Ecology and Evolution (Aug 2024)
Unlocking rivers' hidden diversity and ecological status using DNA metabarcoding in Northwest Spain
Abstract
Abstract Rivers are crucial ecosystems supporting biodiversity and human well‐being, yet they face increasing degradation globally. Traditional river biomonitoring methods based on morphological identification of macroinvertebrates present challenges in terms of taxonomic resolution and scalability. This study explores the application of DNA metabarcoding analysis in both bulk and environmental DNA (eDNA) samples for comprehensive assessment of macrozoobenthic biodiversity, detection of invasive and endangered species, and evaluation of river ecological status in northwestern Spain. DNA metabarcoding of homogenized bulk samples and water eDNA revealed a mean of 100 and 87 macrozoobenthos species per sample respectively. However, the specific composition was significantly different with only 27.3% of the total species being shared. It was not possible to identify all the OTUs to species level; only 17.43% and 49.4% of the OTUs generated could be identified to species level in the bulk and eDNA samples, respectively. Additionally, a total of 11 exotic species (two first records for the Iberian Peninsula and another three first records for Asturias region) and one endangered species were detected by molecular tools. Molecular methods showed significant correlations with morphological identification for EQR values (Ecological Quality Ratio) of IBMWP index, yet differences in inferred river ecological status were noted, with bulk samples tending to indicate higher status. Overall, DNA metabarcoding offers a promising approach for river biomonitoring, providing insights into biodiversity, invasive species, and ecological status within a single analysis. Further optimization and intercalibration are required for its implementation in routine biomonitoring programmes, but its scalability and multi‐tasking capabilities position it as a valuable tool for integrated monitoring of river ecosystems.
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