NCEAS Project 12637

Spatial statistical models for stream networks: Synthesis and new directions

  • Erin E. Peterson
  • Daniel J. Isaak
  • Jay M. Ver Hoef

ActivityDatesFurther Information
Working Group4th—8th April 2011Participant List  
Working Group8th—12th August 2011Participant List  

Spatial autocorrelation quantitatively represents the degree of statistical dependency between random variables using spatial relationships (Cressie 1993). It is an intrinsic characteristic of freshwater stream environments, where watersheds are nested within one another and sites are connected by stream flow through directed networks. Analyzing spatially correlated data requires the use of spatial statistical methodologies because the assumption of independence is violated, making many conventional statistical methods inappropriate (Cressie 1993). Spatial statistical methods have only recently been developed that represent the unique spatial configuration, longitudinal connectivity, flow volume, and flow direction found in freshwater ecosystems (Cressie et al. 2006, Ver Hoef et al. 2006, Peterson and Ver Hoef 2010, Ver Hoef and Peterson 2010). These methods provide significant potential advancements for ecological research and aquatic monitoring because spatial statistical models can be used to quantify patterns of spatial autocorrelation across multiple scales, to make predictions at unobserved sites with estimates of prediction uncertainty, and yield unbiased regression parameter estimates relating ecological variables to the environment (Cressie 1993). Our proposed working group will extend the capabilities of spatial statistical models for stream networks to include additional functionality available in traditional spatial statistical methods, so that a wider range of ecological and management questions can be fully addressed. To accomplish this goal, we will: 1) identify the most pressing needs in terms of analytical capabilities (i.e., what would be most useful for informing science and management) and begin developing these models, with possible extensions to include space-time models, generalized linear mixed models, computing for massive datasets, and others as identified by the working group, 2) assess the current state of software and functionality and determine whether it is sufficient to meet those needs, and develop new ones in conjunction with the previous objective, and 3) intensely analyze a single, nationally important, large, multivariate, stream dataset collected across the Northwestern (NW) United States (US) to gain ecological insights, evaluate methods, and demonstrate the new spatial statistical modeling capabilities.

TypeProducts of NCEAS Research
Journal Article Isaak, Daniel J.; Peterson, Erin E.; Ver Hoef, Jay M.; Wenger, Seth J.; Falke, Jeffrey A.; Torgersen, Christian E.; Sowder, Colin; Steel, E. A.; Fortin, Marie-Josée; Ruesch, Aaron S.; Som, Nicholas A.; Monestiez, Pascal. 2014. Applications of spatial statistical network models to stream data . Wiley Interdisciplinary Reviews: Water. Vol: 1. Pages 277-294.
Journal Article McGuire, Kevin J.; Torgersen, Christian E.; Likens, Gene; Buso, Donald C.; Lowe, Winsor H.; Bailey, Scott W. 2014. Network analysis reveals multiscale controls on streamwater chemistry. Proceedings of the National Academy of Sciences of the United States of America. Vol: 111(19). Pages 7030-7035.
Software Peterson, Erin E. 2011. Spatial Tools for the Analysis of River Systems (STARS) ArcGIS Toolset.
Presentations Peterson, Erin E.; Ver Hoef, Jay M.; Last, A. 2011. STARS and the SSN Package: Analysis tools for spatial statistical modeling in stream networks. Spatial Statistical Conference, Queensland University of Technology, May 10, 2011.
Presentations Peterson, Erin E.; Ver Hoef, Jay M.; Last, A. 2012. STARS and the SSN Package: Analysis tools for spatial statistical modeling in stream networks. Presentation at the International Envirometrics Society Meeting, Hyderabad, India, January 3, 2012.
Journal Article Peterson, Erin E.; Ver Hoef, Jay M.; Isaak, Daniel J.; Falke, Jeffrey A.; Fortin, Marie-Josée; Jordan, Chris E.; McNyset, Kristina M.; Monestiez, Pascal; Ruesch, Aaron S.; Sengupta, Aritra; Som, Nicholas A.; Steel, E. A.; Theobald, David M.; Torgersen, Christian E.; Wenger, Seth J. 2013. Modeling dendritic ecological networks in space: An integrated network perspective. Ecology Letters. Vol: 16(5). Pages 707-719. (Online version)
Journal Article Peterson, Erin E.; Ver Hoef, Jay M. In press. STARS: An ArcGIS toolset used to calculate the spatial information needed to fit spatial statistical models to stream network data. Journey of Statistical Software.
Journal Article Ruesch, Aaron S.; Torgersen, Christian E.; Lawler, Joshua J.; Olden, Julian D.; Peterson, Erin E.; Volk, Carol J.; Lawrence, David J. 2012. Projected climate-induced habitat loss for Salmonids in the John Day River Network, Oregon, U.S.A.. Conservation Biology. Vol: 26. Pages 873-882. (Online version)
Journal Article Som, Nicholas A.; Monestiez, Pascal; Ver Hoef, Jay M.; Zimmerman, Dale; Peterson, Erin E. 2014. Spatial sampling on streams: principles for inference on aquatic networks. Environmetrics. Vol: 25(5). Pages 306-323.
Presentations Ver Hoef, Jay M.; Peterson, Erin E.; Isaak, Daniel J. 2012. Spatial statistical models for stream networks. OneNOAA Science Seminar (Webinar), NOAA Northwest Fisheries Science Center, Seattle, April 26, 2012. Seattle.
Journal Article Ver Hoef, Jay M.; Peterson, Erin E.; Clifford, David; Shah, Rohan. In press. SSN: An R package for spatial statistical modeling on stream networks. Journal of Statistical Software.