“An Open Invitation to Empirical Biologists”

Abstract

What is the process by which one translates conjectures about how nature works into quantitative mathematical models that can then be used to test hypotheses, delineate the dimensions of the possible, or generate new ideas for further experiments? I suspect that for many empirical biologists, this may seem like a fairly mysterious pathway, in part, because as pointed out by Otto and Day in one of the two books under review here, primary research papers commonly leave out many of the intermediate steps in model development due to space and presentation constraints. Not surprisingly, the importance of model interpretation is a theme common to both books being reviewed here. Biologists need to be able to understand and articulate key model results from incomplete information. Over the past several decades, there has been a diversity of books written about modeling approaches in ecology and evolutionary biology, ranging from those aimed at developing or promoting the use of specific techniques or approaches (e.g., the use of computer simulations to address evolutionary problems, Wilson 2000; evolutionary game theory, Maynard Smith 1982) to those broadly focused on the synthesis of particular fields (e.g., human epidemiology, Anderson and May 1991) through to those developing and exploring conceptually new areas (e.g., the theory of island biogeography, MacArthur & Wilson 1967; deterministic chaos in ecological systems, May 1973; the dynamics of cultural evolution, Boyd & Richerson 1985). Sitting at one end of this spectrum are those books that require no biological knowledge at all,