A functional trait-based indicator value index for rapid bioassessment using benthic macroinvertebrate families

Abstract

Traditional indicator species approaches are widely used in freshwater bioassessment due to their simplicity, low cost, and operational efficiency. This study develops a Functional Trait-Based Indicator Value Index (FIVI) by integrating functional traits with the indicator species framework for benthic macroinvertebrates in the Yangtze River Basin. FIVI links functional trait-based indicator values of families to biological integrity gradients derived from the integration of total family richness and functional richness across diverse waterbody types (mainstem, primary and secondary tributaries, streams, river-isolated lakes, and river-connected lakes), thereby enhancing ecological interpretability and operational applicability. We classified 182 families into ten functional groups and quantified their indicator values across five biological integrity grades using specificity and fidelity metrics. These values were further translated into indicator grades and combined with dominance information to construct FIVI. Comparative validation of five configurations identified the top three dominant families as the optimal formulation. Across six waterbody types, FIVI showed more even grade distributions and stronger responses to anthropogenic disturbance gradients than the Biological Monitoring Working Party (BMWP) and the Family Biotic Index (FBI). Multi-dimensional evaluations demonstrated that FIVI achieved superior discriminatory power, higher precision, stronger responsiveness, lower misclassification rates, and greater relevancy to anthropogenic disturbance gradients. By linking functional traits with biological integrity gradients, FIVI provides an ecologically interpretable and operationally simple tool for rapid bioassessment across diverse freshwater ecosystems. The functional group classification and corresponding indicator value lookup tables enable efficient field application, supporting large-scale freshwater ecosystem monitoring and management decisions.