INFERRING THE MINIMUM PH OF STREAMS FROM MACROINVERTEBRATES USING WEIGHTED AVERAGING REGRESSION AND CALIBRATION

1. Data on macroinvertebrates and stream chemistry were collected from sixty-four streams in Finland. Weighted averaging (WA) regression and calibration models were constructed to infer the minimum pH of stream s from their invertebrate assemblages. The purpose was to develop an i nstrument for biological assessment and monitoring of stream acidifica tion. The WA method was compared with simpler approaches, based on qua litative invertebrate data and pH tolerance limits, that are widely us ed. 2. Performance of the two approaches was assessed in terms of corr elation between the inferred and observed minimum pH within the 'train ing set', and in terms of root mean squared differences (predicted - o bserved) (RMSEP) estimated by cross-validation or bootstrap resampling techniques. The models were further tested using independent data fro m the literature representative of a wide geographical range. 3. The p redictive power of the WA models was reasonable (RMSEP 0.40-0.44 pH un its) in the training set and consistently better than that of the tole rance limit method. In contrast to the latter, the WA models were able to infer a minimum pH above 5.5, suggesting they could detect the ear ly stages of acidification. 4. The WA models performed better than the tolerance limit method in inferring pH from the independent literatur e, further demonstrating the superiority and generality of the WA appr oach. 5. The weighted averaging technique could be an effective and wi dely applicable tool for contemporary biological monitoring and assess ment using aquatic invertebrates.