Inferring the minimum pH of streams from macroinvertebrates using weighted averaging regression and calibration

Abstract

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 streams from their invertebrate assemblages. The purpose was to develop an instrument for biological assessment and monitoring of stream acidification. The WA method was compared with simpler approaches, based on qualitative invertebrate data and pH tolerance limits, that are widely used.2. Performance of the two approaches was assessed in terms of correlation between the inferred and observed minimum pH within the ‘training set’, and in terms of root mean squared differences (predicted – observed) (RMSEP) estimated by cross‐validation or bootstrap resampling techniques. The models were further tested using independent data from the literature representative of a wide geographical range.3. The predictive power of the WA models was reasonable (RMSEP 0.40–0.44 pH units) in the training set and consistently better than that of the tolerance 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 early stages of acidification.4. The WA models performed better than the tolerance limit method in inferring pH from the independent literature, further demonstrating the superiority and generality of the WA approach.5. The weighted averaging technique could be an effective and widely applicable tool for contemporary biological monitoring and assessment using aquatic invertebrates.