Developing an improved biomonitoring tool for fine sediment: Combining expert knowledge and empirical data
Bilotta, Gary S.
Brazier, Richard E.
Copyright © 2015 The Authors. Published by Elsevier Ltd. This is an open access article published Under a Creative Commons license.
The Proportion of Sediment-sensitive Invertebrates (PSI) index is a biomonitoring tool that is designed to identify the degree of sedimentation in rivers and streams. Despite having a sound biological basis, the tool has been shown to have only a moderate correlation with fine sediment, which although comparable to other pressure specific indices, limits confidence in its application. The aim of this study was to investigate if the performance of the PSI index could be enhanced through the use of empirical data to supplement the expert knowledge and literature which were used to determine the original four fine sediment sensitivity ratings. The empirical data used, comprised observations of invertebrate abundance and percentage fine sediment, collected across a wide range of reference condition temperate stream and river ecosystems (model training dataset n = 2252). Species were assigned sensitivity weights within a range based on their previously determined sensitivity rating. Using a range of weights acknowledges the breadth of ecological niches that invertebrates occupy and also their differing potential as indicators. The optimum species-specific sensitivity weights were identified using non-linear optimisation, as those that resulted in the highest Spearman's rank correlation coefficient between the Empirically-weighted PSI (E-PSI) scores and deposited fine sediment in the model training dataset. The correlation between percentage fine sediment and E-PSI scores in the test dataset (n = 252) was eight percentage points higher than the correlation between percentage fine sediment and the original PSI scores (E-PSI r<inf>s</inf> = -0.74, p < 0.01 compared to PSI r<inf>s</inf> = -0.66, p < 0.01). This study demonstrates the value of combining a sound biological basis with evidence from large empirical datasets, to test and enhance the performance of biomonitoring tools to increase confidence in their application.
Vol. 54, pp. 82 - 86