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Review

Unusual Animal Behavior as a Possible Candidate of Earthquake Prediction

by
Masashi Hayakawa
1,2,* and
Hiroyuki Yamauchi
3
1
Hayakawa Institute of Seismo Electromagnetics Co., Ltd. (Hi-SEM), UEC (The University of Electro-Communications) Alliance Center #521, 1-1-1 Kojima-cho, Chofu 182-0026, Japan
2
Advanced Wireless & Communication Research Center (AWCC), UEC (The University of Electro-Communications), 1-5-1 Chofugaoka, Chofu 182-8585, Japan
3
Earthquake Prediction Research Center, Ichigaya Science and Technology Innovation Center, 2F, 3-8 Ichigayatamachi, Shinjuku-ku, Tokyo 162-0843, Japan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(10), 4317; https://doi.org/10.3390/app14104317
Submission received: 26 March 2024 / Revised: 13 May 2024 / Accepted: 13 May 2024 / Published: 20 May 2024
(This article belongs to the Special Issue Feature Review Papers in Applied Physics)

Abstract

:
Short-term (with a lead time of about one week) earthquake (EQ) prediction is one of the most challenging subjects in geoscience and applied science; however, it is highly required by society because it is of essential importance in mitigating the human and economic losses associated with EQs. Electromagnetic precursors have recently been agreed to be the most powerful candidate for short-term prediction, because a lot of evidence has been accumulated on the presence of electromagnetic precursors (not only from the lithosphere, but also from the atmosphere and ionosphere) prior to EQs during the last three decades. On the other hand, unusual animal behavior associated with EQs, which is the main topic of this review, has been investigated as a macroscopic phenomenon for many years, with a much longer history than the study of seismo-electromagnetics. So, in this paper, we first summarize the previous research work on this general unusual animal behavior with reference to its relationship with EQs, and then we pay the greatest attention to our own previous work on dairy cows’ milk yield changes. We recommend this unusual animal behavior as an additional potential tool for short-term EQ prediction, which may be a supplement to the above seismo-electromagnetic effects. Finally, we will present our latest case study (as an example) on unusual changes of cows’ milk yields for a particular recent Tokyo EQ on 7 October 2021, and further propose that electromagnetic effects might be a possible sensory mechanism of unusual animal behavior, suggesting a close link between electromagnetic effects and unusual animal behavior.

1. Introduction

The purpose of the present paper is to make an extensive review of the potential importance of unusual animal behavior (especially cows’ milk yield changes) associated with earthquakes (EQs) in the general challenging scientific field of short-term (with a lead time of about one week) EQ prediction [1,2,3,4,5,6,7,8]. We first review the previous studies of general unusual animal behavior associated with EQs as a possible EQ predictor; here, “general” means that earlier works were based on the observations of not only different kinds of animals (large and small), but also birds, fish, and so on. Then, some recent works have suggested the link of those unusual animal behaviors with electromagnetic EQ precursors, which is becoming the most promising candidate for short-term EQ prediction, but this link needs further extensive studies in the future. One typical branch of such unusual animal behavior is the change of dairy cows’ milk yields, and we have had important recent achievements on this, which is the major focus of our review paper.
So, the construction of this paper is as follows. Section 2 deals with the presentation of previous work on the general unusual animal behaviors associated with EQs, including the suggested relationship of those unusual animal behaviors with seismo-electromagnetic effects. Section 3 deals with this new scientific field of electromagnetic effects, especially electromagnetic radiation (or waves) before the EQs. Then, in Section 4 we will present the recent works on the change of cows’ dairy milk yields in possible association with EQs, which will be a plausible future candidate for short-term EQ prediction, and Section 5 presents an event study for a recent Tokyo EQ on 7 October 2021 and its effects on milk yield changes in the Kanto district. We can re-confirm clear anomalies near a station close to the EQ epicenter. Finally, these unusual cow milk yield changes are compared with our previous work on different electromagnetic effects, suggesting a strong link between the two. The last section, Section 6, is the summary.

2. Review of General Studies of Unusual Animal Behavior in Association with EQs

“Unusual animal behavior”, which has been known since a long time ago as a macroscopic phenomenon, is regarded as a classical branch of short-term EQ prediction [9,10,11,12,13,14,15,16,17,18,19,20], even though its mechanism is poorly understood [14,21]. Based on many anecdotal and retrospective data in different countries, Rikitake (2001) [21] summarized unusual animal behaviors in terms of the three important plots: (i) EQ magnitude (M) versus D, (ii) log10 T versus M, and (iii) the occurrence histogram of log10 T using two parameters (epicentral distance (D) of an anomaly and lead time (T)). He found not only short-term precursors, but also medium-term (with a lead time of a few months) and imminent (a lead time of the order of one day) precursors. Further, he suggested different candidates of animal sensory mechanisms including not only conventional mechanical (ground motion), chemical, but also electromagnetic environmental changes, and he paid more attention to electromagnetic effects.
In an attempt to better understand the animal sensory mechanism, Hayakawa (2013) [22] performed an extensive comparison of Rikitake’s statistical results on general unusual animal behavior with those of various seismo-electromagnetic effects. Electromagnetic effects include radio emissions in different frequency ranges (DC, ULF (ultra-low frequency)/ELF (extremely low frequency), VLF (very low frequency)/LF (low frequency), HF (high frequency) bands) and seismo-atmospheric and seismo-ionospheric perturbations described by many scientists during the last few decades (as summarized in Molchanov and Hayakawa (2008) [6] and Hayakawa and Hobara (2010) [2]). In this study, he paid particular attentios to the three plots by Rikitake [21] mentioned above, and he came to the conclusion, by extensive comparison of temporal evolutions, that lower frequency radio emissions (such as ULF lithospheric radiation and ULF/ELF atmospheric radiation) are a plausible candidate as a sensory mechanism of animals among many possibilities, suggesting a close link between unusual animal behavior and electromagnetic effects.
Several works on the observations of unusual animal behavior followed [23,24,25,26,27,28], and further a laboratory experiment was performed by Nishimura et al. (2010) [29], who applied the ULF/ELF radio waves on lizards resulting in a behavioral change of the lizards, giving support to Hayakawa’s (2013) [22] conclusion.

3. Electromagnetic Effects as the Major Component of Short-Term EQ Prediction

For the purpose of short-term EQ prediction, we are strongly required to find EQ precursors [1,2,3,4]. Then during the last few decades, crucial efforts have been made in this field, and enormous progress has been achieved in finding various kinds of EQ precursors (e.g., [3,5,6,7]). Specifically, we have found that EQ precursors are not seismological effects, but rather electromagnetic effects, which take place mainly before an EQ (sometimes together with some after-effects). Even though the main agents are the nucleation and fracturing processes in the lithosphere due to the accumulated pressure in fault regions, various phenomena are found to take place not only in the atmosphere but also in the upper atmosphere (ionosphere), which was revealed with both ground- and satellite-based observations (e.g., [6,7,8]). Finally, we have found that three layers of the Earth, that is, lithosphere, atmosphere, and ionosphere, are strongly coupled with other—a phenomenon which is called “lithosphere-atmosphere-ionosphere coupling (LAIC)” [30,31,32]. Further, it is surprising that the upper-most ionosphere seems to be the most sensitive to the pre-EQ lithospheric activity, and so the recent greatest concern of our scientific society is to elucidate the mechanism of the LAIC process. Even though a few hypotheses have been proposed so far [3,6,8,9,33,34,35,36,37,38,39], it is not well established which channel is most plausible at the moment, or it may be possible that different mechanisms are working for different EQs. In order to gain more insights into the LAIC process, Ouzounov et al. (Eds) (2018) [7] have emphasized the importance of multi-parameter observations; however, such multi-parameter and multi-layer observations are difficult to carry out due to the difficulty of the multidisciplinary nature of observations, so only a limited number of papers have been published only recently [40,41,42,43,44,45,46,47]. Hayakawa et al. (2022) [46] is a typical example of such multiparameter observations for the 7 October 2021 Tokyo EQ, for which cows’ unusual anomalies are presented in this paper. It is no doubt that these electromagnetic phenomena will remain as the highest priority for short-term EQ prediction.

4. Recent Work on Dairy Cows’ Milk Yield Changes

Dairy cows’ milk yield change is one particular branch of unusual animal behavior. There have been early works published on dairy cows’ milk yield changes as in the following papers, [48,49,50,51], and therein.
Recently, our group has made a lot of important contributions. Yamauchi et al. (2014) [52] thoroughly elaborated the study of changes of dairy cows’ milk yields in relation to EQs. They surveyed the unusual animal behavior of pets using a questionnaire, and additionally they explored how the dairy cows’ milk yield varied before the EQ. Their finding was that there was a significant depletion in cows’ milk yields 3–6 days before the disastrous 2011 Tohoku EQ, as a case study. Yamauchi et al. (2017) [53] have then extended their case study to statistical evaluations, and their statistical result revealed that the dairy cows’ milk yield indicated a significant depletion again, but approximately three weeks before an EQ, using long-term observation data, and suggested a link with ULF radio emissions from the lithosphere. Also, they have obtained a promising probability gain of 6.8 based on the confusion matrix. This value seems to be sufficiently high for short-term EQ prediction purposes, even when compared with those of some promising candidates of electromagnetic effects [6]. Further, Hayakawa et al. (2016) [54] have investigated the correlation of cows’ milk yields with seismicity and ULF magnetic field variations, and their conclusion is that these cows’ milk yields show a depletion about 17–18 days before an EQ, and it was also found that ULF lithospheric radiation happens, at least, during the period of low milk yields of dairy cows. Finally, we can say from our previous work that the cows’ milk yield change is acceptable as an EQ predictor, but its lead time is very variable at approximately 2–3 weeks.
A recent good review paper on unusual animal behavior has been published by Woith et al. (2023) [55], who indicated the weakness and deficits of papers published so far because of the short period of data presentation before going into any definite conclusion of the sensory mechanism of animals; however, they have suggested the importance of conventional foreshocks from the seismological sense.

5. Our Latest Result for a Particular Tokyo EQ on 7 October 2021

We here present, as an example, our latest case study: the unusual depletion of dairy cows’ milk yields for a particular Tokyo target EQ (7 October 2021). Because Hayakawa et al. (2022) [46] have already reported on extensive multi-parameter and multi-layer electromagnetic observations (based on ground- and satellite-based measurements) for this EQ, in order to provide further information to elucidate the LAIC process as mentioned in Section 2, we first present the temporal evolutions of daily cows’ milk yields at four monitoring stations in the Chiba prefecture in the Tokyo district for our particular EQ. Then, the temporal changes of cows’ milk yields will be intensively compared with those of electromagnetic precursors (of various kinds of seismogenic emissions in Hayakawa et al. (2022) [46]) to gain any insight into the study of the likely sensory mechanism of cows.

5.1. The EQ Treated in This Paper

The EQ of interest took place in the Tokyo district on 7 October 2021 at 22 h 41 min JST (or 7 October 2021, 13 h 41 min UT). Its epicenter is located at the geographic coordinates of 35°35.4′ N, 140°06.2′ E in one of several fault regions in the Tokyo district, as shown in Figure 1. The EQ magnitude was M = 5.9, according to the Japan Meteorological Agency, and its depth was rather large, in the range of ~70 km. Even though the magnitude of this EQ is not so large as other case studies, the Tokyo people are afraid of the next huge Tokyo EQ since the 1923 Kanto (Tokyo) EQ, so we think it is worthwhile to examine in detail whether there are several existing EQ precursors for any EQs with a moderate magnitude happening in the Tokyo district [25].

5.2. Monitoring Stations of Dairy Cows’ Milk Yields and Observational Results

5.2.1. Monitoring Stations

The EPRC (Earthquake Prediction Research Center, Tokyo, Japan) has been continuing the monitoring of dairy cows’ milk yields in the farms at several stations in and around the Tokyo district with the help of ORION Machinery Co., Ltd. (Nagano, Japan). All the farms featured a free stall barn with one-way traffic automatic milking systems (Mione, GEA Farm Technologies, Düsseldorf, Germany). Individual milk yields automatically recorded at each milking of all lactating cows using DairyPlan C21 (GEA Farm Technologies, Düsseldorf, Germany) were used for our analysis. Figure 1 illustrates the positions of several monitoring stations used in this paper by red circles, which are all located in the Chiba prefecture. In the order of decreasing latitude: (1) Asahi (geographic coordinates: 35.74° N, 140.70° E), (2) Chiba-shi (city) (35.52° N, 140.24° E), (3) Isumi (35.20° N, 140.33° E), and (4) Minami-boso (35.03° N, 139.98° E). Compared with the epicentral location of the Tokyo EQ in Figure 1, we can list the epicentral distances for all these four stations: (1) D = 57 km, (2) D = 15 km, (3) D = 48 km, and (4) D = 63 km. In the following sections, we will show the observational results at the stations starting from the shortest distance to farthest distance.

5.2.2. Observational Results

Chiba Station (D = 15 km)

Figure 2a illustrates the temporal (daily) variation in average milk yield of 88 Holstein dairy cows at the station in Chiba-shi (city) in Figure 1 by a red curve. The abscissa is the date around the EQ day of 7 October (indicated by a red vertical broken line), and our study time period is about one month preceding the EQ and one week after the EQ. The ordinate is the variational value of cows’ milk yield (daily average of all individuals; in kg) around the zero level (zero means the mean value for the past week). In order to identify an anomaly, we have adopted a statistical criterion of plus/minus 1.5σ (where σ is standard deviation), but our previous work [52] suggested that the normal anomaly of cows’ milk yield is only a depletion or a decrease in milk yields. Milk yields in dairy cows can be influenced by the meteorological parameters of ambient temperature and humidity as well as the number of days after calving. The former temperature–humidity index is used as an index of heat stress for cows (see details in Yamauchi et al. (2014) [52]), and this effect should be removed in this figure. When the red line of cows’ milk yield is within the blue area of plus/minus 1.5σ, the milk yield does not exhibit any anomaly. Also, the error bars indicate the standard deviation of individual milk yields at the station. Figure 2a illustrates the observed result at the station in Chiba-shi, which is located at the shortest distance (D = 15 km) from the EQ epicenter. Even though there are some missing values due to a problem in the data transfer system for 22 September and 30 September to 3 October, the graph exhibits very clear abnormal changes of cows’ milk yield during the period of 26 September to 28 September (i.e., 11 days to 9 days before the EQ) and also the size of the anomaly is extremely large, such that it exceeds −3σ. This prolonged anomaly with a deep depletion in milk yields is believed to be a very clear precursor to this Tokyo EQ. However, we have to mention that there is an enhancement after the EQ, although we wonder whether that it might be an after-effect of the EQ.

Isumi Station

Figure 2b illustrates the milk yield of 60 Holstein dairy cows at the Isumi station, with the same notations as in Figure 2a. The epicentral distance of this station is D = 48 km. It is seen from this figure that we can detect abnormal depletions in cows’ milk yield on 10 September (or 28 days before the EQ), 23 September (or 14 days before the EQ), and 7 October (the day of EQ). All of these depletions are just below −1.5σ, so these are not so conspicuous when compared with the anomaly in Chiba-shi in Figure 2a. However, we can recognize a prolonged period of depletion over three successive days, 24, 25, and 26, after the anomalous day of 23 September.

Asahi Station

Figure 2c illustrates the temporal (daily) variation of milk yield of 78 Holstein dairy cows at the station in Asahi, with the notations exactly the same as previous figures. The epicentral distance for this station is D = 57 km. Even though we can notice a tendency of decrease in cows’ milk yield on 4 October (a few days before the EQ), the observed milk yield remained within our criterion of plus/minus 1.5σ, and we can conclude that no anomaly is observed at this station.

Minami-Boso Station

Figure 2d illustrates the temporal evolution of milk yields of 41 Holstein dairy cows at Minami-boso station (D = 63 km). Generally, it is found that the daily cows’ milk yield remains within the blue zone (±1.5σ), but we notice anomalous changes in the milk yield; that is, around 26 September (or 11 days before the EQ) we find a significant depletion in cows’ milk yield exceeding −2σ, and a slight depletion below the −1.5σ level on 29 September (8 days before the EQ). These are likely to be clear depletions in the milk yield. Also, we happen to notice an enhancement in milk yield on 10 and 11 September, but we wonder whether this is unusual or not because the anomaly typically appears as a depletion.

5.2.3. Summary of the Observed Results on Cows’ Milk Yield

Four monitoring stations of daily cows’ milk yield have been investigated: (1) Chiba-shi station (D = 15 km), (2) Isumi station (D = 48 km), (3) Asahi station (D = 57 km), and (4) Minami-boso station (D = 63 km), in the order of small to large epicentral distance of the stations from the EQ epicenter of the Tokyo EQ on 7 October 2021.
Changes of cows’ milk yield have been detected with the use of conventional statistical analysis based on our criterion. We can summarize the results as follows:
(a)
Conspicuous changes in daily cows’ milk yields at the closest station of Chiba-shi with D = 15 km were observed. At this station, a very clear depletion in cows’ milk yield was detected during a period of a few days from 26 September to 28 September. The maximum depletion was observed on 27 September (or 9 days before the EQ), and the degree of depletion was the greatest, exceeding −3σ, definitely because of the shortest distance from the EQ epicenter.
(b)
On the other hand, the behavior of cows’ milk yields at farther stations of Isumi (D = 43 km), Asahi (D = 57 km), and Minami-boso (D = 63 km) looks very variable, in such a way that Minami-boso, at the farthest distance, yielded a significant anomaly and Isumi exhibited a small anomaly exceeding −1.5σ, with no effect at Asahi.
(c)
The lead times for some stations are found to be 8–14 days, which seems to be very consistent with our previous statistical work [52,53,56].
(d)
Multi-stationed monitoring of cows’ milk yields is of crucial importance as a means of short-term EQ prediction.

5.2.4. Sensory Mechanism of Animals Based on a Comparison of Anomalies of Cows’ Milk Yield with Electromagnetic Anomalies for This EQ

This target Tokyo EQ has already been extensively investigated by using multi-parameter electromagnetic observations [46]. As is described in Hayakawa et al. (2022) [46], the people in the Tokyo district are afraid of the next huge EQ since the 1923 Kanto (Tokyo) devastating EQ, so we wanted to explore whether there exists any EQ precursors to a modest EQ (M = 5.9) like this Tokyo EQ on 7 October 2021. Here we will review their multi-parameter observations for this Tokyo EQ, which include various physical parameters for the three layers of the Earth, such as (i) lithospheric ULF radiation (lithospheric parameter), (ii) lower atmospheric parameters (meteorological parameters (T (temperature)/Hum (humidity), ACP (atmospheric chemical potential), and ULF/ELF atmospheric radio emissions), (iii) stratospheric AGW (atmospheric gravity waves), (iv) lower ionospheric perturbations (studied by two independent phenomena of ULF depression and subionospheric VLF/LF propagation anomalies), and (v) upper ionospheric parameter (NmF2 (ionosonde) and GPS (Global Positioning Satellites) TEC (total electron content)). These electromagnetic phenomena will be investigated with special reference to their comparisons with those of cow’s milk yield changes in order to better understand the sensory mechanism of animals.
Figure 3 is depicted as a graph, based on the summary table in Hayakawa et al. (2022) [46]. We have also included the anomalies of dairy cows’ milk yield for the sake of comparison in the top part of Figure 3. Here we will compare the temporal variations of cows’ milk yields with those of different electromagnetic effects, starting from the upper atmosphere to the lowest region of lithosphere.
As seen from Figure 3, it is found that the ionosphere is not perturbed in the upper F region, but only the lower ionosphere is perturbed just before the EQ when using two independent methods of subionospheric VLF propagation anomalies and ULF depression effect, and only just around the EQ (with VLF subionospheric propagation anomalies), i.e., a few days before and after the EQ. A subionospheric VLF anomaly is observed in Chofu (Tokyo) with the signals from the Australian NWC (North West Cape) transmitter as the reliable evidence of lower ionospheric perturbation. However, we have to add a few words on the ULF depression as observed at Kakioka ULF observatory (36.23° N, 140.18° E). The epicentral distance of this Kakioka station is marginal (just around the threshold) to detect ULF lithospheric radiation, but the covering area with the ULF depression effect is much larger than that for ULF lithospheric radiation as summarized in Schekotov et al. (2013) [57] and Hayakawa et al. (2023) [58], so the ULF depression observed at Kakioka is a strong signature of the lower ionospheric perturbation. When discussing the seismogenic ionospheric perturbation, we should be very cautious about the space weather conditions (e.g., geomagnetic activity) around the EQ date, and it was already confirmed in Hayakawa et al. (2022) [46] that the geomagnetic activity before the EQ was rather quiet for about a month before the EQ. Even though the ionospheric perturbations are definitely generated by the pre-EQ agent in the lithosphere through a few possible channels (e.g., [3,6,7]), a significant difference in the temporal evolutions of both ionospheric perturbation and cows’ milk yield changes suggests that our unusual animal behavior has nothing to do with the ionospheric perturbation.
Next, we compare the anomalies of cows’ milk yield with atmospheric physical parameters: (i) atmospheric meteorological parameters (T (temperature)/Hum (humidity) and ACP (atmospheric chemical potential)) or (ii) ULF/ELF atmospheric radio emissions. The meteorological parameters (T/Hum and ACP) are found to be disturbed over a prolonged period as in Figure 3, but Hayakawa et al. (2022) [46] have described that a noticeable correlation with the EQ is observed at the Mishima and Kofu stations (both over 100 km west of Tokyo) with neighboring faults. However, the Tokyo meteorological station (close to the EQ epicenter) exhibited no clear anomaly probably because of the many environmental noises in the urban regions. Further, the heat stress due to the temperature and humidity effects has already been discriminated in the analysis of cows’ milk yield, as mentioned before. So, this meteorological effect is not related to the unusual animal behavior. Then, we move on to the next possibility of atmospheric ULF/ELF radio emissions. In Hayakawa et al. (2022) [46], the observation of atmospheric ULF/ELF radio emissions was carried out at Nakatsugawa (near Nagoya), but unfortunately, the analysis period started only on 30 September 2021 (only one week before the EQ). The observational result showed that this ULF/ELF radiation is found to be persistently observed for a long period as in Figure 3, so that when taking account of the fact that such ULF/ELF atmospheric radio emissions are observed on the very first day of our analysis (30 September), we can speculate that this ULF/ELF atmospheric radiation must be present even a few days before the start of our analysis period (as indicated by ??? in the figure). So, the prolonged depletion of cows’ milk yields at Chiba-shi (26–28 September) is likely to overlap with the initial phase of the generation of atmospheric impulsive ULF/ELF radiation. The direction of this ULF/ELF radiation found at Nakatsugawa indicated that the azimuthal direction of the observed ULF/ELF radiation coincides with the direction of the EQ epicenter. Hence, we can suppose that ULF/ELF radio emissions are definitely generated in and around the EQ epicenter (close to the station of Chiba-shi), and the cows there are likely to respond to these ULF/ELF radio emissions very much, leading to the strongest anomaly as shown by depletion in milk yields in Chiba-shi with the most intense ULF/ELF amplitudes, together with some anomalies at the other stations of Minami-boso and Isumi (with less ULF/ELF intensity due to the distance effect from the EQ epicenter). Hence, we suggest that ULF/ELF atmospheric radiation is plausible as a sensory mechanism of cows.
Finally, we move on to the discussion of ULF lithospheric radiation (e.g., [58,59,60]). In Figure 3 we have indicated no anomaly for lithospheric ULF radiation at Kakioka, but we need some comments on this. The distance between the Kakioka observatory and the EQ epicenter is about 80 km, so we need to estimate the detectability threshold (or critical) distance for the ULF detection. The threshold distance for this EQ magnitude (M = 5.9) is estimated with the empirical formula by Hayakawa et al. (2023) [58] to be D = 56 km, so no anomaly at Kakioka does not mean that ULF radiation is not generated in and around the EQ epicenter. We had established our own ULF/ELF station at Asahi (not exactly at the same place as the cows’ monitoring station in previous sections, but very close to it) [61] (D = 57 km) closer to the EQ epicenter than Kakioka, but the ULF data there was not available during the period of interest because of a malfunction of the equipment. In fact, we had already performed a similar study for another Kobe EQ on 12 April 2013 (not the famous 1995 Kobe EQ) with M~6 (nearly the same EQ magnitude as this Tokyo EQ), and based on the comparison of the cows’ milk yield anomaly with different electromagnetic phenomena, we found that ULF lithospheric radiation is the most likely driver for this Kobe EQ [56] (but in this study, we had no data for atmospheric ULF/ELF radiation). Further, a recent paper by Panagopoulos et al. (2020) [62] has suggested DC/ULF radiation as a sensory mechanism. Finally, we can say that the connection of ULF lithospheric radiation with anomalous changes in cows’ milk yields, remain pending in this paper.

6. Conclusions

We have reviewed the general stream of short-term EQ prediction, and it is recently agreed that the most promising candidate for short-term EQ prediction is electromagnetic effects. These seismogenic effects appear not only in the lithosphere, but also in the atmosphere and ionosphere. On the other hand, unusual animal behavior is not well accepted in the academic society as a possible EQ predictor, probably because the mechanism is not well understood, even though it has been investigated for a long time. We paid the greatest attention to the studies of dairy cows’ milk yield changes as one particular aspect of unusual animal behavior in possible association with EQs. Finally, we come to the conclusion that this kind of unusual cows’ milk yield changes (or depletions) have a high potential as a plausible candidate for short-term EQ prediction, being a very useful supplement to the most promising electromagnetic precursors. This conclusion is strongly supported by our own case study (as an example) for the 2021 Tokyo EQ, in which conspicuous anomalies of depletions of cows’ milk yields are observed at a monitoring station close to the EQ epicenter. Furthermore, we have tried to understand the sensory mechanism of the cows’ behavioral anomaly by comparing the anomalies in cows’ milk yields with the temporal evolutions of various electromagnetic effects as already reported in Hayakawa et al. (2022) [46]. It is likely that the most promising candidate as a sensory mechanism of changes in cows’ milk yields is ULF/ELF atmospheric electromagnetic radiation (probably lightning-like) [58], suggesting a strong link between unusual animal behavior and electromagnetic effects.

Author Contributions

Conceptualization, M.H., writing—original draft preparation, M.H., methodology, H.Y., writing—review and editing, M.H. and H.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to thank Orion Machinery Co., Ltd. (Nagano, Japan) for their assistance in milking data collection.

Conflicts of Interest

Author Masashi Hayakawa was employed by the company Hayakawa Institute of Seismo Electromagnetics Co., Ltd. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Uyeda, S.; Nagao, T.; Kamogawa, M. Short-Term Earthquake Prediction: Current Status of Seismo-Electromagnetics. Tectonophysics 2009, 470, 205–213. [Google Scholar] [CrossRef]
  2. Hayakawa, M.; Hobara, Y. Current Status of Seismo-Electromagnetics for Short-Term Earthquake Prediction. Geomat. Nat. Hazards Risk 2010, 1, 115–155. [Google Scholar] [CrossRef]
  3. Hayakawa, M. Earthquake Prediction with Radio Techniques; Wiley: Singapore, 2016; ISBN 978-1-118-77016-0. [Google Scholar]
  4. Hayakawa, M. Earthquake Precursor Studies in Japan. In Pre-Earthquake Processes: A Multidisciplinary Approach to Earthquake Prediction Studies; American Geophysical Union (AGU): Washington, DC, USA, 2018; pp. 7–18. ISBN 978-1-119-15694-9. [Google Scholar]
  5. Pulinets, S.; Boyarchuk, K. Ionospheric Precursors of Earthquakes; Springer: Berlin/Heidelberg, Germany, 2004; ISBN 978-3-540-20839-6. [Google Scholar]
  6. Molchanov, O.A.; Hayakawa, M. Seismo-Electromagnetics and Related Phenomena: History and Latest Results; Terrapub: Tokyo, Japan, 2008; ISBN 978-4-88704-143-1. [Google Scholar]
  7. Ouzounov, D.; Pulinets, S.; Hattori, K.; Taylor, P. Pre-Earthquake Processes: A Multidisciplinary Approach to Earthquake Prediction Studies; American Geophysical Union: Hoboken, NJ, USA, 2018; ISBN 978-1-119-15693-2. [Google Scholar]
  8. Sorokin, V.M.; Chmyrev, V.M.; Hayakawa, M. Electrodynamic Coupling of Lithosphere—Atmosphere—Ionosphere of the Earth; Nova Science Publishers: New York, NY, USA, 2015. [Google Scholar]
  9. Evernden, J. Abnormal Animal Behavior Prior to Earthquakes; U.S. Geological Survey Office of Earthquake Studies, U.S. Department of Commerce, National Technical Information Service: Alexandria, VA, USA, 1976.
  10. Buskirk, R.E.; Frohlich, C.; Latham, G.V. Unusual Animal Behavior before Earthquakes: A Review of Possible Sensory Mechanisms. Rev. Geophys. 1981, 19, 247–270. [Google Scholar] [CrossRef]
  11. Tributsch, H. When the Snakes Awake: Animals and Earthquake Prediction; MIT Press: Cambridge, MA, USA, 1984; ISBN 978-0-262-70025-2. [Google Scholar]
  12. Kirschvink, J.L. Earthquake Prediction by Animals: Evolution and Sensory Perception. Bull. Seismol. Soc. Am. 2000, 90, 312–323. [Google Scholar] [CrossRef]
  13. Bhargava, N.; Katiyar, V.; Sharma, M.; Pradhan, P. Earthquake Prediction through Animal Behavior: A Review. Indian J. Biomech. Spec. Issue 2009, 7–8. [Google Scholar]
  14. Rikitake, T. Biosystem Behaviour as an Earthquake Precursor. Tectonophysics 1978, 51, 1–20. [Google Scholar] [CrossRef]
  15. Skidmore, W.D.; Baum, S.J. Biological Effects in Rodents Exposed to 108 Pulses of Electromagnetic Radiation. Health Phys. 1974, 26, 391–398. [Google Scholar] [CrossRef] [PubMed]
  16. Gawthrop, W.H.; Johnson, R.; Haberman, R.E.; Wyss, M. Preliminary Experiments on the Behavior of Mice before Rock Fracture in the Laboratory. In Abnormal Animal Behavior Prior to Earthquakes, I; U.S. Department of Commerce, National Technical Information Service: Alexandria, VA, USA, 1976; pp. 205–212. [Google Scholar]
  17. Mulilis, J.; White, M. Behaviors of the Catfish Corydoras Aeneus for Use in Earthquake Prediction. Earthq. Predict. Res. 1986, 4, 47–67. [Google Scholar]
  18. Rikitake, T. Nature of Macro-Anomaly Precursory to an Earthquake. J. Phys. Earth 1994, 42, 149–163. [Google Scholar] [CrossRef]
  19. Asano, M. Catfish Can Sense Electricity. Earthq. J. 1998, 26, 52–59. [Google Scholar]
  20. Yamanaka, C.; Asahara, H.; Matsumoto, H.; Ikeya, M. Wideband Environmental Electromagnetic Wave Observation Searching for Seismo-Electromagnetic Signals and Simultaneous Observation of Catfish Behavior -The Cases for the Western Tottori and the Geiyo Earthquakes-. J. Atmos. Electr. 2002, 22, 277–290. [Google Scholar] [CrossRef]
  21. Rikitake, T. Predictions and Precursors of Major Earthquakes: The Science of Macro-Anomaly Precursory to an Earthquake; Terra Scientific Publishing Company: Tokyo, Japan, 2001. [Google Scholar]
  22. Hayakawa, M. Possible Electromagnetic Effects on Abnormal Animal Behavior Before an Earthquake. Animals 2013, 3, 19–32. [Google Scholar] [CrossRef]
  23. Yokoi, S.; Ikeya, M.; Yagi, T.; Nagai, K. Mouse Circadian Rhythm before the Kobe Earthquake in 1995. Bioelectromagnetics 2003, 24, 289–291. [Google Scholar] [CrossRef] [PubMed]
  24. Ikeya, M. Earthquakes and Animals: From Folk Legends to Science; World Scientific Pub Co., Inc.: Hackensack, NJ, USA, 2004; ISBN 978-981-238-591-8. [Google Scholar]
  25. Li, Y.; Liu, Y.; Jiang, Z.; Guan, J.; Yi, G.; Cheng, S.; Yang, B.; Fu, T.; Wang, Z. Behavioral Change Related to Wenchuan Devastating Earthquake in Mice. Bioelectromagnetics 2009, 30, 613–620. [Google Scholar] [CrossRef]
  26. Grant, R.A.; Halliday, T. Predicting the Unpredictable; Evidence of Pre-Seismic Anticipatory Behaviour in the Common Toad. J. Zool. 2010, 281, 263–271. [Google Scholar] [CrossRef]
  27. Fidani, C. Biological Anomalies around the 2009 L’Aquila Earthquake. Animals 2013, 3, 693–721. [Google Scholar] [CrossRef]
  28. Wikelski, M.; Mueller, U.; Scocco, P.; Catorci, A.; Desinov, L.V.; Belyaev, M.Y.; Keim, D.; Pohlmeier, W.; Fechteler, G.; Martin Mai, P. Potential Short-Term Earthquake Forecasting by Farm Animal Monitoring. Ethology 2020, 126, 931–941. [Google Scholar] [CrossRef]
  29. Nishimura, T.; Okano, H.; Tada, H.; Nishimura, E.; Sugimoto, K.; Mohri, K.; Fukushima, M. Lizards Respond to an Extremely Low-Frequency Electromagnetic Field. J. Exp. Biol. 2010, 213, 1985–1990. [Google Scholar] [CrossRef]
  30. Sorokin, V.M.; Chmyrev, V.M.; Hayakawa, M. A Review on Electrodynamic Influence of Atmospheric Processes to the Ionosphere. Open J. Earthq. Res. 2020, 9, 113–141. [Google Scholar] [CrossRef]
  31. Molchanov, O.; Fedorov, E.; Schekotov, A.; Gordeev, E.; Chebrov, V.; Surkov, V.; Rozhnoi, A.; Andreevsky, S.; Iudin, D.; Yunga, S.; et al. Lithosphere-Atmosphere-Ionosphere Coupling as Governing Mechanism for Preseismic Short-Term Events in Atmosphere and Ionosphere. Nat. Hazards Earth Syst. Sci. 2004, 4, 757–767. [Google Scholar] [CrossRef]
  32. Pulinets, S.; Ouzounov, D. Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) Model—A Unified Concept for Earthquake Precursors Validation. J. Asian Earth Sci. 2011, 41, 371–382. [Google Scholar] [CrossRef]
  33. Freund, F. Stress-Activated Positive Hole Charge Carriers in Rocks and the Generation of Pre-Earthquake Signals. In Electromagnetic Phenomena Associated with Earthquakes; Hayakawa, M., Ed.; Transworld Research Network: Trivandrum, India, 2009; pp. 41–96. [Google Scholar]
  34. Hayakawa, M.; Asano, T.; Rozhnoi, A.; Solovieva, M. Very-Low- to Low-Frequency Sounding of Ionospheric Perturbations and Possible Association with Earthquakes. In Pre-Earthquake Processes: A Multidisciplinary Approach to Earthquake Prediction Studies; American Geophysical Union (AGU): Washington, DC, USA, 2018; pp. 275–304. ISBN 978-1-119-15694-9. [Google Scholar]
  35. Li, M.; Shen, X.; Parrot, M.; Zhang, X.; Zhang, Y.; Yu, C.; Yan, R.; Liu, D.; Lu, H.; Guo, F.; et al. Primary Joint Statistical Seismic Influence on Ionospheric Parameters Recorded by the CSES and DEMETER Satellites. J. Geophys. Res. Space Phys. 2020, 125, e2020JA028116. [Google Scholar] [CrossRef]
  36. Akhoondzadeh, M.; De Santis, A.; Marchetti, D.; Piscini, A.; Cianchini, G. Multi Precursors Analysis Associated with the Powerful Ecuador (MW = 7.8) Earthquake of 16 April 2016 Using Swarm Satellites Data in Conjunction with Other Multi-Platform Satellite and Ground Data. Adv. Space Res. 2018, 61, 248–263. [Google Scholar] [CrossRef]
  37. De Santis, A.; Marchetti, D.; Pavón-Carrasco, F.J.; Cianchini, G.; Perrone, L.; Abbattista, C.; Alfonsi, L.; Amoruso, L.; Campuzano, S.A.; Carbone, M.; et al. Precursory Worldwide Signatures of Earthquake Occurrences on Swarm Satellite Data. Sci. Rep. 2019, 9, 20287. [Google Scholar] [CrossRef] [PubMed]
  38. Freund, F.; Mignan, A.; Ouillon, G.; Sornette, D. The Global Earthquake Forecasting System: Towards Using Non-Seismic Precursors for the Prediction of Large Earthquakes. Eur. Phys. J. Spec. Top. 2021, 230, 1–490. [Google Scholar]
  39. Liu, J.-Y.T.; Shen, X.; Chang, F.-Y.; Chen, Y.-I.; Sun, Y.-Y.; Chen, C.-H.; Pulinets, S.; Hattori, K.; Ouzounov, D.; Tramutoli, V.; et al. Spatial Analyses on Pre-Earthquake Ionospheric Anomalies and Magnetic Storms Observed by China Seismo-Electromagnetic Satellite in August 2018. Geosci. Lett. 2024, 11, 4. [Google Scholar] [CrossRef]
  40. Ouzounov, D.; Pulinets, S.; Davidenko, D.; Rozhnoi, A.; Solovieva, M.; Fedun, V.; Dwivedi, B.N.; Rybin, A.; Kafatos, M.; Taylor, P. Transient Effects in Atmosphere and Ionosphere Preceding the 2015 M7.8 and M7.3 Gorkha–Nepal Earthquakes. Front. Earth Sci. 2021, 9, 757358. [Google Scholar] [CrossRef]
  41. De Santis, A.; Cianchini, G.; Marchetti, D.; Piscini, A.; Sabbagh, D.; Perrone, L.; Campuzano, S.A.; Inan, S. A Multiparametric Approach to Study the Preparation Phase of the 2019 M7.1 Ridgecrest (California, United States) Earthquake. Front. Earth Sci. 2020, 8, 540398. [Google Scholar] [CrossRef]
  42. Sasmal, S.; Chowdhury, S.; Kundu, S.; Politis, D.Z.; Potirakis, S.M.; Balasis, G.; Hayakawa, M.; Chakrabarti, S.K. Pre-Seismic Irregularities during the 2020 Samos (Greece) Earthquake (M = 6.9) as Investigated from Multi-Parameter Approach by Ground and Space-Based Techniques. Atmosphere 2021, 12, 1059. [Google Scholar] [CrossRef]
  43. Marchetti, D.; De Santis, A.; Shen, X.; Campuzano, S.A.; Perrone, L.; Piscini, A.; Di Giovambattista, R.; Jin, S.; Ippolito, A.; Cianchini, G.; et al. Possible Lithosphere-Atmosphere-Ionosphere Coupling Effects Prior to the 2018 Mw = 7.5 Indonesia Earthquake from Seismic, Atmospheric and Ionospheric Data. J. Asian Earth Sci. 2020, 188, 104097. [Google Scholar] [CrossRef]
  44. Parrot, M.; Tramutoli, V.; Liu, T.J.Y.; Pulinets, S.; Ouzounov, D.; Genzano, N.; Lisi, M.; Hattori, K.; Namgaladze, A. Atmospheric and Ionospheric Coupling Phenomena Associated with Large Earthquakes. Eur. Phys. J. Spec. Top. 2021, 230, 197–225. [Google Scholar] [CrossRef]
  45. Hayakawa, M.; Izutsu, J.; Schekotov, A.; Yang, S.-S.; Solovieva, M.; Budilova, E. Lithosphere–Atmosphere–Ionosphere Coupling Effects Based on Multiparameter Precursor Observations for February–March 2021 Earthquakes (M~7) in the Offshore of Tohoku Area of Japan. Geosciences 2021, 11, 481. [Google Scholar] [CrossRef]
  46. Hayakawa, M.; Schekotov, A.; Izutsu, J.; Yang, S.-S.; Solovieva, M.; Hobara, Y. Multi-Parameter Observations of Seismogenic Phenomena Related to the Tokyo Earthquake (M = 5.9) on 7 October 2021. Geosciences 2022, 12, 265. [Google Scholar] [CrossRef]
  47. D’Arcangelo, S.; Regi, M.; De Santis, A.; Perrone, L.; Cianchini, G.; Soldani, M.; Piscini, A.; Fidani, C.; Sabbagh, D.; Lepidi, S.; et al. A Multiparametric-Multilayer Comparison of the Preparation Phase of Two Geophysical Events in the Tonga-Kermadec Subduction Zone: The 2019 M7.2 Kermadec Earthquake and 2022 Hunga Ha’apai Eruption. Front. Earth Sci. 2023, 11, 1267411. [Google Scholar] [CrossRef]
  48. Qidong, D.; Pu, J.; Jones, L.M.; Molnar, P. A Preliminary Analysis of Reported Changes in Ground Water and Anom alous Animal Behavior Before the 4 February 1975 Haicheng Earthquake. In Earthquake Prediction; American Geophysical Union (AGU): Washington, DC, USA, 1981; pp. 543–565. ISBN 978-1-118-66574-9. [Google Scholar]
  49. Nikonov, A.A. Abnormal Animal Behaviour as a Precursor of the 7 December 1988 Spitak, Armenia, Earthquake. Nat. Hazards 1992, 6, 1–10. [Google Scholar] [CrossRef]
  50. Rushen, J.; de Passillé, A.M.B.; Munksgaard, L. Fear of People by Cows and Effects on Milk Yield, Behavior, and Heart Rate at Milking1. J. Dairy Sci. 1999, 82, 720–727. [Google Scholar] [CrossRef]
  51. Rushen, J.; Munksgaard, L.; Marnet, P.G.; DePassillé, A.M. Human Contact and the Effects of Acute Stress on Cows at Milking. Appl. Anim. Behav. Sci. 2001, 73, 1–14. [Google Scholar] [CrossRef]
  52. Yamauchi, H.; Uchiyama, H.; Ohtani, N.; Ohta, M. Unusual Animal Behavior Preceding the 2011 Earthquake off the Pacific Coast of Tohoku, Japan: A Way to Predict the Approach of Large Earthquakes. Animals 2014, 4, 131–145. [Google Scholar] [CrossRef]
  53. Yamauchi, H.; Hayakawa, M.; Asano, T.; Ohtani, N.; Ohta, M. Statistical Evaluations of Variations in Dairy Cows’ Milk Yields as a Precursor of Earthquakes. Animals 2017, 7, 19. [Google Scholar] [CrossRef]
  54. Hayakawa, M.; Asano, T.; Schekotov, A.; Yamauchi, H. A Study on the Correlation of Milk Yield of Cows with Seismicity and ULF Magnetic Field Variations. Open J. Earthq. Res. 2016, 5, 206–218. [Google Scholar] [CrossRef]
  55. Woith, H.; Petersen, G.M.; Hainzl, S.; Dahm, T. Review: Can Animals Predict Earthquakes? Bull. Seismol. Soc. Am. 2018, 108, 1031–1045. [Google Scholar] [CrossRef]
  56. Hayakawa, M.; Yamauchi, H.; Ohtani, N.; Ohta, M.; Tosa, S.; Asano, T.; Schekotov, A.; Izutsu, J.; Potirakis, S.M.; Eftaxias, K. On the Precursory Abnormal Animal Behavior and Electromagnetic Effects for the Kobe Earthquake (M~6) on April 12, 2013. Open J. Earthq. Res. 2016, 5, 165–171. [Google Scholar] [CrossRef]
  57. Schekotov, A.; Fedorov, E.; Molchanov, O.A.; Hayakawa, M. Low Frequency Electromagnetic Precursors as a Prospect for Earthquake Prediction. Earthq. Predict. Stud. Seismo Electromagn. 2013, 81–99. [Google Scholar]
  58. Hayakawa, M.; Schekotov, A.; Izutsu, J.; Nickolaenko, A.P.; Hobara, Y. Seismogenic ULF/ELF Wave Phenomena: Recent Advances and Future Perspectives. Open J. Earthq. Res. 2023, 12, 45–113. [Google Scholar] [CrossRef]
  59. Hattori, K. ULF Geomagnetic Changes Associated with Major Earthquakes. In Earthquake Prediction Studies Seismo Electromagnetics; Hayakawa, M., Ed.; Terrapub: Tokyo, Japan, 2013; pp. 129–152. [Google Scholar]
  60. Hayakawa, M.; Hattori, K.; Ohta, K. Monitoring of ULF (Ultra-Low-Frequency) Geomagnetic Variations Associated with Earthquakes. Sensors 2007, 7, 1108–1122. [Google Scholar] [CrossRef]
  61. Hayakawa, M.; Schekotov, A.; Yamaguchi, H.; Hobara, Y. Observation of Ultra-Low-Frequency Wave Effects in Possible Association with the Fukushima Earthquake on 21 November 2016, and Lithosphere–Atmosphere–Ionosphere Coupling. Atmosphere 2023, 14, 1255. [Google Scholar] [CrossRef]
  62. Panagopoulos, D.J.; Balmori, A.; Chrousos, G.P. On the Biophysical Mechanism of Sensing Upcoming Earthquakes by Animals. Sci. Total Environ. 2020, 717, 136989. [Google Scholar] [CrossRef]
Figure 1. Locations of the epicenter of the 7 October 2021 Tokyo EQ (indicated by EQ), and four monitoring stations of dairy cows’ milk yields in the Chiba Prefecture (indicated by red circles) (Chiba-shi (city), Asahi, Isumi, and Minami-boso stations).
Figure 1. Locations of the epicenter of the 7 October 2021 Tokyo EQ (indicated by EQ), and four monitoring stations of dairy cows’ milk yields in the Chiba Prefecture (indicated by red circles) (Chiba-shi (city), Asahi, Isumi, and Minami-boso stations).
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Figure 2. (a) Temporal (daily) evolution of cows’ milk yields (denoted by red curve) around the mean (zero level) at the station in Chiba-shi with the shortest distance from the EQ epicenter (D = 15 km). The vertical bar for each day is the error bar. The blue area indicates the normal condition within our criterion of plus/minus 1.5σ (σ is the standard deviation), and we define an anomaly outside this blue region, but mainly exceeding −1.5σ. The EQ day is indicated by a vertical red broken line, and the analysis period is one month before and one week after the EQ. (b) Same as (a), but for Isumi (D = 48 km), (c) Asahi (D = 57 km), and (d) Minami-boso (D = 63 km).
Figure 2. (a) Temporal (daily) evolution of cows’ milk yields (denoted by red curve) around the mean (zero level) at the station in Chiba-shi with the shortest distance from the EQ epicenter (D = 15 km). The vertical bar for each day is the error bar. The blue area indicates the normal condition within our criterion of plus/minus 1.5σ (σ is the standard deviation), and we define an anomaly outside this blue region, but mainly exceeding −1.5σ. The EQ day is indicated by a vertical red broken line, and the analysis period is one month before and one week after the EQ. (b) Same as (a), but for Isumi (D = 48 km), (c) Asahi (D = 57 km), and (d) Minami-boso (D = 63 km).
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Figure 3. Summary of temporal evolutions of cows’ milk yields (top panel), together with other various electromagnetic anomalies (from Hayakawa et al. (2022) [46]). From the top to the bottom, lithospheric, atmospheric, and ionospheric effects. Blue horizontal lines with arrows at both ends and blue dots (when an anomaly appears on one day) refer to anomalies in relevant physical parameters.
Figure 3. Summary of temporal evolutions of cows’ milk yields (top panel), together with other various electromagnetic anomalies (from Hayakawa et al. (2022) [46]). From the top to the bottom, lithospheric, atmospheric, and ionospheric effects. Blue horizontal lines with arrows at both ends and blue dots (when an anomaly appears on one day) refer to anomalies in relevant physical parameters.
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Hayakawa, M.; Yamauchi, H. Unusual Animal Behavior as a Possible Candidate of Earthquake Prediction. Appl. Sci. 2024, 14, 4317. https://doi.org/10.3390/app14104317

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Hayakawa M, Yamauchi H. Unusual Animal Behavior as a Possible Candidate of Earthquake Prediction. Applied Sciences. 2024; 14(10):4317. https://doi.org/10.3390/app14104317

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Hayakawa, Masashi, and Hiroyuki Yamauchi. 2024. "Unusual Animal Behavior as a Possible Candidate of Earthquake Prediction" Applied Sciences 14, no. 10: 4317. https://doi.org/10.3390/app14104317

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