Postal Address:  
Institute of Environmental Physics
INF 229, 69120 Heidelberg, GERMANY
Visit: Im Neuenheimer Feld 229, Room 202
Voice: (+49) 6221 54-6379
ORCID: 0000-0003-3858-0904
Skype: Yunus.Sevinchan
GitLab (internal): @yunus @blsqr
GitHub @blsqr

Research Interests

  • Isolating fundamental processes in evolving systems
    • What is the nature of hierarchy formation and what determines a system's evolvability?
    • How can evolutionary systems be represented in computer models?
  • Studying the behaviour of complex and evolving systems using computational modelling
    • How do ecosystems emerge and what makes them resilient?
    • How does collective behaviour and cooperation arise?

Evolution can be seen as the universal generator of complexity in our environment. Systems that behave evolutionary, in the Darwinian sense, span vastly different time and length scales [Szathmáry, 2015], ranging from pre-biotic evolution in chemical reaction networks [Vasas et al., 2012; Nghe et al., 2015] and the origin of eukaryotes [Koonin, 2014; Martin, 2017] to eusocial animal societies [Wilson & Wilson, 2007], and technological [Solé et al., 2013] and cultural evolution [Brewer et al., 2017; Lenton et al., 2021]. These systems exhibit emergent behaviour like speciation, cooperation, group formation, and hierarchical organisation [Bourke, 2009]. With evolution playing a role in so many domains, increasing the understanding of these processes remains a very intriguing field of research.

Overall, I have a keen interest in connecting theoretical approaches and model building to observations in research on ecology, human behaviour, and their impact on each other. These observations should be used for re-evaluating the premises of a model, and lead to a better understanding of the underlying mechanisms of evolutionary systems. Ultimately, I hope that research in this field will also lead to a better understanding of the dynamics of System Earth and the role of humankind in it.



  • Yunus Sevinchan: Evolution Mechanics: a conceptual framework for the hierarchical unfolding of life. In preparation, based on my doctoral thesis.
  • Yunus Sevinchan: Retaining structural information in evolutionary food web models by including environment-based inheritance channels. In preparation, based on my doctoral thesis.
  • Yunus Sevinchan: Evolution Mechanics and Perspectives on Food Web Ecology. Dissertation, DOI: 10.11588/heidok.00030750
  • Yunus Sevinchan, Harald Mack, Benjamin Herdeanu, Lukas Riedel, and Kurt Roth: Boosting Group-level Synergies by Using a Shared Modeling Framework. In: Computational Science – ICCS 2020. Lecture Notes in Computer Science, vol 12143, 2020. DOI: 10.1007/978-3-030-50436-6_32 (Preprint).
  • Lukas Riedel, Benjamin Herdeanu, Harald Mack, Yunus Sevinchan, and Julian Weninger: Utopia: A comprehensive and collaborative modeling framework for complex and evolving systems. Journal of Open Source Software, 2020. DOI: 10.21105/joss.02165 
  • Yunus Sevinchan, Benjamin Herdeanu, Jeremias Traub: dantro: a Python package for handling, transforming, and visualizing hierarchically structured data. Journal of Open Source Software, 2020. DOI: 10.21105/joss.02316

Research Software

Research questions like the above (and others in the CCEES field) are typically addressed by investigating computer models, requiring efficient and reliable research software.

I am involved in the development and maintenance of the following software packages:

All of the above are available under open-source licenses. If you run into any issues with these tools, feel free to raise an issue in the corresponding GitLab projects, or drop me an e-mail.

Doctoral project (2018-21)

In my doctoral studies, I used numerical simulations to investigate resilience in evolving ecological interaction networks and I (together with my colleagues) conceptualised the Evolution Mechanics framework that aims to describe hierarchy formation in evolving systems.

I defended my doctoral thesis Nov. 3rd 2021. Please refer to my dissertation (linked in Publication list above) for more details.

Evolution Mechanics

The question how hierarchically modularised structures arise from simpler ones is of central importance when desiring to understand our world. The Evolution Mechanics framework aims to find a concise description of the mechanisms by which evolutionary systems unfold into hierarchically organised modules. While inspired by the evolution of biological life, Evolution Mechanics is abstracted from it and takes a more general perspective, providing a consistent language to address the fundamental processes giving rise to the complexity we observe all around and within us.

The framework arose as the product of many discussions with Kurt Roth, Benjamin Herdeanu, Harald Mack, and others. In my dissertation, I provide an in-depth formulation of this framework (or: my perspective of it).

Food Web Resilience

On the more practical side, I studied ecosystem resilience using numerical simulations. To that end, I re-implemented and extended a model by Korinna Allhoff et al. [2015] and studied its response to different kinds of perturbations. In addition, I characterised to which extent this model can be understood as behaving in a self-organised critical way.

I found that the studied food web model does not retain sufficient structural information across a perturbation to influence the recovery of the food web. I then propose a number of ways how such a model can be extended to allow a meaningful investigation of food web resilience.

Generalised Ecosystem Evolution

A part of my research that did not make it into my dissertation focussed on the question of group formation in complex evolutionary interaction networks. To that end, I formulated a computer model of evolving ecological networks [Brännström, 2012; Landi et al., 2018]. The network includes trophic as well as mutualistic interactions and is embedded in a non-trivial environment. It takes into account the resource flow through the network, the utilisation by its entities, their non-trophic interactions as well as effects mediated or forced by the environment, while the network structure changes adaptively.

Recently, Kotil and Vetsigian [2018; Kelsic et al., 2015] have shown how evolutionary stable communities can form among microbial species, and Grilli et al. [2017; Bairey et al., 2016] demonstrated the stabilising effect of higher-order interactions in ecological networks. As part of my doctoral studies I tried to elevate these approaches to models of spatially distributed and evolving ecological networks and evaluate whether their conclusions hold in this more complex setting. The aim was to arrive at a model that offers a framework to investigate not only group formation, but also isolate eco-evolutionary dynamics and study its resilience against perturbations or long-term environmental change. This framework was then to be used to explore abstractions and the minimal necessities for a certain phenomenology to take place.

The formulated and implemented model turned out to be too difficult to get a grip on and was put on the backlog for possible future consideration in a simplified form. Despite the dead end I arrived at with this particular model, it provided a number of useful insights regarding modelling approaches and model complexity.


MSc Thesis Project (2017)

In my master thesis project I used computational modelling to investigate how abstracted populations harvest energy from the resources in their environment. To that end, I implemented an evolutionary interaction network which models complex resource and population dynamics and allows the populations to gain innovations that allow them to tap new resources.

Example of possible network entities. Energy source (yellow); non-renewable and renewable resources (grey); populations (green) tapping those resources; energy sink (red).

Once a population gets to a certain size, it splits into two and the offspring population can pick up mutations both in its internal properties and its innovations, i.e. its abilities to tap certain resources. The below figure shows an example of the resulting evolution of one single population parameter, the size beyond which a population splits:

 Exemplary simulation run showing the evolution of the split size parameter of the populations. The initial value of the starting population is 1000. The lines' extension corresponds to the lifetime of the population; additionally, the colour denotes the time of splitting from the parent population. 

The model was testing ground for the feasibility of modelling an interaction network with both complex propagation dynamics (from node to node) as well as complex internal dynamics (within individual nodes) and evolutionary mechanisms. It is further expanded within my doctoral studies.