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

• Published in: Environmental modelling & software : with environment data news 2014-10, Vol.60, p.320-330
• Main Authors: Frieden, Jennifer C., Peterson, Erin E., Angus Webb, J., Negus, Peter M.
• Format: Article
• Language: English
• Subjects: Animal and plant ecology; Animal, plant and microbial ecology; Aquatic monitoring; Autocorrelation; Biological and medical sciences; Dispersion; Flexibility; Fresh water ecosystems; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Heterogeneity; Macroinvertebrate index; Mathematical models; Methods and techniques (sampling, tagging, trapping, modelling...); Modelling; Scale; Spatial autocorrelation; Spatial statistics; Streams; Synecology
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2014.06.019

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. •Spatial stream-network models had more predictive power than non-spatial models.•The scale and pattern of spatial autocorrelation varied for all three indices.•The choice of spatial weights strongly impacted model performance.•Index composition may affect the scale and pattern of spatial autocorrelation.•Insight into ecological processes may be gained by modelling spatial covariance.