Can we detect ecosystem critical transitions and signals of changing resilience from paleo-ecological records?
Can we detect ecosystem critical transitions and signals of changing resilience from paleo-ecological records?
Nonlinear responses to changing external pressures are increasingly studied in real-world ecosystems. However, as many of the changes observed by ecologists extend beyond the monitoring record, the occurrence of critical transitions, where the system is pushed from one equilibrium state to another, remains difficult to detect. Paleo-ecological records thus represent a unique opportunity to expand our temporal perspective to consider regime shifts and critical transitions, and whether such events are the exception rather than the rule. Yet, sediment core records can be affected by their own biases, such as sediment mixing or compression, with unknown consequences for the statistics commonly used to assess regime shifts, resilience, or critical transitions. To address this shortcoming, we developed a protocol to simulate paleolimnological records undergoing regime shifts or critical transitions to alternate states and tested, using both simulated and real core records, how mixing and compression affected our ability to detect past abrupt shifts. The smoothing that is built into paleolimnological data sets apparently interfered with the signal of rolling window indicators, especially autocorrelation. We thus turned to time-varying autoregressions (online dynamic linear models, DLMs; and time-varying autoregressive state-space models, TVARSS) to evaluate the possibility of detecting regime shifts and critical transitions in simulated and real core records. For the real cores, we examined both varved (annually laminated sediments) and non-varved cores, as the former have limited mixing issues. Our results show that state-space models can be used to detect regime shifts and critical transitions in some paleolimnological data, especially when the signal-to-noise ratio is strong. However, if the records are noisy, the online DLM and TVARSS have limitations for detecting critical transitions in sediment records.
aquatic transitions, eutrophication, online dynamic linear model, paleolimnology, regime shifts, resilience indicators, time-varying autoregressions, time-varying autoregressive state-space
Taranu, Zofia E.
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Carpenter, Stephen R.
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Frossard, Victor
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Jenny, Jean Philippe
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Thomas, Zoë
4b512d3a-3478-4270-9fdd-61256aa640d3
Vermaire, Jesse C.
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Perga, Marie Elodie
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8 October 2018
Taranu, Zofia E.
83b907a1-d0ba-4f8e-888d-885e9de3d76a
Carpenter, Stephen R.
93aeed1f-303d-4df0-bab1-45b322b74f68
Frossard, Victor
f333fdd9-0d45-42f2-b9b7-0175f16aef4f
Jenny, Jean Philippe
6ef18634-b657-4473-963c-987d34eb3019
Thomas, Zoë
4b512d3a-3478-4270-9fdd-61256aa640d3
Vermaire, Jesse C.
9befc63d-d5b2-4e36-b06f-801b197b1d7d
Perga, Marie Elodie
a04e0382-5b17-4730-8b88-47c3d902cd61
Taranu, Zofia E., Carpenter, Stephen R., Frossard, Victor and Thomas, Zoë
,
et al.
(2018)
Can we detect ecosystem critical transitions and signals of changing resilience from paleo-ecological records?
Ecosphere, 9 (10), [e02438].
(doi:10.1002/ecs2.2438).
Abstract
Nonlinear responses to changing external pressures are increasingly studied in real-world ecosystems. However, as many of the changes observed by ecologists extend beyond the monitoring record, the occurrence of critical transitions, where the system is pushed from one equilibrium state to another, remains difficult to detect. Paleo-ecological records thus represent a unique opportunity to expand our temporal perspective to consider regime shifts and critical transitions, and whether such events are the exception rather than the rule. Yet, sediment core records can be affected by their own biases, such as sediment mixing or compression, with unknown consequences for the statistics commonly used to assess regime shifts, resilience, or critical transitions. To address this shortcoming, we developed a protocol to simulate paleolimnological records undergoing regime shifts or critical transitions to alternate states and tested, using both simulated and real core records, how mixing and compression affected our ability to detect past abrupt shifts. The smoothing that is built into paleolimnological data sets apparently interfered with the signal of rolling window indicators, especially autocorrelation. We thus turned to time-varying autoregressions (online dynamic linear models, DLMs; and time-varying autoregressive state-space models, TVARSS) to evaluate the possibility of detecting regime shifts and critical transitions in simulated and real core records. For the real cores, we examined both varved (annually laminated sediments) and non-varved cores, as the former have limited mixing issues. Our results show that state-space models can be used to detect regime shifts and critical transitions in some paleolimnological data, especially when the signal-to-noise ratio is strong. However, if the records are noisy, the online DLM and TVARSS have limitations for detecting critical transitions in sediment records.
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More information
Accepted/In Press date: 17 August 2018
Published date: 8 October 2018
Additional Information:
Funding Information:A significant portion of this paper was made possible thanks to the valuable input, statistical modeling, and R code provided by Dr. Anthony R Ives, to whom we are most grateful. We are very grateful to Drs. Mark Stevenson and Suzanne McGowan for providing data on Lake Anarry. We also thank Dr. Irene Gregory-Eaves for fruitful discussions and comments on earlier drafts of the manuscript. We are also very thankful to Drs. Peter R. Leavitt, Lynda Bunting, Teresa Buchaca, Jordi Catalan, Isabelle Domaizon, Piero Guilizzoni, Andrea Lami, Heather Moorhouse, Frances R. Pick, Patrick L. Thompson, Rolf D. Vinebrooke, and the late Giuseppe Morabito for providing sediment core archive data that helped parametrize the compression model. This paper is a contribution for the PAGES Aquatic Transition working group, from which the original idea emerged. ZET is funded through an NSERC postdoctoral grant. SRC is supported by the North Temperate Lakes Long-Term Ecological Research Program (NSF DEB-1440297) and NSF grants (DEB-1455461 and DEB-1754712) from the Ecosystems Studies Program.
Publisher Copyright:© 2018 The Authors
Keywords:
aquatic transitions, eutrophication, online dynamic linear model, paleolimnology, regime shifts, resilience indicators, time-varying autoregressions, time-varying autoregressive state-space
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Local EPrints ID: 476110
URI: http://eprints.soton.ac.uk/id/eprint/476110
ISSN: 2150-8925
PURE UUID: 5124f652-2c53-46e2-b1a8-21e166e723b0
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Date deposited: 12 Apr 2023 14:20
Last modified: 18 Mar 2024 04:10
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Author:
Zofia E. Taranu
Author:
Stephen R. Carpenter
Author:
Victor Frossard
Author:
Jean Philippe Jenny
Author:
Zoë Thomas
Author:
Jesse C. Vermaire
Author:
Marie Elodie Perga
Corporate Author: et al.
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