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Individual Highlight

New Class of Statistical Model Developed for Stream Networks

Photo of Spatial statistical stream network models can use data from a relatively small number of sites, left map, to make unbiased predictions across larger river networks, right map. National Oceanic and Atmospheric Administration (NOAA) and Commonwealth Scientific and Industrial Research Organisation (CSIRO)Spatial statistical stream network models can use data from a relatively small number of sites, left map, to make unbiased predictions across larger river networks, right map. National Oceanic and Atmospheric Administration (NOAA) and Commonwealth Scientific and Industrial Research Organisation (CSIRO)Snapshot : Researchers and their cooperators developed a new class of statistical model for stream networks that applies to most types of stream data and provides more accurate and new types of information.

Principal Investigators(s) :
Isaak, Daniel J.  
Research Location : Western U.S.
Research Station : Rocky Mountain Research Station (RMRS)
Year : 2013
Highlight ID : 552

Summary

Stream network structure, connectivity, and flow direction strongly affect spatial patterns in stream attributes. Most statistical analyses ignore network structure and may provide biased results for stream data. Forest Service researchers their cooperators developed a new class of statistical model for stream networks that applies to most types of stream data and provides more accurate and new types of information when data are spatially correlated. Statistical stream models accurately represent spatial patterns on stream networks by incorporating statistical programs that account for the direction of stream flow and changes at tributary junctions. Freeware GIS tools and statistical programs for running the network models are available on the Rocky Mountain Research Station's web site http://www.fs.fed.us/rm/boise/AWAE/projects/SpatialStreamNetworks.shtml. Stream network models can be used with any type of data collected on streams, including distribution, abundance, and genetic attributes of aquatic organisms, stream temperature and water quality parameters, or channel habitat characteristics. This approach has several benefits including: new information developed at low cost by aggregating existing databases, resource agencies easily sharing data, and many new types of sampling design.

Forest Service Partners

External Partners

 
  • CSIRO, Australia
  • NOAA, Alaska

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