Improving the predictive power of spatial statistical models of stream macroinvertebrates using weighted autocovariance functions

Spatial statistical stream-network models are useful for modelling physicochemical data, but to-date have not been fit to macroinvertebrate data. Spatial stream-network models were fit to three macroinvertebrate indices: percent pollution-tolerant taxa, taxa richness and the number of taxalacking out-of-network movement (in-stream dispersers). We explored patterns of spatial autocorrelation in the indices and found that the 1) relative strength of in-stream and Euclidean spatial autocorrelation varied between indices; 2) spatial models outperformed non-spatial models; and 3) the spatial-weighting scheme used to weight tributaries had a substantial impact on model performance for the in-stream dispersers; with weights based on percent stream slope, used as a surrogate for velocity because of its potential effect on dispersal and habitat heterogeneity, producing more accurate predictions than other spatial-weighting schemes. These results demonstrate the flexibility of the modelling approach and its ability to account for multi-scale patterns and processes within the aquatic and terrestrial landscape.