Measuring complexity to infer changes in the dynamics of ecological systems under stress |
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| Institution: | 1. Institute of Integrative Biology, Adaptation to a Changing Environment, ETH Zurich, Switzerland;2. Grupo de Lógica, Lenguaje e Información, University of Seville, Spain;3. Algorithmic Nature Group, LABoRES, Paris, France;1. Free University of Bozen-Bolzano, Faculty of Science and Technology, Bolzano, Italy;2. Aston University, Nonlinearity and Complexity Research Group, Engineering and Applied Science, Birmingham B4 7ET, UK;1. Department of Information and Communications Engineering, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain;2. Department of Computer Science, University College Cork, Cork, Ireland;1. Dept. Física, Universitat Politècnica Catalunya - BarcelonaTech, 08860 Castelldefels, Catalonia, Spain;2. Department of Materials Science and Metallurgical Engineering, University of Barcelona, Spain;1. Department of Botany, Faculty of Science, University of South Bohemia, Brani?ovská 31, CZ-370 05, ?eské Budějovice, Czech Republic;2. Terrestrial Ecology Group, Department of Ecology, Autonomous University of Madrid, 28049, Madrid, Spain;1. IIIA, Institut d''Investigació en Intel·ligència Artificial – CSIC, Consejo Superior de Investigaciones Científicas, Campus UAB s/n, 08193, Bellaterra, Spain;2. DEIC, Dep. Enginyeria de la Informació i de les Comunicacions, UAB, Universitat Autònoma de Barcelona, Campus UAB s/n, 08191, Bellaterra, Spain;3. UAB, Universitat Autónoma de Barcelona, Campus UAB s/n, 08193, Bellaterra, Spain;4. School of Informatics, University of Skövde, 54128 Skövde, Sweden |
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| Abstract: | Despite advances in our mechanistic understanding of ecological processes, the inherent complexity of real-world ecosystems still limits our ability in predicting ecological dynamics especially in the face of on-going environmental stress. Developing a model is frequently challenged by structure uncertainty, unknown parameters, and limited data for exploring out-of-sample predictions. One way to address this challenge is to look for patterns in the data themselves in order to infer the underlying processes of an ecological system rather than to build system-specific models. For example, it has been recently suggested that statistical changes in ecological dynamics can be used to infer changes in the stability of ecosystems as they approach tipping points. For computer scientists such inference is similar to the notion of a Turing machine: a computational device that could execute a program (the process) to produce the observed data (the pattern). Here, we make use of such basic computational ideas introduced by Alan Turing to recognize changing patterns in ecological dynamics in ecosystems under stress. To do this, we use the concept of Kolmogorov algorithmic complexity that is a measure of randomness. In particular, we estimate an approximation to Kolmogorov complexity based on the Block Decomposition Method (BDM). We apply BDM to identify changes in complexity in simulated time-series and spatial datasets from ecosystems that experience different types of ecological transitions. We find that in all cases, KBDM complexity decreased before all ecological transitions both in time-series and spatial datasets. These trends indicate that loss of stability in the ecological models we explored is characterized by loss of complexity and the emergence of a regular and computable underlying structure. Our results suggest that Kolmogorov complexity may serve as tool for revealing changes in the dynamics of ecosystems close to ecological transitions. |
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| Keywords: | Kolmogorov complexity Resilience Compression Early-warning Ecological stability Tipping point |
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