Population model

Abstract: Matrix population models are discrete-time structured population models in which individuals are classified into discrete stages (age classes, size classes, developmental stages, spatial locations, etc.).

Abstract: Preface * Ackn. * Prologue * Part I: Simple Single-Species Models * 1 Continuous Population Models * 2 Discrete Population Models * 3 Continuous single-species Population Models with Delays * Part II: Models for Interacting Species * 4 Introduction and Mathematical Preliminaries * 5 Continuous models for two interacting populations * 6 Harvesting in two-species models * Part III: Structured Population Models * 7 Basic ideas of Mathematical Epidemiology * 8 Models for population with age structure * Epilogue * Answers to selected Exercises * References

Abstract: Management of many species is currently based on an inadequate under- standing of their population dynamics. Lack of age-specific demographic information, particularly for long-lived iteroparous species, has impeded development of useful models. We use a Lefkovitch stage class matrix model, based on a preliminary life table developed by Frazer (1983a), to point to interim management measures and to identify those data most critical to refining our knowledge about the population dynamics of threatened log- gerhead sea turtles (Caretta caretta). Population projections are used to examine the sen- sitivity of Frazer's life table to variations in parameter estimates as well as the likely response of the population to various management alternatives. Current management practices appear to be focused on the least responsive life stage, eggs on nesting-beaches. Alternative protection efforts for juvenile loggerheads, such as using turtle excluder devices (TEDs), may be far more effective.

Abstract: 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