Network based early warning indicators of vegetation changes in a land-atmosphere model

Z. Yin*, S. C. Dekker, M. Rietkerk, B. J. J. M. van den Hurk, H. A. Dijkstra

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Numerous model studies demonstrate that ecosystems might not shift smoothly with a gradual change in resource concentration. At specific points, vegetation can suddenly shift from one stable state to another. To predict such undesirable shifts, statistical indicators are proposed for early warning prediction. These so-called classical indicators can address whether vegetation state is moving towards the tipping point of an abrupt transition, however when the transition will occur is hard to predict. Recent studies suggest that complex network based indicators can improve early warning signals of abrupt transitions in complex dynamic systems. In this study, both classical and network based indicators are tested in a coupled land-atmosphere ecological model in which a scale-dependent hydrology-infiltration feedback and a large scale vegetation-precipitation feedback are represented. Multiple biomass equilibria are found in the model and abrupt transitions can occur when rainfall efficiency is decreased. Interaction network based indicators of these transitions are compared with classical indicators, such as the lag-1 autocorrelation and Moran's coefficient, with particular focus on the transition associated with desertification. Two criteria are used to evaluate the quality of these early warning indicators and several high quality network based indicators are identified. (C) 2016 Elsevier B.V. All rights reserved.

Original languageEnglish
Pages (from-to)68-78
Number of pages11
JournalEcological Complexity
Volume26
DOIs
Publication statusPublished - Jun 2016
Externally publishedYes

Keywords

  • Abrupt transition
  • Early warning signals
  • Network based indicator
  • Tipping point
  • Critical slowing down
  • Land-atmosphere model
  • ARID ECOSYSTEMS
  • CATASTROPHIC SHIFTS
  • WOODY COVER
  • PATTERNED ECOSYSTEMS
  • CRITICAL TRANSITIONS
  • SEMIARID ECOSYSTEMS
  • CLIMATE-CHANGE
  • REGIME SHIFTS
  • SLOWING-DOWN
  • WEST-AFRICA

Cite this

Yin, Z. ; Dekker, S. C. ; Rietkerk, M. ; van den Hurk, B. J. J. M. ; Dijkstra, H. A. / Network based early warning indicators of vegetation changes in a land-atmosphere model. In: Ecological Complexity. 2016 ; Vol. 26. pp. 68-78.
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abstract = "Numerous model studies demonstrate that ecosystems might not shift smoothly with a gradual change in resource concentration. At specific points, vegetation can suddenly shift from one stable state to another. To predict such undesirable shifts, statistical indicators are proposed for early warning prediction. These so-called classical indicators can address whether vegetation state is moving towards the tipping point of an abrupt transition, however when the transition will occur is hard to predict. Recent studies suggest that complex network based indicators can improve early warning signals of abrupt transitions in complex dynamic systems. In this study, both classical and network based indicators are tested in a coupled land-atmosphere ecological model in which a scale-dependent hydrology-infiltration feedback and a large scale vegetation-precipitation feedback are represented. Multiple biomass equilibria are found in the model and abrupt transitions can occur when rainfall efficiency is decreased. Interaction network based indicators of these transitions are compared with classical indicators, such as the lag-1 autocorrelation and Moran's coefficient, with particular focus on the transition associated with desertification. Two criteria are used to evaluate the quality of these early warning indicators and several high quality network based indicators are identified. (C) 2016 Elsevier B.V. All rights reserved.",
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Network based early warning indicators of vegetation changes in a land-atmosphere model. / Yin, Z.; Dekker, S. C.; Rietkerk, M.; van den Hurk, B. J. J. M.; Dijkstra, H. A.

In: Ecological Complexity, Vol. 26, 06.2016, p. 68-78.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Network based early warning indicators of vegetation changes in a land-atmosphere model

AU - Yin, Z.

AU - Dekker, S. C.

AU - Rietkerk, M.

AU - van den Hurk, B. J. J. M.

AU - Dijkstra, H. A.

PY - 2016/6

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N2 - Numerous model studies demonstrate that ecosystems might not shift smoothly with a gradual change in resource concentration. At specific points, vegetation can suddenly shift from one stable state to another. To predict such undesirable shifts, statistical indicators are proposed for early warning prediction. These so-called classical indicators can address whether vegetation state is moving towards the tipping point of an abrupt transition, however when the transition will occur is hard to predict. Recent studies suggest that complex network based indicators can improve early warning signals of abrupt transitions in complex dynamic systems. In this study, both classical and network based indicators are tested in a coupled land-atmosphere ecological model in which a scale-dependent hydrology-infiltration feedback and a large scale vegetation-precipitation feedback are represented. Multiple biomass equilibria are found in the model and abrupt transitions can occur when rainfall efficiency is decreased. Interaction network based indicators of these transitions are compared with classical indicators, such as the lag-1 autocorrelation and Moran's coefficient, with particular focus on the transition associated with desertification. Two criteria are used to evaluate the quality of these early warning indicators and several high quality network based indicators are identified. (C) 2016 Elsevier B.V. All rights reserved.

AB - Numerous model studies demonstrate that ecosystems might not shift smoothly with a gradual change in resource concentration. At specific points, vegetation can suddenly shift from one stable state to another. To predict such undesirable shifts, statistical indicators are proposed for early warning prediction. These so-called classical indicators can address whether vegetation state is moving towards the tipping point of an abrupt transition, however when the transition will occur is hard to predict. Recent studies suggest that complex network based indicators can improve early warning signals of abrupt transitions in complex dynamic systems. In this study, both classical and network based indicators are tested in a coupled land-atmosphere ecological model in which a scale-dependent hydrology-infiltration feedback and a large scale vegetation-precipitation feedback are represented. Multiple biomass equilibria are found in the model and abrupt transitions can occur when rainfall efficiency is decreased. Interaction network based indicators of these transitions are compared with classical indicators, such as the lag-1 autocorrelation and Moran's coefficient, with particular focus on the transition associated with desertification. Two criteria are used to evaluate the quality of these early warning indicators and several high quality network based indicators are identified. (C) 2016 Elsevier B.V. All rights reserved.

KW - Abrupt transition

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KW - Tipping point

KW - Critical slowing down

KW - Land-atmosphere model

KW - ARID ECOSYSTEMS

KW - CATASTROPHIC SHIFTS

KW - WOODY COVER

KW - PATTERNED ECOSYSTEMS

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KW - SLOWING-DOWN

KW - WEST-AFRICA

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JO - Ecological Complexity

JF - Ecological Complexity

SN - 1476-945X

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