An important ecological goal is understanding patterns of coexistence and exclusion of species in the same geographic area. Because most multispecies ecological systems are exteremely complex, it is probably impossible to describe all the component populations in terms of their physiological status or demographic characteristics such as age distributions and birth and death rates. One current method of studying such ecological systems is to design a generalized model, based on a few premises or simplifying assumptions, which will generate testable predictions. If, upon being tested, these predictions are falsified, the simplifying assumptions are changed accordingly; but if the predictions are verified, the assumptions are often considered to be substantiated. This hypothetico-deductive approach has influenced ecologists to focus on more general questions rather than on specific factors which may have small effects and which, when applied to more general situations, lead to intractable mathematical equations. Here I emphasize, with Levins (1966) and Cohen (1968), that the use of generalized models can be misleading because of uncertainty as to whether a positive result depends on the reality of the model and the simplifying assumptions, or on entirely unrelated factors. I therefore urge that, whenever possible, simplifying assumptions themselves be tested as hypotheses and resultant models be based on a thorough understanding of the relevant natural history and, when possible, on experimental manipulation. Here I will test predictions generated by two models which might be used in the construction of a general competition-based theory of the structure of an intertidal community. In both cases, the predictions generated by these two models are verified, suggesting that their simplifying assumptions are based on parameters sufficient to describe the community process in question. In both cases, however, acceptance of the superficially plausible models is due to inadequate appreciation of natural history. Simple experimental manipulation shows that each model makes the right prediction for the wrong reason.
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