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R-language scripts for RIVPACS-type predictive modeling

             A RIVPACS-type predictive model predicts the taxonomic assemblage of macroinvertebrates, fish, or periphyton that one would expect to find in an aquatic ecosystem, if that ecosystem were in a minimally-disturbed "reference" condition. The expected assemblage is then compared with the assemblage that is observed by sampling the ecosystem. Discrepancies between the two assemblages indicate the degree of ecosystem stress or impairment.

            A full discussion of predictive modeling methods is provided by the Western Center for Monitoring and Assessment of Freshwater Ecosystems, http://129.123.10.240/wmcportal/DesktopDefault.aspx.

          PREDICTIVE_MODEL_SCRIPTS_V4.2.1.ZIP contains scripts for building and applying predictive models. The scripts are written in the R computing language, which is available free from http://www.r-project.org/

Version 4.2 (January 1,  2011) is now available.   See new features below (***).

Download PREDICTIVE_MODEL_SCRIPTS_V4.2.1.ZIP.

            The scripts are written for use by experienced R programmers. Users will need to modify some scripts to suit their particular data sets.

Features include:

  1. Creation and manipulation of site-by-taxa data matrices, including random subsampling to a fixed count.

  2. Options for different dissimilarity measures and clustering algorithms, including flexible-beta clustering and options for dendrogram pruning..

  3. Options for all-subsets or stepwise discriminant function analysis.

  4. Predictions for new sites, including assessment of site outlier status. *** Save final model as an R object and export to users, with a stand-alone script for assessing new sites. *** Export your custom model for submission to the Western Center for Monitoring’s web-accessible modeling system.

  5. Calibration and predictions for null models.

  6. O/E and BC indices.

  7. *** Detailed comparisons of expected and observed taxa at user-selected sites.

  8. *** Use a Random Forest model, rather than discriminant functions, to predict site group membership.

 For additional information see the following articles, available from the senior author:

Van Sickle, J. 2008. An index of compositional dissimilarity between observed and expected assemblages.
Journal of the North American Benthological Society 27, 227-235.

Van Sickle, J., D.P. Larsen and C.P. Hawkins. 2007. Exclusion of rare taxa affects performance of the O/E index in bioassessments. Journal of the North American Benthological Society 26, 319-331.

Van Sickle, J., David D. Huff, and C.P. Hawkins (2006). Selecting discriminant function models for predicting the expected richness of aquatic macroinvertebrates. Freshwater Biology 51, 359-372.

Van Sickle, J., C.P. Hawkins, D.P. Larsen and A.T. Herlihy. (2005). A null model for the expected macroinvertebrate assemblage in streams. Journal of the North American Benthological Society 24, 178-191.

 

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