◽  
Management Objectives ◽  
Control Rules ◽  
Average Biomass ◽  
Harvest Control Rules

In this paper we developed a simulation model to evaluate a range of acceptable biological catch (ABC) control rules to determine their relative performance at achieving common fishery management objectives. We explored a range of scenarios to determine robustness of a control rule to different situations and found that across scenarios the control rules that used a buffer to account for scientific uncertainty when setting the ABC were able to limit the frequency of overfishing. Modest buffers when setting the ABC were generally effective at limiting overfishing, but larger buffers resulted in higher average biomass, similar long-term benefits to the fishery (high yield, low variability in yield), more rapid recovery of depleted populations, and a lower risk of the population being overfished, and these results were robust to the level of uncertainty in the assessment model estimates. In addition, fixing the ABC over the interval between assessments and having a short interval between assessments was generally more effective at meeting management objectives than using projections and having a long assessment interval.

scholarly journals Rebuilding depleted fisheries towards BMSY under uncertainty: harvest control rules outperform combined management measures

2020 ◽  
Author(s):  
Ming Sun ◽  
Yunzhou Li ◽  
Yiping Ren ◽  
Yong Chen
Keyword(s):  
Management Practices ◽  
Maximum Sustainable Yield ◽  
Natural Mortality ◽  
Short Term ◽  
Sources Of Uncertainty ◽  
Control Rules ◽  
Management Measures ◽  
Strategy Evaluation ◽  
Harvest Control Rules

Abstract Rebuilding depleted fisheries towards sustainable levels, such as BMSY, is challenging under uncertainty. Although a substantial amount of research has highlighted the importance of accounting for uncertainty in fisheries management, tactical measures remain to be identified. We consider two approaches to achieve this goal: (i) the naive maximum sustainable yield (MSY) approach, combining management measures based on effort control, catch quotas, and spatial–temporal closures, and (ii) the harvest control rules (HCRs) approach, developing HCRs based on short-term or long-term targets. A suite of strategies is developed accordingly and tested with management strategy evaluation for their performance under four sources of uncertainty that may negatively impact management effects, including reduced recruitment strength, increased natural mortality, inadequate implementation error, and varying levels of temporal effort aggregation. Combining management measures using the naive MSY approach is found to perform poorly in tackling uncertainty. Complex HCRs that account for both short-term and long-term BMSY targets can mitigate the adverse effects of uncertainty. The rebuilding target can be only achieved by compromising yield, especially when uncertainties with natural mortality and recruitment are present. Strategies based on catch quotas are prone to all sources of uncertainty, indicating latent risks in many current management practices.

scholarly journals Performance of harvest control rules in a variable environment

2010 ◽  
Vol 67 (5) ◽  
pp. 1051-1062 ◽  
Author(s):  
Thomas Brunel ◽  
Gerjan J. Piet ◽  
Ralf van Hal ◽  
Christine Röckmann
Keyword(s):  
Dynamic Models ◽  
Generalized Additive Models ◽  
Maximum Sustainable Yield ◽  
Additive Models ◽  
Precautionary Approach ◽  
Variable Environment ◽  
Control Rules ◽  
Stock Biomass ◽  
Stock Recruitment ◽  
Harvest Control Rules

AbstractBrunel, T., Piet, G. J., van Hal, R., and Röckmann, C. 2010. Performance of harvest control rules in a variable environment. – ICES Journal of Marine Science, 67: 1051–1062. Population dynamic models used for fisheries management assume that stocks are isolated entities, ignoring the influence of environmental factors on stock productivity. An operating model parameterized for North Sea cod, plaice, and herring is developed, in which the link between recruitment and environment is assumed to be known and described by generalized additive models. This tool is used to compare the performance of harvest control rules (HCRs) when recruitment is independent of the environment or when recruitment is affected by an environment varying according to different scenarios. The first HCR exploited the stock with a fixed fishing mortality (F) corresponding to maximum sustainable yield, and in the second HCR, F was set equal to the precautionary approach F (i.e. Fpa), but reduced from Fpa when stock biomass fell below Bpa. The performance of the HCRs altered only slightly in a randomly varying environment compared with a constant one. For a detrimental change in the environment, however, no HCR could prevent a massive decrease in stock size. The performance of the HCRs was also influenced by the stock characteristics, such as recruitment variability or the shape of the stock–recruitment relationship. The performance of “environmental” HCRs (eHCRs), in which F varies depending on environmental conditions, was compared with that of conventional HCRs. The gain in using the eHCR was small, except for a detrimental change in the environment, where the eHCR performed markedly better than a conventional HCR. The benefits of using the eHCR were the greatest for the stock with the strongest environment–recruitment relationship.

Improved approximations for estimation of size-transition probabilities within size-structured models

2019 ◽  
Vol 76 (8) ◽  
pp. 1305-1313
Author(s):  
Russell B. Millar ◽  
Christopher D. Nottingham
Keyword(s):  
Transition Probabilities ◽  
Stock Assessment ◽  
Approximation Error ◽  
Maximum Sustainable Yield ◽  
Individual Variability ◽  
Size Class ◽  
Assessment Model ◽  
Accurate Approximation ◽  
Diverse Range ◽  
Incremental Growth

Modelling annual growth of individuals in a size-structured model requires calculation of the size-transition probabilities for moving from one size class to another. This requires evaluation of two-dimensional integrals when there is individual variability in growth. For computational simplicity, it is common to approximate the integrals by setting all individuals in a size class to the midsize of that class or by ignoring the individual variability. We develop a more accurate approximation that assumes a uniform distribution in size within each size class. The approximation is fast and hence feasible for Bayesian models in which the matrix of transition probabilities must be computed for each posterior sample. The improved accuracy of the new approximation is shown to hold over a diverse range of formulations for incremental growth. For the New Zealand Paua 5A (Haliotis iris) stock assessment model, it was found to reduce the average approximation error of the size-transition probabilities by 86% and 98% compared with the midpoint and deterministic growth approximations, respectively. Moreover, the midpoint and deterministic approximations inflated the estimated maximum sustainable yield by 6% and 8%, respectively, and the current biomass by almost 30% in comparison with the more accurate approximation.

scholarly journals Institutional designs to face the dark side of total allowable catches

2007 ◽  
Vol 64 (4) ◽  
pp. 851-857 ◽  
Author(s):  
Ikerne del Valle ◽  
Kepa Astorkiza
Keyword(s):  
Dark Side ◽  
Policy Makers ◽  
Control Rules ◽  
Opportunistic Behaviour ◽  
Self Interest ◽  
Conflicting Interests ◽  
Endogenous Process ◽  
Harvest Control Rules ◽  
Stock Abundance

Abstract del Valle, I., and Astorkiza, K. 2007. Institutional designs to face the dark side of total allowable catches. – ICES Journal of Marine Science, 64: 851–857. Setting total allowable catches (TACs) is an endogenous process in which different agents and institutions, often with conflicting interests and opportunistic behaviour, try to influence policy-makers. Far from being the benevolent social planners many would wish them to be, these policy-makers may also pursue self-interest when making final decisions. Although restricted knowledge of stock abundance and population dynamics, and weakness in enforcement, have effects, these other factors may explain why TAC management has failed to guarantee sustainable exploitation of fish resources. Rejecting the exogeneity of the TAC and taking advantage of fruitful debate on economic policy (i.e. the rules vs. discretion debate, and that surrounding the independence of central banks), two institutional developments are analysed as potential mechanisms to face up to misconceptions about TACs: long-term harvest control rules and a central bank of fish.

scholarly journals Substituting model-based indicators in Harvest Control Rules by observations using fuzzy logic methodology

2017 ◽  
Vol 75 (3) ◽  
pp. 977-987
Author(s):  
Arne Eide
Keyword(s):  
Fuzzy Logic ◽  
Mathematical Models ◽  
Stock Assessment ◽  
Decision Rules ◽  
Initial Information ◽  
Stock Size ◽  
Control Rules ◽  
Stock Biomass ◽  
Size Evaluation ◽  
Harvest Control Rules

Abstract Harvest Control Rules are predefined heuristic decision rules to provide quota advices for managed fisheries. Frequently statistical methods and biological assumptions expressed in mathematical models, are used to provide the Harvest Control Rules with initial information (indicators values). The aim of this article is to investigate a possible way forward of replacing these inputs by quantities of measurable observations, e.g. catch-at-age statistics. The article presents a method by which recruitment indexes and stock biomass indicators are obtained by non-parametric use of annual catch-at-age records, without filtering the raw data (observations) through mathematical models. Two related methods, applied on three empirical cases, are provided: First, showing that recruitment strengths of the Northeast Arctic cod, haddock, and saithe stocks, obtained by fuzzy logic methodology, are satisfactory captures by the use of catch-at-age data. Second, stock size indicators are estimated for the three species by the same catch-at-age data. The second task turns out to be more challenging than the first, but also in the case of stock size evaluation, the suggested procedure provides reasonable results when compared to standard stock assessment methods.

Assessing the sensitivity of data-limited methods (DLMs) to the estimation of life-history parameters from length–frequency data

2018 ◽  
Vol 75 (10) ◽  
pp. 1563-1572 ◽  
Author(s):  
Ming Sun ◽  
Chongliang Zhang ◽  
Yong Chen ◽  
Binduo Xu ◽  
Ying Xue ◽  
...  
Keyword(s):  
Life History ◽  
Stock Assessment ◽  
Stock Management ◽  
Larimichthys Polyactis ◽  
Life History Parameters ◽  
Control Rules ◽  
Strategy Evaluation ◽  
Management Procedures ◽  
Spawning Potential Ratio ◽  
Harvest Control Rules

Data-limited methods (DLMs) in stock assessment may provide potential critical information for data-limited stock management. However, the sensitivity of those methods to life-history parameters is largely unknown, resulting in extra uncertainty and consequent risks. In the present study, we designed six parallel workflows (WFs) to incorporate classic and state-of-the-art methods of estimating life-history parameters and examined their influences on the assessment of small yellow croaker (Larimichthys polyactis) in Haizhou Bay, China. The sensitivity was evaluated with three objectives: (i) the evaluation of stock status with the spawning potential ratio following different assumptions; (ii) the length-based harvest control rules derived from three management procedures; and (iii) the management performance of these harvest control rules with simulation of management strategy evaluation. The results showed considerable sensitivity regarding the three objectives to the estimations with different WFs, indicating the previous practice of credulously accepting empirical values and indiscriminately selecting references are inadvisable. We also identified the most appropriate WFs used for different purposes with limited data, aiming to provide more reliable inputs for effective fisheries management.

Parameterizing age-structured models from a fisheries management perspective

2008 ◽  
Vol 65 (8) ◽  
pp. 1586-1600 ◽  
Author(s):  
Steven J.D. Martell ◽  
William E. Pine ◽  
Carl J. Walters
Keyword(s):  
Analytical Solution ◽  
Maximum Sustainable Yield ◽  
Assessment Model ◽  
Informative Priors ◽  
Biological Variables ◽  
Management Perspective ◽  
Stock Recruitment ◽  
Age Structured ◽  
Management Advice ◽  
Two Parameters

Age-structured models are widely used in fisheries stock assessments and contain two very important parameters that determine the rate and amount of harvest that can be safely taken: the compensation rate in juvenile survival (κ) and the unfished biomass (Bo). These two parameters are often confounded. It is common for relative abundance indices to lack contrast, and the use of informative priors, or fixing at least one of these parameters, is necessary to develop management advice. Providing management advice proceeds by transforming estimates of biological variables such as Bo and κ into management variables such as the maximum sustainable yield (C*) and the fishing mortality rate that would achieve this yield (F*). There is no analytical solution for the transformation of Bo, κ to C*, or F* for age-structured models with commonly used stock–recruitment functions and therefore must be done numerically. The opposite transition, however, does have an analytical solution for both the Beverton–Holt and Ricker recruitment models with partial selectivities for all age classes. Use of these analytical solutions allows for age-structured assessment models to be directly parameterized in terms of the management variables C* and F*. The effects of informative priors on C* and F* on the results of the assessment model are completely transparent to management.

scholarly journals Probability of stochastic depletion: an easily interpreted diagnostic for stock assessment modelling and fisheries management

2014 ◽  
Vol 72 (2) ◽  
pp. 428-435 ◽  
Author(s):  
James T. Thorson ◽  
Olaf P. Jensen ◽  
Ray Hilborn
Keyword(s):  
Stock Assessment ◽  
Biological Effects ◽  
Decision Support Tool ◽  
Maximum Sustainable Yield ◽  
Support Tool ◽  
Management Actions ◽  
Stock Assessments ◽  
Harvest Control Rule ◽  
Recruitment Variability ◽  
Spawning Biomass

Abstract Marine fish populations have high variation in cohort strength, and the production of juveniles (recruitment) may have persistent positive or negative residuals (autocorrelation) after accounting for spawning biomass. Autocorrelated recruitment will occur whenever average recruitment levels change between oceanographic regimes or due to predator release, but may also indicate persistent environmental and biological effects on shorter time-scales. Here, we use estimates of recruitment variability and autocorrelation to simulate the stationary distribution of spawning biomass for 100 real-world stocks when unfished, fished at FMSY, or fished following a harvest control rule where fishing mortality decreases as a function of spawning biomass. Results show that unfished stocks have spawning biomass (SB) below its deterministic equilibrium value (SB0) 58% of the time, and below 0.5SB0 5% of the time on average across all stocks. Similarly, stocks fished at the level producing deterministic maximum sustainable yield (FMSY) are below its deterministic prediction of spawning biomass (SBMSY) 60% of the time and below 0.5SBMSY 8% of the time. These probabilities are greater for stocks with high recruitment variability, positive autocorrelation, and high natural mortality—traits that are particularly associated with clupeids and scombrids. An elevated probability of stochastic depletion, i.e. biomass below the deterministic equilibrium expectation, implies that management actions required when biomass drops below a threshold may be triggered more frequently than expected. Therefore, we conclude by suggesting that fisheries scientists routinely calculate these probabilities during stock assessments as a decision support tool for fisheries managers.

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