Detecting early warning signals of tree mortality in boreal North America using multiscale satellite data

2018 ◽  
Vol 24 (6) ◽  
pp. 2284-2304 ◽  
Author(s):  
Brendan M. Rogers ◽  
Kylen Solvik ◽  
Edward H. Hogg ◽  
Junchang Ju ◽  
Jeffrey G. Masek ◽  
...  
Keyword(s):  
North America ◽  
Satellite Data ◽  
Early Warning ◽  
Tree Mortality ◽  
Warning Signals ◽  
Early Warning Signals

scholarly journals Early-Warning Signals of Individual Tree Mortality Based on Annual Radial Growth

2019 ◽  
Vol 9 ◽  
Author(s):  
Maxime Cailleret ◽  
Vasilis Dakos ◽  
Steven Jansen ◽  
Elisabeth M. R. Robert ◽  
Tuomas Aakala ◽  
...  
Keyword(s):  
Early Warning ◽  
Radial Growth ◽  
Tree Mortality ◽  
Warning Signals ◽  
Individual Tree ◽  
Early Warning Signals

scholarly journals Efficacy of early warning signals and spectral periodicity for predicting transitions in bipolar patients: An actigraphy study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yoram K. Kunkels ◽  
Harriëtte Riese ◽  
Stefan E. Knapen ◽  
Rixt F. Riemersma - van der Lek ◽  
Sandip V. George ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Early Warning ◽  
Type I ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Research Fields ◽  
Mood Episode ◽  
Final Sample ◽  
Bipolar Patients ◽  
Direction Opposite

AbstractEarly-warning signals (EWS) have been successfully employed to predict transitions in research fields such as biology, ecology, and psychiatry. The predictive properties of EWS might aid in foreseeing transitions in mood episodes (i.e. recurrent episodes of mania and depression) in bipolar disorder (BD) patients. We analyzed actigraphy data assessed during normal daily life to investigate the feasibility of using EWS to predict mood transitions in bipolar patients. Actigraphy data of 15 patients diagnosed with BD Type I collected continuously for 180 days were used. Our final sample included eight patients that experienced a mood episode, three manic episodes and five depressed episodes. Actigraphy data derived generic EWS (variance and kurtosis) and context-driven EWS (autocorrelation at lag-720) were used to determine if these were associated to upcoming bipolar episodes. Spectral analysis was used to predict changes in the periodicity of the sleep/wake cycle. The study procedures were pre-registered. Results indicated that in seven out of eight patients at least one of the EWS did show a significant change-up till four weeks before episode onset. For the generic EWS the direction of change was always in the expected direction, whereas for the context-driven EWS the observed effect was often in the direction opposite of what was expected. The actigraphy data derived EWS and spectral analysis showed promise for the prediction of upcoming transitions in mood episodes in bipolar patients. Further studies into false positive rates are suggested to improve effectiveness for EWS to identify upcoming bipolar episode onsets.

Early warning signals using AVaRs of infinitely divisible GARCH models — evidence from stock index markets

2015 ◽  
Vol 47 (43) ◽  
pp. 4630-4652 ◽  
Author(s):  
Chia-Chien Chang ◽  
Te-Chung Hu ◽  
Chiu-Fen Kao ◽  
Ya-Chi Chang
Keyword(s):  
Early Warning ◽  
Stock Index ◽  
Garch Models ◽  
Infinitely Divisible ◽  
Warning Signals ◽  
Early Warning Signals

scholarly journals Early warning signals in plant disease outbreaks

2019 ◽  
Vol 393 ◽  
pp. 12-19 ◽  
Author(s):  
S. Orozco-Fuentes ◽  
G. Griffiths ◽  
M.J. Holmes ◽  
R. Ettelaie ◽  
J. Smith ◽  
...  
Keyword(s):  
Early Warning ◽  
Plant Disease ◽  
Disease Outbreaks ◽  
Warning Signals ◽  
Early Warning Signals

scholarly journals Relation between stability and resilience determines the performance of early warning signals under different environmental drivers

2015 ◽  
Vol 112 (32) ◽  
pp. 10056-10061 ◽  
Author(s):  
Lei Dai ◽  
Kirill S. Korolev ◽  
Jeff Gore
Keyword(s):  
Early Warning ◽  
Alternative Stable States ◽  
Basin Of Attraction ◽  
Environmental Drivers ◽  
Ecological Communities ◽  
Stable States ◽  
Warning Signals ◽  
Early Warning Signals ◽  
The Stability ◽  
Stability And Resilience

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.

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