Publications
Ecological theory and statistical methods, applied to community ecology, macroecology, macroevolution and conservation.
- Publications
- 126
- Citations
- 31,641
- h-index
- 58
Citations per year
Florian Hartig
Theoretical ecologist and ecological data scientist
Full Professor (W3),
University of Regensburg
About me
As a theoretical ecologist and ecological data scientist, I currently head the Theoretical Ecology Group and serve as Deputy Dean of Research of the Faculty of Biology and Preclinical Medicine. My path to Regensburg led through Berlin, Uppsala, Leipzig and Freiburg, as shown below.
- 2004 – 2005 MSc in Physics (Theoretical Particle Physics)Department of Theoretical Physics, Uppsala University, Sweden2001 – 2006 Studies in PhysicsFreie Universität Berlin, Germany
- 2006 – 2009 Dr. rer. nat., Department of Mathematics/Computer ScienceOsnabrück University (2009); research at UFZ LeipzigThesis: Metapopulations, Markets and the Individual
- 2009 – 2012 Postdoctoral fellowHelmholtz Centre for Environmental Research (UFZ), Leipzig
- 2012 – 2016 LecturerUniversity of FreiburgIn the group of Prof. Carsten Dormann
- 2026 – now Full Professor (W3) for Theoretical EcologyUniversity of Regensburg
- Head of the Professorship for Theoretical Ecology (Theoretical Ecology Group)
- Interim head of the Chair for Plant Ecology
- Deputy Dean of Research, Faculty of Biology and Preclinical Medicine
- Coordinator and head of the examination committee, MSc Ecology, Evolution & Conservation (EEC)
2016 – 2026 W2 Professor for Theoretical EcologyUniversity of Regensburg
Hover over, click or tab to a stage for details.Tap a stage for details. Full CV →
Research interests
With my research group, I study the mechanisms and principles underlying biodiversity patterns, using computer simulations, statistical methods and, more recently, algorithms from machine learning and artificial intelligence.
News from the group, such as new papers and new team members: group news page →
My research centres on five themes: how communities and biotic interaction networks assemble; what drives biodiversity patterns in macroecology and macroevolution; how ecological systems behave, studied with process-based models; how such models can be integrated with data; and statistics and machine learning for ecology. To find out more about these topics, please click on the topics in the ringlist below.
Understanding the assembly of communities and biotic interaction networks
Understanding the assembly of communities and biotic interaction networks
Why do some species coexist while others exclude each other, and how do networks of competition, pollination and other interactions arise? With my group, I combine data from forest plots, interaction networks and eDNA surveys with models to infer and test mechanisms such as density dependence and trait matching.
Mechanisms behind biodiversity patterns in macroecology and macroevolution
Mechanisms behind biodiversity patterns in macroecology and macroevolution
Why are there more species in the tropics, and how did today’s distribution of species richness arise? I link ecological and evolutionary processes such as speciation, dispersal, extinction and adaptation to large-scale patterns, using eco-evolutionary simulations such as gen3sis and phylogenetic models of diversification.
Systems ecology and process-based simulation models
Systems ecology and process-based simulation models
How do forests, vegetation and populations respond to climate and land use? I develop and apply process-based models, from forest growth and dynamic vegetation models to population models, to understand and project the dynamics of ecological systems. R interfaces such as r3PG and rLPJGUESS make these models accessible.
Model-data integration, simulation-based inference and Bayesian statistics
Model-data integration, simulation-based inference and Bayesian statistics
How can complex simulation models be fitted to data, and how certain are their predictions? My group develops methods to calibrate process-based and stochastic models, from approximate Bayesian computation to Bayesian calibration and uncertainty quantification, and software such as BayesianTools that makes these methods usable.
Statistics, machine learning and deep learning in ecology
Statistics, machine learning and deep learning in ecology
How can statistics and machine learning deliver robust inference, not only predictions, from ecological data? I develop and test such methods; R packages from my group, such as DHARMa for residual diagnostics, sjSDM for joint species distribution models and cito for deep learning, bring them into everyday practice, and I apply them to predict and monitor biodiversity, for example in the BaySenseAI project.
Overview
The outcome of our activities is documented through research papers, software, teaching and outreach. To find out more about this, see below.
Software
Open-source R packages for the methods we develop, from regression diagnostics to Bayesian inference and deep learning.
- R packages
- 9
- CRAN downloads
- 1,355,717
Downloads per year
Teaching & Outreach
Ecology, statistics, data science and machine learning for BSc, MSc and PhD students; coordinator of the MSc Ecology, Evolution & Conservation. Outreach through a blog, videos, a podcast and the media.
- Regular courses
- 9
- Open online books
- 4
Citations and h-index per Google Scholar, as of 29 October 2026; downloads from the Posit (RStudio) CRAN mirror, through 28 October 2026.