Prof. Dr. Carsten F. Dormann

Telefon: +49 761 203-3749
Telefax: +49 761 203-3751
eMail: carsten.dormann@biom.uni-freiburg.de 




Frau Eva Meier

Telefon: +49 761 203-3749 
Telefax: +49 761 203-3751 
eMail: eva.meier@biom.uni-freiburg.de 




Biometrie und Umweltsystemanalyse

Albert-Ludwigs-Universität Freiburg

Tennenbacher Straße 4 
79106 Freiburg i. Br.


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Prof. Dr. Carsten Dormann

Bild Dormann 

Carsten Dormann, Prof. Dr.

Department of Biometry and Environmental System Analysis

Tennenbacher Straße 4, 79106 Freiburg, Germany
Room 03.056

phone: ++49 761 203-3749
fax: ++49 761 203-3751  
Email: carsten [DOT] dormann [AT] biom [DOT] uni-freiburg [DOT] de (to reduce spam)





Full list of scientific publications

Zur Vorlesung "Statistik" für BSc Umweltnaturwissenschaften, Waldwirtschaft und Umwelt, Geographie jetzt das Lehrbuch:

Errata    |    Daten zum Buch

Ein früheres Machwerk für Umsteiger auf R:

Dormann, C.F., I. Kühn. 2008. Statistische Analyse biologischer Daten (mit dem freien Programmpaket R). 2. Auflage.

download (2.7 MB)   |    Daten zu Dormann & Kühn 


Research Interests


    In my research, I seek to understand the mechanistic functioning of ecological communities. My research methods fall into three categories: statistical ecology, manipulative field experiments and mechanistic modelling. They go hand-in-hand:
    I employ statistical methods to explore potential causes of ecological pattern, thereby generating hypotheses on the processes and mechanisms underlying these pattern. My main field or research is here species distribution modelling, i.e. the analysis of large-scale data sets on the occurrence pattern of plant and animal species, as well as regional biodiversity pattern. However, my interest ranges much wider into any aspect of statistical ecology.
    For plant communities, we then try to manipulate processes we suspect to be important. These have mainly been resource competition and herbivory, but also mycorrhization. Experiments take place in the field as well as in the lab, in greenhouses and botanic gardens. My favourite system is low-growing vegetation: arctic tundra (where I did my PhD), salt marshes or temperate grasslands.
    Finally, I want to learn from data about mechanisms through modelling. The idea is that a process model is an hypothesis to be tested with data, similar in fact to descriptive field studies.

    Over the last years, like many ecologists I have become interested in (and often disillusioned by) ecosystem services as a framework to join ecological science and the (in my view) softer sciences of economics and social sciences. Our current research investigates the possibilities for embracing a more evidence-based over the current expert opinion approach to a science-society interface.

    Details and projects: The data-theory interface and the role of statistical ecology
    This interface between theory and data is a very challenging one, since theoretical ecology is very advanced in this field, but appropriate experimental data are very hard to come by. In this field I try to bring as much theoretical-mechanistic understanding into field work and experiments. I believe that theory requires experimental support to become usefull, while at the same time theory actually provides the hypotheses to be tested. So, in principle, ecological theory and experiments should lead a harmonious relationship. Due to different research traditions, this is not always the case, and I try to do my bit to help bridge the existing gaps.

    Plant community processes
    I could show that competition is of high importance for plant community structure for different systems (e.g. salt marshes, High Arctic tundra, temperate grassland, laborartory bryophyte communities). When I tried to transfer this understanding into community theory, the theoretical predictions and the experimental data did not match (Dormann & Roxburgh 2005). Plant biodiversity will affect local biogeochemical processes, so it is of relevance to properly understand the mechanisms that produce biodiversity in the first place.

    Species Distribution Modelling
    Throughout the world, large datasets on species occurrences are available. Their analysis can be used to understand the drivers of species occurrences and species richness, and possibly to project their ranges under GEC. However, the analysis of these data is burdened with statistical problems (such as spatial autocorrelation) and ecological assumptions (such as lack of adaptation to new environmental conditions). Investigating the consequences of violating these assumptions and finding solutions to some of the problems is my aim. In particular the integration of important ecological processes (biotic interactions, microevolution, dispersal/migration, changing limiting factors) are the future steps in my research.

    Pollination networks
    In many studies, visitation recordings of pollinators to plants have been analysed to investigate the stability of pollination networks to Global Environmental Change or loss of pollinators. However, several assumptions behind the sophisticated analysis of pollination networks are untested (e.g that the observed network is representative), others are untenable (e.g. that temporal changes in pollination network structure are negligible). Within a larger research project (Biotic Ecosystem Services), I work on simulations to investigate violations of some assumptions. For that purpose I also maintain an R-package devoted to the visualisation and analysis of bipartite networks (see package bipartite on CRAN: www.r-project.org).

    Landscape-scale pattern of pollination and biocontrol services
    From various descriptive studies we have gained an intuitive insight into how landscape structure and composition affects the diversity and abundance of pollinators and pest-suppressing biocontrol agents. At the landscape scale, manipulative experiments are nigh impossible, so an alternative approach, spatially explicit mechanistic models, comes into play. By formulating our knowledge as behavioural rules or as mechanistic processes, we predict species abundances in time and space, which can then be validated with field data. Once a model is able to a priori predict correctly, we can use land-use and climate change scenarios to explore how pollination and biocontrol services will be affected.

Current PhD researchers (main/co-supervisor)


Previous PhD/PostDoc researchers


Benutzerspezifische Werkzeuge