Whether changes in animal behavior allow for short-term earthquake predictions has been debated for a long time. Before, during and after the 2016/2017 earthquake sequence in Italy, we deployed bio-logging tags to continuously observe the activity of farm animals (cows, dogs, and sheep) close to the epicenter of the devastating magnitude M6.6 Norcia earthquake (Oct–Nov 2016) and over a subsequent longer observation period (Jan–Apr 2017). Relating 5,304 (in 2016) and 12,948 (in 2017) earthquakes with a wide magnitude range (0.4 ≤ M ≤ 6.6) to continuously measured animal activity, we detected how the animals collectively reacted to earthquakes. We also found consistent anticipatory activity prior to earthquakes during times when the animals were in a building (stable), but not during their time on a pasture. We detected these anticipatory patterns not only in periods with high, but also in periods of low seismic activity. Earthquake anticipation times (1–20 hr) are negatively correlated with the distance between the farm and earthquake hypocenters. Our study suggests that continuous bio-logging of animal collectives has the potential to provide statistically reliable patterns of pre-seismic activity that could yield valuable insights for short-term earthquake forecasting. Based on a priori model parameters, we provide empirical threshold values for pre-seismic animal activities to be used in real-time observation stations.
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): BAS/1/1339-01-01
Acknowledgements: We are indebted to the Angeli family for the help, support, and understanding despite their personal hardships. P.M.M. acknowledges L. Parisi and L. Lombardo (both at KAUST) for their help in processing the earthquake data collected by the INGV Rome (Istituto Nazionale di Geofisica and Volcanologica). This study was funded by the Max Planck Society and partially by King Abdullah University of Science and Technology (KAUST), BAS/1/1339-01-01. We also acknowledge funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's ExcellenceStrategy—EXC 2117—422037984. We thank all participants of the URAGAN—ICARUS collaboration in the Russian-German space partnership.