Fish eDNA metabarcoding unveils genetic diversity patterns and habitat fragmentation
Stream ecosystems are highly diverse and provide essential ecosystem services, yet they are experiencing unprecedented biodiversity loss. This decline is commonly assessed through changes in species richness and abundance, while changes in genetic diversity remain largely underrepresented. Maintaining genetic diversity in streams primarily depends on longitudinal connectivity to ensure migratory pathways and population exchange in fish communities. Environmental DNA (eDNA) metabarcoding has emerged as an efficient, non-invasive monitoring approach. Importantly, eDNA metabarcoding is based on the detection of genetic variants, which also enables the assessment of intraspecific genetic variation compared to traditional monitoring methods. Here, we present a proof-of-concept study conducted along the River Ruwer, a tributary of the Moselle (Germany) where extensive renaturation efforts have been undertaken. Using fine scale eDNA metabarcoding along the entire watercourse, we assessed spatial patterns in species composition and genetic diversity. These results were complemented by conventional electrofishing surveys and a citizen science eDNA sampling campaign across the wider catchment. Overall, we detected 18 fish species within 159 genetic variants. Both species richness (rho=-0.94, p=0.05) and genetic diversity (rho=-0.88, p=0.05) decreased significantly along the watercourse, with distinct upstream and downstream communities separated by the waterfall. Only four species occurred in both sections. Genetic diversity was markedly lower upstream of the barrier, with several unique genetic variants found exclusively in this isolated section, even when compared to the broader regional dataset derived from citizen science sampling. Our findings highlight the importance of restoring longitudinal connectivity in stream ecosystems and demonstrate the added value of genetic information in biodiversity monitoring. Our conventional eDNA-based approach, which was not-specifically optimized towards detection of population dynamics, proved to be a comprehensive tool for whole-river assessments, capable of revealing genetic isolation patterns that remain undetected by conventional monitoring approaches.