Exploring hidden parasite diversity during a ParasiteBlitz across a coastal habitat gradient using environmental DNA...

Area of Science:

• Parasitology
• Environmental DNA (eDNA) Metabarcoding
• Aquatic Ecology

Background:

• Parasite biodiversity assessments have historically lagged behind other biodiversity assessments.
• Environmental DNA (eDNA) metabarcoding offers a promising approach for detecting parasites in various environmental matrices.
• Limited studies have comprehensively evaluated eDNA metabarcoding for parasite detection across different aquatic habitats and sampling methods.

Purpose of the Study:

• To implement and evaluate eDNA metabarcoding for assessing parasite diversity in four connected aquatic habitats.
• To compare the effectiveness of different sampling methods (sediment, active water filtration, passive water collection) for parasite detection.
• To identify parasite operational taxonomic units (OTUs) and assess the diversity and community structure across habitats.

Main Methods:

• Collected sediment and water samples from four aquatic habitats in coastal South Carolina.
• Utilized five amplicon libraries targeting mitochondrial COI and 18S ribosomal RNA genes for various parasite groups.
• Processed over 5.8 million sequences to identify parasite amplicon sequence variants (ASVs) and operational taxonomic units (OTUs).

Main Results:

• Identified over 1,000 parasite ASVs, corresponding to approximately 600 parasite OTUs across six parasite groups.
• Microsporidians exhibited the highest diversity, and their assay demonstrated the greatest fidelity.
• Actively-filtered water samples captured all six parasite groups, while sediment captured four groups with higher ASV counts.

Conclusions:

• eDNA metabarcoding is effective and efficient for assessing parasite diversity during intensive surveys.
• The optimal sampling method varied by parasite group and habitat, indicating a need for tailored approaches.
• Enhanced parasite sequence databases and primer development are crucial for improving eDNA-based parasite detection.