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.

The roles of biological reference points and operational control points in management procedures for the sablefish (Anoplopoma fimbria) fishery in British Columbia, Canada

2013 ◽  
Vol 40 (4) ◽  
pp. 318-328 ◽  
Author(s):  
SEAN P. COX ◽  
ALLEN R. KRONLUND ◽  
ASHLEEN J. BENSON
Keyword(s):  
British Columbia ◽  
Stock Assessment ◽  
Reference Points ◽  
Control Points ◽  
Operational Control ◽  
Short Term ◽  
Anoplopoma Fimbria ◽  
Control Rules ◽  
Management Procedures ◽  
Harvest Control Rules

SUMMARYBiological reference points (BRPs) in fisheries policy are typically sensitive to stock assessment model assumptions, thus increasing uncertainty in harvest decision-making and potentially blocking adoption of precautionary harvest policies. A collaborative management strategy evaluation approach and closed-loop simulation modelling was used to evaluate expected fishery economic and conservation performance of the sablefish (Anoplopoma fimbria) fishery in British Columbia (Canada), in the presence of uncertainty about BRPs. Comparison of models derived using two precautionary harvest control rules, which each complied with biological conservation objectives and short-term economic objectives given by industry, suggested that both rules were likely to avert biomass decline below limit BRPs, even when stock biomass and production were persistently overestimated by stock assessment models. The slightly less conservative, industry-preferred harvest control rule also avoided short-term economic losses of c. CAN$ 2.7–10 million annually, or 10–50% of current landed value. Distinguishing between the role of BRPs in setting fishery conservation objectives and operational control points that define harvest control rules improved the flexibility of the sablefish management system, and has led to adoption of precautionary management procedures.

Using management strategy evaluation to establish indicators of changing fisheries

2019 ◽  
Vol 76 (9) ◽  
pp. 1653-1668 ◽  
Author(s):  
T.R. Carruthers ◽  
A.R. Hordyk
Keyword(s):  
Management Strategy ◽  
Statistical Power ◽  
Stock Assessment ◽  
Model Misspecification ◽  
Management Approach ◽  
Data Types ◽  
Quality Of Data ◽  
Management Strategy Evaluation ◽  
Strategy Evaluation ◽  
Management Procedures

A new indicator is described that uses multivariate posterior predictive data arising from management strategy evaluation (MSE) to detect operating model misspecification (exceptional circumstances) due to changing system dynamics. The statistical power of the indicator was calculated for five case studies for which fishery stock assessments have estimated changes in recruitment, natural mortality rate, growth, fishing efficiency, and size selectivity. The importance of the component data types that inform the indicator was also calculated. The indicator was tested for multiple types of management procedures (e.g., catch limits by stock assessment, size limits, spatial closures) given varying qualities of data. The statistical power of the indicator could be high even over short time periods and depended on the type of system change and quality of data. Statistical power depended strongly on the type of management approach, suggesting that indicators should be established that rigorously account for feedbacks between proposed management and observed data. MSE processes should use alternative operating models to evaluate protocols for exceptional circumstances to ensure they are of acceptable statistical power.

scholarly journals Designing tools to evaluate fishery management strategies: can the scientific community deliver?

2007 ◽  
Vol 64 (6) ◽  
pp. 1077-1084 ◽  
Author(s):  
Jon T. Schnute ◽  
Mark N. Maunder ◽  
James N. Ianelli
Keyword(s):  
Programming Languages ◽  
Scientific Community ◽  
Fishery Management ◽  
Stock Assessment ◽  
Management Strategies ◽  
Academic Disciplines ◽  
Strategy Evaluation ◽  
Management Procedures ◽  
Human Skills ◽  
Testing Management

Abstract Schnute, J. T., Maunder, M. N., and Ianelli, J. N. 2007. Designing tools to evaluate fishery management strategies: can the scientific community deliver? – ICES Journal of Marine Science, 64: 1077–1084. Techniques for quantitative fishery management have evolved rapidly during a period when computers, programming languages, and computational algorithms have also changed dramatically. Despite these advances, many stock assessment methods remain untested. A process of management strategy evaluation (MSE) could potentially rectify this problem, but it would require a framework in which to conduct systematic tests. We survey the tools currently used for stock assessments and discuss the development of new standards for testing management procedures. A successful project would depend on human skills scattered among various nations, organizations, and academic disciplines. Analogies from civil engineering illustrate the discipline and collaboration required for an effective outcome. If the world community of fishery scientists could design, build, and support such a project, it would revolutionize the theory, teaching, and practice of scientific fishery management.

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.

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 A stepwise stochastic simulation approach to estimate life history parameters for data-poor fisheries

2016 ◽  
Vol 73 (12) ◽  
pp. 1874-1884 ◽  
Author(s):  
Marc O. Nadon ◽  
Jerald S. Ault
Keyword(s):  
Life History ◽  
Stock Assessment ◽  
Probability Distributions ◽  
Size Composition ◽  
History Data ◽  
Life History Parameters ◽  
Life History Data ◽  
Novel Approach ◽  
Cost Efficient ◽  
Species Specific

Coastal fisheries are typically characterized by species-rich catch compositions and limited management resources, which typically leads to notably data-poor situations for stock assessment. Some parsimonious stock assessment approaches rely on cost-efficient size composition data, but these also require estimates of life history parameters associated with natural mortality, growth, and maturity. These parameters are unavailable for most exploited stocks. Here, we present a novel approach that uses a local estimate of maximum length and statistical relationships between key life history parameters to build multivariate probability distributions that can be used to parameterize stock assessment models in the absence of species-specific life history data. We tested this approach on three fish species for which empirical length-at-age and maturity data were available (from Hawaii and Guam) and calculated probability distributions of spawning potential ratios (SPR) at different exploitation rates. The life history parameter and SPR probability distributions generated from our data-limited analytical approach compared well with those obtained from bootstrap analyses of the empirical life history data. This work provides a useful new tool that can greatly assist fishery stock assessment scientists and managers in data-poor situations, typical of most of the world’s fisheries.

scholarly journals Ensemble models for data-poor assessment: accounting for uncertainty in life-history information

2019 ◽  
Vol 76 (4) ◽  
pp. 870-883 ◽  
Author(s):  
Merrill B Rudd ◽  
James T Thorson ◽  
Skyler R Sagarese
Keyword(s):  
Life History ◽  
Learning Algorithm ◽  
Stock Assessment ◽  
Model Averaging ◽  
Biological Information ◽  
Parameter Distribution ◽  
Life History Parameter ◽  
Multivariate Distributions ◽  
Life History Parameters ◽  
Parameter Values

Abstract Length measurements from fishery catch can be used in data-limited assessments to estimate important population parameters to guide management, but results are highly sensitive to assumptions about biological information. Ideally, local life history studies inform biological parameters. In the absence of reliable local estimates, scientists and managers face the difficult task of agreeing on fixed values for life-history parameters, often leading to additional uncertainty unquantified in the assessment or indecision defaulting to status-quo management. We propose an ensemble approach for incorporating life history uncertainty into data-limited stock assessments. We develop multivariate distributions of growth, mortality, and maturity parameter values, then use bivariate interpolation and stacking as an ensemble learning algorithm to propagate uncertainty into length-based, data-limited stock assessment models. Simulation testing demonstrated that stacking across life history parameter values leads to improved interval coverage over simple model averaging or assuming the parameter distribution means when the true life-history parameter values are unknown. We then applied the stacking approach for a U.S. Caribbean stock where the Scientific and Statistical Committee did not accept the assessment due to uncertainty in life history parameters. Stacking can better characterize uncertainty in stock status whenever life-history parameters are unknown but likely parameter distributions are available.

scholarly journals Stock Assessment of Small Yellow Croaker (Larimichthys polyactis) Off the Coast of China Using Per-Recruit Analysis Based on Bayesian Inference

2021 ◽  
Vol 8 ◽  
Author(s):  
Lixin Zhu ◽  
Changzi Ge ◽  
Zhaoyang Jiang ◽  
Chunli Liu ◽  
Gang Hou ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Mortality Rate ◽  
Growth Parameters ◽  
Stock Assessment ◽  
Larimichthys Polyactis ◽  
Small Yellow Croaker ◽  
Spawning Stock ◽  
Key Factor ◽  
The Status ◽  
Spawning Potential Ratio

This paper presents a framework for quantifying uncertainty in per-recruit analysis for small yellow croaker (Larimichthys polyactis) fisheries in China, in which credible estimates of life history parameters from Bayesian inference were used to generate the distribution for a quantity of interest. Small yellow croakers were divided into five spatial groups. The status of each group was examined using a yield-per-recruit (YPR) model and a spawning stock biomass-per-recruit (SSBPR) model. The optimal length at first capture (Lcopt) was proposed to recover the biomass. The maximum observed age in the current stocks (3 years) and the maximum recorded age (≥20 years) were adopted in per-recruit analysis. Our results suggest that the framework can quantify uncertainty well in the output of per-recruit analysis for small yellow croaker. It is suited to other fish species. The SSBPR at FMSY (SSBPRMSY) is a better benchmark than the spawning potential ratio (SPR) at FMSY because SSBPRMSY had a unimodal distribution. The SSBPR analysis can lead to a more conservative Lcopt than the YPR analysis. The key factor influencing the assessment conclusions may be the growth parameters rather than the natural mortality rate for a stock with a younger maximum age. Overfishing likely occurred for all groups and recruitment overfishing may not occur if the maximum age is maintained at 3 years. Increasing lengths at first capture to the recommended values can help this population recover. However, Fcur is too high for small yellow croakers to attain the maximum recorded age. Both reducing fishing mortality rate and increasing length at first capture are needed to attain the maximum recorded age.

Life history parameters as basis for the initial recognition of stock management units in horse mackerel (Trachurus trachurus)

2008 ◽  
Vol 89 (2) ◽  
pp. 167-180 ◽  
Author(s):  
P. Abaunza ◽  
L.S. Gordo ◽  
M.T. García Santamaría ◽  
S.A. Iversen ◽  
A.G. Murta ◽  
...  
Keyword(s):  
Life History ◽  
Horse Mackerel ◽  
Stock Management ◽  
Trachurus Trachurus ◽  
Management Units ◽  
Life History Parameters

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.

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