Improving the performance of macroinvertebrate based multi-metric indices by incorporating functional traits and an index performance-driven approach

Human-driven multiple pressures impact freshwater ecosystems worldwide, reducing biodiversity, and impacting ecosystem functioning and services provided to human societies. Multi-metric indices (MMIs) are suitable tools for tracking the effects of anthropogenic pressures on freshwater ecosystems because they incorporate various biological metrics responding to multiple pressures at different levels of biological organization. However, the performance and applicability of MMIs depend on their metrics' selection and their calibration against natural environmental gradients. In this study, we aimed to unravel i) how incorporating functional trait-based metrics affects the performance of MMIs, ii) how disentangling the natural environmental gradients from anthropogenic pressures effects affects the performance of MMIs, and iii) how the performance of MMIs developed using a metric performance-driven approach compares with MMIs developed using an index performance-driven approach. We carried out a field survey measuring abiotic and biotic variables at 53 sites in the Karun River basin (Iran) in 2018. For functional trait-based metrics, we used 15 macroinvertebrate traits and calculated community-weighted mean trait values and functional diversity indices. We used random forest modeling to account for the effect of natural environmental gradients on each metric. Based on our results, incorporating functional traits increased the MMI performance significantly and facilitated ecological interpretation of MMIs. Both taxonomic and functional components of macroinvertebrate assemblages co-varied strongly with natural environmental gradients, and accounting for these covariations improved the performance of MMIs. Finally, we found that index performance-driven MMIs performed better in terms of precision, bias, sensitivity, and responsiveness than metric performance-driven MMIs. [Display omitted] •Multi-metric indices (MMIs) track anthropogenic effects on freshwater ecosystems.•Adjustment for natural environmental gradients improves MMIs performance.•Incorporating functional traits improves MMIs performance and interpretation.•Index performance-driven MMIs outperform metric performance-driven MMIs.