Small Bugs, Big Data: Metagenomics for Arthropod Biodiversity Monitoring

Area of Science:

• Ecology
• Genomics
• Bioinformatics

Background:

• Obtaining genome-wide data from complex environmental samples is feasible but challenging for species identification and population genomics.
• Metabarcoding is a common method for biodiversity assessment, but may miss subtle genetic variations.

Purpose of the Study:

• To compare metagenomic sequencing with metabarcoding for analyzing arthropod bulk samples.
• To assess the utility of metagenomics for inferring species presence, population structure, and ecological interactions.

Main Methods:

• Applied metagenomic sequencing to 40 arthropod bulk samples collected via Malaise traps.
• Compared metagenomic results with metabarcoding data from the same samples.
• Utilized a custom genome database for taxonomic classification and population genomic analyses.

Main Results:

• Metagenomics achieved genus-level classification consistent with metabarcoding, detecting all identified genera.
• Conservative filtering in metagenomics excluded some low-abundance taxa but provided genome-level data like haplotype diversity and heterozygosity.
• Population structure was inferred for abundant species, revealing hybrid origins in ants and distinctiveness in bumblebees. Plant DNA by-catch suggested arthropod-plant interactions.

Conclusions:

• Metagenomics offers valuable biodiversity monitoring and population genomics insights beyond metabarcoding.
• Effective use requires careful consideration of filtering criteria and comprehensive reference databases.
• This approach has potential for discovering ecological associations and understanding population genetics.