Estimating intraspecific genetic diversity from community DNA metabarcoding data.

Area of Science:

• Ecology
• Genetics
• Bioinformatics

Background:

• DNA metabarcoding generates species composition data but often loses intraspecific diversity due to sequencing errors and operational taxonomic unit (OTU) clustering.
• Cytochrome c oxidase subunit I (COI) haplotype information, while limited, can be valuable for phylogeography and understanding taxon distribution.

Purpose of the Study:

• To develop and test a novel method for extracting haplotype information from freshwater macroinvertebrate metabarcoding datasets.
• To combine sequence denoising and abundance-based filtering to infer intraspecific genetic diversity from bulk samples.

Main Methods:

• Integrated sequence denoising strategies (from microbial research) with abundance-based filtering.
• Developed a new haplotyping approach implemented in the R package "JAMP" for COI amplicon datasets.
• Tested the method on a mock community with known haplotypes and 18 real-world monitoring samples using multiple primer sets and replicates.

Main Results:

• All 15 haplotypes were detected in the mock community; rigorous filtering successfully removed most spurious haplotypes while retaining expected ones.
• Monitoring samples yielded 177-200 OTUs per primer set, with an average of 2.40-3.30 haplotypes per OTU.
• Recovered intraspecific diversity revealed population structures consistent across replicates and primer pairs, with some taxa showing clear phylogeographic patterns (e.g., north-south clines).

Conclusions:

• A strategy for inferring intraspecific genetic diversity from invertebrate metabarcoding data was successfully developed.
• The method recovered intraspecific diversity for many species, identifying potentially isolated populations for further phylogeographic investigation.
• Metabarcoding-informed haplotyping shows significant promise for biomonitoring, providing insights into both species and underlying genetic diversity.