scholarly journals Population changes in rattail species on the Chatham Rise

2021 ◽  
Author(s):  
◽  
Kathleen Large
Keyword(s):  
Maximum Likelihood ◽  
Stock Assessment ◽  
Production Model ◽  
Likelihood Method ◽  
Model Parameters ◽  
Important Species ◽  
Surplus Production ◽  
Surplus Production Model ◽  
Abundance Indices ◽  
The Impact

<p>The aim of this project was to conduct a stock assessment to determine the population dynamic characteristics of rattail species taken as bycatch in the hoki, hake and ling fishery on the Chatham Rise. No quantitative assessment of the current size of rattail populations , and how these may have changed over time, has been carried out before. There is interest in the need to quantify the impact of commercial fishing on the rattail populations, as rattails (Macrouridae family) are considered to be an ecologically important species complex in the deep ocean, and there may be the potential for the development of a commercial fishery based on their value as processed fishmeal. The minimum data required for a stock assessment are an abundance index and a catch history. Abundance indices are available for over 20 species of rattail produced from scientific surveys conducted annually on the Chatham Rise since 1992. Catch histories for individual rattail species in the same area are not available. A method was developed to reconstruct commercial catches of rattails from commercial effort data and survey catch and effort data. A surplus production model was fitted to the reconstructed catch data and survey abundance indices, using maximum likelihood and Bayesian methods to estimate model parameters and uncertainty. A surplus production model has two components: an observation model for abundance indices and a process model for population dynamics. Maximum likelihood estimation was applied to a model that specified errors for the observations only, and this produced estimates that had wide confidence intervals. A Bayesian approach was then taken to fit a statespace version of the model that incorporates errors associated with the observation and process models. While the Bayesian method produced more plausible parameter estimates (in comparison to the maximum likelihood method) and parameter uncertainty was reduced, our analysis indicated the posterior estimates were highly sensitive to the specification of different priors. There may be several reasons for these results, including: the small number of observations, lack of contrast in the data and mis-specification of the model. Meaningful estimates of the absolute size of rattail populations are not possible with these results, where estimates can vary by orders of magnitude depending on prior specification. This implies that more work needs to be done to develop more effective methods that can be used to help inform decisions regarding the management of these fish populations. Improving data collection, investigating informative priors and extending/respecifying the model are considered worthwhile avenues of future work to improve stock assessments of rattails.</p>

scholarly journals Population changes in rattail species on the Chatham Rise

2021 ◽  
Author(s):  
◽  
Kathleen Large
Keyword(s):  
Maximum Likelihood ◽  
Stock Assessment ◽  
Production Model ◽  
Likelihood Method ◽  
Model Parameters ◽  
Important Species ◽  
Surplus Production ◽  
Surplus Production Model ◽  
Abundance Indices ◽  
The Impact

<p>The aim of this project was to conduct a stock assessment to determine the population dynamic characteristics of rattail species taken as bycatch in the hoki, hake and ling fishery on the Chatham Rise. No quantitative assessment of the current size of rattail populations , and how these may have changed over time, has been carried out before. There is interest in the need to quantify the impact of commercial fishing on the rattail populations, as rattails (Macrouridae family) are considered to be an ecologically important species complex in the deep ocean, and there may be the potential for the development of a commercial fishery based on their value as processed fishmeal. The minimum data required for a stock assessment are an abundance index and a catch history. Abundance indices are available for over 20 species of rattail produced from scientific surveys conducted annually on the Chatham Rise since 1992. Catch histories for individual rattail species in the same area are not available. A method was developed to reconstruct commercial catches of rattails from commercial effort data and survey catch and effort data. A surplus production model was fitted to the reconstructed catch data and survey abundance indices, using maximum likelihood and Bayesian methods to estimate model parameters and uncertainty. A surplus production model has two components: an observation model for abundance indices and a process model for population dynamics. Maximum likelihood estimation was applied to a model that specified errors for the observations only, and this produced estimates that had wide confidence intervals. A Bayesian approach was then taken to fit a statespace version of the model that incorporates errors associated with the observation and process models. While the Bayesian method produced more plausible parameter estimates (in comparison to the maximum likelihood method) and parameter uncertainty was reduced, our analysis indicated the posterior estimates were highly sensitive to the specification of different priors. There may be several reasons for these results, including: the small number of observations, lack of contrast in the data and mis-specification of the model. Meaningful estimates of the absolute size of rattail populations are not possible with these results, where estimates can vary by orders of magnitude depending on prior specification. This implies that more work needs to be done to develop more effective methods that can be used to help inform decisions regarding the management of these fish populations. Improving data collection, investigating informative priors and extending/respecifying the model are considered worthwhile avenues of future work to improve stock assessments of rattails.</p>

scholarly journals Censored catch data in fisheries stock assessment

2005 ◽  
Vol 62 (6) ◽  
pp. 1118-1130 ◽  
Author(s):  
T.R. Hammond ◽  
V.M. Trenkel
Keyword(s):  
Stock Assessment ◽  
Recursive Partitioning ◽  
Maximum Sustainable Yield ◽  
Production Model ◽  
Model Parameters ◽  
Commercial Fish ◽  
Surplus Production ◽  
Stock Assessments ◽  
Surplus Production Model ◽  
Simulation Parameters

Abstract Landings statistics can be lower than true catches because many fish are discarded or landed illegally. Since many discards do not survive, treating landings as true catches can lead to biased stock assessments. This paper proposes treating catch as censored by bounding it below by the landings, L, and above by cL (for scalar c > 1). We demonstrate the approach with a simulation study, using a Schaefer surplus production model. Parameters were estimated in a Bayesian framework with BUGS software using two sets of priors. Both the traditional true-catch method and a survey-and-effort method (which was landings free) performed worse on average than the censored approach, as measured by the Bayes risk associated with estimates of maximum sustainable yield (MSY) and of an index of depletion (X). Recursive partitioning (regression trees) was used to associate simulation parameters to best-performing methods, showing that higher commercial fish catchability favoured the censored method at estimating X. In conclusion, censored methods provide a means of dealing with discarding and misreporting that can outperform some traditional alternatives.

Reference priors for Bayesian fisheries models

2002 ◽  
Vol 59 (9) ◽  
pp. 1492-1502 ◽  
Author(s):  
Russell B Millar
Keyword(s):  
Prior Knowledge ◽  
Population Analysis ◽  
Production Model ◽  
Model Parameters ◽  
Jeffreys Prior ◽  
Unknown Parameters ◽  
Prior Distributions ◽  
Reference Priors ◽  
Surplus Production ◽  
Surplus Production Model

Bayesian models require the specification of prior distributions for all unknown parameters, and this formal utilization of prior knowledge (if any) can be used to great advantage in some fisheries. However, regardless of whether prior knowledge about model parameters is available, specification of prior distributions is seldom unequivocal. This work addresses the problem of specifying default priors for several common fisheries models. To maintain consistency of terminology with the statistical literature, such priors are herein called reference priors to recognize that they can be interpreted as providing a sensible reference point against which the implications of alternative priors can be compared. Here, the Jeffreys' prior is demonstrated for the Ricker and Beverton–Holt stock–recruit curves, von Bertalanffy growth curve, Schaefer surplus production model, and sequential population analysis. The Jeffreys' priors for relevant derived parameters are demonstrated, including the steepness parameter of the Beverton–Holt stock–recruit curve. The sequential population analysis example is used to show that the Jeffreys' prior should not be automatically accepted as a reference prior in all models—this needs to be decided on a case-by-case basis.

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 Stock assessment of Bombayduck Harpodon nehereus (Hamilton, 1822) from Gujarat coast using non-equilibrium production model

2017 ◽  
Vol 64 ◽  
Author(s):  
K. Mohammed Koya ◽  
K. R. Sreenath ◽  
M. Muktha ◽  
Gyanranjan Dash ◽  
Swathipriyanka Sen ◽  
...  
Keyword(s):  
Stock Assessment ◽  
Northern Region ◽  
Maximum Sustainable Yield ◽  
Production Model ◽  
Catch Per Unit Effort ◽  
Surplus Production ◽  
Surplus Production Model ◽  
Gujarat Coast ◽  
Model Approach ◽  
Non Equilibrium

Bombayduck Harpodon nehereus, harvested mainly by dol nets (stationary bag nets), has been a prolific fishery in the northern region of Arabian Sea and Bay of Bengal. Biomass and maximum sustainable yield (MSY) estimates for the Bombayduck stock in the Saurashtra region were obtained from a non-equilibrium surplus production model approach utilising catch per unit effort (CPUE) time series derived from fish landing data. Fox model was found to be the most appropriate defining model and the results demonstrated that the stock is currently being overexploited.

scholarly journals Maximum Likelihood Estimation of the VAR(1) Model Parameters with Missing Observations

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Helena Mouriño ◽  
Maria Isabel Barão
Keyword(s):  
Stochastic Process ◽  
Missing Data ◽  
Maximum Likelihood ◽  
Moving Average ◽  
Practical Importance ◽  
Likelihood Estimation ◽  
Model Parameters ◽  
Missing Observations ◽  
Data Set ◽  
The Impact

Missing-data problems are extremely common in practice. To achieve reliable inferential results, we need to take into account this feature of the data. Suppose that the univariate data set under analysis has missing observations. This paper examines the impact of selecting an auxiliary complete data set—whose underlying stochastic process is to some extent interdependent with the former—to improve the efficiency of the estimators for the relevant parameters of the model. The Vector AutoRegressive (VAR) Model has revealed to be an extremely useful tool in capturing the dynamics of bivariate time series. We propose maximum likelihood estimators for the parameters of the VAR(1) Model based on monotone missing data pattern. Estimators’ precision is also derived. Afterwards, we compare the bivariate modelling scheme with its univariate counterpart. More precisely, the univariate data set with missing observations will be modelled by an AutoRegressive Moving Average (ARMA(2,1)) Model. We will also analyse the behaviour of the AutoRegressive Model of order one, AR(1), due to its practical importance. We focus on the mean value of the main stochastic process. By simulation studies, we conclude that the estimator based on the VAR(1) Model is preferable to those derived from the univariate context.

scholarly journals DEPLESI SUMBER DAYA IKAN TUNA DAN CAKALANG DI INDONESIA

2019 ◽  
Vol 13 (2) ◽  
pp. 167
Author(s):  
Maulana Firdaus ◽  
Akhmad Fauzi ◽  
A Faroby Falatehan
Keyword(s):  
Resource Depletion ◽  
Production Model ◽  
Economic Potential ◽  
Average Volume ◽  
Negative Numbers ◽  
Average Value ◽  
Surplus Production ◽  
Fish Resources ◽  
Surplus Production Model ◽  
The World

ABSTRAKTuna dan cakalang memiliki potensi ekonomi yang besar di Indonesia. Beberapa penelitian menunjukkan bahwa kedua komoditas ini telah menunjukkan gejala over fishing di dunia, termasuk Indonesia. Penelitian ini bertujuan untuk mengestimasi seberapa besar deplesi ikan tuna dan cakalang di Indonesia. Deplesi sumber daya dihitung melalui perkiraan stok dan tingkat hasil lestari dengan menggunakan model produksi surplus dan estimasi parameter menggunakan metoda Clarke Yoshimoto Pooley (CYP). Nilai deplesi diperoleh dari perkalian volume deplesi dengan unit rent. Hasil penelitian menunjukkan bahwa volume rata-rata deplesi sumber daya ikan tuna dan cakalang pada periode 1992-2015 adalah (-) 2.828 ton per tahun. Rata-rata nilai deplesi sumber daya ikan tuna dan cakalang menunjukkan angka negatif, yaitu (-) Rp131,89 miliar per tahun. Nilai negatif ini menunjukkan bahwa selama periode 1992-2015, stok sumber daya ikan tuna dan cakalang mengalami penurunan sebesar 2.828 ton per tahun dengan nilai potensi kerugian atau kehilangan akibat penurunan stok yang mencapai Rp131,89 miliar per tahun.Title: Tuna And Skipjack Resources Depletion In IndonesiaABSTRACTTuna and Skipjack has a great economic potential in Indonesia. Several studies have shown that these commodities have symptomed of over-fishing in the world, including Indonesia. This study aims to estimate the extent of tuna and skipjack depletion in Indonesia. Resource depletion is calculated through stock estimates and sustainable yield levels using surplus production model and parameter estimation of Clark Yoshimoto Pooley (CYP) method. Depletion value is obtained from multiplication of depletion volume with unit rent. Results of the study showed that the average volume of depletion of tuna and skipjack resources in the period 1992-2015 was (-) 2,828 tons per year. The average value of tuna and skipjack resource depletion showed negative numbers, ie (-) IDR 131.89 billion per year. This negative value indicates that during the period 1992-2015, the stock of tuna and skipjack fish resources decreased by 2.828 tons per year with the potential value of loss or loss due to a decrease in stock which reached IDR131,89 billion per year. 

scholarly journals New Lindley Half Cauchy Distribution: Theory and Applications

2020 ◽  
Vol 9 (4) ◽  
pp. 1-7
Keyword(s):  
Maximum Likelihood ◽  
Real Data ◽  
Cauchy Distribution ◽  
Least Square ◽  
Cumulative Distribution ◽  
Likelihood Method ◽  
Estimation Methods ◽  
Model Parameters ◽  
Data Set ◽  
Von Mises

In this paper, we have defined a new two-parameter new Lindley half Cauchy (NLHC) distribution using Lindley-G family of distribution which accommodates increasing, decreasing and a variety of monotone failure rates. The statistical properties of the proposed distribution such as probability density function, cumulative distribution function, quantile, the measure of skewness and kurtosis are presented. We have briefly described the three well-known estimation methods namely maximum likelihood estimators (MLE), least-square (LSE) and Cramer-Von-Mises (CVM) methods. All the computations are performed in R software. By using the maximum likelihood method, we have constructed the asymptotic confidence interval for the model parameters. We verify empirically the potentiality of the new distribution in modeling a real data set.

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