Modelling and Quantitative Methods in Fisheries

With numerous real-world examples, Modelling and Quantitative Methods in Fisheries, Second Edition provides an introduction to the analytical methods used by fisheries’ scientists and ecologists. By following the examples using Excel, readers see the nuts and bolts of how the methods work and better understand the underlying principles. Excel workbooks are available for download from CRC Press website.

In this second edition, the author has revised all chapters and improved a number of the examples. This edition also includes two entirely new chapters:


* Characterization of Uncertainty covers asymptotic errors and likelihood profiles and develops a generalized Gibbs sampler to run a Markov chain Monte Carlo analysis that can be used to generate Bayesian posteriors
* Sized-Based Models implements a fully functional size-based stock assessment model using abalone as an example


This book continues to cover a broad range of topics related to quantitative methods and modelling. It offers a solid foundation in the skills required for the quantitative study of marine populations. Explaining important and relatively complex ideas and methods in a clear manner, the author presents full, step-by-step derivations of equations as much as possible to enable a thorough understanding of the models and methods.

Malcolm Haddon is a senior fisheries modeller for CSIRO in Hobart, Tasmania, Australia. Prior to joining CSIRO, Dr. Haddon was an associate professor at the University of Tasmania, head of fisheries at Australian Maritime College, a senior research fellow at the University of Sydney, editor of the New Zealand Journal of Marine and Freshwater Research, and a lecturer at Victoria University of Wellington. He has conducted stock assessments on Tasmanian rock lobster, giant crab, and abalone. Now at CSIRO, he continues to produce stock assessments of abalone but also for an array of Australian Commonwealth fisheries.

Fisheries and Modelling
Fish Population Dynamics
The Objectives of Stock Assessment
Characteristics of Mathematical Models
Types of Model Structure


Simple Population Models
Introduction
Assumptions—Explicit and Implicit
Density-Independent Growth
Density-Dependent Models
Responses to Fishing Pressure
The Logistic Model in Fisheries
Age-Structured Models
Simple Yield-per-Recruit


Model Parameter Estimation
Models and Data
Least Squared Residuals
Nonlinear Estimation
Likelihood
Bayes’ Theorem
Concluding Remarks


Computer-Intensive Methods
Introduction
Resampling
Randomization Tests
Jackknife Methods
Bootstrapping Methods
Monte Carlo Methods
Bayesian Methods
Relationships between Methods
Computer Programming


Randomization Tests
Introduction
Hypothesis Testing
Randomization of Structured Data


Statistical Bootstrap Methods
The Jackknife and Pseudo Values
The Bootstrap
Bootstrap Statistics
Bootstrap Confidence Intervals
Concluding Remarks


Monte Carlo Modelling
Monte Carlo Models
Practical Requirements
A Simple Population Model
A Non-Equilibrium Catch Curve
Concluding Remarks


Characterization of Uncertainty
Introduction
Asymptotic Standard Errors
Percentile Confidence Intervals Using Likelihoods
Likelihood Profile Confidence Intervals
Percentile Likelihood Profiles for Model Outputs
Markov Chain Monte Carlo (MCMC)
Conclusion


Growth of Individuals
Growth in Size
von Bertalanffy Growth Model
Alternatives to von Bertalanffy
Comparing Growth Curves
Concluding Remarks


Stock Recruitment Relationships
Recruitment and Fisheries
Stock Recruitment Biology
Beverton-Holt Recruitment Model
Ricker Model
Deriso’s Generalized Model
Residual Error Structure
The Impact of Measurement Errors
Environmental Influences
Recruitment in Age-Structured Models
Concluding Remarks


Surplus Production Models
Introduction
Equilibrium Methods
Surplus Production Models
Observation Error Estimates
Beyond Simple Models
Uncertainty of Parameter Estimates
Risk Assessment Projections
Practical Considerations
Conclusions

Age-Structured Models
Types of Models
Cohort Analysis
Statistical Catch-at-Age
Concluding Remarks

Size-Based Models
Introduction
The Model Structure
Conclusion

Appendix: The Use of Excel in Fisheries

Bibliography

Index