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Fire is a potential significant driver of soil erosion in the Mediterranean area, as it results in the partial removal of vegetation and the alteration of organic matter, affecting soil structure and stability. The erosion of particle sizes is influenced by the intensity and duration of rainfall, regulated by disturbance regimes and vegetation cover. We hypothesize that, during the Holocene, high fire frequency and intensity under precipitation control may have contributed to soil erosion, while plant cover and composition could have mitigated it. This plant cover, in turn, might have been influenced by biomass burning. To test this hypothesis, we conducted a detailed analysis of sediments spanning the last 11,500 years from a small mountain lake, Corsica, situated in the black pine forest belt. The high temporal resolution analysis (∼10 years per sample), used granulometry and loss-on-ignition as proxies for erosion and ecosystem productivity in both the lake and watershed, and fire and plant macroremains as fire and tree cover proxies, respectively. The correlation between particle size and the contents of organic or mineral matter with mean fire return intervals (FRI) revealed significant patterns. Long fire intervals were associated with more fine and coarse sands, whereas short mean FRI positively correlated with clay and, fine and coarse silt, along with higher total mineral and organic matter contents. These findings suggest that fires were more frequent when rain duration was sustained but runoff intensity was lower. Conversely, wildfires were less frequent during intense runoff periods (wetter climate). Unlike fire frequency, fire severity did not correlate with erosion, and tree cover and plant richness had minimal to no effect. The 8.2 kyr event was characterized by runoff transporting primarily coarse sands, i.e. a dry period with very intense rains. This suggests that the system is primarily top-down controlled by climate. Multimillennial erosion trends are influenced by fire frequency and precipitation regimes, whereas vegetation does not seem to have a mitigating effect on this process.

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The emergence and selection of antibiotic resistance is a major public health problem worldwide. The presence of antibiotic-resistant bacteria (ARBs) in natural and anthropogenic environments threatens the sustainability of efforts to reduce resistance in human and animal populations. Here, we use mathematical modeling of the selective effect of antibiotics and contaminants on the dynamics of bacterial resistance in water to analyze longitudinal spatio-temporal data collected in hospital and urban wastewater between 2012 and 2015. Samples were collected monthly during the study period at four different sites in Haute-Savoie, France: hospital and urban wastewater, before and after water treatment plants. Three different categories of exposure variables were collected simultaneously: 1) heavy metals, 2) antibiotics and 3) surfactants for a total of 13 drugs/molecules; in parallel to the normalized abundance of 88 individual genes and mobile genetic elements, mostly conferring resistance to antibiotics. A simple hypothesis-driven model describing weekly antibiotic resistance gene (ARG) dynamics was proposed to fit the available data, assuming that normalized gene abundance is proportional to antibiotic resistant bacteria (ARB) populations in water. The detected compounds were found to influence the dynamics of 17 genes found at multiple sites. While mercury and vancomycin were associated with increased ARG and affected the dynamics of 10 and 12 identified genes respectively, surfactants antagonistically affected the dynamics of three genes. The models proposed here make it possible to analyze the relationship between the persistence of resistance genes in the aquatic environment and specific compounds associated with human activities from longitudinal data. Our analysis of French data over 2012–2015 identified mercury and vancomycin as co-selectors for some ARGs.

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With about 8000 marine benthic species, the amphipod crustaceans form one of the richest animal groups of the worldwide Ocean. They have colonized a wide range of soft-and hard-bottom natural and artificial habitats extending from the intertidal to hadal zones. Moreover, they show a broad size spectrum, with numerous giant species exceeding 20 cm in length and some species smaller than 2 mm. When biofouling artificial hard surfaces, some tube-building species can form very dense populations comprising up to 100,000 individuals per square meter. Amphipods are important prey for fish and mammals. Along with cephalopod juveniles, they are also included in the trophic diet of shorebirds that consume amphipods mostly during the low tide on tidal flats. They display diel migration, which reinforces the predation by demersal fish in the suprabenthic zone just above the sea bed, as well as by pelagic fish in the water column. Despite their importance in terms of biodiversity and trophic transfer, no general overview is available on the role of benthic amphipods in marine ecosystem food webs. Various methods, including laboratory and field experiments, as well as the analysis of stomach contents and DNA extraction, have been used to identify the prey/predator trophic links. Based on an extensive literature review, this study discusses the role of marine benthic amphipods as potential food for higher trophic levels in natural and artificial hard-bottom communities created via the construction of offshore wind farms.

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Assessing long-term changes in groundwater is crucial for understanding the impacts of climate change on aquifers and for managing water resources.However, long-term groundwater level (GWL) records are often scarce, limiting the understanding of historical trends and variability. In this paper, we present a deep learning approach to reconstruct GWLs up to several decades back in time using recurrent-based neural networks with wavelet pre-processing and climate reanalysis data as inputs. GWLs are reconstructed using two different reanalysis datasets with distinct spatial resolutions (ERA5: 0.25 • x 0.25 • & ERA20C: 1 • x 1 •) and monthly time resolution, and the performance of the simulations were evaluated. New insights: Long term GWL timeseries are now available for northern France, corresponding to extended versions of observational timeseries back to early 20th century. All three types of piezometric behaviours could be reconstructed reliably and consistently capture the multidecadal variability even at coarser resolutions, which is crucial for understanding long-term hydroclimatic trends and cycles. GWLs'multidecadal variability was consistent with the Atlantic multidecadal oscillation. From a synthetic experiment involving a modified long-term observational time series, we highlighted the need for longer training datasets for some lowfrequency signals. Nevertheless, our study demonstrated the potential of using DL models together with reanalysis data to extend GWL observations and improve our understanding of groundwater variability and climate interactions.

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Abstract Background Major advances over the past decade in molecular ecology are providing access to soil fungal diversity in forest ecosystems worldwide, but the diverse functions and metabolic capabilities of this microbial community remain largely elusive. We conducted a field survey in montane old-growth broadleaved and conifer forests, to investigate the relationship between soil fungal diversity and functional genetic traits. To assess the extent to which variation in community composition was associated with dominant tree species (oak, spruce, and fir) and environmental variations in the old-growth forests in the Jade Dragon Snow Mountain in Yunnan Province, we applied rDNA metabarcoding. We also assessed fungal gene expression in soil using mRNA sequencing and specifically assessed the expression of genes related to organic matter decomposition and nutrient acquisition in ectomycorrhizal and saprotrophic fungi. Results Our taxonomic profiling revealed striking shifts in the composition of the saprotrophic and ectomycorrhizal guilds among the oak-, fir-, and spruce-dominated forests. The core fungal microbiome comprised only ~ 20% of the total OTUs across all soil samples, although the overlap between conifer-associated communities was substantial. In contrast, seasonality and soil layer explained only a small proportion of the variation in community structure. However, despite their highly variable taxonomic composition, fungal guilds exhibited remarkably similar functional traits for growth-related and core metabolic pathways across forest associations, suggesting ecological redundancy. However, we found that the expression profiles of genes related to polysaccharide and protein degradation and nutrient transport notably varied between and within the fungal guilds, suggesting niche adaptation. Conclusions Overall, our metatranscriptomic analyses revealed the functional potential of soil fungal communities in montane old-growth forests, including a suite of specialized genes and taxa involved in organic matter decomposition. By linking genes to ecological traits, this study provides insights into fungal adaptation strategies to biotic and environmental factors, and sheds light on the importance of understanding functional gene expression patterns in predicting ecosystem functioning.

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