Kontakt

 

Leitung

Prof. Dr. Carsten F. Dormann

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

 

Sekretariat:

 

Frau Eva Meier

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

 

Anschrift:

 

Biometrie und Umweltsystemanalyse

Albert-Ludwigs-Universität Freiburg

Tennenbacher Straße 4 
79106 Freiburg i. Br.

 

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Prof. Dr. Carsten Dormann

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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) Professor at the University of Freiburg (since 2011)

Habilitation (a somewhat anachronisitc rung of the German academic career ladder) at the Agricultural Faculty of the University of Göttingen (2008)

PostDoc and Senior Research Scientist at the Helmholtz Centre for Environmental Research-UFZ, Department Computational Landscape Ecology (2002-2011)

PhD in Plant Ecology, University of Aberdeen, under the guidance of Dr. Sarah Woodin and Prof. Dr. Steve Albon (2001)

Diploma (equivalent to an MSc) in Biology, University of Kiel (1996)

Publications

  

Full list of scientific publicationsParStatcover.jpg

Zur Vorlesung "Statistik" für BSc Umweltnaturwissenschaften, Waldwirtschaft und Umwelt, Geographie das Lehrbuch (2. Auflage 2017):          Daten zum Buch    |    Errata zur 1. Auflage

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

    Comparing, challenging and improving the toolbox of statistical ecology.

    Ecological data sets are often very small (less than 100 data points), but the complexity of the system allows for dozens to hundreds of potential predictors and explanations of the data's origin. Some statistical approaches commonly employed fail to meet assumptions or are poorly tested - with unknown effect on our ecological knowledge. Formally testing such approaches (with simulated data) and reviewing challenging topics in ecological analyses is important to my research (spatial autocorrelation, null models, collinearity, etc).

    Formal statistical integration of (ecological) models and data.

    Ecologists are often happy to get their research qualitatively right. For more faith in our understanding of the living world, I think we should strive for more rigour: a more formal representation of our ecological understanding (or what we think we know) in models, quantitative predictions to new conditions, and statistically explicit comparison with new data. Being more interested in the how-to than the specific system, such models may be forest growth models, community processes or population dynamics.

    Evidence-focus of environmental science.

    The complexity of the environmental systems and the multitude of approaches used to study them has allowed for some slip in scientific rigour. In analogy to evidence-based medicine, a transparent quest for causal mechanisms and evaluation of published studies for their actual level of evidence are, in my humble opinion, unnegotiable.

    Lots of other things:

    species distribution modelling, plant-pollinator interactions, experimental climate change, statistical teaching, scientific credibility, patterns of biodiversity

Current PhD researchers (main/co-supervisor)

 

Previous PhD/PostDoc researchers

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