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...