NEIGHBORHOOD MODELS OF CLONAL GROWTH IN THE WHITE CLOVER TRIFOLIUM-REPENS

The importance of spatially local conditions in plants and the movemen t of clones over time are complications that have limited the developm ent of population dynamic models for clonal plant species. We develop a field-calibrated, spatial model of white clover (Trifolium repens) p opulation dynamics. We relate rates of stolen growth and dieback obser ved in the field to local clover density, and we use field data on the morphology of clover clones to develop a stochastic simulation of how clones spread through space. The size and shape of clover clones vari ed considerably in the field but still could be depicted accurately by our simulation model. Stolen growth in white clover increased with de nsity at low clover densities and decreased with density at high clove r densities. To our knowledge, this is the first demonstration of posi tive, density-dependent growth in a clonal plant species. Stolon dieba ck rates were density-independent. An unstable, lower ''equilibrium'' point, together with local (as opposed to global) density dependence, caused the formation and maintenance of patch structure in our model. Model predictions indicated that lawn populations should consist of a mosaic of clover and grass patches, the largest of which persist for s ubstantial periods of time. Clover aggregations persisted in our simul ations despite increased dispersal from areas of high population growt h. Incorporation into the model of natural variation for clonal growth rates (at each local density of clover) had little effect on model pr edictions. We tested model predictions with 4 yr of data on the spatia l and temporal distribution of white clover in a lawn population. We f ound positive and significant spatial autocorrelation at low distance classes (up to 192 cm), negative and significant spatial autocorrelati on at intermediate distance classes (288-481 cm), and positive and sig nificant spatial autocorrelation at high distance classes (577-673 cm) . Temporal autocorrelation analyses indicated the location of clover a nd grass patches cycled over time. Thus, our field data revealed signi ficant patch structure that corresponded to model predictions but did not support the prediction that patch structure is maintained for exte nded periods of time.