Complexity in Ecological Systems

Ecology has been eminently a descriptive science despite some pio­ neering work by theoreticians such as Lotka, Volterra, Nicholson, and others. Description is a first step toward understanding a system. How­ ever, such a first step needs to be accompanied by the development of a theoretical framework in order to achieve real insight and, whenever possible, predictive power. Ecologists are increasingly facing the chal­ lenge of predicting the consequences of human-induced changes in the biosphere. For example, we need to better understand how biodiversity declines as more habitat is destroyed, or how harvested populations are driven to extinction as harvesting rates are increased. Toward that end, it is necessary to integrate field and experimental ecology with theory (Odum, 1977). This integration is much more common in the physi­ cal sciences, where theory and experimental data have always marched together (May, 2004). This book describes a theoretical view of ecosystems based on how they self-organize to produce complex patterns. It focuses on very simple models that, despite their simplicity, encapsulate fundamental properties of how ecosystems work. They are based on the nonlinear interactions observed in nature and predict the existence of thresholds and discontinuities that can challenge the usual linear way of thinking. These models have shown the possibility of ecosystem self-organization at several scales. Simple nonlinear rules are able to generate complex patterns. This view contrasts with traditional approaches to ecological complexity based on extrinsic explanations. Thus, population cycles were said to be complex because multiple variables and external influ­ ences such as the climate are involved; spatial patterns in the distribu­ tion of a species were said to be nonhomogeneous because the spatial distribution of nutrients or perturbations is also heterogeneous; com­ munities were said to be complex because there are hundreds of species interacting in a somewhat random way. This book presents theoretical evidence of the potential of nonlinear ecological interactions to gener­ ate nonrandom, self-organized patterns at all levels. In time, nonlinear density-dependence can generate complex dynamics such as determin­ istic chaos (chapter 2); in space, the combination of local nonlinear growth and short-range dispersal can generate a myriad of spatiotem­ poral patterns (chapter 3); at the community level, simple buildup mechanisms such as the preferential attachment of species to gener­ alists can generate complex, invariant ecological networks (chapter 6).