Interactions between macroinvertebrate taxa and complex environmental gradients influencing abundance and distribution in the Umatilla River, Northeastern Oregon

Melissa A. Scherr, David E. Wooster, Sujaya Rao

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1 Scopus citations

Abstract

Non-parametric multiplicative regression was used to develop regression models for six macroinvertebrate genera present in the Umatilla River in northeastern Oregon. For all taxa, elevation, turbidity, and conductivity were identified as predictors. They described between 25.5% and 63.4% of the variation in the abundance of the six taxa. Sensitivity analysis of the parameters showed the relative importance of the model parameters as predictors of abundance, with conductivity having a broader range of sensitivity values than either elevation or turbidity. Daily average temperature, percentage of algal cover, depth, and width were also shown to be possible predictors of taxa abundance. As all the variables examined in this study are potentially impacted by non-target effects of agriculture in rivers, these results indicated that agriculture disturbance in the adjacent landscape affects the abundance and distribution of benthic indicator taxa.

Original languageEnglish (US)
Pages (from-to)255-266
Number of pages12
JournalJournal of Freshwater Ecology
Volume26
Issue number2
DOIs
StatePublished - Jun 2011

Bibliographical note

Funding Information:
We thank Craig Contor and Billy Goodrich of the CTUIR Department of Natural Resources for lending insight and experience on the Umatilla River and for providing assistance with land owner permissions. Anne Madsen, Andrew MacMillan, Nathan Scherr, Jodie Lovern, and Patrick Christensen assisted with field and laboratory work. Bill Gerth assisted with identification of macroinvertebrates, and Bruce McCune and Sandy DeBano provided helpful comments on statistical analysis. This manuscript was improved with the editing help of Jeff Miller. Funding for this project was provided by the National Science Foundation Graduate Research Fellowship Program.

Keywords

  • Abundance predictors
  • Agricultural disturbance
  • NPMR
  • Sensitivity analysis

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