Registration now open: Spatial Statistical Stream Network Models training workshop: 2nd Annual in Boise, Idaho (May 15-17)

A new class of spatial statistical network model (SSNM) for data measured on stream networks has recently been developed & free software is available for implementing the models. SSNMs account for network topology (i.e., flow direction, stream size, tributary confluences) and offer significant improvements over many traditional statistical techniques that were developed originally for terrestrial applications. SSNMs are applicable to common types of stream data (e.g., water quality attributes, biological surveys, habitat conditions) through application of several distributions (e.g., Gaussian, binomial, Poisson). The models also account for spatial autocorrelation among measurements, which makes them powerful tools for mining information from large datasets aggregated from multiple sources.

This 3-day workshop will consist of a 1-day short-course & 2 days of working with course instructors to apply SSNMs to participant’s datasets. Note that attendance is limited, so the 1st day short-course will also be offered as a webinar to provide a broader audience with an introductory overview.

The workshop will include:
1. Overviews of two sets of free software (STARS ArcGIS toolset; SSN package for R Statistical Software)

2. Demonstration of the software for:
a) Parameter estimation and modelling continuous, presence/absence (binomial), and count data (Poisson);
b) Kriging predictions at unsampled locations and block kriging for discrete areas;
c) Uncertainty estimation;
d) Simulation and visualization techniques for space-time stream data;
e) Monitoring design considerations (developing new designs & supplementing existing ones);

3. Discussions of when spatial statistical techniques are most useful and the many new applications that are now possible for stream data;

4. Working with instructors to apply the SSNMs to participant’s data

The attached flier describes the workshop in more detail & those interested in attending may register here: Note that a good working knowledge of statistics and the R statistical program are needed to benefit from workshop participation.

Key Background Literature for SSNMs (& hyperlinks to articles)
1. Ver Hoef, J.M., E.E. Peterson, & D. Theobald. 2006. Spatial statistical models that use flow and stream distance. Environmental and Ecological Statistics 13:449-464.
2. Peterson, E.E., & J.M. Ver Hoef. 2010. A mixed-model moving-average approach to geostatistical modeling in stream networks. Ecology 91:644-651.
3. Peterson E.E. and J.M. Ver Hoef. 2014. STARS: An ArcGIS toolset used to calculate the spatial information needed to fit spatial statistical models to stream network data. Journal of Statistical Software 56(2):1-17.
4. Ver Hoef J.M., E.E. Peterson, D. Clifford, and R. Shah. 2014. SSN: An R package for spatial statistical modeling on stream networks. Journal of Statistical Software 56(3):1-42.
5. Peterson E.E., J.M. Ver Hoef, D.J. Isaak, J.A. Falke., M.J. Fortin, C. Jordan, K. McNyset, P. Monestiez, A.S. Ruesch, A. Sengupta, N. Som, A. Steel, D.M. Theobald, C.T. Torgersen, & S.J. Wenger. 2013. Modeling dendritic ecological networks in space: an integrated network perspective. Ecology Letters 16:707-719.
6. Isaak, D.J., E.E. Peterson, J.M. Ver Hoef, S. Wenger, J. Falke, C. Torgersen, C. Sowder, A. Steel, M.J. Fortin, C. Jordan, A. Reusch, N. Som, P. Monestiez. 2014. Applications of spatial statistical network models to stream data. Wiley Interdisciplinary Reviews - WATER 1 doi: 10.1002/wat2.1023