Modelling and Quantitative Methods in 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

Biography

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.

The text remains true to the author’s initial aim of providing an introduction to the analytical methods currently being used in quantitative biology and fisheries science. It is important to remember when reading this book that there are few texts that students can truly consult on fisheries science without a detailed understanding of stock assessment and fisheries management practices—this text continues to bridge that gap.
The material has been revised and improvements made to a number of the examples. Two concerns and reservations that I commented on in my previous review have been addressed by the inclusion of two new chapters—one on characterizing uncertainty covering asymptotic errors and likelihood profiles, and the other on size-based models using abalone as an example. The book is lavishly illustrated throughout with the use of Microsoft Excel workbooks which adds to the flexibility, availability and ease of use of the text. I recommend the text both as a course companion and for private study.
—Carl M. O’Brien, International Statistical Review, 2012

This update of Malcolm Haddon’s book is timely and appreciated. The authors breadth of interest and experience really comes through. Fisheries science, and much of ecological science, has become highly numerical. Modelling, statistics, and scientific programming are now sought-after skills. This book doesn’t just tell you about these, but gives you the tools, and even the R code to do it. I would expect everyone from graduate students through to professionals to gain useful knowledge from this book.
- Matt Dunn, NIWA

Praise for the First Edition:
The book is a good introduction to modeling for students and practitioners. The emphasis is on population models, with chapters on parameter estimation, randomization tests, resampling methods, Monte Carlo methods, stock-recruitment, and age-structures models. One helpful feature is the use of spreadsheet examples to illustrate the methods.
—Fisheries, 2002