Emergent properties of plants competing in silico for space and light: Seeing the tree from the forest

Abstract

A spatially explicit, reiterative algorithm (SERA) is presented and used to predict multiple aspects of plant population and community dynamics. Using simple physical principles and empirically derived relationships, SERA provides an analytical venue to test alternative hypotheses about individual functional traits governing ecological or evolutionary processes at the population or community level of complexity. Our analyses show that, as a result of competition for light and space, individual-level features scale up to produce species ensemble properties such as the scaling of self-thinning, size-dependent mortality, realistic size-frequency distributions, and a broad spectrum of empirically observed relationships for the species examined. SERA also predicts the competitive exclusion of conifers by angiosperms and the age at which reproductive maturity is achieved by different species. SERA serves as a null hypothesis by demonstrating that biologically complex phenomena, including widely observed scaling relationships at the species-ensemble level, can emerge from the operation of simple and transparent "rules" governing competition for space and light.