METAbarcoding for METAcommunities: towards a genetic approach to community ecology (META2)

The Challenge: measuring how species move

Understanding and predicting how biological communities respond to changing environments is a topic of prime concern for both science and society. While dispersal is the primary driver of community structure, traditional ecology lacks the tools to measure it accurately.

• The Data Gap: Tracking how species—especially "non-charismatic" but vital groups like insects—move and colonize new habitats is difficult.

• Implication for conservation: Without precise dispersal data, planning efficient conservation strategies remains a challenge.

The Innovation: The META2 Framework

META2 bridges the gap between ecological theory and practical biomonitoring. By leveraging advanced genetic techniques, we are moving beyond single-species tracking to whole-community analysis.

Key Technological Pillars:

• Metabarcoding: An emerging technique used to infer species composition from environmental DNA (eDNA) or bulk samples.

• Haplotype: By using haplotypes as a measure of relatedness, META2 tracks the recent movement of organisms across landscapes.

• Metacommunity: We treat local communities as interconnected networks linked by dispersal, providing a landscape view on conservation ecology.

Project Goals & Impact

META2  scales biodiversity surveys from dozens to hundreds of species simultaneously, making it possible to implement comprehensive conservation strategies.

• Data generation: Creation of a haplotype dataset from field work (dung beetles samples) using a metabarcoding approach.

• Knowledge gain: Improve understanding on dispersal and niche-based processes in metacommunities. In particular:

  1. Develop a framework suitable for analysing metabarcoding data.

  2. Investigate whether species distribution relate to altitude and land use.

• Implication for conservation: Use the results to guide conservation strategies.