A simple plankton model for the Oregon upwelling ecosystem: Sensitivity and validation against time-series ocean data

2011 ◽  
Vol 222 (6) ◽  
pp. 1222-1235 ◽  
Author(s):  
James J. Ruzicka ◽  
Thomas C. Wainwright ◽  
William T. Peterson
Keyword(s):  
Time Series ◽  
Ecosystem Sensitivity ◽  
Plankton Model

scholarly journals Unifying ecosystem resistance, resilience, and recovery from extreme stress into a single statistical framework

2019 ◽  
Author(s):  
Nathan P. Lemoine
Keyword(s):  
Time Series ◽  
Global Change ◽  
Observation Error ◽  
Impulse Response Functions ◽  
Legacy Effects ◽  
Ecosystem Responses ◽  
Ecosystem Sensitivity ◽  
Extreme Stress ◽  
Statistical Framework ◽  
Biotic Change

AbstractNatural communities and ecosystems are currently experiencing unprecedented rates of environmental and biotic change. While gradual shifts in average conditions, such as rising mean air temperatures, can significantly alter ecosystem function, ecologists recently acknowledged that the most damaging consequences of global change will probably emanate from both a higher prevalence and increased intensity of extreme climatic stress events. Given the potential ecological and societal ramifications of more frequent disturbances, it is imperative that we identify which ecosystems are most vulnerable to global change by accurately quantifying ecosystem responses to extreme stress. Unfortunately, the lack of a standardized method for estimating ecosystem sensitivity to drought makes drawing general conclusions difficult. There is a need for estimates of resistance/resilience/legacy effects that are free of observation error, not biased by stochasticity in production or rainfall, and standardizes stress magnitude among many disparate ecosystems relative to normal interannual variability. Here, I propose a statistical framework that estimates all three components of ecosystem response to stress using standardized language (resistance, resilience, recovery, and legacy effects) while resolving all of the issues described above. Coupling autoregressive time series with exogenous predictors (ARX) models with impulse response functions (IRFs) allows researchers to statistically subject all ecosystems to similar levels of stress, estimate legacy effects, and obtain a standardized estimate of ecosystem resistance and resilience to drought free from observation error and stochastic processes inherent in raw data. This method will enable researchers to rigorously compare resistance and resilience among locations using long-term time series, thereby improving our knowledge of ecosystem responses to extreme stress.

The LDE-Type Flare Occurrence (1969-1993)

1994 ◽  
Vol 144 ◽  
pp. 279-282
Author(s):  
A. Antalová
Keyword(s):  
Time Series ◽  
Fourier Analysis ◽  
Sunspot Cycle ◽  
List Type ◽  
Type Number ◽  
Solar Cycles ◽  
Type Iv ◽  
Long Duration ◽  
Cycle Maximum ◽  
Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

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