AN INSTRUCTIONAL APPROACH TO MODELING IN MICROEVOLUTION

Microevolution has been described as the evolutionary change that occurs in local breeding populations (Dobzhansky 1951). This, of course, is the general theme of basic population genetics, a significant element of many biology courses, from introductory biology and genetics to advanced courses in evolution and population biology. As Wallace (1981) so clearly articulates, the beauty of population genetics lies in its abstractions, or models, rather than with a detailed listing or description of the actual events. This is not meant to denigrate the importance of empirical studies, but rather to stress the role that models serve in the theoretical framework of the subject, on which the empirical studies depend. Historically, models and model building have been closely associated with the development of various aspects of population biology. The classic works of Fisher (1930), Wright (1931) and Haldane (1932) in population genetics are only a few of many examples. Modeling not only continues to play a major role in population genetics, but functions in a similar way in other areas of population biology, as exemplified by the work of Maynard-Smith (1974) in population ecology. Because modeling and the use of models are an important aspect of population biology, a part of our instruction to students should include the importance, utility and development of models. As with a number of other biologists (Spain 1982; Hedrick 1984 & Price 1985), one of my goals as an instructor is to assist students in developing an appreciation for models and how they can enhance our understanding of biological processes, in particular microevolutionary processes. With this in mind, I have developed a course in population genetics and evolution which incorporates instruction in modeling and the development of microcomputer models, along with the traditional declarative aspects of the material in micro and macroevolution. This paper describes this approach as well as some of the ways the models can be used to enhance understanding of the processes being studied. The Instructional Plan At the beginning of the course, the foundations of modeling and simulation are first presented in a lecture-demonstration fashion. Students are encouraged to apply these principles to the construction of simple mathematical expressions of events such as exponential and logistic growth. Throughout the semester, one period per week is set aside to individually examine existing microcomputer models, develop skill in BASIC programming, or, as a group project, develop a specific model in microevolution. Because all biology majors at Ithaca College are required to complete a year of college level mathematics, and because the majority of students have also had an introductory course in programming, the initial introduction to modeling and programming goes very quickly. For example, by the fourth week of the semester the students usually are capable of writing a program that will simulate a simple model of complete selection against a recessive gene. Table 1 is a listing of a typical program; it shows the modest level of programming skill needed for this simulation. To assist the students in their programming, I have assembled a simplified manual on Applesoft BASIC for student use. I also provide an initialized programming diskette containing a general purpose graphic utility file constructed for use in this class. This graphics file, which we use in all our microcomputer models, allows students to display the numeric data from the simulation models in the form of line graphs. Following the initial introduction to programming and modeling, the students, working as a team with me serving as a resource person, approach the major class project of the semester-the modeling of one of the phenomena of microevolution. This includes such topics as genetic drift, a general selection model and migration. Instead of constructing a programming flow chart, our usual format is first to outline what the model must do biologically. This includes all the possible events we can easily follow. However, I learned early in the development of this inSteven Thompson is an associate professor of biology at Ithaca College, Ithaca, NY 14850. He received a B.S. in general studies (science) from Portland State College and an M.S. and Ph.D in zoology (genetics) from Oregon State University. He has published articles on genetics and on the application of computers in biology education and is the author of a commercially published statistics software package. At Ithaca College, he teaches genetics and introductory biology and is the biology coordinator of the Science Education Program.