Regime shifts occur disproportionately faster in larger ecosystems
Regime shifts occur disproportionately faster in larger ecosystems
Regime shifts can abruptly affect hydrological, climatic and terrestrial systems, leading to degraded ecosystems and impoverished societies. While the frequency of regime shifts is predicted to increase, the fundamental relationships between the spatial-temporal scales of shifts and their underlying mechanisms are poorly understood. Here we analyse empirical data from terrestrial (n=4), marine (n=25) and freshwater (n=13) environments and show positive sub-linear empirical relationships between the size and shift duration of systems. Each additional unit area of an ecosystem provides an increasingly smaller unit of time taken for that system to collapse, meaning that large systems tend to shift more slowly than small systems but disproportionately faster. We substantiate these findings with five computational models that reveal the importance of system structure in controlling shift duration. The findings imply that shifts in Earth ecosystems occur over ‘human’ timescales of years and decades, meaning the collapse of large vulnerable ecosystems, such as the Amazon rainforest and Caribbean coral reefs, may take only a few decades once triggered.
Cooper, Gregory
37b901f5-a53f-4790-a674-206c6c21debe
Willcock, Simon P
89d9767e-8076-4b21-be9d-a964f5cc85d7
Dearing, John
dff37300-b8a6-4406-ad84-89aa01de03d7
1 December 2020
Cooper, Gregory
37b901f5-a53f-4790-a674-206c6c21debe
Willcock, Simon P
89d9767e-8076-4b21-be9d-a964f5cc85d7
Dearing, John
dff37300-b8a6-4406-ad84-89aa01de03d7
Cooper, Gregory, Willcock, Simon P and Dearing, John
(2020)
Regime shifts occur disproportionately faster in larger ecosystems.
Nature Communications, 11 (1), [1175].
(doi:10.1038/s41467-020-15029-x).
Abstract
Regime shifts can abruptly affect hydrological, climatic and terrestrial systems, leading to degraded ecosystems and impoverished societies. While the frequency of regime shifts is predicted to increase, the fundamental relationships between the spatial-temporal scales of shifts and their underlying mechanisms are poorly understood. Here we analyse empirical data from terrestrial (n=4), marine (n=25) and freshwater (n=13) environments and show positive sub-linear empirical relationships between the size and shift duration of systems. Each additional unit area of an ecosystem provides an increasingly smaller unit of time taken for that system to collapse, meaning that large systems tend to shift more slowly than small systems but disproportionately faster. We substantiate these findings with five computational models that reveal the importance of system structure in controlling shift duration. The findings imply that shifts in Earth ecosystems occur over ‘human’ timescales of years and decades, meaning the collapse of large vulnerable ecosystems, such as the Amazon rainforest and Caribbean coral reefs, may take only a few decades once triggered.
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s41467-020-15029-x
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Accepted/In Press date: 14 February 2020
e-pub ahead of print date: 10 March 2020
Published date: 1 December 2020
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Funding Information:
G.S.C. and J.A.D. gratefully acknowledge a research studentship and financial support respectively from the Deltas, Vulnerability and Climate Change: Migration and Adaptation (DECCMA) project under the Collaborative Adaptation Research Initiative in Africa and Asia (CARIAA) program with financial support from the UK Government’s Department for International Development (DFID) and the International Development Research Centre (IDRC), Canada (Grant No. 107642-001). The views expressed in this work are those of the creators and do not necessarily represent those of DFID and IDRC or its Boards of Governors. S.W. was funded by UKRI project numbers: NE/L001322/1, NE/T00391X/1, ES/R009279/1 and ES/R006865/1. We thank Professor Peter Langdon (University of Southampton) for providing early contributions to the concepts of this paper and Dr Rong Wang (Nanjing Institute of Geography and Limnology) for preliminary data collection and analysis. We also thank Dr James Dyke (University of Southampton), Professor Felix Eigenbrod (University of Southampton) and Dr Robert Cooke (University of Southampton) for their comments on earlier versions of the paper. We also wish to acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work.
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© 2020, The Author(s).
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Local EPrints ID: 438464
URI: http://eprints.soton.ac.uk/id/eprint/438464
ISSN: 2041-1723
PURE UUID: 97b97028-8234-4ac0-9076-ae1a79d2d94e
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Date deposited: 10 Mar 2020 17:33
Last modified: 06 Jun 2024 01:42
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Gregory Cooper
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