Improving data-limited stock assessment with sporadic stock index information in stock reduction analysis

2020 ◽  
Vol 77 (5) ◽  
pp. 857-868
Author(s):  
Lisha Guan ◽  
Yong Chen ◽  
Robert Boenish ◽  
Xianshi Jin ◽  
Xiujuan Shan
Keyword(s):  
Time Series ◽  
Life History ◽  
Stock Assessment ◽  
Assessment Methods ◽  
Stock Index ◽  
Fish Stocks ◽  
Stock Status ◽  
Index Development ◽  
Data Requirements

As most exploited fisheries lack a coherent time series of biomass index, development of data-limited stock assessment methods such as stock reduction analysis (SRA), is critical for fishery stock assessment due to their modest data requirements for estimating stock status and overfishing catch limits. In this study, we propose that sporadic time series of biomass indices, if available, may be fully utilized to inform priors of recent relative biomass (BT/B1) for data-limited stocks. We evaluated the performance of SRA incorporating this index-based prior by comparing two other common SRA priors (a deterministic prior set at 40% of the unfished biomass and a catch-based prior) with estimates from the likelihood-based assessments of 91 fish stocks from the RAM Legacy database. We extended our analysis by evaluating performance based on life history attributes and two depletion levels with BT/BMSY equaling 1 as the breakpoint. Results suggest index-based priors enhance accuracy for fish stocks at both depletion levels. We demonstrate that performance of SRA can be affected by three factors: the reliability of priors for BT/B1, recent depletion level, and life history.

scholarly journals Quirky patterns in time-series of estimates of recruitment could be artefacts

2014 ◽  
Vol 72 (1) ◽  
pp. 111-116 ◽  
Author(s):  
M. Dickey-Collas ◽  
N. T. Hintzen ◽  
R. D. M. Nash ◽  
P-J. Schön ◽  
M. R. Payne
Keyword(s):  
Time Series ◽  
Stock Assessment ◽  
Meta Analysis ◽  
Reference Points ◽  
Fish Stocks ◽  
Stock Recruitment ◽  
Modelling Assumptions ◽  
Meta Analyses ◽  
Recruitment Model ◽  
Recruitment Time

Abstract The accessibility of databases of global or regional stock assessment outputs is leading to an increase in meta-analysis of the dynamics of fish stocks. In most of these analyses, each of the time-series is generally assumed to be directly comparable. However, the approach to stock assessment employed, and the associated modelling assumptions, can have an important influence on the characteristics of each time-series. We explore this idea by investigating recruitment time-series with three different recruitment parameterizations: a stock–recruitment model, a random-walk time-series model, and non-parametric “free” estimation of recruitment. We show that the recruitment time-series is sensitive to model assumptions and this can impact reference points in management, the perception of variability in recruitment and thus undermine meta-analyses. The assumption of the direct comparability of recruitment time-series in databases is therefore not consistent across or within species and stocks. Caution is therefore required as perhaps the characteristics of the time-series of stock dynamics may be determined by the model used to generate them, rather than underlying ecological phenomena. This is especially true when information about cohort abundance is noisy or lacking.

scholarly journals Linking the performance of a data-limited empirical catch rule to life-history traits

2020 ◽  
Vol 77 (5) ◽  
pp. 1914-1926
Author(s):  
Simon H Fischer ◽  
José A A De Oliveira ◽  
Laurence T Kell
Keyword(s):  
Time Series ◽  
Life History ◽  
Life Histories ◽  
Life History Traits ◽  
Growth Parameter ◽  
Reference Points ◽  
Total Allowable Catch ◽  
Fish Stocks ◽  
Wide Range ◽  
Spawning Stock

Abstract Worldwide, the majorities of fish stocks are data-limited and lack fully quantitative stock assessments. Within ICES, such data-limited stocks are currently managed by setting total allowable catch without the use of target reference points. To ensure that such advice is precautionary, we used management strategy evaluation to evaluate an empirical rule that bases catch advice on recent catches, information from a biomass survey index, catch length frequencies, and MSY reference point proxies. Twenty-nine fish stocks were simulated covering a wide range of life histories. The performance of the rule varied substantially between stocks, and the risk of breaching limit reference points was inversely correlated to the von Bertalanffy growth parameter k. Stocks with k>0.32 year−1 had a high probability of stock collapse. A time series cluster analysis revealed four types of dynamics, i.e. groups with similar terminal spawning stock biomass (collapsed, BMSY, 2BMSY, 3BMSY). It was shown that a single generic catch rule cannot be applied across all life histories, and management should instead be linked to life-history traits, and in particular, the nature of the time series of stock metrics. The lessons learnt can help future work to shape scientific research into data-limited fisheries management and to ensure that fisheries are MSY compliant and precautionary.

scholarly journals Scientific Methods to Understand Fish Population Dynamics and Support Sustainable Fisheries Management

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 574
Author(s):  
Shewit Gebremedhin ◽  
Stijn Bruneel ◽  
Abebe Getahun ◽  
Wassie Anteneh ◽  
Peter Goethals
Keyword(s):  
Developing Countries ◽  
Population Dynamics ◽  
Life History ◽  
Freshwater Fish ◽  
Life History Traits ◽  
Stock Assessment ◽  
Freshwater Ecosystems ◽  
Sustainable Fisheries ◽  
Fish Stocks ◽  
The World

Fisheries play a significant role in the livelihoods of the world population, while the dependence on fisheries is acute in developing countries. Fisheries are consequently a critical element for meeting the sustainable development (SDG) and FAO goals to reduce poverty, hunger and improve health and well-being. However, 90% of global marine fish stocks are fully or over-exploited. The amount of biologically unsustainable stocks increased from 10% in 1975 to 33% in 2015. Freshwater ecosystems are the most endangered ecosystems and freshwater fish stocks are worldwide in a state of crisis. The continuous fish stock decline indicates that the world is still far from achieving SDG 14 (Life Below Water), FAO’s Blue Growth Initiative goal and SDG 15 (Life on Land, including freshwater systems). Failure to effectively manage world fish stocks can have disastrous effects on biodiversity and the livelihoods and socio-economic conditions of millions of people. Therefore, management strategies that successfully conserve the stocks and provide optimal sustainable yields are urgently needed. However, successful management is only possible when the necessary data are obtained and decision-makers are well informed. The main problem for the management of fisheries, particularly in developing countries, is the lack of information on the past and current status of the fish stocks. Sound data collection and validation methods are, therefore, important. Stock assessment models, which support sustainable fisheries, require life history traits as input parameters. In order to provide accurate estimates of these life history traits, standardized methods for otolith preparation and validation of the rate of growth zone deposition are essential. This review aims to assist researchers and fisheries managers, working on marine and freshwater fish species, in understanding concepts and processes related to stock assessment and population dynamics. Although most examples and case studies originate from developing countries in the African continent, the review remains of great value to many other countries.

Comparative performance of data-poor CMSY and data-moderate SPiCT stock assessment methods when applied to data-rich, real-world stocks

2020 ◽  
Author(s):  
Paul Bouch ◽  
Cóilín Minto ◽  
Dave G Reid
Keyword(s):  
Stock Assessment ◽  
Assessment Methods ◽  
Production Model ◽  
Fishing Mortality ◽  
Comparative Performance ◽  
Fish Stocks ◽  
Surplus Production ◽  
Stock Biomass ◽  
Surplus Production Model ◽  
The Status

Abstract All fish stocks should be managed sustainably, yet for the majority of stocks, data are often limited and different stock assessment methods are required. Two popular and widely used methods are Catch-MSY (CMSY) and Surplus Production Model in Continuous Time (SPiCT). We apply these methods to 17 data-rich stocks and compare the status estimates to the accepted International Council for the Exploration of the Sea (ICES) age-based assessments. Comparison statistics and receiver operator analysis showed that both methods often differed considerably from the ICES assessment, with CMSY showing a tendency to overestimate relative fishing mortality and underestimate relative stock biomass, whilst SPiCT showed the opposite. CMSY assessments were poor when the default depletion prior ranges differed from the ICES assessments, particularly towards the end of the time series, where some stocks showed signs of recovery. SPiCT assessments showed better correlation with the ICES assessment but often failed to correctly estimate the scale of either F/FMSY of B/BMSY, with the indices lacking the contrast to be informative about catchability and either the intrinsic growth rate or carrying capacity. Results highlight the importance of understanding model tendencies relative to data-rich approaches and warrant caution when adopting these models.

scholarly journals Simulation testing the robustness of stock assessment models to error: some results from the ICES strategic initiative on stock assessment methods

2014 ◽  
Vol 72 (1) ◽  
pp. 19-30 ◽  
Author(s):  
J.J. Deroba ◽  
D.S. Butterworth ◽  
R.D. Methot ◽  
J.A.A. De Oliveira ◽  
C. Fernandez ◽  
...  
Keyword(s):  
Large Scale ◽  
Stock Assessment ◽  
Assessment Methods ◽  
Fish Stocks ◽  
Production Models ◽  
Self Testing ◽  
Stock Assessments ◽  
The World ◽  
Simulation Testing ◽  
Pseudo Data

Abstract The World Conference on Stock Assessment Methods (July 2013) included a workshop on testing assessment methods through simulations. The exercise was made up of two steps applied to datasets from 14 representative fish stocks from around the world. Step 1 involved applying stock assessments to datasets with varying degrees of effort dedicated to optimizing fit. Step 2 was applied to a subset of the stocks and involved characteristics of given model fits being used to generate pseudo-data with error. These pseudo-data were then provided to assessment modellers and fits to the pseudo-data provided consistency checks within (self-tests) and among (cross-tests) assessment models. Although trends in biomass were often similar across models, the scaling of absolute biomass was not consistent across models. Similar types of models tended to perform similarly (e.g. age based or production models). Self-testing and cross-testing of models are a useful diagnostic approach, and suggested that estimates in the most recent years of time-series were the least robust. Results from the simulation exercise provide a basis for guidance on future large-scale simulation experiments and demonstrate the need for strategic investments in the evaluation and development of stock assessment methods.

scholarly journals Complex assessments or simple management procedures for efficient fisheries management: a comparative study

2014 ◽  
Vol 72 (1) ◽  
pp. 262-274 ◽  
Author(s):  
H. F. Geromont ◽  
D. S. Butterworth
Keyword(s):  
Fishery Management ◽  
Stock Assessment ◽  
Assessment Methods ◽  
Essential Requirement ◽  
Fish Stocks ◽  
Control Rules ◽  
Management Procedures ◽  
Simple Rules ◽  
Sound Management

Abstract Complex stock assessment methods are data- and expertise-hungry, with the annual updates of catch-at-age data and models typically seen as an essential requirement for sound management. But are the heavy commitments of resources required for this level of annual intervention really necessary to achieve efficient long-term fishery management? This question is addressed through a retrospective analysis of management performance over the last 20 years for four North Atlantic fish stocks. The assessments for two of these stocks have exhibited fairly strong retrospective patterns. The actual assessment advice for these stocks was provided based on complex assessment methods making use of age data. The outcomes are compared with what could have been achieved with much simpler catch control rules based upon age-aggregated survey indices alone. Even for the stocks whose assessments exhibit retrospective patterns, these simple rules can achieve virtually equivalent catch and risk performance, with much less interannual TAC variability, compared with what actually occurred over the past 20 years.

Estimating uncertainty of data limited stock assessments

2016 ◽  
Vol 74 (1) ◽  
pp. 69-77 ◽  
Author(s):  
Alexandros Kokkalis ◽  
Anne Maria Eikeset ◽  
Uffe H. Thygesen ◽  
Petur Steingrund ◽  
Ken H. Andersen
Keyword(s):  
Stock Assessment ◽  
Assessment Method ◽  
Fish Stocks ◽  
Stock Status ◽  
North East ◽  
The North ◽  
Stock Biomass ◽  
Uncertainty Estimates ◽  
Stock Assessments ◽  
Spawning Stock

Many methods exist to assess the fishing status of data-limited stocks; however, little is known about the accuracy or the uncertainty of such assessments. Here we evaluate a new size-based data-limited stock assessment method by applying it to well-assessed, data-rich fish stocks treated as data-limited. Particular emphasis is put on providing uncertainty estimates of the data-limited assessment. We assess four cod stocks in the North-East Atlantic and compare our estimates of stock status (F/Fmsy) with the official assessments. The estimated stock status of all four cod stocks followed the established stock assessments remarkably well and the official assessments fell well within the uncertainty bounds. The estimation of spawning stock biomass followed the same trends as the official assessment, but not the same levels. We conclude that the data-limited assessment method can be used for stock assessment and that the uncertainty estimates are reliable. Further work is needed to quantify the spawning biomass of the stock.

Relationships between temperature and Pacific hake distribution vary across latitude and life-history stage

2020 ◽  
Vol 639 ◽  
pp. 185-197 ◽  
Author(s):  
MJ Malick ◽  
ME Hunsicker ◽  
MA Haltuch ◽  
SL Parker-Stetter ◽  
AM Berger ◽  
...  
Keyword(s):  
Life History ◽  
Environmental Effects ◽  
Environmental Conditions ◽  
Vancouver Island ◽  
Additive Models ◽  
Life History Stage ◽  
Fish Stocks ◽  
Merluccius Productus ◽  
Multiple Dimensions ◽  
Effects Of Temperature

Environmental conditions can have spatially complex effects on the dynamics of marine fish stocks that change across life-history stages. Yet the potential for non-stationary environmental effects across multiple dimensions, e.g. space and ontogeny, are rarely considered. In this study, we examined the evidence for spatial and ontogenetic non-stationary temperature effects on Pacific hake Merluccius productus biomass along the west coast of North America. Specifically, we used Bayesian additive models to estimate the effects of temperature on Pacific hake biomass distribution and whether the effects change across space or life-history stage. We found latitudinal differences in the effects of temperature on mature Pacific hake distribution (i.e. age 3 and older); warmer than average subsurface temperatures were associated with higher biomass north of Vancouver Island, but lower biomass offshore of Washington and southern Vancouver Island. In contrast, immature Pacific hake distribution (i.e. age 2) was better explained by a nonlinear temperature effect; cooler than average temperatures were associated with higher biomass coastwide. Together, our results suggest that Pacific hake distribution is driven by interactions between age composition and environmental conditions and highlight the importance of accounting for varying environmental effects across multiple dimensions.

scholarly journals A Neutrosophic Forecasting Model for Time Series Based on First-Order State and Information Entropy of High-Order Fluctuation

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 455 ◽  
Author(s):  
Hongjun Guan ◽  
Zongli Dai ◽  
Shuang Guan ◽  
Aiwu Zhao
Keyword(s):  
Time Series ◽  
Information Entropy ◽  
Stock Exchange ◽  
Time Series Forecasting ◽  
High Order ◽  
Stock Index ◽  
Neutrosophic Set ◽  
Forecasting Model ◽  
Forecasting Models ◽  
Proposed Model

In time series forecasting, information presentation directly affects prediction efficiency. Most existing time series forecasting models follow logical rules according to the relationships between neighboring states, without considering the inconsistency of fluctuations for a related period. In this paper, we propose a new perspective to study the problem of prediction, in which inconsistency is quantified and regarded as a key characteristic of prediction rules. First, a time series is converted to a fluctuation time series by comparing each of the current data with corresponding previous data. Then, the upward trend of each of fluctuation data is mapped to the truth-membership of a neutrosophic set, while a falsity-membership is used for the downward trend. Information entropy of high-order fluctuation time series is introduced to describe the inconsistency of historical fluctuations and is mapped to the indeterminacy-membership of the neutrosophic set. Finally, an existing similarity measurement method for the neutrosophic set is introduced to find similar states during the forecasting stage. Then, a weighted arithmetic averaging (WAA) aggregation operator is introduced to obtain the forecasting result according to the corresponding similarity. Compared to existing forecasting models, the neutrosophic forecasting model based on information entropy (NFM-IE) can represent both fluctuation trend and fluctuation consistency information. In order to test its performance, we used the proposed model to forecast some realistic time series, such as the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), the Shanghai Stock Exchange Composite Index (SHSECI), and the Hang Seng Index (HSI). The experimental results show that the proposed model can stably predict for different datasets. Simultaneously, comparing the prediction error to other approaches proves that the model has outstanding prediction accuracy and universality.

Review of the life history, fisheries, and stock assessment for small tunas in the Atlantic Ocean

2021 ◽  
Author(s):  
Flávia Lucena-Frédou ◽  
Bruno Mourato ◽  
Thierry Frédou ◽  
Pedro G. Lino ◽  
Rubén Muñoz-Lechuga ◽  
...  
Keyword(s):  
Life History ◽  
Atlantic Ocean ◽  
Stock Assessment
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