Influence of geographical extent on typology‐ and model‐based assessments of taxonomic completeness of river macroinvertebrates

Abstract

Summary1. River InVertebrate Prediction and Classification System (RIVPACS)‐type predictive models are increasingly used to assess the biological condition of freshwaters, but management schemes may also be based on a priori groupings of similar water bodies (typologies) to control for natural variation in biota. The two approaches may lead to disagreements of the biological status of a site, depending on, for example, the spatial scale at which assessments are conducted.2. We used data from 96 reference and 134 potentially impacted sites from Western and Central Finland to compare RIVPACS‐type models and a simple size‐based typology of rivers for the assessment of taxonomic completeness (the quotient of the Observed‐to‐Expected number of predicted taxa, O / E) of riffle macroinvertebrates. We specifically examined how geographical extent influences bioassessment performance (accuracy, precision and sensitivity to detect impact) of the two approaches. To fully examine the behaviour of the O / E‐index with the two approaches at differing spatial scales, we performed all assessments with a full range of thresholds for predicted taxa occurrence probabilities (pt from 0+ to 0.9).3. Both approaches performed consistently better than the corresponding null models. At the larger extent (i.e. assessment encompassing the whole study area), the RIVPACS‐approach performed in all aspects better than the typology‐approach. However, at the smaller extent (i.e. regional assessments) the RIVPACS‐type models and the typologies showed similar accuracy to predict the actual fauna (mean E), similar precision (SD) of cross‐validated O / E and similar sensitivity to detect sites with human impairment.4. SD(O / E) decreased (i.e. precision increased) consistently with increasing pt. However, both approaches were most sensitive at intermediate pt:s (c. 0.2–0.6) when taxa with low predicted occurrence probabilities were excluded.5. Our results show that RIVPACS‐type predictive models are less susceptible to variations in spatial scale, whereas the performance of a priori typologies increases with decreasing spatial extent. Thus, RIVPACS‐type models are more efficient for large‐scale bioassessments, but at restricted spatial scales, or with an otherwise biologically meaningful stratification, simple a priori classifications can be equally useful for the assessment of taxonomic completeness of river macroinvertebrates.