scholarly journals Drought impacts on tree growth of two pine species along an altitudinal gradient and their use as early-warning signals of potential shifts in tree species distributions

2016 ◽  
Vol 381 ◽  
pp. 157-167 ◽  
Author(s):  
Laura Marqués ◽  
J. Julio Camarero ◽  
Antonio Gazol ◽  
Miguel A. Zavala
Keyword(s):  
Tree Growth ◽  
Early Warning ◽  
Tree Species ◽  
Altitudinal Gradient ◽  
Species Distributions ◽  
Warning Signals ◽  
Early Warning Signals ◽  
Drought Impacts ◽  
Potential Shifts ◽  
Tree Species Distributions

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

Using a dynamic forest model to predict tree species distributions

2016 ◽  
Vol 25 (3) ◽  
pp. 347-358 ◽  
Author(s):  
Alvaro G. Gutiérrez ◽  
Rebecca S. Snell ◽  
Harald Bugmann
Keyword(s):  
Tree Species ◽  
Species Distributions ◽  
Forest Model ◽  
Tree Species Distributions
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