Towards a multimetric index for the assessment of Dutch streams using benthic macroinvertebrates

This study describes the development of a macroinvertebrate based multimetric index for two stream types, fast and slow running streams, in the Netherlands within the AQEM project. Existing macroinvertebrate data (949 samples) were collected from these stream types from all over the Netherlands. All sites received a ecological quality (post-)classification ranging from 1 (bad status) to 4 (good status) based on biotic and abiotic variables, using a combination of multivariate analysis and expert-judgement. A number of bioassessment metrics was tested for both stream types (fast and slow running streams) to examine their power to discriminate between streams of different ecological quality within each stream type. A metric was selected for inclusion in the final multimetric index when there was no overlap of the 25th and 75th percentile between one (or more) ecological quality class(es). Out of all metrics tested, none could distinguish between all four ecological quality classes without overlap of the 25th and 75th percentile between one or more of the classes. Instead, metrics were selected that could distinguish between one (or more) ecological quality class(es) and all others. Finally, 10 metrics were selected for the assessment of slow running streams and 11 metrics for the assessment of fast running streams. Class boundaries were established, to make the assignment of scores to the individual metrics possible. The class boundaries were set at the 25th and/or 75th percentile of the individual metric values. The individual metrics were combined into a multimetric index. Calibration showed that 67% of the samples from slow running streams and 65% of the samples from fast running streams were classified in accordance to their post-classification. In total, only 8% of the samples differed more than one quality class from the post-classification. The multimetric index was validated with data collected in the Netherlands from 82 sites for the purpose of the AQEM project. Validation showed that 54% of the streams were classified correctly