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Early warning signals of recovery in complex systems

Citation

Clements, CF and McCarthy, MA and Blanchard, JL, Early warning signals of recovery in complex systems, Nature Communications, 10, (1) Article 1681. ISSN 2041-1723 (2019) [Refereed Article]


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Copyright Statement

Copyright 2019 the authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

DOI: doi:10.1038/s41467-019-09684-y

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.

Item Details

Item Type:Refereed Article
Keywords:early warning signals, tipping points, marine fish populations
Research Division:Environmental Sciences
Research Group:Environmental management
Research Field:Conservation and biodiversity
Objective Division:Animal Production and Animal Primary Products
Objective Group:Fisheries - wild caught
Objective Field:Fisheries - wild caught not elsewhere classified
UTAS Author:Blanchard, JL (Professor Julia Blanchard)
ID Code:144013
Year Published:2019
Funding Support:Australian Research Council (DP170104240)
Web of Science® Times Cited:30
Deposited By:Ecology and Biodiversity
Deposited On:2021-04-14
Last Modified:2021-05-04
Downloads:17 View Download Statistics

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