ComBi

Local Adapation and Chromosomal Inversions

Nature showcases a fascinating mosaic of adaptations, with species evolving to adapt to their ever-changing environments. Chromosomal inversions, structural mutations that reverse gene order and link different gene variants, play a key role in this process. These inversions, often influenced by environmental factors like temperature and altitude, contribute to reproductive isolation and new species emergence. However, the exact mechanisms behind their contribution to local adaptation and the formation of new species remain unclear.

This project explores whether genomic structure facilitates the establishment of locally adaptive mutations or if natural selection and adaptive genome content drive the evolution of structural variation. By developing new statistical methods and mathematical models, and using machine learning to analyze data from sticklebacks and fruit flies, the project aims to demonstrate how chromosomal inversions contribute to local adaptation.

Team: Stefan Struett, Karolina Wachala, Stephan Peischl
Project Partners: Thomas Flatt, Katie Peichel

Supported by SNSF grant 10001034 (SNSF Link)

Automated Machine learning to identify drivers of microbial community composition

The compositions of microbial communities are of central importance for ecosystem functioning and host-associated health. Recent advancements in metagenomic sequencing have facilitated the acquisition of microbiome composition data. However, current analytical methods often fall short in exploring the environmental factors shaping these communities, especially in complex settings with numerous variables.

Given the crucial ecologic role of microbiomes and methodological gaps, this project aims to develop an analytic pipeline that explores environmental variables and ranks them by their association with the microbial community compositions at hand. The developed pipeline implements best practices of conditional data analysis (CoDa) and uses state-of-the-art ordination, clustering, and machine learning techniques to handle data with minimal supervision and assumptions.

Team: Ianis Vilela, Stephan Peischl
Co-Supervisors: Claudia Bank, Adamantia Kapopoulou

Evolutionary Rescue

We study mathematical models of evolutionary rescue in tempoprally and spatially changing extended populations.

Team: Matteo Tomasini, Stephan Peischl

Key publication: When does gene flow facilitate evolutionary rescue?

Range Expansions

Range expansions have profound effects on neutral and functional genomic diversity. We study the impact of gene surfing on the build up of expansion load and the evolution of fintess and dispersal related traits during range expansions.

Team: Stephan Peischl
Collaborators: Laurent Excoffier, Kim Gilbert, Mark Kirkpatrick and others

Key publication: On the accumulation of deleterious mutations during range expansions

Evolution in regions of low recombination

We study how deleterious mutations can affect patterns of diversity and demographic inference in regions of low recombination.

Team: Stefan Struett, Stephan Peischl
Collaborator: Laurent Excoffier

Key publication: A generalized structured coalescent for purifying selection without recombination

Teaching mathematical modelling with interactive web apps

R Shiny apps can improve the dissemination of mathematical models in ecology and evolution. We study how these interactive tools can be used most effectively in higher-education.

Team: Ana-Hermina Ghenu & Stephan Peischl
Project Partner: Sandra Grinschgl

Funded by the University of Bern Digitalization Strategy 2030 and Innovation Office.
Click here to participate in our study!

Mutation Load Dynamics

We study when and how deleterious mutations can accumulate in genomes. This includes investigating the demographic and evolutionary processes that shape the distribution and effects of deleterious mutations in natural populations.

Team: Stephan Peischl
Collaborators: Gerald Heckel, Laurent Excoffier, Flavia Schlichta, Kim Gilbert and others

Key publication: Demographic history and genomic consequences of 10,000 generations of isolation in a wild mammal