The impact of benthic microbial communities in sediment dispersion and bedform preservation: a view from the oldest microbially induced sedimentary structures in South America

2021 ◽  
Vol 52 (2) ◽  
Author(s):  
Lucas Veríssimo Warren ◽  
Filipe Giovanini Varejão ◽  
Fernanda Quaglio ◽  
Lucas Inglez ◽  
Fernanda Buchi ◽  
...  
2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yukiko Hirabayashi ◽  
Haireti Alifu ◽  
Dai Yamazaki ◽  
Yukiko Imada ◽  
Hideo Shiogama ◽  
...  

AbstractThe ongoing increases in anthropogenic radiative forcing have changed the global water cycle and are expected to lead to more intense precipitation extremes and associated floods. However, given the limitations of observations and model simulations, evidence of the impact of anthropogenic climate change on past extreme river discharge is scarce. Here, a large ensemble numerical simulation revealed that 64% (14 of 22 events) of floods analyzed during 2010-2013 were affected by anthropogenic climate change. Four flood events in Asia, Europe, and South America were enhanced within the 90% likelihood range. Of eight snow-induced floods analyzed, three were enhanced and four events were suppressed, indicating that the effects of climate change are more likely to be seen in the snow-induced floods. A global-scale analysis of flood frequency revealed that anthropogenic climate change enhanced the occurrence of floods during 2010-2013 in wide area of northern Eurasia, part of northwestern India, and central Africa, while suppressing the occurrence of floods in part of northeastern Eurasia, southern Africa, central to eastern North America and South America. Since the changes in the occurrence of flooding are the results of several hydrological processes, such as snow melt and changes in seasonal and extreme precipitation, and because a climate change signal is often not detectable from limited observation records, large ensemble discharge simulation provides insights into anthropogenic effects on past fluvial floods.


2021 ◽  
pp. 1-8
Author(s):  
Thaísa Araújo ◽  
Helena Machado ◽  
Dimila Mothé ◽  
Leonardo dos Santos Avilla

Abstract Climatic and environmental changes, as well as human action, have been cited as potential causes for the extinction of megafauna in South America at the end of the Pleistocene. Among megamammals lineages with Holarctic origin, only horses and proboscideans went extinct in South America during this period. This study aims to understand how the spatial extent of habitats suitable for Equus neogeus and Notiomastodon platensis changed between the last glacial maximum (LGM) and the middle Holocene in order to determine the impact that climatic and environmental changes had on these taxa. We used species distribution modeling to estimate their potential extent on the continent and found that both species occupied arid and semiarid open lands during the LGM, mainly in the Pampean region of Argentina, southern and northeastern Brazil, and parts of the Andes. However, when climate conditions changed from dry and cold during the LGM to humid and warm during the middle Holocene, the areas suitable for these taxa were reduced dramatically. These results support the hypothesis that climatic changes were a driving cause of extinction of these megamammals in South America, although we cannot rule out the impact of human actions or other potential causes for their extinction.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Verónica Lloréns-Rico ◽  
Sara Vieira-Silva ◽  
Pedro J. Gonçalves ◽  
Gwen Falony ◽  
Jeroen Raes

AbstractWhile metagenomic sequencing has become the tool of preference to study host-associated microbial communities, downstream analyses and clinical interpretation of microbiome data remains challenging due to the sparsity and compositionality of sequence matrices. Here, we evaluate both computational and experimental approaches proposed to mitigate the impact of these outstanding issues. Generating fecal metagenomes drawn from simulated microbial communities, we benchmark the performance of thirteen commonly used analytical approaches in terms of diversity estimation, identification of taxon-taxon associations, and assessment of taxon-metadata correlations under the challenge of varying microbial ecosystem loads. We find quantitative approaches including experimental procedures to incorporate microbial load variation in downstream analyses to perform significantly better than computational strategies designed to mitigate data compositionality and sparsity, not only improving the identification of true positive associations, but also reducing false positive detection. When analyzing simulated scenarios of low microbial load dysbiosis as observed in inflammatory pathologies, quantitative methods correcting for sampling depth show higher precision compared to uncorrected scaling. Overall, our findings advocate for a wider adoption of experimental quantitative approaches in microbiome research, yet also suggest preferred transformations for specific cases where determination of microbial load of samples is not feasible.


2021 ◽  
Author(s):  
Jinglie Zhou ◽  
Susanna M. Theroux ◽  
Clifton P. Bueno de Mesquita ◽  
Wyatt H. Hartman ◽  
Ye Tian ◽  
...  

AbstractWetlands are important carbon (C) sinks, yet many have been destroyed and converted to other uses over the past few centuries, including industrial salt making. A renewed focus on wetland ecosystem services (e.g., flood control, and habitat) has resulted in numerous restoration efforts whose effect on microbial communities is largely unexplored. We investigated the impact of restoration on microbial community composition, metabolic functional potential, and methane flux by analyzing sediment cores from two unrestored former industrial salt ponds, a restored former industrial salt pond, and a reference wetland. We observed elevated methane emissions from unrestored salt ponds compared to the restored and reference wetlands, which was positively correlated with salinity and sulfate across all samples. 16S rRNA gene amplicon and shotgun metagenomic data revealed that the restored salt pond harbored communities more phylogenetically and functionally similar to the reference wetland than to unrestored ponds. Archaeal methanogenesis genes were positively correlated with methane flux, as were genes encoding enzymes for bacterial methylphosphonate degradation, suggesting methane is generated both from bacterial methylphosphonate degradation and archaeal methanogenesis in these sites. These observations demonstrate that restoration effectively converted industrial salt pond microbial communities back to compositions more similar to reference wetlands and lowered salinities, sulfate concentrations, and methane emissions.


2021 ◽  
Author(s):  
Markus Deppner ◽  
Bedartha Goswami

<p>The impact of the El Niño Southern Oscillation (ENSO) on rivers are well known, but most existing studies involving streamflow data are severely limited by data coverage. Time series of gauging stations fade in and out over time, which makes hydrological large scale and long time analysis or studies of rarely occurring extreme events challenging. Here, we use a machine learning approach to infer missing streamflow data based on temporal correlations of stations with missing values to others with data. By using 346 stations, from the “Global Streamflow Indices and Metadata archive” (GSIM), that initially cover the 40 year timespan in conjunction with Gaussian processes we were able to extend our data by estimating missing data for an additional 646 stations, allowing us to include a total of 992 stations. We then investigate the impact of the 6 strongest El Niño (EN) events on rivers in South America between 1960 and 2000. Our analysis shows a strong correlation between ENSO events and extreme river dynamics in the southeast of Brazil, Carribean South America and parts of the Amazon basin. Furthermore we see a peak in the number of stations showing maximum river discharge all over Brazil during the EN of 1982/83 which has been linked to severe floods in the east of Brazil, parts of Uruguay and Paraguay. However EN events in other years with similar intensity did not evoke floods with such magnitude and therefore the additional drivers of the 1982/83  floods need further investigation. By using machine learning methods to infer data for gauging stations with missing data we were able to extend our data by almost three-fold, revealing a possible heavier and spatially larger impact of the 1982/83 EN on South America's hydrology than indicated in literature.</p>


2014 ◽  
Vol 29 (3) ◽  
pp. 315-330
Author(s):  
Yanina García Skabar ◽  
Matilde Nicolini

During the warm season 2002-2003, the South American Low-Level Jet Experiment (SALLJEX) was carried out in southeastern South America. Taking advantage of the unique database collected in the region, a set of analyses is generated for the SALLJEX period assimilating all available data. The spatial and temporal resolution of this new set of analyses is higher than that of analyses available up to present for southeastern South America. The aim of this paper is to determine the impact of assimilating data into initial fields on mesoscale forecasts in the region, using the Brazilian Regional Atmospheric Modeling System (BRAMS) with particular emphasis on the South American Low-Level Jet (SALLJ) structure and on rainfall forecasts. For most variables, using analyses with data assimilated as initial fields has positive effects on short term forecast. Such effect is greater in wind variables, but not significant in forecasts longer than 24 hours. In particular, data assimilation does not improve forecasts of 24-hour accumulated rainfall, but it has slight positive effects on accumulated rainfall between 6 and 12 forecast hours. As the main focus is on the representation of the SALLJ, the effect of data assimilation in its forecast was explored. Results show that SALLJ is fairly predictable however assimilating additional observation data has small impact on the forecast of SALLJ timing and intensity. The strength of the SALLJ is underestimated independently of data assimilation. However, Root mean square error (RMSE) and BIAS values reveal the positive effect of data assimilation up to 18-hours forecasts with a greater impact near higher topography.


2019 ◽  
Vol 364 ◽  
pp. 591-599 ◽  
Author(s):  
María T. Gómez-Sagasti ◽  
Lur Epelde ◽  
Mikel Anza ◽  
Julen Urra ◽  
Itziar Alkorta ◽  
...  

2017 ◽  
Author(s):  
Taha Soliman ◽  
Sung-Yin Yang ◽  
Tomoko Yamazaki ◽  
Holger Jenke-Kodama

Structure and diversity of microbial communities are an important research topic in biology, since microbes play essential roles in the ecology of various environments. Different DNA isolation protocols can lead to data bias and can affect results of next-generation sequencing. To evaluate the impact of protocols for DNA isolation from soil samples and also the influence of individual handling of samples, we compared results obtained by two researchers (R and T) using two different DNA extraction kits: (1) MO BIO PowerSoil® DNA Isolation kit (MO_R and MO_T) and (2) NucleoSpin® Soil kit (MN_R and MN_T). Samples were collected from six different sites on Okinawa Island, Japan. For all sites, differences in the results of microbial composition analyses (bacteria, archaea, fungi, and other eukaryotes), obtained by the two researchers using the two kits, were analyzed. For both researchers, the MN kit gave significantly higher yields of genomic DNA at all sites compared to the MO kit (ANOVA; P <0.006). In addition, operational taxonomic units for some phyla and classes were missed in some cases: Micrarchaea were detected only in the MN_T and MO_R analyses; the bacterial phylum Armatimonadetes was detected only in MO_R and MO_T; and WIM5 of the phylum Amoebozoa of eukaryotes was found only in the MO_T analysis. Our results suggest the possibility of handling bias; therefore, it is crucial that replicated DNA extraction be performed by at least two technicians for thorough microbial analyses and to obtain accurate estimates of microbial diversity.


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