Northwest Fisheries Science Center

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Document Type: Journal Article
Center: NWFSC
Document ID: 7946
Title: Spatial autocorrelation and statistical analysis on stream networks: overview and applications
Author: D. J. Isaak, Erin E. Peterson, Jay M. Ver Hoef, Seth J. Wenger, Jeff A. Falke, C. E. Torgersen, Colin Sowder, E. Ashley Steel, Marie-Josee Fortin, Chris E. Jordan, Aaron S. Ruesch, Nicholas Som, Pascal Monestiez
Publication Year: 2014
Journal: WIREs Water
Keywords: stream network,spatial statistics,autocorrelation,semivariogram,kriging,Torgergram
Abstract:

1. Streams and rivers host a significant portion of Earth’s biodiversity and provide important ecosystem services for human populations. Accurate information regarding the status and trends of stream resources is vital for their effective conservation and management.
2. Most statistical techniques applied to data measured on stream networks were developed for terrestrial applications and are not optimized for streams. A new class of spatial statistical model, based on valid covariance structures for stream networks, can be used with many common types of stream survey data (e.g., water chemistries, habitat conditions, biological attributes) through application of appropriate distributions (e.g., Gaussian, binomial, Poisson).
3. The spatial statistical network models account for spatial autocorrelation (i.e., nonindependence) among measurements, which allows their application to databases with clustered, non-random measurement locations. Large amounts of stream survey data exist in many areas where spatial statistical analyses could be used to develop novel insights, improve predictions at unsampled sites, and aid in the design of efficient monitoring strategies at relatively low cost.
4. We review the topic of spatial autocorrelation and its effects on statistical inference, demonstrate the use of spatial statistics with stream datasets relevant to common research and management questions, and discuss additional applications and development potential for spatial statistics on stream networks.
5. Free software for implementing the spatial statistical network models has been developed that enables custom applications with many stream databases.

Theme: Habitats to Support Sustainable Fisheries and Recovered Populations
Foci: Characterize relationships between habitat and ecosystem processes, climate variation, and the viability of organisms.