An Introduction to the Practice of Ecological Modeling

Abstract

odeling has become an important tool in the study of ecological systems,as a scan of the table of con tents of a ny major eco l ogical journ a l makes abundantly clear. A number of books have recently been published that provide excellent advice on mo del construction, building, and use (e.g.,Gotelli 1995, Gurney and Nisbet 1998, Roughgarden 1998) and add to the classic literature on mo deling ecological systems and their dynamics (e.g., Maynard Smith 1974, Nisbet and Gurney 1 9 8 2 ) . Un fortu n a tely, h owever, l i t t l e — i f a ny — of t h i s growing literature on e cological modeling addresses the motivation to model and the initial stages of the modeling process, information that beginning students would find useful. Fast computers and graphical software packages have removed much of the drudgery of creating models with a programming language and opened new avenues of model construction,use,and even misuse. There are many reasons why a student might want to consider modeling as a component of his or her education. Models p rovide an opportunity to explo re ideas regarding ecological systems that it may not be possible to field-test for logistical, political, or financial reasons. Often, learning occurs from apparently st range results and unexpected sur prises. The process of formulating an e cological model is ext remely helpful for organizing one’s thinking , bringing hidden assumptions to light, and identifying data needs. More and more,students want to “do something” with modeling but are not sure how to get started. The goals of this article are to outline issues concerning the value of ecological models and some possible motivations for mo deling, and to provide an entry point to the established modeling literature so that those who are beginning to think about using models in their research can integrate modeling usefully. We therefore envision the typical reader to be an advanced undergraduate, a beginning graduate student, or a new modeler. We first consider some of the values of models and the motivation for modeling. We then discuss the steps involved in developing a mo del from an initial idea to something that is implemented on a computer, outlining some of the decisions that must be ma de along the way. Many excellent texts and journal articles deal with the technical details of models and model construction; we do not attempt to replace this literature, but rather try to make the reader aware of the issues that must be considered and point to some of the sources we have found particularly useful. We b egin with the assumption that the reader has decided that he or she would like to “do something” with modeling as part of his or her research (Figure 1). It is important to recognize the difference between models and the modeling process. A model is a representation of a particular thing, idea, or condition. Models can be as simple as a verbal statement about a subje ct or two boxes c onnected by an arrow to represent some r elationship. Alternatively, models can be ext remely c omplex and detailed, such as a mathematical description of the pathways o f nitrogen t ransformations within ecosystems. The model ing process is the series of steps taken to convert an idea first into a conceptual model and then into a quantitative m odel . Because part of what eco l ogists do is revi s e hypotheses and collect new data, the model and the view of nature that it r epresents often und ergo many changes from the initial conception to what is d eemed the final product. The discussion that follows is organized to consider issues in a sequence similar to what a new modeler would encounter. Because individuals’ backgrounds differ, the sequence is not fixed. We map one possible route through the sorts of decisions that will most lik ely need to be considered; this course is derived from our individual experiences plus the collective knowledge o f our reviewers. We begin with conceptual models because many people, even self-labeled nonmodelers, formulate conceptual models.