The [MnO|SiO2,Al2O3,FeO,MgO] balanced ratio (i.e. the isometric log-ratio of the MnO concentration relative to the concentration of SiO2, Al2O3, FeO and MgO) of chlorite and of whole-rock composition is an effective discriminant between Mesozoic stratigraphic formations in the Magallanes Basin (Chile). The MnO content in chlorite is only controlled by the host rock chemistry and is dependent on the geological environment. The MnO content in chlorite remains unchanged at low-grade metamorphic conditions. Single-grain chlorite analysis (n = 1042, electron microprobe) and whole-rock analysis (n = 40, X-ray fluorescence) were used to discriminate stratigraphic formations and to decipher differences in the depositional environment in the Magallanes Basin. The samples are from one Upper Jurassic and three Cretaceous sedimentary units that were affected either by low-grade regional metamorphism or by Miocene contact metamorphism. The highest [MnO|SiO2,Al2O3,FeO,MgO] values are recorded in the upper Zapata Formation. The Punta Barrosa, Cerro Toro and Tobífera Formations show slightly lower [MnO|SiO2,Al2O3,FeO,MgO] values. Elevated [MnO|SiO2,Al2O3,FeO,MgO] values at the transition between Zapata and Punta Barrosa Formations record an oxygenated shallow marine environment that can be linked to the closure of the Rocas Verdes Basin and the onset of fold-and-thrust belt formation. Decreasing [MnO|SiO2,Al2O3,FeO,MgO] values from the Punta Barrosa towards the Cerro Toro Formation indicate gradually increasing water depths during the Upper Cretaceous that correlate well with the global sea level.
The traditional study of palaeoseismic trenches, involving logging, stratigraphic and structural interpretation, can be time consuming and affected by
Abstract. Given the dimensions of the Amazon basin (7.5 million km2), its internal dynamics, increasing anthropogenic strain on this large biome, and its global role as one of two continental biospheric tipping elements, it appears crucial to have data-based knowledge on carbon and nitrogen concentrations and pools as well as on possible intra-annual dynamics. We quantified carbon (Ct, Corg), nitrogen (N) and sulfur (S) concentrations in litter (ORG) and mineral soil material (TOP 0–20 cm, BOT 30–50 cm) of upland (terra firme) oxisols across Amazonas state and present a first pool calculation. Data are based on triplicate seasonal sampling at 29 sites (forest and post-forest) within the binational project EcoRespira-Amazon (ERA). Repeated sampling increased data accuracy and allows for interpreting intra-annual (seasonal) and climate-change related dynamics. Extreme conditions between the dry season in 2016 and the subsequent wet season (ENSO-related) show differences more clearly. Median CNS in the Amazon basin TOP soils (Ct 1.9, Corg 1.6, N 0.15, S 0.03 wt-% under forest canopy) as well as Corg / N ratios show concentrations similar to European soils (FOREGS, GEMAS). TOP Ct concentrations ranged from 1.02 to 3.29 wt-% (medianForest 2.17 wt-%; medianPost-Forest 1.75 wt-%), N from 0.088 to 0.233 wt-% (medianForest 0.17 wt-%; medianPost-Forest 0.09 wt-%) and S from 0.012 to 0.051 wt.-% (medianForest 0.03 wt.-%; medianPost-Forest 0.02 wt-%). Corg / N ratios ranged from 6 to 14 (median 10). A first pool calculation (hectare-based) illustrates forest versus post-forest changes. The elements are unevenly distributed in the basin with generally higher CNS values in the central part (Amazonas graben) as compared to the southern part of the basin. Deforestation and drought conditions lead to C and N losses – within 50 years after deforestation, C and N losses average 10 to 15 %. Regional climate change with increased drought will likely speed up carbon and nitrogen losses.
In the geosciences it is still uncommon to include measurement uncertainties into statistical methods such as discriminant analysis, but, especially for trace elements, measurement uncertainties are frequently of relevant size. Uncertainties can be reported by each measured variable, by each observation or by individual cells (i.e., each observation has an individual uncertainty for each variable). Most methods incorporating uncertainties use the uncertainties as weights for the variables or observations of the data set. The method proposed in this contribution uses variance additivity properties and generalised least squares to calculate better estimates of group variances and group means, which then influence the decision rules of linear and quadratic discrimination algorithms. This methodological framework allows incorporation of cell-wise uncertainties, and would be largely valid if the information about co-dependency between variable errors within each observation were reported. The method is also appropriate for incorporating uncertainties into compositional data sets-for example, those formed by concentrations, proportions, percentages or any other form of information about the relative abundance of a set of components forming a whole-even if such uncertainties are nearly never reported considering this compositional nature. The methods are illustrated by means of case studies with simulated data.
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