The emergence of environmental homeostasis in complex ecosystems


Dyke, James G. and Weaver, Iain S. (2013) The emergence of environmental homeostasis in complex ecosystems PLoS Computational Biology, 9, (5), pp. 1-9. (doi:10.1371/journal.pcbi.1003050). (PMID:23696719).

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Description/Abstract

The Earth, with its core-driven magnetic field, convective mantle, mobile lid tectonics, oceans of liquid water, dynamic climate and abundant life is arguably the most complex system in the known universe. This system has exhibited stability in the sense of, bar a number of notable exceptions, surface temperature remaining within the bounds required for liquid water and so a significant biosphere. Explanations for this range from anthropic principles in which the Earth was essentially lucky, to homeostatic Gaia in which the abiotic and biotic components of the Earth system self-organise into homeostatic states that are robust to a wide range of external perturbations. Here we present results from a conceptual model that demonstrates the emergence of homeostasis as a consequence of the feedback loop operating between life and its environment. Formulating the model in terms of Gaussian processes allows the development of novel computational methods in order to provide solutions. We find that the stability of this system will typically increase then remain constant with an increase in biological diversity and that the number of attractors within the phase space exponentially increases with the number of environmental variables while the probability of the system being in an attractor that lies within prescribed boundaries decreases approximately linearly. We argue that the cybernetic concept of rein control provides insights into how this model system, and potentially any system that is comprised of biological to environmental feedback loops, self-organises into homeostatic states.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1371/journal.pcbi.1003050
ISSNs: 1553-734X (print)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QH Natural history > QH301 Biology
Organisations: Electronics & Computer Science
ePrint ID: 354179
Date :
Date Event
20 March 2013Accepted/In Press
16 May 2013Published
Date Deposited: 03 Jul 2013 12:00
Last Modified: 17 Apr 2017 15:19
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/354179

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