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The surface deformation associated with the 2010 M-w 8.8 Maule earthquake in Chile was recorded in great detail before, during and after the event. The high data quality of the continuous GPS (cGPS) observations has facilitated a number of studies that model the postseismic deformation signal with a combination of relocking, afterslip and viscoelastic relaxation using linear rheology for the upper mantle. Here, we investigate the impact of using linear Maxwell or power-law rheology with a 2D geomechanical-numerical model to better understand the relative importance of the different processes that control the postseismic deformation signal. Our model results reveal that, in particular, the modeled cumulative vertical postseismic deformation pattern in the near field (< 300 km from the trench) is very sensitive to the location of maximum afterslip and choice of rheology. In the model with power-law rheology, the afterslip maximum is located at 20-35 km rather than > 50 km depth as suggested in previous studies. The explanation for this difference is that in the model with power-law rheology the relaxation of coseismically imposed differential stresses occurs mainly in the lower crust. However, even though the model with power-law rheology probably has more potential to explain the vertical postseismic signal in the near field, the uncertainty of the applied temperature field is substantial, and this needs further investigations and improvements.
Management of agricultural soil quality requires fast and cost-efficient methods to identify multiple stressors that can affect soil organisms and associated ecological processes. Here, we propose to use soil protists which have a great yet poorly explored potential for bioindication. They are ubiquitous, highly diverse, and respond to various stresses to agricultural soils caused by frequent management or environmental changes. We test an approach that combines metabarcoding data and machine learning algorithms to identify potential stressors of soil protist community composition and diversity. We measured 17 key variables that reflect various potential stresses on soil protists across 132 plots in 28 Swiss vineyards over 2 years. We identified the taxa showing strong responses to the selected soil variables (potential bioindicator taxa) and tested for their predictive power. Changes in protist taxa occurrence and, to a lesser extent, diversity metrics exhibited great predictive power for the considered soil variables. Soil copper concentration, moisture, pH, and basal respiration were the best predicted soil variables, suggesting that protists are particularly responsive to stresses caused by these variables. The most responsive taxa were found within the clades Rhizaria and Alveolata. Our results also reveal that a majority of the potential bioindicators identified in this study can be used across years, in different regions and across different grape varieties. Altogether, soil protist metabarcoding data combined with machine learning can help identifying specific abiotic stresses on microbial communities caused by agricultural management. Such an approach provides complementary information to existing soil monitoring tools that can help manage the impact of agricultural practices on soil biodiversity and quality.