Human-triggered magnification of erosion rates in European Alps since the Bronze Age.

A major feature of the Anthropocene is the drastic increase in global soil erosion. Soil erosion is threatening Earth habitability not only as soils are an essential component of the Earth system but also because societies depend on soils. However, proper quantification of the impact of human activities on erosion over thousands of years is still lacking. This is particularly crucial in mountainous areas, where the highest erosion rates are recorded. Here we use the Lake Bourget catchment, one of the largest in the European Alps, to estimate quantitatively the impact of human activities on erosion. Based on a multi-proxy, source-to-sink approach relying on isotopic geochemistry, we discriminate the effects of climate fluctuations from those of human activities on erosion over the last 10,000 years. We demonstrate that until 3800 years ago, climate is the only driver of erosion. From that time on, climate alone cannot explain the measured rates of erosion. Thanks to an unprecedented regional paleoenvironmental reconstruction, we highlight that the development of pastoralism at high altitudes from the Bronze Age onwards and the extension of agriculture starting in the Middle Ages were key factors in the drastic increase in erosion observed in the Alps.

Shifts in sediment bacterial communities reflect changes in depositional environments in a fluviatile context.

Sediments are complex heterogeneous matrices allowing to some extent the recording of past environmental conditions by integrating sediment characteristics, contamination and the microbial community assembly. In aquatic environments, abiotic environmental filtering is considered the primary deterministic mechanism shaping microbial communities in sediments. However, the number and relative contributions of geochemical and physical factors associated with biotic parameters (reservoir of microorganisms) complicate our understanding of community assembly dynamics. In this study, the sampling of a sedimentary archive in a site alternately subjected to contrasting inputs from the Eure and the Seine Rivers allowed us to study the response of microbial communities to changes in depositional environment over time. The coupling of the quantification and sequencing of the gene encoding the 16S rRNA with analyses of grain size, organic matter and major and trace metal contents demonstrated that microbial communities reflected contrasting sedimentary inputs over time. Total organic carbon (TOC) was the main factor influencing microbial biomass, while the quantity and quality of organic matter (R400, RC/TOC), major elements (i.e. Al, Fe, Ti) and trace metals (i.e. Zn, Pb, Cu, Cr, Ni, As, Co, Ag, Sb) shaped the structure of the microbial community. Besides the effect of geochemical factors, a specific microbial signature was associated with the contrasting sedimentary sources, highlighting the importance of the microbial reservoir in the assembly of microbial communities. Indeed, the main genera identified in the facies influenced by the Eure River were affiliated with the phyla Desulfobacterota (Syntrophus, Syntrophorhabdus, Smithella, Desulfatiglans), Firmicutes (Clostridium_sensu_stricto_1), Proteobacteria (Crenothrix), Verrucomicrobiota (Luteolibacter), while the contributions of the Seine River were characterised by some halophilic genera Salirhabdus (Firmicutes), Haliangium (Myxococcota) and SCGC-AB-539-J10 (Chloroflexi). This study sheds light on the overall processes determining the assembly of microbial communities in sediments and the importance of associating geochemical factors with reservoirs of microorganisms inherited from sediment sources.

Sedimentary structure discrimination with hyperspectral imaging in sediment cores.

Hyperspectral imaging (HSI) is a non-destructive, high-resolution imaging technique that is currently under significant development for analyzing geological areas with remote devices or natural samples in a laboratory. In both cases, the hyperspectral image provides several sedimentary structures that must be separated to temporally and spatially describe the sample. Sediment sequences are composed of successive deposits (strata, homogenite, flood) that are visible depending on sample properties. The classical methods to identify them are time-consuming, have a low spatial resolution (millimeters) and are generally based on naked-eye counting. In this study, we compare several supervised classification algorithms to discriminate sedimentological structures in lake sediments. Instantaneous events in lake sediments are generally linked to extreme geodynamical events (e.g., floods, earthquakes), so their identification and counting are essential to understand long-term fluctuations and improve hazard assessments. Identification and counting are done by reconstructing a chronicle of event layer occurrence, including estimation of deposit thicknesses. Here, we applied two hyperspectral imaging sensors (Visible Near-Infrared, VNIR, 60 µm, 400-1000 nm; Short Wave Infrared, SWIR, 200 µm, 1000-2500 nm) on three sediment cores from different lake systems. We highlight that the SWIR sensor is the optimal one for creating robust classification models with discriminant analyses (prediction accuracies of 0.87-0.98). Indeed, the VNIR sensor is impacted by the surface reliefs and structures that are not in the learning set, which causes mis-classification. These observations are also valid for the combined sensor (VNIR-SWIR) and the RGB images. Several spatial and spectral pre-processing were also compared and enabled one to highlight discriminant information specific to a sample and a sensor. These works show that the combined use of hyperspectral imaging and machine learning improves the characterization of sedimentary structures compared to conventional methods.

High-resolution prediction of organic matter concentration with hyperspectral imaging on a sediment core.

In the case of environmental samples, the use of a chemometrics-based prediction model is highly challenging because of the difficulty in experimentally creating a well-ranged reference sample set. In this study, we present a methodology using short wave infrared hyperspectral imaging to create a partial least squares regression model on a cored sediment sample. It was applied to a sediment core of the well-known Lake Bourget (Western Alps, France) to develop and validate a model for downcore high resolution LOI550 measurements used as a proxy of the organic matter. In lake and marine sediment, the organic matter content is widely used, for example, to reconstruct carbon flux variations through time. Organic matter analysis through routine analysis methods is time- and material-consuming, as well as not spatially resolved. A new instrument based on hyperspectral imaging allows high spatial and spectral resolutions to be acquired all along a sediment core. In this study, we obtain a model characterized by a 0.95 r prediction, with 0.77 wt% of model uncertainty based on 27 relevant wavelengths. The concentration map shows the variation inside each laminae and flood deposit. LOI550 reference values obtained with the loss on ignition are highly correlated to the inc/coh ratio used as a proxy of the organic matter in X-ray fluorescence with a correlation coefficient of 0.81. This ratio is also correlated with the averaged subsampled hyperspectral prediction with a r of 0.65.