Prof. Dr. Sebastian Höhna
Professor für Theoretische Geobiologie
Research Interests
I am focusing on several aspects of theoretical and computational phylogenetics. Using our server facilities our primary aim is to develop new statistical models and computational methods to answer research questions in phylogenetics, which include:
- Estimating phylogenetic relationships
- Estimating divergence times and rates of molecular evolution
- Inferring rates and mode of lineage diversification (speciation and extinction)
- Analyzing causes of conflicting gene trees.
Robust Estimation of Gene Trees
Gene trees provide fundamental information about the processes in genetic evolution which is contained in the variation of their topology and branching times. In this work we are focused on how to reliably estimate gene trees by developing more robust statistical methods.
Research projects:
- Von Genomen zur Makroevolution: Entwicklung von phylogenetischen Methoden zur Genbaum, Speziesbaum und Divergenzraten Schätzung (DFG, seit 2022)
Understanding Discordance Between Species Trees and Gene Trees
The following biological scenarios can cause incongruent gene trees: (a) simple population genetic processes (i.e., the coalescent), (b) migration and thus gene-flow between species/populations , and (c) gene-duplication and gene-loss. We are working on more realistic models that incorporate different causes of gene-tree incongruence such as the multispecies coalescent with migration model. This work also entails new algorithm development and theory design for more efficient computation of species trees from thousands of gene trees.
Inference of Macroevolutionary Processes
Our ultimate goal is to infer diversification rates through time and among lineages and identify correlations to genetic, phenotypic and/or environmental factors that impact diversification rate. For example, we developed a statistical method to identify correlations between rates of diversification and environmental variables, such as changes in atmospheric CO2. In our future work, we want to incorporate fossil occurrence information in our models of lineage diversification.
The State-Dependent OU Model
Joint inference of continuous trait adaptation and selective regime history using the state-dependent Ornstein-Uhlenbeck model. Trait evolution studies the evolution of homologous characters across phylogeny. These characters are at above-sequence level, including but not limited to gene expression, physiological, and morphological data. Many phylogenetic comparative methods (PCMs) have been developed to study trait evolution, yet to what extent these models reflect realistic evolutionary processes is still under debate. Here, we have developed the state-dependent Ornstein-Uhlenbeck model, a Bayesian approach to jointly infer the evolution of a continuous trait and the discrete selective regimes.
Modelling gene expression evolution in fireflies
Gene expression is a key driver of trait variation, particularly among closely related species. This project aims to develop innovative methods to model gene expression evolution using Brownian motion and Ornstein-Uhlenbeck processes. Specially, we focus on within-species variance, a critical yet underexplored aspect of gene expression. The firefly family (Lampyridae) serves as a novel study system due to their recurrent sexual dimorphism across the phylogeny. As sexual dimorphism is inherently linked to sex-biased gene expression, this makes fireflies an ideal model for investigating sex-biased gene expression evolution.
Aktuelle Forschungsprojekte
(DFG, priority programm SPP 2349 GEvol , seit 2023)Macroevolutionary dynamics and biodiversity
Biodiversity is modeled by the process of speciation and extinction. There is clear evidence from both living and extinct species that biodiversity is extremely variable through time and among species. However, we still do not know what factors drive speciation and extinction rates on a macroevolutionary level (that is, beyond species boundaries). The goal of this project is to combine statistical, computational, neontological – i.e. relating to species living today – and paleobiological approaches to study macroevolutionary dynamics.
Aktuelles Forschungsprojekt
- Which Factors Drive Macroevolutionary Rates of Speciation and Extinction (MacDrive) (EU ERC Starting Grant, seit 2023)
Bayesian phylogenetic inference using probabilistic graphical models and an interpreted language. RevBayes provides a flexible framework for performing Bayesian statistical analysis of phylogeny and related topics, such as divergence time estimation, diversification rate estimation, continuous and discrete trait evolution, and historical biogeography.
An R package for Diversification Rate Estimation and Fast Simulation of Reconstructed Phylogenetic Trees under Tree-Wide Time-Heterogeneous Birth-Death Processes Including Mass-Extinction Events. TESS both provides the likelihood function as well as reversible-jump method to estimate global diversification rates and rate shifts.
A module for Phylogenetic Estimation of Shifts in the Tempo of Origination. Under the birth-death-shift process (Höhna et al. 2019, biorxiv), the diversification rate is allowed to shift across the phylogeny, where branch-specific diversification rates, as well as the number of rate shifts can be inferred. Pesto is implemented using an efficient estimation algorithm, allowing for fast inferences even for large phylogenies with thousands of species.
An R package for convergence diagnostics in Bayesian Phylogenetic MCMC. This R package helps in convergence assessment for phylogenetic inference not only in continuous parameters but also in the trees sampled as well. More information and a tutorial on convergence assessment using Convenience can be found in the