Lisa Hülsmann

Research group leader

Contact

Ecosystem Analysis and Simulation (EASI) Lab, University of Bayreuth
email: lisa.huelsmann@uni-bayreuth.de

Research Interests

  • Forest ecology
  • Demographic diversity
  • Vegetation models
  • Statistics and environmental data integration

Curriculum Vitae

2021-now Juniorprofessor for Ecosystem Analysis and Simulation at University of Bayreuth, Germany
2018-2021 Junior research group leader at University of Regensburg, Germany
2017-2018 Lecturer at University of Regensburg, Germany
2016-2017 Postdoctoral fellow at WSL Birmensdorf, Switzerland
2012-2016 PhD studies at ETH Zurich and WSL Birmensdorf, Switzerland
2008-2012 Master studies in Hydrogeology and Environmental Geoscience at University of Goettingen, Germany
2005-2008 Bachelor studies in Forest Science and Ecology at University of Goettingen, Germany

Selected publications

For a full list of publications see Scopus, ResearcherID, Google Scholar & Research Gate.

  • Hülsmann, L., Chisholm R. A. & Hartig, F. (2021). Is variation in conspecific negative density dependence driving tree diversity patterns at large scales? Trends in Ecology and Evolution. 36, 151-163. [journal]
  • Bugmann, H., Seidl, R., Hartig, F., … Hülsmann, L., … Reyer, C.P.O. 2019. Tree mortality submodels drive simulated long-term forest dynamics: assessing 15 models from the stand to global scale. Ecosphere. 10, e02616. [journal]
  • Hülsmann, L. & Hartig, F. (2018). Comment on “Plant diversity increases with the strength of negative density dependence at the global scale”. Science, 360. doi: 10.1126/science.aar2435. [journal]
  • Hülsmann, L., Bugmann, H., Cailleret, M. & Brang, P. (2018). How to kill a tree: Empirical mortality models for eighteen species and their performance in a dynamic forest model. Ecological Applications. 28, 522-540. [journal]
  • Hülsmann, L., Bugmann, H. & Brang, P. (2017). How to predict tree death from inventory data – lessons from a systematic assessment of European tree mortality models. Canadian Journal of Forerst Research, 47, 890-900. [journal]