Stochastic simulation of clonal growth in the tall goldenrod, Solidago altissima.

Abstract

As clonal plants grow they move through space. The movement patterns that result can be complex and difficult to interpret without the aid of models. We developed a stochastic simulation model of clonal growth in the tall goldenrod, Solidago altissima. Our model was calibrated with field data on the clonal expansion of both seedlings and established clones, and model assumptions were verified by statistical analyses.When simulations were based on empirical distributions with long rhizome lengths, there was greater dispersal, less leaf overlap, and less spatial aggregation than when simulations were based on distributions with comparatively short rhizome lengths. For the field data that we utilized, variation in rhizome lengths had a greater effect than variation for either branching angles or "rhizome initiation points" (see text). We also found that observed patterns of clonal growth in S. altissima did not cause the formation of "fairy rings". However, simulations with an artificial distribution of branching angles demonstrate that "fairy rings" can result solely from a plant's clonal morphology.Stochastic simulation models that incorporated variation in rhizome lengths, branching angles, and rhizome initiation points produced greater dispersal and less leaf overlap than deterministic models. Thus, variation for clonal growth parameters may increase the efficiency of substrate exploration by increasing the area covered and by decreasing the potential for intraclonal competition. We also demonstrated that ramet displacements were slightly, but consistently lower in stochastic simulation models than in random-walk models. This difference was due to the incorporation of details on rhizome bud initiation into stochastic simulation models, but not random-walk models. We discuss the advantages and disadvantages of deterministic, stochastic simulation, and random-walk models of clonal growth.