scholarly journals Fitting a non-parametric stock–recruitment model in R that is useful for deriving MSY reference points and accounting for model uncertainty

2012 ◽  
Vol 70 (1) ◽  
pp. 56-67 ◽  
Author(s):  
Noel G. Cadigan
Keyword(s):  
Confidence Intervals ◽  
Model Uncertainty ◽  
Fishery Management ◽  
Reference Points ◽  
Parametric Models ◽  
Parametric Approach ◽  
Stock Size ◽  
Stock Recruitment ◽  
Recruitment Model ◽  
Non Parametric

Abstract Cadigan, N. G. 2013. Fitting a non-parametric stock–recruitment model in R that is useful for deriving MSY reference points and accounting for model uncertainty. – ICES Journal of Marine Science, 70:56–67. Modelling the relationship between parental stock size and subsequent recruitment of fish to a fishery is often required when deriving reference points, which are a fundamental component of fishery management. A non-parametric approach to estimate stock–recruitment relationships is illustrated using a simulated example and nine case studies. The approach preserves compensatory density dependence in which the recruitment rate monotonically decreases as stock size increases, which is a basic assumption of commonly used parametric stock–recruitment models. The implications of the non-parametric estimates on maximum sustainable yield (MSY) reference points are illustrated. The approach is used to provide non-parametric bootstrapped confidence intervals for reference points. The efficacy of the approach is investigated using simulations. The results demonstrate that the non-parametric approach can provide a more realistic estimation of the stock–recruitment relationship when informative data are available compared with common parametric models. Also, bootstrap confidence intervals for MSY reference points based on different parametric stock–recruitment models often do not overlap. The confidence intervals based on the non-parametric approach tend to be much wider, and reflect better uncertainty due to stock–recruit model choice.

scholarly journals An explicit solution for calculating optimum spawning stock size from Ricker’s stock recruitment model

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1623 ◽  
Author(s):  
Mark D. Scheuerell
Keyword(s):  
Explicit Solution ◽  
Model Fitting ◽  
Maximum Sustainable Yield ◽  
Reference Points ◽  
Stock Size ◽  
Points Of Interest ◽  
Spawning Stock ◽  
Stock Recruitment ◽  
Explicit Formulae ◽  
Recruitment Model

Stock-recruitment models have been used for decades in fisheries management as a means of formalizing the expected number of offspring that recruit to a fishery based on the number of parents. In particular, Ricker’s stock recruitment model is widely used due to its flexibility and ease with which the parameters can be estimated. After model fitting, the spawning stock size that produces the maximum sustainable yield (SMSY) to a fishery, and the harvest corresponding to it (UMSY), are two of the most common biological reference points of interest to fisheries managers. However, to date there has been no explicit solution for either reference point because of the transcendental nature of the equation needed to solve for them. Therefore, numerical or statistical approximations have been used for more than 30 years. Here I provide explicit formulae for calculating bothSMSYandUMSYin terms of the productivity and density-dependent parameters of Ricker’s model.

scholarly journals An explicit solution for calculating optimum spawning stock size from Ricker's stock recruitment curve

2015 ◽  
Author(s):  
Mark D. Scheuerell
Keyword(s):  
Explicit Solution ◽  
Model Fitting ◽  
Maximum Sustainable Yield ◽  
Reference Points ◽  
Stock Size ◽  
Points Of Interest ◽  
Spawning Stock ◽  
Stock Recruitment ◽  
Explicit Formulae ◽  
Recruitment Model

Ricker’s stock recruitment model is widely used to describe the spawner-offspring relationship for fishes. After model fitting, the spawning stock size that produces the maximum sustainable yield (SMSY), and the harvest corresponding to it (UMSY), are two of the most common biological reference points of interest to fisheries managers. However, to date there has been no explicit solution for either reference point because of the transcendental nature of the equation needed to solve for them. Therefore, numerical or statistical approximations have been used for more than 30 years. Here I provide explicit formulae for calculating both SMSY and UMSY in terms of the productivity and density-dependent parameters from Ricker’s model.

scholarly journals An explicit solution for calculating optimum spawning stock size from Ricker's stock recruitment curve

2015 ◽  
Author(s):  
Mark D. Scheuerell
Keyword(s):  
Explicit Solution ◽  
Model Fitting ◽  
Maximum Sustainable Yield ◽  
Reference Points ◽  
Stock Size ◽  
Points Of Interest ◽  
Spawning Stock ◽  
Stock Recruitment ◽  
Explicit Formulae ◽  
Recruitment Model

Ricker’s stock recruitment model is widely used to describe the spawner-offspring relationship for fishes. After model fitting, the spawning stock size that produces the maximum sustainable yield (SMSY), and the harvest corresponding to it (UMSY), are two of the most common biological reference points of interest to fisheries managers. However, to date there has been no explicit solution for either reference point because of the transcendental nature of the equation needed to solve for them. Therefore, numerical or statistical approximations have been used for more than 30 years. Here I provide explicit formulae for calculating both SMSY and UMSY in terms of the productivity and density-dependent parameters from Ricker’s model.

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 Non-linearity in stock–recruitment relationships of Atlantic cod: insights from a multi-model approach

2019 ◽  
Vol 77 (4) ◽  
pp. 1492-1502 ◽  
Author(s):  
Camilla Sguotti ◽  
Saskia A Otto ◽  
Xochitl Cormon ◽  
Karl M Werner ◽  
Ethan Deyle ◽  
...  
Keyword(s):  
Environmental Variables ◽  
Gadus Morhua ◽  
Fishery Management ◽  
Atlantic Cod ◽  
Stock Assessment ◽  
Fish Stock ◽  
Recovery Potential ◽  
Stock Recruitment ◽  
Model Approach ◽  
Non Parametric

Abstract The stock–recruitment relationship is the basis of any stock prediction and thus fundamental for fishery management. Traditional parametric stock–recruitment models often poorly fit empirical data, nevertheless they are still the rule in fish stock assessment procedures. We here apply a multi-model approach to predict recruitment of 20 Atlantic cod (Gadus morhua) stocks as a function of adult biomass and environmental variables. We compare the traditional Ricker model with two non-parametric approaches: (i) the stochastic cusp model from catastrophe theory and (ii) multivariate simplex projections, based on attractor state-space reconstruction. We show that the performance of each model is contingent on the historical dynamics of individual stocks, and that stocks which experienced abrupt and state-dependent dynamics are best modelled using non-parametric approaches. These dynamics are pervasive in Western stocks highlighting a geographical distinction between cod stocks, which have implications for their recovery potential. Furthermore, the addition of environmental variables always improved the models’ predictive power indicating that they should be considered in stock assessment and management routines. Using our multi-model approach, we demonstrate that we should be more flexible when modelling recruitment and tailor our approaches to the dynamical properties of each individual stock.

scholarly journals A Non-Parametric Approach for Survival Analysis of Component-Based Software

2020 ◽  
Vol 5 (2) ◽  
pp. 309-318
Author(s):  
Sandeep Chopra ◽  
Lata Nautiyal ◽  
Preeti Malik ◽  
Mangey Ram ◽  
Mahesh K. Sharma
Keyword(s):  
Survival Analysis ◽  
Failure Rate ◽  
Growth Models ◽  
Reliability Estimation ◽  
Parametric Models ◽  
Model Parameters ◽  
Parametric Approach ◽  
Estimation Model ◽  
Failure Data ◽  
Non Parametric

Reliability of a software or system is the probability of system to perform its functions adequately for the stated time period under specific environment conditions. In case of component-based software development reliability estimation is a crucial factor. Existing reliability estimation model falls into two broad categories parametric and non-parametric models. Parametric models approximate the model parameters based on the assumptions of fundamental distributions. Non-parametric models enable parameter estimation of the software reliability growth models without any assumptions. We have proposed a novel non-parametric approach for survival analysis of components. Failure data is collected based on which we have calculated failure rate and reliability of the software. Failure rate increases with the time whereas reliability decreases with the time.

scholarly journals Stock-recruitment models, principles of management and rules for regulating the fishery on the main Chukotka stocks of sockeye and chum salmon

2021 ◽  
Vol 201 (3) ◽  
pp. 735-751
Author(s):  
E. A. Shevlyakov ◽  
M. G. Feldman ◽  
A. N. Kanzeparova
Keyword(s):  
Sockeye Salmon ◽  
Chum Salmon ◽  
Fishery Management ◽  
Pacific Salmon ◽  
Far East ◽  
Simulation Models ◽  
Processing Capacity ◽  
Spawning Stock ◽  
Stock Recruitment ◽  
Recruitment Model

Fishery pressure on populations of pacific salmons has increased in the Rusian Far East in the last decade because of growing fishing and processing capacity, so measures for the fishery regulation are necessary, as the regime of pass days in rivers and marine coastal areas. Chukotka is now almost the only region where such restrictions are still absent. However, if the interest of fishery industry to the stocks of pacific salmon in Chukotka will grow, a successful scientifically based strategy of fishery should be developed to maintain exploitation of the stocks without exceeding the limits of excessive use. Year-to-year time series on spawning stock and recruitment of chum salmon in the Anadyr area and sockeye salmon in the Meynypilgyn area were analysed for development of recruitment models and establishment of general principles for adaptive fishery management. Nonlinear adaptive fishery management based on principles of buffer managing is proposed and tested under various regimes of landing using the stock simulation models accounting deviations from the standard stock-recruitment model. There is concluded that the level of exploitation is much lower than optimal for the Anadyr chum salmon, whereas escapement for spawning of the Meynypilgyn sockeye salmon should be increased in cases of low spawning stock of this species.

The estimation and robustness of FMSY and alternative fishing mortality reference points associated with high long-term yield

2012 ◽  
Vol 69 (9) ◽  
pp. 1468-1480 ◽  
Author(s):  
Jan Horbowy ◽  
Anna Luzeńczyk
Keyword(s):  
Total Yield ◽  
Reference Points ◽  
Superior Performance ◽  
Equilibrium Yield ◽  
Spawning Stock ◽  
Stock Recruitment ◽  
Yield Per Recruit ◽  
Different Sources ◽  
Recruitment Model

Equations for equilibrium yield and biomass are presented and used to derive FMSY and alternative reference points (ARPs). ARPs are analogues of the traditional points based on yield-per-recruit (YPR) or spawning stock-per-recruit (SPR) (F0.1, F40%, F50%), but refer to the equilibrium total yield or biomass. The method combines YPR and SPR analysis with stock–recruitment relationships. The sensitivity of FMSY and ARPs to the range of available stock–recruitment data, recruitment variance, various steepness levels in the stock–recruitment models, assessment variance, and bias are tested. The analysis showed that in most cases, F40%B and F50%B, defined by the equilibrium biomass (B), were the most robust relative to the different sources of uncertainty. However, in the case of the misspecification of the stock–recruitment relationship, FMSY showed superior performance. F40%B for the Beverton and Holt stock–recruitment model and F50%B for the Ricker recruitment model can be recommended as conservative fishing mortalities associated with high long-term yield. For the considered steepness, they produced yield up to 5%–10% lower than yield at FMSY.

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