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Tropical Lake Sentani in the Indonesian Province Papua consists of four separate basins and is surrounded by a catchment with a very diverse geology. We characterized the surface sediment (upper 5 cm) of the lake's four sub-basins based on multivariate statistical analyses (principal component analysis, hierarchical clustering) of major element compositions obtained by X-ray fluorescence scanning. Three types of sediment are identified based on distinct compositional differences between rivers, shallow/proximal and deep/distal lake sediments. The different sediment types are mainly characterized by the correlation of elements associated with redox processes (S, Mn, Fe), carbonates (Ca), and detrital input (Ti, Al, Si, K) derived by river discharge. The relatively coarse-grained river sediments mainly derive form the mafic catchment geology and contribution of the limestone catchment geology is only limited. Correlation of redox sensitive and detrital elements are used to reveal oxidation conditions, and indicate oxic conditions in river samples and reducing conditions for lake sediments. Organic carbon (TOC) generally correlates with redox sensitive elements, although a correlation between TOC and individual elements change strongly between the three sediment types. Pyrite is the quantitatively dominant reduced sulfur mineral, monosulfides only reach appreciable concentrations in samples from rivers draining mafic and ultramafic catchments. Our study shows large spatial heterogeneity within the lake's sub-basins that is mainly caused by catchment geology and topography, river runoff as well as the bathymetry and the depth of the oxycline. We show that knowledge about lateral heterogeneity is crucial for understanding the geochemical and sedimentological variations recorded by these sediments. The highly variable conditions make Lake Sentani a natural laboratory, with its different sub-basins representing different depositional environments under identical tropical climate conditions.
Based on an analysis of continuous monitoring of farm animal behavior in the region of the 2016 M6.6 Norcia earthquake in Italy, Wikelski et al., 2020; (Seismol Res Lett, 89, 2020, 1238) conclude that animal activity can be anticipated with subsequent seismic activity and that this finding might help to design a "short-term earthquake forecasting method." We show that this result is based on an incomplete analysis and misleading interpretations. Applying state-of-the-art methods of statistics, we demonstrate that the proposed anticipatory patterns cannot be distinguished from random patterns, and consequently, the observed anomalies in animal activity do not have any forecasting power.
Brownian motion and viscoelastic anomalous diffusion in homogeneous environments are intrinsically Gaussian processes. In a growing number of systems, however, non-Gaussian displacement distributions of these processes are being reported. The physical cause of the non-Gaussianity is typically seen in different forms of disorder. These include, for instance, imperfect "ensembles" of tracer particles, the presence of local variations of the tracer mobility in heteroegenous environments, or cases in which the speed or persistence of moving nematodes or cells are distributed. From a theoretical point of view stochastic descriptions based on distributed ("superstatistical") transport coefficients as well as time-dependent generalisations based on stochastic transport parameters with built-in finite correlation time are invoked. After a brief review of the history of Brownian motion and the famed Gaussian displacement distribution, we here provide a brief introduction to the phenomenon of non-Gaussianity and the stochastic modelling in terms of superstatistical and diffusing-diffusivity approaches.