Strong and weak tests of macroecological theory

Macroecology is a rapidly growing branch of ecology. The essence of macroecology can be summarized as a two-step process: 1) find large-scale patterns and 2) find the explanations/mechanisms for those patterns. Much of the work on step 1 has focused on identifying the shape of various curves. This includes some of the most famous of all macroecological patterns: the power-law (S=cA) species area relationship (SPAR), the hollow curve distribution of species abundances (SAD), and the skewed-lognormal distribution of body size (BSD). A large number of papers have debated the correct curve shapes. For example it has been suggested that SPARs should really be logarithmic, sigmoidal, or asymptotic (such as the Michaelis–Menton) instead of the power law (Connor and McCoy 1979, Sugihara 1981, Connor et al. 1983, Williams 1995, Lomolino 2000, 2002, Lomolino and Weiser 2001, Williamson et al. 2001). Over two-dozen different distributions have been suggested for the SAD (Pielou 1977, Tokeshi 1993). Moreover, much of the work on step 2 has proceeded by testing theories according to whether they produce curves of the correct shape (i.e. the shape observed in nature). In short, it is fair to say that macroecology has been practiced as a study of curve shapes. I argue that this focus on curve shapes is unfortunate and even counterproductive for macroecology. To see why, let us explore in more detail how macroecological mechanisms are identified. Unfortunately, macroecological questions are, almost by definition, too large to perform the type of replicated, manipulative, controlled experiments that form the backbone of scientific progress in much of the rest of ecology. Thus, the search for mechanism usually proceeds through theory. A theory is built which suggests that if certain mechanisms are important, then certain predictions should be true. If the predictions fail, the mechanisms lose credence; if they succeed, the mechanisms accumulate support. Of course, the strength of this method depends on the quality of the predictions. In practice, often the sole prediction made is the shape of the curve (with parameters free to maximize fit). The weakness of this mode of testing is most painfully obvious in the search for mechanisms in one of the oldest and most well known patterns in macroecology: the species abundance distribution (SAD). The SAD describes the relative abundances of different species within a community – many rare species and a few highly abundant species (known as a hollow curve). Well over two dozen different curves have been suggested as the ‘‘right’’ curve with more coming every year (Pielou 1977, Tokeshi 1993, Sichel 1997, Harte et al. 1999, Dewdney 2000, Hubbell 2001). This work on SADs has proceeded as I previously described: a hypothesized mechanism leads to a theory that makes one prediction about curve shape. Macroecologists ought to worry that dozens of theories have been proposed and all fit the data. Macroecologists must also worry that few if any curves (and their mechanisms) have been decisively rejected. In short, this mode of testing has failed to reject most of the dozens of proposed theories. In the rest of this paper, I will first try to demonstrate that testing exclusively by curve fitting is very weak and a poor way for macroecology to proceed in the search for mechanisms. Then, I will look at some ways to make stronger tests.