| Description |
xvi, 449 pages : illustrations ; 25 cm |
| Series |
Chapman & Hall book |
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Chapman & Hall book.
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| Contents |
Contents note continued: 10.9.1.Strategies for Including Stock Recruitment Relationships -- 10.9.2.Steepness -- 10.9.3.Beverton--Holt Redefined -- 10.10.Concluding Remarks -- Appendix 10.1 Derivation of Beverton---Holt Equations -- Appendix 10.2 Derivation of the Ricker Equations -- Appendix 10.3 Deriving the Beverton---Holt Parameters -- 11.Surplus Production Models -- 11.1.Introduction -- 11.1.1.Stock Assessment Modelling Options -- 11.1.2.Surplus Production -- 11.2.Equilibrium Methods -- 11.3.Surplus Production Models -- 11.3.1.Russell's Formulation -- 11.3.2.Alternative Fitting Methodology -- 11.4.Observation Error Estimates -- 11.4.1.Outline of Method -- 11.4.2.In Theory and Practice -- 11.4.3.Model Outputs -- 11.5.Beyond Simple Models -- 11.5.1.Introduction -- 11.5.2.Changes in Catchability -- 11.5.3.The Limits of Production Modelling -- 11.6.Uncertainty of Parameter Estimates -- 11.6.1.Likelihood Profiles -- 11.6.2.Bootstrap Confidence Intervals and Estimates of Bias -- |
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Contents note continued: 11.7.Risk Assessment Projections -- 11.7.1.Introduction -- 11.7.2.Bootstrap Projections -- 11.7.3.Projections with Set Catches -- 11.7.4.Projections with Set Effort -- 11.8.Practical Considerations -- 11.8.1.Introduction -- 11.8.2.Fitting the Models -- 11.9.Concluding Remarks -- Appendix 11.1 Derivation of Equilibrium-Based Stock Production -- Appendix 11.2 The Closed Form of the Estimate of the Catchability Coefficient -- Version 1 Constant q -- Version 2 Additive Increment to Catchability -- Version 3 Constant Proportional Increase---qinc -- Appendix 11.3 Simplification of the Maximum Likelihood Estimator -- 12.Age-Structured Models -- 12.1.Types of Models -- 12.1.1.Introduction -- 12.1.2.Age-Structured Population Dynamics -- 12.1.3.Fitting Age-Structured Models -- 12.2.Cohort Analysis -- 12.2.1.Introduction -- 12.2.2.The Equations -- 12.2.3.Pope's and MacCall's Approximate Solutions -- 12.2.4.Newton's Method -- 12.2.5.Terminal F Estimates -- |
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Contents note continued: 12.2.6.Potential Problems with Cohort Analysis -- 12.2.7.Concluding Remarks on Cohort Analysis -- 12.3.Statistical Catch-at-Age -- 12.3.1.Introduction -- 12.3.2.The Equations -- 12.3.3.Fitting to Catch-at-Age Data -- 12.3.4.Fitting to Fully Selected Fishing Mortality -- 12.3.5.Adding a Stock Recruitment Relationship -- 12.3.6.Other Auxiliary Data and Different Criteria of Fit -- 12.3.7.Relative Weight to Different Contributions -- 12.3.8.Characterization of Uncertainty -- 12.3.9.Model Projections and Risk Assessment -- 12.4.Concluding Remarks -- Appendix 12.1 Weight-at-Age Data and Optimum Fit to Catch-at-Age Model -- 13.Size-Based Models -- 13.1.Introduction -- 13.1.1.Stock Assessment Modelling Options -- 13.2.The Model Structure -- 13.3.Concluding Remarks -- Appendix A The Use of Excel in Fisheries -- A.1.Introduction -- A.1.1.Getting Started -- A.2.Workbook Skills -- A.2.1.Tools/Options, Auditing, and Customization -- A.2.2.Data Entry -- |
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Contents note continued: 2.3.1.Exponential Growth -- 2.3.2.Standard Transformations -- 2.3.3.Why Consider Equilibrium Conditions? -- 2.4.Density-Dependent Models -- 2.4.1.An Upper Limit and Persistence -- 2.4.2.The Logistic Model of Growth -- 2.4.3.Discrete Logistic Model -- 2.4.4.Stability Properties -- 2.4.5.Dynamic Behaviour -- 2.5.Responses to Fishing Pressure -- 2.6.The Logistic Model in Fisheries -- 2.7.Age-Structured Models -- 2.7.1.Age-Structured and Exponential Growth Models -- 2.7.2.Annual versus Instantaneous Mortality Rates -- 2.7.3.Selection of a Target Fishing Mortality -- 2.8.Simple Yield-per-Recruit -- 2.8.1.Is There an Optimum Fishing Mortality Rate? -- 2.8.2.What Is the Optimum Age or Size at First Capture? -- 2.8.3.From Empirical Table to Mathematical Model -- 2.8.4.The Model Structure and Assumptions -- 2.8.5.The Model Equations -- 2.8.6.Yield-per-Recruit Management Targets -- 2.8.7.Management Targets and Limits -- |
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Contents note continued: 2.8.8.Uncertainties in Yield-per-Recruit Analyses -- 2.8.9.Types of Overfishing -- 3.Model Parameter Estimation -- 3.1.Models and Data -- 3.1.1.Fitting Data to a Model -- 3.1.2.Which Comes First, the Data or the Model? -- 3.1.3.Quality of Fit versus Parsimony versus Reality -- 3.1.4.Uncertainty -- 3.1.5.Alternative Criteria of Goodness of Fit -- 3.2.Least Squared Residuals -- 3.2.1.Introduction -- 3.2.2.Selection of Residual Error Structure -- 3.3.Nonlinear Estimation -- 3.3.1.Parameter Estimation Techniques -- 3.3.2.Graphical Searches for Optimal Parameter Values -- 3.3.3.Parameter Correlation and Confounding Effects -- 3.3.4.Automated Directed Searches -- 3.3.5.Automated Heuristic Searches -- 3.4.Likelihood -- 3.4.1.Maximum Likelihood Criterion of Fit -- 3.4.2.The Normal Distribution -- 3.4.3.Probability Density -- 3.4.4.Likelihood Definition -- 3.4.5.Maximum Likelihood Criterion -- 3.4.6.Likelihoods with the Normal Probability Distribution -- |
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Contents note continued: 3.4.7.Equivalence with Least Squares -- 3.4.8.Fitting a Curve Using Normal Likelihoods -- 3.4.9.Likelihoods from the Lognormal Distribution -- 3.4.10.Fitting a Curve Using Lognormal Likelihoods -- 3.4.11.Likelihoods with the Binomial Distribution -- 3.4.12.Multiple Observations -- 3.4.13.Likelihoods from the Poisson Distribution -- 3.4.14.Likelihoods from the Gamma Distribution -- 3.4.15.Likelihoods from the Multinomial Distribution -- 3.5.Bayes' Theorem -- 3.5.1.Introduction -- 3.5.2.Bayes' Theorem -- 3.5.3.Prior Probabilities -- 3.5.4.An Example of a Useful Informative Prior -- 3.5.5.Noninformative Priors -- 3.6.Concluding Remarks -- 4.Computer-Intensive Methods -- 4.1.Introduction -- 4.2.Resampling -- 4.3.Randomization Tests -- 4.4.Jackknife Methods -- 4.5.Bootstrapping Methods -- 4.6.Monte Carlo Methods -- 4.7.Bayesian Methods -- 4.8.Relationships between Methods -- 4.9.Computer Programming -- 5.Randomization Tests -- 5.1.Introduction -- |
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Contents note continued: 5.2.Hypothesis Testing -- 5.2.1.Introduction -- 5.2.2.Standard Significance Testing -- 5.2.3.Significance Testing by Randomization Test -- 5.2.4.Mechanics of Randomization Tests -- 5.2.5.Selection of a Test Statistic -- 5.2.6.Ideal Test Statistics -- 5.3.Randomization of Structured Data -- 5.3.1.Introduction -- 5.3.2.More Complex Examples -- 6.Statistical Bootstrap Methods -- 6.1.The Jackknife and Pseudovalues -- 6.1.1.Introduction -- 6.1.2.Parameter Estimation and Bias -- 6.1.3.Jackknife Bias Estimation -- 6.2.The Bootstrap -- 6.2.1.The Value of Bootstrapping -- 6.2.2.Empirical versus Theoretical Probability Distributions -- 6.3.Bootstrap Statistics -- 6.3.1.Bootstrap Standard Errors -- 6.3.2.Bootstrap Replicates -- 6.3.3.Parametric Confidence Intervals -- 6.3.4.Bootstrap Estimate of Bias -- 6.4.Bootstrap Confidence Intervals -- 6.4.1.Percentile Confidence Intervals -- 6.4.2.Bias-Corrected Percentile Confidence Intervals -- |
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Contents note continued: 6.4.3.Other Bootstrap Confidence Intervals -- 6.4.4.Balanced Bootstraps -- 6.5.Concluding Remarks -- 7.Monte Carlo Modelling -- 7.1.Monte Carlo Models -- 7.1.1.The Uses of Monte Carlo Modelling -- 7.1.2.Types of Uncertainty -- 7.2.Practical Requirements -- 7.2.1.The Model Definition -- 7.2.2.Random Numbers -- 7.2.3.Nonuniform Random Numbers -- 7.2.4.Other Practical Considerations -- 7.3.A Simple Population Model -- 7.4.A Nonequilibrium Catch Curve -- 7.4.1.Ordinary Catch Curve Analysis -- 7.4.2.The Influence of Sampling Error -- 7.4.3.The Influence of Recruitment Variability -- 7.5.Concluding Remarks -- 8.Characterization of Uncertainty -- 8.1.Introduction -- 8.2.Asymptotic Standard Errors -- 8.3.Percentile Confidence Intervals Using Likelihoods -- 8.4.Likelihood Profile Confidence Intervals -- 8.5.Percentile Likelihood Profiles for Model Outputs -- 8.6.Markov Chain Monte Carlo (MCMC) -- 8.7.Concluding Remarks -- 9.Growth of Individuals -- |
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Contents note continued: 9.1.Growth in Size -- 9.1.1.Uses of Growth Information -- 9.1.2.The Data -- 9.1.3.Historical Usage -- 9.2.Von Bertalanffy Growth Model -- 9.2.1.Growth in Length -- 9.2.2.Growth in Weight -- 9.2.3.Seasonal Growth -- 9.2.4.Fitting to Tagging Data -- 9.2.5.Extensions to Fabens Method -- 9.2.6.Comparability of Growth Curves -- 9.2.7.Growth from Modal Progression -- 9.3.Alternatives to Von Bertalanffy -- 9.3.1.A Generalized Model -- 9.3.2.Model Selection---AIC and BIC -- 9.3.3.Polynomial Equations -- 9.3.4.Problems with the von Bertalanffy Growth Function -- 9.4.Comparing Growth Curves -- 9.4.1.Nonlinear Comparisons -- 9.4.2.An Overall Test of Coincident Curves -- 9.4.3.Likelihood Ratio Tests -- 9.4.4.Kimura's Likelihood Ratio Test -- 9.4.5.Less than Perfect Data -- 9.4.6.A Randomization Version of the Likelihood Ratio Test -- 9.5.Concluding Remarks -- Appendix 9.1 Derivation of the Fabens Version of the von Bertalanffy Growth Equation -- |
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Contents note continued: A.2.3.Movement around Worksheets -- A.2.4.Range Selection -- A.2.5.Formatting and Naming Cells and Ranges -- A.2.6.Formulas -- A.2.7.Functions -- A.2.7.1.=SumProduct() -- A.2.7.2.=Frequency() and Countif() -- A.2.7.3.=Linest() -- A.2.7.4.=Vlookup() -- A.2.7.5.Other Functions -- A.3.Visual Basic for Applications -- A.3.1.Introduction -- A.3.2.An Example Macro -- A.3.3.Using the Solver Inside a Macro -- A.4.Concluding Remarks |
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Contents note continued: Appendix 9.2 Derivation of the Maximum Likelihood Estimator for the von Bertalanffy Curve -- 10.Stock Recruitment Relationships -- 10.1.Recruitment and Fisheries -- 10.1.1.Introduction -- 10.1.2.Recruitment Overfishing -- 10.1.3.The Existence of a Stock Recruitment Relationship -- 10.2.Stock Recruitment Biology -- 10.2.1.Properties of "Good" Stock Recruitment Relationships -- 10.2.2.Data Requirements---Spawning Stock -- 10.2.3.Data Requirements---Recruitment -- 10.3.Beverton---Holt Recruitment Model -- 10.3.1.The Equations -- 10.3.2.Biological Assumptions/Implications -- 10.4.Ricker Model -- 10.4.1.The Equation -- 10.4.2.Biological Assumptions/Implications -- 10.5.Deriso's Generalized Model -- 10.5.1.The Equations -- 10.6.Residual Error Structure -- 10.7.The Impact of Measurement Errors -- 10.7.1.Appearance over Reality -- 10.7.2.Observation Errors Obscuring Relationships -- 10.8.Environmental Influences -- 10.9.Recruitment in Age-Structured Models -- |
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Machine generated contents note: 1.Fisheries and Modelling -- 1.1.Fish Population Dynamics -- 1.2.The Objectives of Stock Assessment -- 1.2.1.Characterizing Stock Dynamics -- 1.2.2.Characterizing Uncertainty -- 1.2.3.Management Objectives -- 1.3.Characteristics of Mathematical Models -- 1.3.1.General Properties -- 1.3.2.Limitations Due to the Modeller -- 1.3.3.Limitations Due to Model Type -- 1.3.4.The Structure of Mathematical Models -- 1.3.5.Parameters and Variables -- 1.4.Types of Model Structure -- 1.4.1.Deterministic/Stochastic -- 1.4.2.Continuous versus Discrete Models -- 1.4.3.Descriptive/Explanatory -- 1.4.4.Testing Explanatory Models -- 1.4.5.Realism/Generality -- 1.4.6.When Is a Model a Theory? -- 2.Simple Population Models -- 2.1.Introduction -- 2.1.1.Biological Population Dynamics -- 2.1.2.The Dynamics of Mathematical Models -- 2.2.Assumptions---Explicit and Implicit -- 2.2.1.All Assumptions Should Be Explicit -- 2.3.Density-Independent Growth -- |
| Notes |
"A Chapman & Hall Book." |
| Bibliography |
Includes bibliographical references (pages 419-433) and index |
| Subject |
Fisheries -- Mathematical models.
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| LC no. |
2010045166 |
| ISBN |
1584885610 (hardcover : alk. paper) |
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9781584885610 (hardcover : alk. paper) |
