The relationship between loggerhead turtle (<Emphasis Type="Italic">Caretta caretta</Emphasis>) movement patterns and Mediterranean currents

Previous studies have shown that loggerhead sea turtles (Caretta caretta), monitored by satellite telemetry, complete long-distance migration between the western and eastern Mediterranean basins following a seasonal pattern. This study investigated if these migration routes may be influenced by surface currents by superimposing the tracks of three loggerhead turtles (curved carapace length >55 cm), migrating from the western to the eastern Mediterranean basin, on Lagrangian data of current developed into pseudo-eulerian speed fields. The average travel speed of the turtles was 1.6 km h⁻¹ and did not depend on the current speed or direction. We observed a connection between surface currents and the turtles’ migration routes, although not a conclusive one. These observations show that neritic stage loggerhead turtles conduct migration in two distinct alternate phases: the first characterized by high and constant speed of travel both when swimming with or against currents and the second typified by low travel speeds and a good concurrence between the trailed routes and the course of the currents. These two phases corresponded to two types of movements, one where the turtle migrates actively to reach a specific destination (either neritic foraging, wintering or nesting ground) and the other, where the turtle drifts with the mesoscale current and forages pelagically. It seemed thus, that the influence of currents on a turtle’s movements depends on the turtle’s momentary behaviour and location of residence.     

Study of the contaminant transport into granite microfractures using nuclear ion beam techniques

Hydrated bentonite is a very plastic material and it is expected to enter in the rock microfractures at the granite/bentonite boundary of a deep geological high-level waste repository. This process is enhanced by the high swelling pressure of the clay. Since bentonite has a very good sorption capability for many radionuclides, the displacement of the clay might lead to a "clay-mediated" contaminant transport into the rock. The aim of this work is to study the contaminant transport into granite microfractures using nuclear ion beam techniques, and to determine to what extent the clay can favour it. To do so, bentonite previously doped with uranium, cesium and europium was put in contact with the surface of granite sheets. Granite sheets contacted with non-doped bentonite and with radionuclide solutions were also prepared as references. This allowed analysing the differences in the diffusion behaviour of the three systems: clay, radionuclides and clay plus radionuclides. A combination of Rutherford backscattering spectrometry (RBS) and other nuclear ion-beam techniques such as particle-induced X-ray emission (PIXE) and microPIXE was used to study the depth and lateral distribution of clay and contaminants inside granite. It was also tried to evaluate not only the diffusion depth and diffusion coefficients but also the different areas of the granite where the diffusants have a preferential access.     

Soil Water Content Estimated by Support Vector Machine for the Assessment of Shallow Landslides Triggering: the Role of Antecedent Meteorological Conditions

Soil water content is a key parameter for representing water dynamics in soils. Its prediction is fundamental for different practical applications, such as identifying shallow landslides triggering. Support vector machine (SVM) is a machine learning technique, which can be used to predict the temporal trend of a quantity since training from past data. SVM was applied to a test slope of Oltrepò Pavese (northern Italy), where meteorological parameters coupled with soil water content at different depths (0.2, 0.4, 0.6, 1.0, 1.2, 1.4 m) were measured. Two SVM models were developed for water content assessment: (i) model 1, considering rainfall amount, air temperature, air humidity, net solar radiation, and wind speed; (ii) model 2, considering the same predictors of model 1 together with antecedent condition parameters (cumulated rainfall of 7, 30, and 60 days; mean air temperature of 7, 30, and 60 days). SVM model 2 showed significantly higher satisfactory results than model 1, for both training and test phases and for all the considered soil levels. SVM models trends were implemented in a methodology of slope safety factor assessment. For a real event occurred in the tested slope, the triggering time was correctly predicted using data estimated by SVM model based on antecedent meteorological conditions. This confirms the necessity of including these predictors for building a SVM technique able to estimate correctly soil moisture dynamics in time. The results of this paper show a promising potential application of the SVM methodologies for modeling soil moisture required in slope stability analysis.