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This interdisciplinary work utilizes techniques developed in artificial intelligence to characterize observations made in ecosystem ecology. Ecosystem datasets are typically highly complex and noisy. The aim was to identify the underlying causalities directly from the data, i.e. without prior assumptions about the functional relationships. The new methodology, based on artificial neural networks, is demonstrated for eddy covariance measurements of carbon fluxes above a German beech forest. The suite of ecophysiological applications encompasses: 1) characterizing ecosystem responses to climatic drivers, 2) testing specific hypotheses, 3) assessing competing semi-empirical equations, 4) evaluating ecosystem models, and 5) as a by-product, interpolating missing data. The results highlight the benefit of this methodology to provide a new link between ecosystem observations and their semi-empirical representations in the modeling world. This, in turn, may help in predicting the effects of changing climatic conditions on the terrestrial biosphere. Awarded the 2012 prize for the best doctoral thesis in Mathematics and Computer Science at the Friedrich Schiller University, Jena.

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Born on April 18th, 1973 in Berlin. Study of physics at Stuttgart University and Queen´s University, Kingston, Canada. Application engineer at Schlumberger Systems GmbH, Munich (2001-2002). PhD student and research associate at Max Planck Institute for Biogeochemistry, Jena (2003-2013), doctoral degree at Friedrich Schiller University, Jena (2012).

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