This work-package is leaded by Emese Meglecz (professor at IMBE)
Describing community composition is central to biodiversity monitoring and thus biodiversity protection. In addition to artificial structures (ARMS, ASUs), we will characterize communities from benthos scrapings, to compare patterns (e.g. spatial connectivity, temporal variation) between installed and recently colonized communities. Several methods are available to accomplish this task, but they need to be adjusted for monitoring hard bottoms due to a very large taxonomic diversity in this environment. Metabarcoding provides a high throughput, automatable way of studying community composition. We will focus on fine-tuning both laboratory and bioinformatics protocols for metabarcoding and aim to establish a robust protocol. To overcome the two limitations of PCR-based metabarcoding (risk of false negatives and contamination, poor quantitative estimations of abundance or biomass) we will design a set of universal baits (for the barcode metazoan molecule COI) to use in capture-enrichment –HTS, and test it on a subset of samples.
Each sample are amplified for the COI fragment using universal primer pairs for Metazoa. Replicates, positive (mock samples of known composition) and negative controls warrant repeatability and reliability. A bioinformatics pipeline designed for this experimental design will be fine-tuned and published. In addition to metabarcoding, representing both validation and alternative methods, we will characterize some samples by classical and cost-effective morphological methods including photo analysis with computer vision algorithms. We will also test a PCR-free-metabarcoding approach with probes designed to capture any metazoan taxon.