How does molecular taxonomy for deriving river health indices correlate with traditional morphological taxonomy?

Abstract

Macroinvertebrate surveys are commonly used for assessing the health of freshwater systems around the world. Traditionally, surveying involves morphologically identifying the families, and sometimes genera, present in samples. Biological indices, derived from taxonomic lists, provide convenient ways to summarise community data and may be fairly insensitive to species-level changes in community compositions. In recent years, molecular techniques for identifying taxa have become increasingly popular and metabarcoding approaches that offer the ability to identify species from mixtures of whole animals (bulk-samples) or from environmental samples have gained much attention. However, generating accurate species lists from metabarcode data is challenging and can be impacted by sample type, choice of primers, community composition within samples, and the availability of reference sequences. This study compares the performance of molecular data extracted from bulk-samples against morphological data in calculating two biological indices (the Stream Invertebrate Grade Number Average Level 2 (SIGNAL2), which is calculated from family-level data, and a genus-level equivalent of this index, SIGNAL_SG) and one biological metric (taxon richness). Further, molecular indices and metrics derived from global, local or mixed reference DNA libraries and with varying degrees of filtering processes applied to them, are compared with respect to the strength of their relationships with morphological indices and metrics. Molecularly derived SIGNAL2 and SIGNAL_SG scores correlated strongly with morphologically derived scores, and were strongest when using a reference library containing a mix of local and global data. Molecularly derived richness metrics were moderately correlated with morphological taxa richness; however, the strongest correlations were observed when taxa that could not be assigned SIGNAL grades were omitted from analyses. This study highlights the utility of using molecular data as an objective and sensitive alternative to traditional freshwater biological assessment using macroinvertebrates.