Relation between stability and resilience determines the performance of early warning signals under different environmental drivers
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Relation between stability.pdf
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Author(s)
Dai, Lei
Korolev, Kirill S.
Gore, Jeff
Date Issued
July 2015
Journal
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences (U.S.)
Citation
Dai, Lei; Korolev, Kirill S. and Gore, Jeff. “Relation Between Stability and Resilience Determines the Performance of Early Warning Signals Under Different Environmental Drivers.” Proceedings of the National Academy of Sciences 112, no. 32 (July 27, 2015): 10056–10061. © 2015 National Academy of Sciences
Version
Final published version
Abstract
Shifting patterns of temporal fluctuations have been found to signal critical transitions in a variety of systems, from ecological communities to human physiology. However, failure of these early warning signals in some systems calls for a better understanding of their limitations. In particular, little is known about the generality of early warning signals in different deteriorating environments. In this study, we characterized how multiple environmental drivers influence the dynamics of laboratory yeast populations, which was previously shown to display alternative stable states [Dai et al., Science, 2012]. We observed that both the coefficient of variation and autocorrelation increased before population collapse in two slowly deteriorating environments, one with a rising death rate and the other one with decreasing nutrient availability. We compared the performance of early warning signals across multiple environments as “indicators for loss of resilience.” We find that the varying performance is determined by how a system responds to changes in a specific driver, which can be captured by a relation between stability (recovery rate) and resilience (size of the basin of attraction). Furthermore, we demonstrate that the positive correlation between stability and resilience, as the essential assumption of indicators based on critical slowing down, can break down in this system when multiple environmental drivers are changed simultaneously. Our results suggest that the stability–resilience relation needs to be better understood for the application of early warning signals in different scenarios.
MIT Department
Massachusetts Institute of Technology. Department of Biology
Massachusetts Institute of Technology. Department of Chemistry
Massachusetts Institute of Technology. Department of Physics
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DOI of Published Version
https://doi.org/10.1073/pnas.1418415112
