Nonlinear forecasting was used to forecast spatially structured population models with complex dynamics,focusing on the effect of dispersal and spatial scale on the predictive capability of nonlinear forecasting (NLF).Dispersal influences NLF ability by its influence on population dynamics.For simple 2-cell models,when dispersal is small,the ability to predict abundance in subpopulations decreased and then increased with increasing dispersal.Spatial heterogeneity,mode of dispersal,and environmental noise did not qualitatively change this result.But results are not clear for complex spatial configurations because of complicated dispersal interactions across subpopulations.Populations undergoing periodic fluctuations could be forecasted perfectly for all deterministic cases that we studied,but less reliably when environmental noise was incorporated.More importantly,for all models that we have examined,NLF was much worse at larger spatial scales as a consequence of the asynchronous dynamics of subpopulations when the dispersal rate was below some critical value.The only difference among models was the critical value of dispersal rate,which varied with growth rate,carrying capacity,mode of dispersal,and spatial configuration.