Exclusion of rare taxa affects the performance of the O/E index in bioassessments

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Journal/Book Title/Conference

Journal of the North American Benthological Society



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

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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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