In times like these, in which we have witnessed several 7+ earthquakes in August along with natural and manmade natural catastrophes – wild fires in Russia, floods in Pakistan, the Gulf of Mexico oil spill, and the gas-line explosion in San Bruno, Calif. – many people are experiencing great stress and upheaval.
Growing numbers are feeling sincere compassion and care for the people, wildlife and environments affected. Global Coherence Initiative Ambassadors and various prayer and meditation groups are sending out love and care to help uplift people affected, create global well-being and relieve suffering.
Last year, GCI sought a better understanding of the interconnectedness we believe exists between all living systems and the earth, and I would like to take this opportunity to report the preliminary results of the GCI , conducted from July 1 to Dec. 31, 2009. First, I would like to start with some examples that illustrate interconnectedness between animals and Earth-related events.
Can animals sense earthquakes?
The belief that earthquakes can be predicted based on animal behavior has been around for centuries, and many reports and stories from eye witnesses support this.
In 373 B.C., historians recorded that animals such as rats, snakes, weasels and centipedes left the Greek city of Helice several days before an earthquake devastated it.
There are many accounts of pet owners claiming to have witnessed their cats, dogs or livestock acting strangely before an earthquake. Precisely what animals sense remains a mystery.
One theory is that wild and domestic creatures feel the earth vibrate before humans do. Some research suggests that animals detect changes in the groundwater level, increases in humidity or electrical changes in the air’s ions or radon gas released from Earth before an earthquake.
Because earthquakes are sudden events, geologists and seismologists have not been able to predict exactly when they will occur. According to estimates based on US Geological Survey (USGS) earthquake statistics, over 1.3 million earthquakes occur each year, but only about 130 of them are in the range of 6.0 magnitude or higher on earthquake scales.
Most modern scientific studies have not considered the possibility that the unusual behavior of domestic and wild animals occurring several days or weeks in advance of an earthquake could be related to it. Buskirk et al. (1981) reports, however, that of 36 earthquakes occurring from 1923 to 1978 in Europe, Asia and the Americas, the most unusual animal behavior occurred near the epicenters within one or two days of the quakes and the species cited were primarily domestic.
In 2008, two days before a devastating earthquake hit China’s Sichuan province; thousands of toads suddenly were seen moving across a bridge in Taizhou (April 26, 2009, see photo). Chinese authorities did not associate that event with the earthquake.
A recent study by Grant and Halliday published in the Journal of Zoology in 2010 looked at a population of Bufo bufo toads 74 km away from the Italian city of L’Aquila, where a devastating 6.3 earthquake hit in April 2009. For this study, the authors used very low-frequency and low-frequency electromagnetic signals to look at perturbations in the ionosphere, which have recently been linked with large earthquakes. Maekawa at al. (2006) established that the ionosphere is disturbed a few days to a week before an earthquake.
In the Grant and Halliday study, breeding toads were monitored at San Ruffino Lake in Italy. Scientists reported there was a 95% decline in the number of male toads on the lake’s breeding ground five days before the quake, which, as the authors state, is highly unusual behavior for toads, because they usually remain active once they start breeding. The number of toads on the breeding ground began rising after the earthquake. Also, pre-seismic perturbations were recorded in the ionosphere before the quake.
The presence of magnetoreceptors in toads and other amphibians may be used for navigation and enable them to detect small changes in geomagnetism and the magnetic fields produced in the ionosphere, and could also be useful in helping them evade impending earthquakes (Kirschvink, 2000). The Grant & Halliday study provides scientific evidence that toads react to seismic events. This interesting and documented case supports a type of interconnectedness between animals and the earth.
GCI’s Interconnectedness Study:
In the past, awareness of an interconnectedness between people and people and the earth and its solar system was strong among many shamanic, indigenous and religious traditions. Most of these traditions believe there is a universal consciousness that pervades and connects everything – the planet, rocks, plants, animals and human beings – in subtle and unseen ways.
Many in modern science now are beginning to accept that we indeed are all part of a vast web of connections that encompass not only life on this planet but the solar system and beyond. Today, the notion of interconnectedness is discussed by scientists and many others who have the challenging task of trying to prove that the interconnectedness of life is not merely a spiritual belief, but scientific fact.
Many studies suggest that this interconnectedness exists, but the more challenging task is developing an understanding of what forces mediate such interconnections. To demonstrate and understand how we are all interconnected is of greatest importance because it is critical for the global community to take increased responsibility and care for each other and the earth, which is sustaining life and providing its many resources.
Methodology: A total of 1,643 GCI Compassion Operatives from 51 countries participated in the study. There were 1,015 participants who filled out 15 or more of the biweekly surveys and 748 who filled out 30 or more. Enough valid data was obtained to conduct a meaningful statistical analysis. Study data was collected six days per week, with participants being given Sundays off. There were 154 survey days.
The main goal of the Interconnectedness study was to gain additional insights into the correlations that indicate interconnectedness among people worldwide to the rhythms and changes in solar and planetary geomagnetic activity.
Thirty-eight items in the surveys were subjected to a factor analysis that confirmed six valid scales; Positive Affect/positive feelings, Well-being, Anxiety, Confusion, Fatigue and Physical Symptoms. A single item related to dream activity also was included in the analysis.
Additional Data: To gain a better understanding of the interconnectedness of participants with planetary and solar activity, correlations between the data obtained from the surveys and environmental variables from the National Space Science Data Centers (NSSDC), the OMNI 2 data set was examined. The OMNI 2 data set contains hourly resolution of the solar wind, magnetic field and plasma data from several spacecrafts that are in geocentric orbit around the earth as well as the L1 Lagrange point. (The L1 point is a point of equilibrium between Earth and the sun’s gravitational field, in which the pull is equal from both the sun and Earth ~225 rE, or Earth radii, in front of the earth.) The data set also contains hourly measures of energetic protons, geomagnetic activity indices – AE, Dst, Kp – and sunspot numbers. The following variables in the OMNI 2 data set had significant correlations to one or more survey scales and were used in our analysis. Below is a brief explanation of these five measures:
Geomagnetic Kp and Ap indices: Daily magnetic field variations occur because of solar radiation changes. Magnetic field changes also can be caused by the interaction of Solar wind with the magnetosphere, by interactions of the ionosphere and the magnetosphere and by variations in the magnetosphere or ionosphere. Kp and Ap magnetic indices were designed to describe these variations in the geomagnetic field.
Kp index: The planetary Kp index is used to find out if there has been a disturbance in Earth’s magnetic field and the severity of the disturbance. The Kp index combines three hours of geomagnetic data measured at 13 magnetic observatories. It is reported on a scale from 0 to 9, with a number higher than 7 indicating a large geomagnetic storm.
Ap-index: The planetary Ap-index also is used for measuring the strength of a disturbance in Earth’s magnetic field.
The A-index is an averaged daily index of geomagnetic activity derived as the average of the eight three-hour “a” indices from a set of eight monitoring stations around the world: www.ngdc.noaa.gov/stp/geomag/kp_ap.html. The Ap index is an averaged planetary A index based on data from a set of specific recording stations. The Ap index ranges from 0/very quiet to 400/extremely disturbed. An Ap index of 30 or greater indicates a local geomagnetic storm. The “a” is related to the three-hour K index according to the following scale:
K = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
a = 0, 3, 7, 15, 27, 48, 80, 140, 240, 400
Solar Wind Speed, Plasma Flow, Speed km/s:
The solar wind is a stream of charged particles ejected from its upper atmosphere. It mostly consists of electrons and protons. The solar wind streams off the sun in all directions at speeds of about 400 km/s (about 1 million miles per hour). The source of the solar wind is the sun’s hot corona. The solar wind is not uniform, however, and it can change speeds as the sun rotates and with solar flares. Earth’s geomagnetic field is what protects the planet and life from the solar wind, and without the geomagnetic field, life as we know it would not be possible on Earth. Variations in wind affect Earth’s magnetic field and can produce storms in its magnetosphere. (The data used in this report is from the Advanced Composition Explorer satellite www.swpc.noaa.gov/ace/).
Figure 2. This NASA picture depicts the sun and solar wind. Courtesy NASA.
F 10.7-cm Solar Radio Flux index, from National Geophysical Data Center (NGDC): The sun emits radio energy with a varying energy level, depending on the number of spots on the solar disk. Solar flux from the entire disk at the frequency of 2800 MHz (10.7 cm wavelength) is recorded by a radio telescope. The global daily value is measured at local noon at the Penticton Radio Observatory in Canada. Currently it is considered one of the best indices of solar activity available.
The Ottawa 10.7-cm Solar Radio Flux is measured at 1700 UT daily and expressed in units of 10 -22 watts/ m 2/Hz.
PC (N) index, from National Geophysical Data Center: The PC (N) index is a 15-minute index for magnetic activity in the North Polar Cap (PC (N)) region of the earth. It is based on data from a single near pole recording station, which monitors the polar cap magnetic activity that is affected by solar wind parameters. The station, Thule, is located in the town of Qaanaaq in Greenland.
Results of the GCI Interconnectedness Study:
The beginning of solar cycle 24 was expected to start up again during the GCI Interconnectedness Study, but there was an unexpected delay in the start of the cycle and thus, very little solar activity in the form of solar flares during the duration of the study. Despite the delay, weak but significant correlations between the survey items and solar and planetary activity could still be seen. Stronger results would likely have been found had the activity of the solar cycle started as predicted, during the GCI Interconnectedness Study.
The following parameters from the GCI survey were found to be significantly correlated to the solar and earth parameters described above: Fatigue, Anxiety, Confusion, Dreams, Positive Affect and Well-being.
Table 1 shows the results of the correlation analysis. The relationship between survey responses and environmental variables is emphasized with yellow shading where the correlation was significant at thep < 0.01 level and also is noted with double asterisks (**). A positive correlation coefficient represents positive linear correlations, while a negative correlation coefficient (minus sign) represents negative correlations. If the two variables examined have a perfect positive correlation, the coefficient would be +1 and a perfect negative correlation would be -1. If there is no correlation, or a weak correlation, the correlation is at or near zero. A positive correlation occurs when both variables increase or decrease together. Negative correlations are obtained, when one variable increases while the other decreases.
Table 1. GCI Interconnectedness Results.
Positive Affect was negatively correlated to the Solar Wind Speed (-.22, p < 0.01), Kp (-.25, p < 0.01), Ap index (-.24, p < 0.01), and polar cap magnetic activity (-.32, p < 0.01). In other words, when Solar Wind Speed, Kp, Ap and Polar Cap Activity increased, Positive Affect among the participants decreased. Positive Affect was positively correlated with the F 10.7 index (.28, p < 0.01).
Well-being scores were negatively correlated with Solar Wind Speed (-.17, p < 0.05), Kp (-.21, p < 0.01) Ap-index (-.20, p < 0.05) and polar cap magnetic activity (-.24, p < 0.01). Well-being was positively correlated to the Solar Radio Flux F 10.7 index (.20, p < 0.05).
Fatigue on the other hand is positively correlated to Solar Wind Speed (.32, p < 0.01), Kp (.28, p < 0.01), Ap index (.27, p < 0.01), and polar cap magnetic activity (.32, p < 0.01). Meaning, when Solar Wind Speed, Kp, Ap and Polar Cap Activity increased, the level of fatigue in participants also increased. Fatigue was negatively correlated with the F 10.7 index (-.34, p < 0.01).
Figure 3 illustrates the relationship between Positive Affect, Fatigue and four of the environmental variables Solar Wind Speed, Kp index, Polar Cap Activity and the F 10.7 Solar Radio Flux activity.
Like Fatigue, Confusion in participants was positively correlated with Solar Wind Speed (0.30, p < 0.01), KP (0.20, p < 0.05) and polar cap magnetic activity (0.24, p < 0.01), i.e. these negative feelings increased as the Solar Wind Speed, Kp and geomagnetic activity at the polar cap increased.
Anxiety also was positively correlated with Solar Wind Speed (.17, p < 0.05) and polar cap magnetic activity (-.19, p < 0.05), and negatively correlated with the Solar Radio Flux F 10.7 index.
Dream Activity was correlated with the Solar Radio Flux (.21, p < 0.05).
The physical symptoms scale did not correlate with any of the environmental variables, likely because of the lack of geomagnetic disturbance. The physical symptom scale included items such as headaches, backaches, muscle tension, fatigue, etc.
Results of quartile data analyses:
To further explore the relationships in the GCI survey data, each of the environmental variables were separately grouped into four signal-range quartiles. Quartile data analysis is, as the name suggests, a partition of the data into four sections, each containing 25 % of the data.
The lowest 25% of daily values for each environmental variable used in the analysis of the 154 survey days were coded Group 1 (low); the daily values ranging from 26%-50% were coded Group 2 (medium); the values from 51%-75% were coded Group 3 (high); and those ranging from 76%-100% were coded Group 4 (highest).
Independently, each of the survey’s emotion and symptom scales was compared and analyzed on quartile levels. For example, the relationship of each of the four quartiles of Positive Affect was analyzed relative to the corresponding levels of Kp as follows: The relationship of Group 1 scores, 0%-25% (low), of Positive Affect to Kp at it lowest – Group 1 – was analyzed; the Group 2 (medium), 26%-50% Positive Affect scores and the Kp Group 2 data were analyzed; etc. The four groups of Positive Affect scores were analyzed using a single factor ANOVA (analysis of variance) with Bonferroni post-hoc correction for multiple comparisons.
Each of the five environmental variables were used to create similar quartile grouping factors to evaluate the differences in the survey scores between groups on the days when the environmental variables were at different intensity levels.
The only significant result obtained for Physical Symptoms was for the Ap index (F = 2.7, p< 0.05). The post-hoc analysis failed to show any additional significant results.
There was a significant effect recorded within the four groups of Fatigue relative to the four levels of Solar Wind Speed (F=6.34, p < 0.001), Kp (F=5.46, p < 0.001), Ap (F=5.11, p < 0.01), Solar Radio Flux (F=5.58, p < 0.001) and Polar Cap Magnetic Activity (F=6.87, p < 0.001). Post-hoc analysis reveals significant differences between the following groups of activity levels: On days when Solar Wind Speed was at its lowest levels, Fatigue was significantly lower than on days when wind speeds were medium (p < 0.05), high (p < 0.01) and highest (p < 0.001). Fatigue levels on the days when the Kp was low were significantly lower than the days when Kp was high (p < 0.05) and highest (p < 0.01). Similarly, Fatigue on days when the Ap index was low were significantly lower that the days when the Ap index was high (p < 0.05) and highest (p < 0.01). Fatigue was significantly lower on the days when the Solar Radio Flux (F 10.7 index) was at its highest (1 vs. 4, p < 0.01), on days when it was high (p < 0.01) and days when it was medium (p < 0.01). Fatigue levels on days when Polar Cap Magnetic Activity was at its lowest and highest were significantly different (p < 0.001). Fatigue on days when the Polar Cap Magnetic Activity was highest was also higher than the days when it was at medium (p < .001) and high (p < 0.05).
Figure 4. Fatigue versus Solar Wind Speed (km/sec)
In Figure 4, the four levels of Fatigue scores are plotted with the corresponding mean quartile level of Solar Wind Speed (km/sec) for reference. There was a significant difference between Fatigue scores on the days when Solar wind was low compared to scores on the days when Solar Wind Speed was medium, high and highest.
Anxiety levels were significantly higher on days when Polar Cap Magnetic Activity was highest than when it was at its lowest (p < 0.01).
Confusion quartile levels had significant differences in relation to activity levels for Solar Wind Speed (F=3.86, p < 0.05); Kp (F=2.74, p < 0.05); Ap (F=3.47, p < 0.05); and Polar Cap Magnetic Activity (F=6.55,p < 0.001). Post-hoc tests showed that Confusion was significantly higher on days when Solar Wind Speed was at its highest compared to days when it was at its lowest. Confusion was greater on days when the Ap index was highest relative to days when levels were medium ( p < 0.05). On days of the highest Polar Cap Magnetic Activity recorded levels of Confusion were higher than on days when the activity was low (p < 0.01), medium (p < 0.001) and high (p < 0.05).
Positive Affect quartile differences in relation to the varying activity levels of Solar Wind Speed were as follows: (F=3.09, p < 0.05), Kp (F=5.00, p < 0.01); Ap index (F=4.87, p < 0.01); and Polar Cap Magnetic Activity (F=5.84, p < 0.001). The post-hoc difference for Positive Affect was between the low and medium Solar wind level days (p < 0.05). Positive Affect/feelings were higher on days when Kp was lowest relative to when it was high (p < 0.01)or highest (p < 0.01). Positive feelings were also higher on days when Ap was lowest relative to when it was high (p < 0.05) or highest (p < 0.05). On days when Polar Cap Magnetic Activity was at its highest, Positive Affect was significantly lower than when the activity was low (p < 0.001), medium (p < 0.05) or high (p < 0.05).
Well-being quartile level differences compared to varying activity levels of Solar Wind Speed were reported as follows: (F=3.37, p < 0.05); Kp (F=3.41, p < 0.05); and Polar Cap Magnetic Activity (F=4.44, p < 0.01). Post-hoc analysis revealed a significant difference in Well-being levels in relation to varying Solar Wind Speeds: Well-being was significantly lower, (p < 0.05), when wind speeds were highest. They were lower on days when Polar Cap Magnetic Activity was at it highest relative to when it was at a low activity level (p < 0.01) or high activity level (p < 0.05).
Figure 5. The lower the Kp, the higher the Positive Affect
In Figure 5, the four levels of Positive Affect scores are plotted with the corresponding mean quartile level of Kp for reference. Positive Affect levels were significantly lower on average on days when Kp was at its high and highest levels.
In short, the analyses of the relationship of emotions and symptoms relative to the environmental quartiles yielded statistically significant data, especially for Fatigue, which had the greatest number of significant effects, followed by Positive Affect, Confusion and Well-being. It is interesting that the emotion and symptom factors – Positive Affect, Well-being, Anxiety, Confusion, Fatigue and Physical Symptoms – had significant results for the Polar Cap Magnetic Activity. Solar Wind Speed changes related to Fatigue, Confusion and Well-being. Changes in Kp and/or Ap indexes related to Fatigue, Positive Affect and Well-being. Fatigue is the only survey scale that showed statistically significant differences to Solar Radio Flux. Predictably, post-hoc tests for emotion and symptom factors on days when environmental variables were at there lowest levels vs. highest levels produced the most statistically meaningful differences.
For those interested in more detailed results and discussion, this will be available in an upcoming GCI Interconnectedness report and journal publication.
It is exciting to see indications of interconnectedness between the GCI Compassion Operatives who participated in the study and the energetic factor such as solar and planetary magnetic field activity. This helps to confirm some of the research that has been discussed in GCI commentaries in the past. For example, environmental scientist Neil Cherry, 2001, reviewed a large number of studies that identified significant physical, biological and health effects that are associated with changes in solar and geomagnetic activity. Consider that the brain and heart rhythms and the Schumann resonances and geomagnetic field line resonances overlap, and changes in these resonances caused by changes in Solar Wind Speed or solar flares can in turn influence the function of human, and animal, brain and heart patterns in the GCI Commentary, Influence of Geomagnetism and Schumann Resonances on Human Health and Behavior, July 15, 2009).
Doronin et al., (1998) noted that the oscillations in the Kp index and brainwave alpha-rhythms have identical periods. Because of the global interconnection between solar and geomagnetic activity, the ionospheric waveguide, Schumann resonances and the human brain and heart, increased solar activity can disturb the biological rhythm of humans and animals and exacerbate existing issues such as Fatigue, Confusion, Anxiety and Dream Activity – as seen in the GCI’s results.
Although solar cycle 24 hadn’t yet picked up its activity during the GCI study period, correlations between the survey items and participants’ well-being could be made and even bigger effects would be expected during increased solar activity. To gain a greater understanding of interconnectedness during solar cycle 24, further studies may be undertaken by GCI in the future.
A big thank you from the GCI team goes out to all the GCI Ambassadors who took the time to participate in the Interconnectedness Study and helped add to the new science investigating the interconnectedness of people with Earth on a global scale.
We are looking forward to continuing on the path of exploring the web of interconnectedness of all living beings, the planet, the solar system and beyond.
In closing, I leave you with the words of modern artist, Alex Grey:
“The infinite vibratory levels, the dimensions of interconnectedness are without end. There is nothing independent. All beings and things are residents in your awareness.”
Annette Deyhle, Ph.D. and GCI Research Team