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Early warning signals of recovery in complex systems

Author

Listed:
  • Christopher F. Clements

    (University of Bristol)

  • Michael A. McCarthy

    (University of Melbourne, Parkville)

  • Julia L. Blanchard

    (University of Tasmania)

Abstract

Early warning signals (EWSs) offer the hope that patterns observed in data can predict the future states of ecological systems. While a large body of research identifies such signals prior to the collapse of populations, the prediction that such signals should also be present before a system’s recovery has thus far been overlooked. We assess whether EWSs are present prior to the recovery of overexploited marine systems using a trait-based ecological model and analysis of real-world fisheries data. We show that both abundance and trait-based signals are independently detectable prior to the recovery of stocks, but that combining these two signals provides the best predictions of recovery. This work suggests that the efficacy of conservation interventions aimed at restoring systems which have collapsed may be predicted prior to the recovery of the system, with direct relevance for conservation planning and policy.

Suggested Citation

  • Christopher F. Clements & Michael A. McCarthy & Julia L. Blanchard, 2019. "Early warning signals of recovery in complex systems," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09684-y
    DOI: 10.1038/s41467-019-09684-y
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    Cited by:

    1. Thomas M. Bury & Daniel Dylewsky & Chris T. Bauch & Madhur Anand & Leon Glass & Alvin Shrier & Gil Bub, 2023. "Predicting discrete-time bifurcations with deep learning," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    2. Sun, Xiaoqian & Wandelt, Sebastian & Zhang, Anming, 2023. "A data-driven analysis of the aviation recovery from the COVID-19 pandemic," Journal of Air Transport Management, Elsevier, vol. 109(C).
    3. Duncan A. O’Brien & Smita Deb & Gideon Gal & Stephen J. Thackeray & Partha S. Dutta & Shin-ichiro S. Matsuzaki & Linda May & Christopher F. Clements, 2023. "Early warning signals have limited applicability to empirical lake data," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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