More than the sum of its parts: Self-organized patterns and emergent properties of ecosystems

doi:10.17520/biods.2020225

Abstract

Abstract:

Over the past 30 years, the self-organization theory has effectively explained the regular spatial patterning of ecosystems and has led to a proliferation of studies investigating spatial patterns in ecology and biology. Indeed, the emergent properties generated by this self-organization process are now recognized as critical to ecosystem functioning. Here, we review this important theoretical framework by assessing the definition and development of the concept of self-organization and by evaluating two fundamental theoretical principles of self-organization theory, the Turing principle and the phase separation principle. We further describe the mathematical models of each principle in the context of different, unique ecosystems, and explain the emergent properties of the Turing principle on ecosystem functioning and the phase separation principle on cell functions, respectively. Finally, we propose three promising future developments for ecological self-organization theory: multi-scale self-organization patterns, transient patterns, and individual behavioral self-organization. Our review provides an assessment of this fundamental ecological theory and offers exciting new research directions and applications.

Key words: spatial self-organization, regular spatial patterning, Turing principle, phase separation principle, ecological model, ecosystem functioning

Zhenpeng Ge, Quanxing Liu. More than the sum of its parts: Self-organized patterns and emergent properties of ecosystems[J]. Biodiv Sci, 2020, 28(11): 1431-1443.

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URL: https://www.biodiversity-science.net/EN/10.17520/biods.2020225

https://www.biodiversity-science.net/EN/Y2020/V28/I11/1431

Figures/Tables 4

Fig. 1 The regular patterns of mussel and “cell”, and the scale-dependent feedback and density-dependent feedback. (a) Mussel bed in the intertidal zone of Wadden Sea, the Netherlands, the image come from the corresponding author of this paper; (b) The recurrence of a cell based on liquid light technology, the image is modified from https://liquidlightlab.com/home.html Copyright Oil art: Steve Pavlovsky/Liquid Light Lab.; (c) Schematic representation of scale-dependent feedback; (d) Schematic representation of density-dependent movement, (c) and (d) are modified from Liu et al (2016).

Fig. 2 Phase diagram and numerical simulations of the equations (7) and (8). In the upper figure, the red solid line is the boundary between homogeneous zone and binodal zone, and the solid blue line is the boundary between binodal zone and spinodal decomposition zone. ‘1’ to ‘5’ in the upper figure correspond to the below five figures which are the numerical simulation results, ‘1’ and ‘5’ are located in homogeneous zone where the system doesn’t have pattern; ‘2’ and ‘4’ are located in binodal zone where the system has spot pattern, ‘3’ is located in the spinodal decomposition zone where the system has labyrinth pattern. The numerical simulation adopts periodic boundary condition, and the corresponding matlab code can be downloaded at https://github.com/liufengyinxue/Liu_Mussel_ PNAS.

Fig. 3 One of the emergent properties of mussel aggregation: reducing the likelihood of predation by seabirds

Fig. 4 The predominant research ideas of self-organization ecology in the future (The English version see appendix 1). After years of development, Turing's principle was found to be unable to fully explain the multi-scale patterns of various ecosystems, as well as the phase separation principle. Therefore, a new theoretical framework needs to be developed in the future to understand the multi-scale patterns of ecosystems. Transient pattern will be paid more attention in the future due to its unique ecological functioning. The response of organisms to environmental change leads to changes in biological behavior, and how this response emerges to the ecosystem level is critical to understanding the self-organization of ecosystems across scales.

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