A goal of this course is to provide exposure to a variety of 
quantitative models and their assumptions.  Students should be 
interested in thinking about the quantitative nature of 
population assessment.  We will explore what it means to conduct
conservation, confront models with data, and provide meaningful
decision support.  Students will be encouraged to integrate
lectures, readings, discussions, and computer exercises.

1.	Historical context and introduction to models
2.	Basic population dynamics
3.	Population monitoring 
4.	Simple linear regression & the Gaussian distribution
5.	Surplus production & MSY
6.	Growth and recruitment models
7.	Probability & likelihood overview
8.	More distributions
9.	Model selection and multimodel inference
10.	Fixed and random effects
11.	Variance partitioning
12.	Hierarchical modeling
13.	Bayesian overview
14.	Models and decision making

Students in this course must comply with the University Honor 
code and Academic Honesty Policy.  Students may discuss 
homework assignments for clarification but any completed 
assignment/test must represent the work of a single 
individual.  Group projects will be explicitly identified in 
class and will represent the collaborative efforts of the 
individuals within the group. Cheating on assignments/tests 
will be handled as described in the Academic Honesty Policy 
Handbook.