01560nas a2200169   4500008004100000245006300041210006300104300001100167490000700178520100800185653002001193653001901213653002101232653002501253100001801278856009401296       1995                        eng d00aSpatial model error analysis using autocorrelation indices0 aSpatial model error analysis using autocorrelation indices  a75 -910 v823 aNo standard techniques yet exist for assessing the predictive performance of models that simulate spatially-explicit processes. Spatial simulation models generate autocorrelation patterns that are a critical aspect of model prediction. These patterns can be quantified using spatial (Moran's I) and spatio-temporal (Griffith's STI) autocorrelation indices. A method of error analysis for spatial simulation models is demonstrated using autocorrelation indices to distinguish among the effects of parameter uncertainty on a stochastic spatial simulation of seed dispersal. The resulting autocorrelation patterns of state variables display a range of nonlinear, counterintuitive effects. In contrast to techniques proposed for spatial model error analysis (contagion, spatial predictability, adjacency, multiple resolution procedure, fractal dimension, interface), autocorrelation indices can be used with interval-scaled data and have well-defined sampling distributions that enable significance testing.10aAutocorrelation10aError analysis10aSpatial patterns10aUncertainty analysis1 aHenebry, G.M.  uhttp://knz.lternet.edu/content/spatial-model-error-analysis-using-autocorrelation-indices