Multilayer networks in ecology and evolution

Networks provide a powerful framework for exploring evolutionary and ecological complexity, generating fundamental insights into the structure, function, and dynamics of biological systems — from gene regulatory networks to ecological communities. More recently, network theory in ecology and evolution has moved beyond single 'monolayer' networks toward multilayer frameworks that capture the higher-dimensional complexity of biological systems, including how interactions among genes, individuals, and species vary across time and space. This approach has already proven valuable for analysing diverse data network types in biological systems, including host-microbiome associations, spatial and co-occurrence networks, seasonal dynamics of ecological communities, gene regulatory and protein interaction networks, and the network architecture of phenotypic plasticity.

This full day workshop will:

1. Provide the theoretical foundations and examples of network and multilayer network analysis as applied to biological and ecological systems;
2. Offer interactive, hands-on sessions where participants build and analyse networks from their own empirical, experimental, or simulated data;
3. Introduce network pattern detection using different metrics (e.g., modularity, centrality, percolation);
4. Cover null model analysis and rarefaction approaches in multilayer networks across genes, traits, interactions, and species; and
5. Explore network dynamics using empirical multilayer datasets. Participants will gain practical experience with available software tools and R packages for multilayer network visualisation and analysis. 

All workshop material will be deposited and shared in a Github repository for long term use and training. 

Target audience

Graduate students and researchers interested in these methods for ecology, evolutionary development biology and genomics — applying multilayer network methods to your own biological or ecological data; visualising your empirical multilayer network; conducting data analysis using metrics tailored to your research question; and interpreting network dynamics in the context of your system.

Participants will:

• Construct multilayer networks from empirical data.
• Apply modularity, centrality and percolation metrics.
• Implement null models for multilayer networks.
• Interpret network dynamics across spatial or temporal layers.
• Assess robustness and rarefaction effects.

You should have familiarity with command line interface and R coding. Introductory materials will be shared beforehand. The full workshop schedule is available here.