Introduction to Ecological and Environmental Modeling

CATALOG DESCRIPTION

Instructor: Ming Xu, Assistant Professor

Prerequisite: College level calculus and basic statistics OR permission of instructor

This course will review the background mathematical and statistical tools necessary for students interested in pursuing ecological and environmental modeling. The course will discuss general model formulation, validation, hypothesis testing, non-linear phenomena, and forecasting. Topics include linear algebra, differential equations, statistical tools, regression and curve fitting, box model, and population dynamics model. This course will be recommended as a prerequisite for advanced Ecosystem Modeling courses.

NARRATIVE JUSTIFICATION

Quantitative analysis is of more and more importance in understanding biological and biophysical processes and in managing environment and natural resources. Basic quantitative skills are indispensable to the students majoring in ecology, natural resource management, and environmental science. However, there are no courses at Cook College, or anywhere on the New Brunswick campus, which are specifically designed to introduce students to ecological and environmental modeling. It is the intent of this course to fill this need. The course is designed for senior undergraduates and graduate students who do not have strong quantitative skills in their previous training.

CREDIT HOURS: 3

James W. Haefner. 1996. Modeling Biological Systems: Principles and Applications. Chapman and Hall, New York.

Grading: Attendance 10% Assignment 30% Mid-term (Essay type) 25% Final (Essay type) 35% Syllabus

Introduction to Ecological and Environmental Modeling (16:215:600:02, INDEX # 34742) Fall 2003

Lectures: Tu. 9:50-11:10 & Th. 1:30-3:00 Unites: 3 Location: HICKMAN HALL 132 (or 145 ENR Building, depending on class size) Instructor: Ming Xu, 132 ENR, 932-9211, [email protected] Office hours: Th. 3:00-4:30 or by appointment

Week Topic Readings Assignment Due*

1 Introduction to modeling Text: Chaps 1, 2 Problem set 1

2 Qualitative model formulation Text: Chap 3 Problem set 2

3 Quantitative model formulation Text: Chap 4 Problem set 3 I: difference and differential equations

4. Quantitative model formulation Text: Chap 4 Problem set 4 II: application of differential equations

5 Quantitative model formulation Text: Chap 4 Problem set 5 III: useful functions

6 Model simulation: computer tools Text: Chap 5 Problem set 6

7 Numerical techniques Text: Chap 6 Problem set 7 Mid-term (Essay type)

8 Parameter estimation Text: Chap 7 Problem set 8 I: linear regression

9 Parameter estimation Text: Chap 7 Problem set 9 II: nonlinear equations

10 Model validation Text Chap 8 Problem set 10

11 Model analysis Text Chap 9 Problem set 11

12 Stochastic models Text Chap 10 Problem set 12

13 Model application Text Chaps 10, 13 Problem set 13

Final (Essay type)