Species Identification in Malaise Trap Samples by DNA Barcoding Based on NGS Technologies and a Scoring Matrix

Area of Science:

• Ecology
• Genomics
• Bioinformatics

Background:

• The German Barcoding of Wildlife (GBOL) and Barcoding of Fauna Bavaria (BFB) initiatives have created a substantial reference library for metazoan species.
• Next-generation molecular biodiversity assessments require efficient and reliable methods for processing large sample volumes.

Purpose of the Study:

• To develop and evaluate a meta-barcoding pipeline for streamlining the analysis of DNA from malaise trap samples.
• To compare the efficiency of pre-sorting arthropod orders versus analyzing a combined sample for biodiversity assessment.

Main Methods:

• A single malaise trap sample was sorted into 12 arthropod orders, with DNA extracted from pooled individuals of each order.
• DNA extracts were amplified using four CO1-5' fragment primer sets and sequenced on an Illumina Mi-SEQ platform.
• A custom local BLAST database was created using existing Hymenoptera, Coleoptera, Diptera, and Lepidoptera barcodes for identification.

Main Results:

• The pipeline identified 529 Barcode Index Numbers (BINs) from pooled samples, with 390 high-score BINs after applying a scoring matrix.
• Pre-sorting yielded approximately 30% more high-score BINs compared to analyzing a combined, non-sorted sample.
• 69% of high-score BINs from sorted samples were also identified in the combined sample, indicating significant overlap but reduced detection.

Conclusions:

• The developed meta-barcoding pipeline offers a fast, efficient, and reliable method for analyzing next-generation sequencing data from malaise trap samples.
• Pre-sorting samples into major taxonomic groups enhances the detection of biodiversity, providing more comprehensive results for molecular assessments.
• This approach facilitates large-scale biodiversity monitoring and the application of reference libraries for ecological studies.