Modeling and simulation in ecotoxicology with applications in matlab and simulink

Exploring roles critical to environmental toxicology, Modeling and Simulation in Ecotoxicology with Applications in MATLAB^® and Simulink^® covers the steps in modeling and simulation from problem conception to validation and simulation analysis. Using the MATLAB and Simulink programming languages, the book presents examples of mathematical functions and simulations, with special emphasis on how to develop mathematical models and run computer simulations of ecotoxicological processes.

Designed for students and professionals with little or no experience in modeling, the book includes:

• General principles of modeling and simulation and an introduction to MATLAB and Simulink
• Stochastic modeling where variability and uncertainty are acknowledged by making parameters random variables
• Toxicological processes from the level of the individual organism, with worked examples of process models in either MATLAB or Simulink
• Toxicological processes at the level of populations, communities, and ecosystems
• Parameter estimation using least squares regression methods
• The design of simulation experiments similar to the experimental design applied to laboratory or field experiments
• Methods of postsimulation analysis, including stability analysis and sensitivity analysis
• Different levels of model validation and how they are related to the modeling purpose

The book also provides three individual case studies. The first involves a model developed to assess the relative risk of mortality following exposure to insecticides in different avian species. The second explores the role of diving behavior on the inhalation and distribution of oil spill naphthalene in bottlenose dolphins. The final case study looks at the dynamics of mercury in Daphnia that are exposed to simulated thermal plumes from a hypothetical power plant cooling system.

Presented in a rigorous yet accessible style, the methodology is versatile enough to be readily applicable not only to environmental toxicology but a range of other biological fields.

Introduction
Theories Underlying Predictive Models
Reasons for Modeling and Simulation
What Does It Take To Be a Modeler?
Why Models Fail: A Cautionary Note
Principles of Modeling and Simulation
Systems
Modeling
Simulation
Introduction to Matlab and Simulink
MATLAB
Simulink
Exercises
Introduction to Stochastic Modeling
Introduction to Probability Distributions
Example Probability Distributions
Discrete-State Markov Processes
Monte Carlo Simulation
Exercises
Modeling Ecotoxicology of Individuals
Toxic Effects on Individuals
Exercises
Modeling Ecotoxicology of Populations, Communities, and Ecosystems
Effects of Toxicants on Aggregated Populations
Effects of Toxicants on Age-Structured Populations
Effects of Toxicants on Communities
Effects of Toxicants on Ecosystems
Exercises
Parameter Estimation
Linear Regression
Nonlinear Regression
Comparison between Linear and Nonlinear Regressions
Exercises
Designing Simulation Experiments
Factorial Designs
Response Surface Designs
Exercises
Analysis of Simulation Experiments
Simulation Output Analysis
Stability Analysis
Sensitivity Analysis
Response Surface Methodology
Exercises
Model Validation
Validation and Reasons for Modeling and Simulation
Testing Hypotheses
Statistical Techniques
Some MATLAB Methods
Exercises
A Model to Predict the Effects of Insecticides on Avian Populations
Problem Definition
Model Development
Model Implementation
Data Requirements
Model Validation
Design Simulation Experiments
Analyze Results of Simulation Experiments
Case Study: Predicting Health Risk to Bottlenose Dolphins from Exposure to Oil Spill Toxicants
Problem Definition
Model Development
Model Implementation
Data Requirements
Model Validation
Design of Simulation Experiments
Analyze Results of Simulation Experiments
Presentation and Implementation of Results
Case Study: Simulating the Effects of Temperature Plumes on the Uptake of Mercury in Daphnia
Problem Definition
Model Development
Model Implementation
Data Requirements
Model Validation
Design of Simulation Experiments
Analyze Results of Simulation Experiments
Presentation and Implementation of Results
Index.