Bio/Bot/Zoo 293 --- Simulating Ecological and Evolutionary Systems
Instructor: Will Wilson
Meeting Details: BioSci Rm 107, Mondays 1 to 3 (or so)
Office and such: BioSci 033, 660-7346, wgw@duke.edu



This course will cover the use of C programming applied to 
ecological and evolutionary problems, ranging from stochastic birth--
death models to foraging models to resource allocation in plants. 
Each simulation will be accompanied by analytic modelling, including 
differential equations and the like, providing a stronger theoretical 
foundation, but the primary tool will be simulation and computational 
techniques. 


There are no prerequisites for taking the course, for example, students 
are not expected to know how to program or work within the UNIX 
computer environment. All students enrolled at Duke have access to 
the acpub computer system and its resources (a UNIX operating system, 
an ANSI-standard C compiler, and the textedit editor), however, 
students are encouraged to use their own computers with whatever 
operating system (UNIX, Windows or Macintosh), editors, and C compilers. 
I may not be able to provide much detailed assistance with those systems, 
however.

At the beginning of the term, I will cover the basic UNIX commands and 
how to use the textedit file editor (my personal favorites). Then, 
during the first part of the term (8 weeks or so), I'll cover basic 
C programming by examining ``simple'' birth-death population models 
from both individual-based simulation and analytic perspectives. I'll 
assign a series of computing projects to familiarize students with 
essential programming concepts and the language C. These projects begin 
with very straightforward programs designed to initiate students with 
editing files, and compiling and running a computer program. After a 
few programming projects, I'll introduce more advanced aspects of C, 
including pointers, structures, and compiler directives, and important 
numerical algorithms including random number and bit generation, 
numerical integration, and visualization techniques. I might give a 
midterm on the above topics.


The important part of the course is an individually motivated 
programming project. It could be either a computational technique 
(genetic algorithm, neural networks, numerical analysis, random 
number generation, etc.) or a biological application of a 
computational method (a published paper or general area).  Ideally, 
two aspects should come together in your project: One, the project 
should relate to your thesis topic (and ideally be a publishable 
piece of work), and two, I want you to demonstrate a mastery of 
programming skills.   After 4 or 5 weeks, students will turn in a 
written outline and give overview talks on their proposed project. 
Towards the end of the term, each student will give a talk and 
write up a paper on the project. An alternative route might be 
to examine recent papers covering current topics in theoretical 
ecology and use them as foundations for individual or group 
programming projects. One goal might be to extend these works and 
produce publishable manuscripts. I might also use such papers as 
the basis of a class assignment.