Where Newton might have taken ecology

A dilemma in ecology Ecologists aspire to build a discipline to both understand the natural world and to provide society with tools to make responsible decisions about the environment. For both of these purposes, most sciences have, at their core, a set of empirical generalizations that predict the behaviour of important properties of nature (e.g., Newton's laws of mechanics, Mendeleev's periodic table, Mendel's genetics). Ecological science, in contrast, has favoured studies to understand processes (competition, population regulation, etc. - i.e., independent variables) rather than models that predict attributes of nature (dependent variables). Classical reductionist scientific training emphasizes studies of mechanisms under controlled experimental conditions. Yet, inferences about nature from experiments are nearly always unjustifiable extrapolations beyond the experimental conditions. Mechanisms that are statistically detectable in experimental systems may contribute very little to the variation of nature. Studies of ecological processes in isolation may contribute to expert understanding, but experts have been shown to be poor predictors of the behaviour of natural systems. A proposed solution The more relevant, often neglected question is: what factors can statistically account for the observed variance of nature? A more Newtonian approach to ecology would: (a) first, specify the properties of nature (i.e., dependent variables) whose variance is of concern; (b) develop models that statistically capture the variance of those properties in nature; (c) demonstrate that those models can predict independent data; and, only last, (d) experimentally test hypotheses about processes that could give rise to the predictable patterns in nature. Why it matters Successful disciplines identify specific goals and measure progress toward those goals. Predictive accuracy of properties of nature is a measure of that progress in ecology. Predictive accuracy is the objective evidence of understanding. It is the most useful tool that science can offer society.