Instructor: John McLaughlin
Office: ES 440
Phone: 650- 7617
E-mail: 
Office Hours:
Course Web Site: http://www.ac.wwu.edu/~jmcl/envr499.htm
Prerequisite: MATH 124 Calculus and Analytic Geometry, ENVR 340 Biostatistical Analysis, or equivalents.
Reading:
Most reading will be from the following book, which will serve as the course "text".
Hilborn, R. and M. Mangel. 1997. The Ecological Detective: Confronting Models with Data. Princeton Univ. Press.
Additional readings (available electronically as noted below, or in the course binder shelved in ES 545):
Dennis, B. 1996. Discussion: Should ecologists become Bayesians? Ecological Applications 6:1095-1103.
Ellison, A. M. 1996. An introduction to Bayesian inference for ecological research and environmental decision-making. Ecological Applications 6:1036-1046.
EVS Environmental Consultants. 1999. Cherry Point: Screening Level Ecological Risk Assessment. Report to WA Dept. Natural Resources, Olympia, WA.; selections.
Kendall, B. E. et al. 1999. Why do populations cycle? A synthesis of statistical and mechanistic modeling approaches. Ecology 80:1789-1805.
Ludwig, D. 1996. Uncertainty and the assessment of extinction probabilities. Ecological Applications 6:1067-1076.
Full text copies of Ecological Applications articles may be viewed electronically with JSTOR: http://www.jstor.org/cgi-bin/jstor/listjournal
A full text copy of the Kendall et al. article may be viewed by searching ProQuest: http://www.library.wwu.edu/cgi-bin/dbasewwwalph.pl
Course Description:
Models play increasingly important roles in the work of professional ecologists. Models can be used to evaluate hypotheses, design experiments, probe ecological complexity, and confront uncertainty. When skillfully combined with data, models can be essential to answering ecological questions. Models intimidate most fledgling ecologists, however. Many different models can be used to describe the same system -- how do you choose which model is best? How do you incorporate uncertainty in a model? How do you combine models with statistical analysis? How do you use models to evaluate multiple hypotheses? How do you build a good model in the first place?
This seminar will provide answers to these and related questions. Several contentious issues will provide context for our discussions, including: outbreaks of agricultural pests, fisheries by-catch, causes of animal population cycles, wildebeest conservation, avian extinction, fisheries management, and reasons for the decline of the Cherry Point herring stock.
The seminar format will consist of discussion of the assigned readings. Each class meeting will be led by two discussion leaders, with two alternates prepared to lead should one or both leaders succumb to illness, etc. The required course "text" is available in the WWU bookstore. The supplementary journal articles are in a course binder in the Huxley Library, ES 545.
Course Evaluation:
Grades (S/U) will be based on participation during discussions (30%), leadership of two discussions (40%), and an analysis of the decline in the Cherry Point herring stock (30%). If the herring analysis is not submitted, the course grade will be an incomplete.
Course Schedule:
|
Week of: |
Topic |
Reading (from text, except as noted) |
|
Sept. 29 |
Introduction and Organization |
|
|
Oct. 2 |
Methods of Scientific Inquiry |
Ch.1, Ch.2 |
|
Oct. 9 |
Probability Distributions and Models |
Ch. 3, Ch. 4 |
|
Oct. 16 |
Model Selection by Sum of Squares |
Ch. 5, Ch. 6 |
|
Oct. 23 |
Application: Understanding Population Cycles |
Kendall, et al. 1999. |
|
Oct. 30 |
Application: Decline of Cherry Point Herring |
Cherry Point ERA |
|
Nov. 6 |
Model Selection by Maximum Likelihood |
Ch. 7 |
|
Nov. 13 |
Maximum Likelihood, part 2 |
Ch. 8 |
|
Nov. 20 |
Bayesian Analysis |
Ch. 9, Ch. 10, Ellison 1996. |
|
Nov. 27 |
Critique of Bayesian Inference |
Ludwig 1996; Dennis 1996. |
|
Nov. 29 |
Discussion: Cherry Point Herring Analysis |
|
|
Dec. 4 |
Cherry Point Herring Analysis due |
|
|
Dec. 11 |
Exam week (no class) |
|
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