Tests for density dependence.

Several statistical tests for 'density dependence' have been proposed in the literature, and so in any practical case the question poses itself which one of these tests to choose. This paper offers a few remarks additional to those made by Fox and Ridsill-Smith (1995) and others. Parametric statistical tested are based on a fully specified mathematical model. Examples of such tests are Bulmer's (1975) first test, and the test of Dennis and Taper (1994). Distribution-free tests are based on far less stringent assumptions. An example of such a test is the one proposed by Pollard et al. (1987). The choice between parametric tests can best be made by considering which one of the underlying mathematical models ist most plausible. If all models are almost equally plausible, considerations of computational requirement and ease of application may be important. Strong doubts concerning the plausibility of mathematical models may lead one to prefer a distribution-free test. An important feature of any test is its power, i.e. the probability of its rejecting the null hypothesis when this hypothesis is not true. Other things being equal, tests are preferable when they have superior powers. But power of a test depends on 'the true state of nature', and the only way to study power quantitatively is by assuming some mathematical model as approximately representing this 'true state'. As any mathematical model can at best only be an approximation to the situation in nature, a mathematical model and the statistical tests based on it should be robust against small deviations from model assumptions. Solow (1990) showed that Bulmer's test is not robust with respect to the assumption that the residuals in the underlying autoregression model be stochastically independent. Contrary to what was suggested by Fox and Ridsill-Smith (1995), who misinterpreted some statements in Reddingius (1990), the present author thinks this is a serious shortcoming of this test since an ecologist cannot assume a priori that important density-independent ecological factors are not somehow serially correlated. Moreover, he is rather sceptical about the usefulness of statistical tests for density dependence. They have contributed more to misunderstandings than to a significant increase in ecological insight. In any case, statistical tests are designed to test hypotheses that are stated before data are collected, and the question which test to use also has to be answered before the data have been collected. Designing and using statistical tests a posteriori to 'detect' things in data mainly leads to confusion and controversy.