Selection of an adequate functional diversity index for stream assessment based on biological traits of macroinvertebrates

Functional diversity (FD) is useful for the evaluation of freshwater ecosystems. The FD of macroinvertebrate families for river water quality (WQ) assessment in the Paute River Basin (PRB), Ecuador, was investigated. Macroinvertebrate samples and data about 26 physical, chemical, microbiological and hydro-geomorphological variables were available. Literature-based biological traits were allocated as scores to the macroinvertebrates data through fuzzy coding. The Generalised Additive Mixed Model (GAMM) was used to assess the performance of six FD indices using the referred 26 WQ descriptive variables. The best performing GAMM led to selecting the index based on functional dendrograms including the species community pool (wFDc) as the most suitable to characterise FD in the PRB. The sub-basins of the PRB were grouped in three classes applying Average Linkage Clustering (ALC) and using wFDc. The Random Forest (RF) algorithm was used with a global efficiency of 89% to assess the ALC clusters consistency and pre-identify the significant WQ descriptive variables, explaining most of the FD variability. The Kruskal-Wallis test was then applied to refine the outcomes of the previous analysis. Twelve WQ descriptive variables were finally identified as the best discriminant predictors for FD, including the riparian vegetation, electric conductivity, dissolved oxygen, total hardness, faecal coliforms and pH. It is believed that the implemented approach successfully assessed the stream WQ status of the PRB upon selecting a suitable macroinvertebrate FD index; as such, it could be applied to other tropical basins for WQ assessment.

Automated summary

This study investigated the functional diversity of macroinvertebrate families for river water quality assessment in the Paute River Basin, Ecuador. The best performing functional density index based on physical, chemical, microbiological and hydro-geomorphological variables was identified. The Random Forest algorithm and Kruskal-Wallis test were used to determine the best predictors, resulting in the identification of 12 variables. This approach successfully assessed the stream water quality and can be applied to other tropical basins.