Florian Grziwotz

PhD Student

Institute for Evolution and Biodiversity
Genome Evolution
Hüfferstraße 1
D-48149 Münster
Tel.: +49 251/83-21093

Nationality: GermanDe


  • Since 2016
    Trainee teacher, Goethe-Gymnasium, Dortmund
  • Since 2013
    PhD in the Genome Evolution Group, University of Münster, Germany
  • 2011 - 2013
    Studies in Mathematics and Biology (M.Ed.), University of Münster, Germany
    Master thesis: "Leiten Gründereffekte eine rapide Artbildung ein - ein mathematischer Ansatz"
  • 2008 - 2011
    Studies in Mathematics and Biology (B.Sc.), University of Münster, Germany
    Bachelor thesis: "Arrows Satz vom Diktator"


  • Jun. Prof. Arndt Telschow, Genome Evolution Group, Institute for Evolution and Biodiversity, University of Münster
  • Prof. Joachim Kurtz, Animal Evolutionary Ecology Group, Institute for Evolution and Biodiversity, University of Münster
  • Prof. Dr. Chih-Hao Hsieh, Institute of Oceanography and Institute of Ecology and Evolutionary Biology, National Taiwan University

Research interests

  • Wolbachia, Polyandry
  • Chaos, Projections
  • Population dynamics

PhD Project description

Evolution can be described as the genetic transformation of reproductive populations over time and space. This definition implies that not individuals but populations evolve. Against this background, my research agenda consists of three parts. (1) What is the effect of intracellular bacteria Wolbachia pipientis on mosquito population dynamics? I analyze published empirical data of Wolbachia infected and uninfected Aedes albopictus populations using non-linear time series analysis (S‐Map) and mathematical modeling. (2) What environmental factors drive mosquito population dynamics? Populations are exposed to environmental factors such as pathogens or climatic conditions. Therefore I try to identify environmental forcing variables based on non‐linear forecasting (multivariate simplex projections) for published empirical data of Aedes aegypti populations. (3) How can we detect evolutionary processes in time series data? Detecting different states in dynamic systems enables the identification of evolving systems and can be applied to evolutionary models (host‐parasite coevolution and red queen dynamics) as well as empirical data. Thus I try do develop a method to detect different states in dynamic systems using state space reconstructions.


  • Telschow A, Grziwotz F, Crain P, Miki T, Mains JW, Sugihara G, Dobson SL & Hsieh CH (2017) Infections of Wolbachia may destabilize mosquito population dynamics. Journal of Theoretical Biology. 10.1016/j.jtbi.2017.05.016 [doi]
  • Grziwotz F, Strauß JF, Hsieh CH, Telschow A (2018) Empirical dynamic modelling identifies differenr responses of Aedes polynesiensis subpopulations to natural environmental variables. Scientific Reports 8:16768 10.1038/s41598-018-34972-w [doi]