Analysis of historic jack pine budworm outbreaks in the Prairie provinces of Canada

1988 ◽  
Vol 18 (9) ◽  
pp. 1152-1158 ◽  
Author(s):  
W. Jan A. Volney
Keyword(s):  
Time Series ◽  
Severity Index ◽  
Jack Pine ◽  
Stationary Time Series ◽  
Sufficient Information ◽  
Outbreak Area ◽  
Jack Pine Budworm ◽  
Annual Time Series ◽  
Time Series Analyses ◽  
Highly Correlated

The annual Forest Insect and Disease Survey reports of the Canadian Forestry Service were used to develop a jack pine budworm (Choristoneurapinus Freeman) defoliation severity index for a 50-year span. The region covered was the western half of the host's (Pinusbanksiana Lamb.) range. An interpretation of this record permitted the construction of an annual time series of the total area moderately to severely or severely defoliated. The area of outbreaks has increased over the period. This trend was removed from the data to obtain a stationary time series. Analyses of the time series showed that there was a statistically significant periodicity to the size of outbreaks. An examination of the sample autocorrelation function revealed that only the past year's outbreak area was significantly correlated with that of the current year's outbreak. The model identified by applying the Box–Jenkins methodology to these results was inadequate, indicating that the series itself does not contain sufficient information for predictions. Outbreak area and the total area burned in Manitoba and Saskatchewan 4–7 years previously were highly correlated. Despite the crudity of the data, these relations could be exploited to develop predictors of outbreak size and occurrence. The significance of these results for forest management in the region is discussed.

Physiological fractals: Visual and statistical evidence across timescales and experimental states

2020 ◽  
Author(s):  
Jeffrey J Kim
Keyword(s):  
Time Series ◽  
Published Data ◽  
Compassion Meditation ◽  
Open Questions ◽  
Mean And Variance ◽  
Time Series Analyses ◽  
The Mean ◽  
Highly Correlated ◽  
Mind Training ◽  
Stable Signal

A marker of engaging in compassion meditation and related processes is an increase in heart-rate variability (HRV), typically interpreted as a marker of parasympathetic nervous response. Whilst insightful, open questions remain. For example, which timescale is best to examine the effects of meditation and related practices on HRV? Furthermore, how might advanced time series analyses – such as stationarity – be able to examine dynamic changes in the mean and variance of the HRV signal across time? Here we apply such methods to previously published data, which measured HRV pre- and post- a two-week compassionate mind training (CMT) intervention. Inspection of these data reveal that a visualization of HRV correlations across resting and compassion meditation states, pre- and post- two-week training, is retained across numerous recording timescales. Here, the fractal-like nature of our data indicate that the accuracy of representing HRV data can exist across timescales, albeit with greater or lesser granularity. Interestingly, inspection of the HRV signal at Time 2 compassion meditation versus Time 1 revealed a more highly correlated (i.e., potentially more stable) signal. We followed up these results with tests of stationarity, which revealed Time 2 had a less stochastic (variable) signal than Time 1, and a measure of distance in the time series, which showed that Time 2 had less of an average difference between rest and meditation than at Time 1. Our results provide novel assessment of visual and statistical markers of HRV change across distinct experimental states.

scholarly journals Physiological fractals: visual and statistical evidence across timescales and experimental states

2020 ◽  
Vol 17 (167) ◽  
pp. 20200334 ◽  
Author(s):  
Jeffrey J. Kim ◽  
Stacey Parker ◽  
Trent Henderson ◽  
James N. Kirby
Keyword(s):  
Time Series ◽  
Parasympathetic Nervous System ◽  
System Response ◽  
Published Data ◽  
Compassion Meditation ◽  
Open Questions ◽  
Mean And Variance ◽  
Time Series Analyses ◽  
Highly Correlated ◽  
Mind Training

A marker of engaging in compassion meditation and related processes is an increase in heart-rate variability (HRV), typically interpreted as a marker of parasympathetic nervous system response. While insightful, open questions remain. For example, which timescale is best to examine the effects of meditation and related practices on HRV? Furthermore, how might advanced time-series analyses––such as stationarity––be able to examine dynamic changes in the mean and variance of the HRV signal across time? Here we apply such methods to previously published data, which measured HRV pre- and post- a two-week compassionate mind training (CMT) intervention. Inspection of these data reveals that a visualization of HRV correlations across resting and compassion meditation states, pre- and post-two-week training, is retained across numerous recording timescales. Here, the fractal-like nature of our data indicates that the accuracy of representing HRV data can exist across timescales, albeit with greater or lesser granularity. Interestingly, inspection of the HRV signal at Time 2 compassion meditation versus Time 1 revealed a more highly correlated (i.e. potentially more stable) signal. We followed up these results with tests of stationarity, which revealed Time 2 had a less stochastic (variable) signal than Time 1, and a measure of distance in the time series, which showed that Time 2 had less of an average difference between rest and meditation than at Time 1. Our results provide novel assessment of visual and statistical markers of HRV change across distinct experimental states.

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