Exclusion of Rare Taxa Affects the Performance of the O/E Index in Bioassessments

Document Type

Article

Journal/Book Title/Conference

Journal of the North American Benthological Society

Volume

26

Publication Date

1-1-2007

Keywords

predictive model, rare species, null model, sensitivity, precision, bioassessment

First Page

319

Last Page

331

Abstract

The contribution of rare taxa to bioassessments based on multispecies assemblages is the subject of continued debate. As a result, users of predictive models such as River InVertebrate Prediction and Classification System (RIVPACS) disagree on whether to exclude locally rare taxa from the O/E index, where O is the number of taxa observed in a sampled assemblage, and E is the number that would be expected if the site were in a minimally disturbed reference condition. We assessed how the bioassessment performance of O/E was affected by the exclusion of taxa with site-specific, model-predicted occurrence probabilities that did not exceed thresholds of PT = 0+, 0.1, 0.2, … , 0.7. We assessed O/E performance for each of 10 predictive models applied to a total of 5685 stream and lake samples from throughout the contiguous USA. For 5 of the 10 cases, the standard deviation (SD) of O/E across reference sites was reduced by at least 0.02 O/E units when locally rare and uncommon taxa were excluded (PT = 0.5) from O/E, as compared with all taxa being included (PT = 0+). These reductions in SD denote increases in precision of the O/E index. We also assessed the sensitivity of O/E, measured by the % of test sites (that is, sites independently assessed as not being in reference condition) that were declared to be outside the reference distribution of O/E scores. Five of our 10 cases showed increases in sensitivity of ≥10% at PT = 0.5 as compared with PT = 0+. All cases that did not show increases in sensitivity or precision also showed no decrease in either of these performance measures as PT increased. A comparison of observed occurrence frequencies of taxa at reference and test sites qualitatively explained the size of the O/E sensitivity response in all 10 cases. This result suggests that effects of rare-taxa exclusion are a direct consequence of these occurrence frequencies rather than of predictive-model structures. Thus, we predict that other assemblage-based bioassessment tools are likely to show improved sensitivity when rare taxa are excluded.

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