10.5061/DRYAD.K30V3
Dai, Lei
Massachusetts Institute of Technology
Korolev, Kirill S.
Boston University
Gore, Jeff
Massachusetts Institute of Technology
Data from: Relation between stability and resilience determines the
performance of early warning signals under different environmental drivers
Dryad
dataset
2015
stability-resilience relation
Population Collapse
Population biology
environmental drivers
early warning signals
2015-07-30T20:41:53Z
2015-07-30T20:41:53Z
en
https://doi.org/10.1073/pnas.1418415112
161352 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
data_deteriorationdata_SUCbifurcationdata_SUCindicator