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Open Access Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming

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Accurate model representation of land–atmosphere carbon fluxes is essential for climate projections. However, the exact responses of carbon cycle processes to climatic drivers often remain uncertain. Presently, knowledge derived from experiments, complemented by a steadily evolving body of mechanistic theory, provides the main basis for developing such models. The strongly increasing availability of measurements may facilitate new ways of identifying suitable model structures using machine learning. Here, we explore the potential of gene expression programming (GEP) to derive relevant model formulations based solely on the signals present in data by automatically applying various mathematical transformations to potential predictors and repeatedly evolving the resulting model structures. In contrast to most other machine learning regression techniques, the GEP approach generates readable models that allow for prediction and possibly for interpretation. Our study is based on two cases: artificially generated data and real observations. Simulations based on artificial data show that GEP is successful in identifying prescribed functions, with the prediction capacity of the models comparable to four state-of-the-art machine learning methods (random forests, support vector machines, artificial neural networks, and kernel ridge regressions). Based on real observations we explore the responses of the different components of terrestrial respiration at an oak forest in south-eastern England. We find that the GEP-retrieved models are often better in prediction than some established respiration models. Based on their structures, we find previously unconsidered exponential dependencies of respiration on seasonal ecosystem carbon assimilation and water dynamics. We noticed that the GEP models are only partly portable across respiration components, the identification of a general terrestrial respiration model possibly prevented by equifinality issues. Overall, GEP is a promising tool for uncovering new model structures for terrestrial ecology in the data-rich era, complementing more traditional modelling approaches.
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Document Type: Research Article

Affiliations: 1: Max Planck Institute for Biogeochemistry, Department Biogeochemical Integration, Hans-Knoell-Str. 10, 07745 Jena, Germany 2: Bio Systems Analysis Group, Institute of Computer Science, Jena Centre for Bioinformatics and Friedrich Schiller University, 07745 Jena, Germany 3: Michael Stifel Center Jena for Data-Driven and Simulation Science, 07745 Jena, Germany 4: CENSE, Departamento de Ciéncias e Engenharia do Ambiente, Faculdade de Ciéncias e Tecnologia, Universidade NOVA de Lisboa, Caparica, Portugal 5: Department of Environment, Stockholm Environment Institute, University of York, York, YO105NG, UK 6: Forest Research, Alice Holt Lodge, Farnham, Surrey, GU10 4LH, UK 7: Biological and Environmental Sciences, School of Natural Sciences, University of Stirling, Stirling, UK 8: German Centre for Integrative Biodiversity Research (iDiv), Deutscher Platz 5e, 04103 Leipzig, Germany

Publication date: January 1, 2017

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