A robust microbiome signature for autism spectrum disorder across different studies using machine learning.

Autism spectrum disorder (ASD) is a highly complex neurodevelopmental disorder characterized by deficits in sociability and repetitive behaviour, however there is a great heterogeneity within other comorbidities that accompany ASD. Recently, gut microbiome has been pointed out as a plausible contributing factor for ASD development as individuals diagnosed with ASD often suffer from intestinal problems and show a differentiated intestinal microbial composition. Nevertheless, gut microbiome studies in ASD rarely agree on the specific bacterial taxa involved in this disorder. Regarding the potential role of gut microbiome in ASD pathophysiology, our aim is to investigate whether there is a set of bacterial taxa relevant for ASD classification by using a sibling-controlled dataset. Additionally, we aim to validate these results across two independent cohorts as several confounding factors, such as lifestyle, influence both ASD and gut microbiome studies. A machine learning approach, recursive ensemble feature selection (REFS), was applied to 16S rRNA gene sequencing data from 117 subjects (60 ASD cases and 57 siblings) identifying 26 bacterial taxa that discriminate ASD cases from controls. The average area under the curve (AUC) of this specific set of bacteria in the sibling-controlled dataset was 81.6%. Moreover, we applied the selected bacterial taxa in a tenfold cross-validation scheme using two independent cohorts (a total of 223 samples-125 ASD cases and 98 controls). We obtained average AUCs of 74.8% and 74%, respectively. Analysis of the gut microbiome using REFS identified a set of bacterial taxa that can be used to predict the ASD status of children in three distinct cohorts with AUC over 80% for the best-performing classifiers. Our results indicate that the gut microbiome has a strong association with ASD and should not be disregarded as a potential target for therapeutic interventions. Furthermore, our work can contribute to use the proposed approach for identifying microbiome signatures across other 16S rRNA gene sequencing datasets.

Methodology for biomarker discovery with reproducibility in microbiome data using machine learning.

BACKGROUND: In recent years, human microbiome studies have received increasing attention as this field is considered a potential source for clinical applications. With the advancements in omics technologies and AI, research focused on the discovery for potential biomarkers in the human microbiome using machine learning tools has produced positive outcomes. Despite the promising results, several issues can still be found in these studies such as datasets with small number of samples, inconsistent results, lack of uniform processing and methodologies, and other additional factors lead to lack of reproducibility in biomedical research. In this work, we propose a methodology that combines the DADA2 pipeline for 16s rRNA sequences processing and the Recursive Ensemble Feature Selection (REFS) in multiple datasets to increase reproducibility and obtain robust and reliable results in biomedical research. RESULTS: Three experiments were performed analyzing microbiome data from patients/cases in Inflammatory Bowel Disease (IBD), Autism Spectrum Disorder (ASD), and Type 2 Diabetes (T2D). In each experiment, we found a biomarker signature in one dataset and applied to 2 other as further validation. The effectiveness of the proposed methodology was compared with other feature selection methods such as K-Best with F-score and random selection as a base line. The Area Under the Curve (AUC) was employed as a measure of diagnostic accuracy and used as a metric for comparing the results of the proposed methodology with other feature selection methods. Additionally, we use the Matthews Correlation Coefficient (MCC) as a metric to evaluate the performance of the methodology as well as for comparison with other feature selection methods. CONCLUSIONS: We developed a methodology for reproducible biomarker discovery for 16s rRNA microbiome sequence analysis, addressing the issues related with data dimensionality, inconsistent results and validation across independent datasets. The findings from the three experiments, across 9 different datasets, show that the proposed methodology achieved higher accuracy compared to other feature selection methods. This methodology is a first approach to increase reproducibility, to provide robust and reliable results.

Optimum k-Nearest Neighbors for Heading Synchronization on a Swarm of UAVs under a Time-Evolving Communication Network.

Heading synchronization is fundamental in flocking behaviors. If a swarm of unmanned aerial vehicles (UAVs) can exhibit this behavior, the group can establish a common navigation route. Inspired by flocks in nature, the k-nearest neighbors algorithm modifies the behavior of a group member based on the k closest teammates. This algorithm produces a time-evolving communication network, due to the continuous displacement of the drones. Nevertheless, this is a computationally expensive algorithm, especially for large groups. This paper contains a statistical analysis to determine an optimal neighborhood size for a swarm of up to 100 UAVs, that seeks heading synchronization using a simple P-like control algorithm, in order to reduce the calculations on every UAV, this is especially important if it is intended to be implemented in drones with limited capabilities, as in swarm robotics. Based on the literature of bird flocks, that establishes that the neighborhood of every bird is fixed around seven teammates, two approaches are treated in this work: (i) the analysis of the optimum percentage of neighbors from a 100-UAV swarm, that is necessary to achieve heading synchronization, and (ii) the analysis to determine if the problem is solved in swarms of different sizes, up to 100 UAVs, while maintaining seven nearest neighbors among the members of the group. Simulation results and a statistical analysis, support the idea that the simple control algorithm behaves like a flock of starlings.

Soil moisture drives differences in the diversity and trophic complexity of high Arctic tundra soils.

The Arctic soil communities play a vital role in stabilizing and decomposing soil carbon, which affects the global carbon cycling. Studying the food web structure is critical for understanding biotic interactions and the functioning of these ecosystems. Here, we studied the trophic relationships of (microscopic) soil biota of two different Arctic spots in Ny-Ålesund, Svalbard, within a natural soil moisture gradient by combining DNA analysis with stable isotopes as trophic tracers. The results of our study suggested that the soil moisture strongly influenced the diversity of soil biota, with the wetter soil, having a higher organic matter content, hosting a more diverse community. Based on a Bayesian mixing model, the community of wet soil formed a more complex food web, in which bacterivorous and detritivorous pathways were important in supplying carbon and energy to the upper trophic levels. In contrast, the drier soil showed a less diverse community, lower trophic complexity, with the green food web (via unicellular green algae and gatherer organisms) playing a more important role in channelling energy to higher trophic levels. These findings are important to better understand the soil communities inhabiting the Arctic, and for predicting how the ecosystem will respond to the forthcoming changes in precipitation regimes.

Integrating stakeholders' inputs to co-design climate resilience adaptation measures in Mediterranean areas with conflicts between wetland conservation and intensive agriculture.

Designing sustainable management strategies in groundwater-dependent socio-economic systems in areas with scarce water resources and protected wetlands is a challenging issue. The high vulnerability of these systems to droughts will be exacerbated even further under future climate change (CC) and socio-economic scenarios. A novel integrated bottom-up/top-down approach is used to identify "climate resilient pathways", from which to co-design adaptation strategies to reduce the impact of potential future CC and socio-economic scenarios. The approach followed two steps (1) the generation of local CC and socio-economic scenarios by downscaling global/regional climate models and (2) the identification and assessment of potential adaptation strategies through an iterative bottom-up/top-down approach. Top-down assessments of the impact of CC have been undertaken by propagating local scenarios within a chain of mathematical models based on expert criteria/assumptions. This allowed us to analyse of the physical vulnerability of the system under different potential CC and socio-economic scenarios by simulating them with a sequential modelling of rainfall-recharge, agriculture, and hydrological processes through a distributed groundwater finite difference model. These model results were discussed with the stakeholders at a first workshop, which aimed to identify potential adaptation strategies. The influence of the adaptation strategies on the future hydrological status was assessed by simulating them through the chain of models. These results were the inputs into the discussions at a second workshop, which aimed to validate and/or improve the results of the first workshop. The methodology was applied in the Upper Guadiana River Basin, where there is a long-standing conflict between wetland conservation and groundwater overexploitation for intensive agriculture. The future horizon 2016-2045 is analysed with the scenarios compatible with the emission scenario RCP4.5. The research has allowed us to conclude that groundwater pumping reduction would be the most robust and effective measure to reduce the impact of CC in the area.

A needle in a haystack: A new metabarcoding approach to survey diversity at the species level of Arcellinida (Amoebozoa: Tubulinea).

Environmental DNA-based diversity studies have increased in popularity with the development of high throughput sequencing technologies. This permits the potential simultaneous retrieval of vast amounts of molecular data from many different organisms and species, thus contributing to a wide range of biological disciplines. Environmental DNA protocols designed for protists often focused on the highly conserved small subunit of the ribosome gene, that does not permit species-level assignments. On the other hand, eDNA protocols aiming at species-level assignments allow a fine level ecological resolution and reproducible results. These protocols are currently applied to organisms living in marine and shallow lotic freshwater ecosystems, often in a bioindication purpose. Therefore, in this study, we present a species-level eDNA protocol designed to explore diversity of Arcellinida (Amoebozoa: Tubulinea) testate amoebae taxa that is based on mitochondrial cytochrome oxidase subunit I (COI). These organisms are widespread in lentic water bodies and soil ecosystems. We applied this protocol to 42 samples from peatlands, estuaries and soil environments, recovering all the infraorders in Glutinoconcha (with COI data), except for Hyalospheniformes. Our results revealed an unsuspected diversity in morphologically homogeneous groups such as Cylindrothecina, Excentrostoma or Sphaerothecina. With this protocol we expect to revolutionize the design of modern distributional Arcellinida surveys. Our approach involves a rapid and cost-effective analysis of testate amoeba diversity living in contrasted ecosystems. Therefore, the order Arcellinida has the potential to be established as a model group for a wide range of theoretical and applied studies.

Electronic structure and reactivity indexes of cobalt clusters, both pure and mixed with NO and [Formula: see text] ([Formula: see text], [Formula: see text] and [Formula: see text]).

Among the most popular motivations for environmental scientists is improving materials that could be useful to fight or avoid pollution. This work shows a study of neutral and cationic cobalt clusters from 4 to 9 atoms ([Formula: see text], q = 0,1 and n = 4-9) to model their separate interaction with contaminant nitric and nitrous oxides. This study is within the framework of the density functional theory in the Kohn-Sham scheme by using BPW91 functional and 6-311G and 6-31G* basis sets to calculate global and local reactivity indexes. The effect of spin multiplicity is also determined. Results on the geometries of pure cobalt clusters agree with previously reported structures. Global minimum energy structures showed a marked preference towards the interaction of nitric and nitrous oxide molecules with cobalt clusters through chemisorptive dissociation, with the dissociation of the corresponding nitrogen oxide. Reactivity indexes reveal an even-odd alternate, which is related to electron counts. Moreover, the chemical potential is lowering after interaction with nitrogen oxides. The Fukui function illustrates the reactive zones with a high probability of chemisorption of more nitrogen oxide molecules.

Marine Vertebrates Impact the Bacterial Community Composition and Food Webs of Antarctic Microbial Mats.

The biological activity of marine vertebrates represents an input of nutrients for Antarctic terrestrial biota, with relevant consequences for the entire ecosystem. Even though microbial mats assemble most of the biological diversity of the non-marine Antarctica, the effects of the local macrofauna on these microecosystems remain understudied. Using 16S rRNA gene sequencing, 13C and 15N stable isotopes, and by characterizing the P and N-derived nutrient levels, we evaluated the effects of penguins and other marine vertebrates on four microbial mats located along the Antarctic Peninsula. Our results show that P concentrations, C/N and N/P ratios, and δ15N values of "penguin-impacted" microbial mats were significantly higher than values obtained for "macrofauna-free" sample. Nutrients derived from penguin colonies and other marine vertebrates altered the trophic interactions of communities within microbial mats, as well as the relative abundance and trophic position of meiofaunal groups. Twenty-nine bacterial families from eight different phyla significantly changed with the presence of penguins, with inorganic nitrogen (NH4 + and NO3 -) and δ15N appearing as key factors in driving bacterial community composition. An apparent change in richness, diversity, and dominance of prokaryotes was also related to penguin-derived nutrients, affecting N utilization strategies of microbial mats and relating oligotrophic systems to communities with a higher metabolic versatility. The interdisciplinary approach of this study makes these results advance our understanding of interactions and composition of communities inhabiting microbial mats from Antarctica, revealing how they are deeply associated with marine animals.

Cyphoderia ampulla (Cyphoderiidae: Rhizaria), a tale of freshwater sailors: The causes and consequences of ecological transitions through the salinity barrier in a family of benthic protists.

The salinity barrier that separates marine and freshwater biomes is probably the most important division in biodiversity on Earth. Those organisms that successfully performed this transition had access to new ecosystems while undergoing changes in selective pressure, which often led to major shifts in diversification rates. While these transitions have been extensively investigated in animals, the tempo, mode, and outcome of crossing the salinity barrier have been scarcely studied in other eukaryotes. Here, we reconstructed the evolutionary history of the species complex Cyphoderia ampulla (Euglyphida: Cercozoa: Rhizaria) based on DNA sequences from the nuclear SSU rRNA gene and the mitochondrial cytochrome oxidase subunit I gene, obtained from publicly available environmental DNA data (GeneBank, EukBank) and isolated organisms. A tree calibrated with euglyphid fossils showed that four independent transitions towards freshwater systems occurred from the mid-Miocene onwards, coincident with important fluctuations in sea level. Ancestral trait reconstructions indicated that the whole family Cyphoderiidae had a marine origin and suggest that ancestors of the freshwater forms were euryhaline and lived in environments with fluctuating salinity. Diversification rates did not show any obvious increase concomitant with ecological transitions, but morphometric analyses indicated that species increased in size and homogenized their morphology after colonizing the new environments. This suggests adaptation to changes in selective pressure exerted by life in freshwater sediments.

Developing a pan-European high-resolution groundwater recharge map - Combining satellite data and national survey data using machine learning.

Groundwater recharge quantification is essential for sustainable groundwater resources management, but typically limited to local and regional scale estimates. A high-resolution (1 km × 1 km) dataset consisting of long-term average actual evapotranspiration, effective precipitation, a groundwater recharge coefficient, and the resulting groundwater recharge map has been created for all of Europe using a variety of pan-European and seven national gridded datasets. As an initial step, the approach developed for continental scale mapping consists of a merged estimate of actual evapotranspiration originating from satellite data and the vegetation controlled Budyko approach to subsequently estimate effective precipitation. Secondly, a machine learning model based on the Random Forest regressor was developed for mapping groundwater recharge coefficients, using a range of covariates related to geology, soil, topography and climate. A common feature of the approach is the validation and training against effective precipitation, recharge coefficients and groundwater recharge from seven national gridded datasets covering the UK, Ireland, Finland, Denmark, the Netherlands, France and Spain, representing a wide range of climatic and hydrogeological conditions across Europe. The groundwater recharge map provides harmonised high-resolution estimates across Europe and locally relevant estimates for areas where this information is otherwise not available, while being consistent with the existing national gridded datasets. The Pan-European groundwater recharge pattern compares well with results from the global hydrological model PCR-GLOBWB 2. At country scale, the results were compared to a German recharge map showing great similarity. The full dataset of long-term average actual evapotranspiration, effective precipitation, recharge coefficients and groundwater recharge is available through the EuroGeoSurveys' open access European Geological Data Infrastructure (EGDI).

The impact of climate change scenarios on droughts and their propagation in an arid Mediterranean basin. A useful approach for planning adaptation strategies.

Areas where there is a scarcity of water frequently experience significant drought periods, which may become exacerbated in the future due to climate change. In this paper we propose a novel and integrated method for a semi-distributed analysis of the impact on potential future meteorological, hydrological, agronomical and operational droughts within a basin. We analyse the propagation and correlation of the different types of droughts, and then this analysis can be used to plan sustainable adaptation strategies. The proposed method is based on sequential applications of different statistical techniques and mathematical models. We have applied several statistical downscaling techniques to generate consistent local future climate scenarios considering both basic and drought statistics. This allows us to analyse the sensitivity of the results to the applied technique and the spatial distribution. A chain of models has been used to propagate climate scenarios to analyse the hydrological, agricultural, and operational impact. We have applied a clustering analysis to historical data to identify homogeneous hydro-climate areas used to analyse the spatial distribution of the impact. The approach has been applied in the Segura basin (in south-eastern Spain). The simulations of the impact in the 3 generated ensemble scenarios on the whole Segura Basin system for the horizon 2071-2100 under the RCP8.5 emission scenario show a significant mean reduction (40.9-59.1%) of the available resources, an increase in pumping rates in aquifers (36.4-42.7%) and lower guarantees (96.3% in the historical period and 75.0-77.6% in the future scenarios) for demand supply. The spatial distribution of the impact is heterogeneous, with the hydro-climate areas near to the coast for agricultural and operational droughts being more affected. An analysis of correlation between the meteorological and operational droughts shows the maximum correlation for a time delay of around 4 months. This information could help to identify when measures to reduce the operational impact should start to be applied when a meteorological drought starts.

Microbial colonizers of microplastics in an Arctic freshwater lake.

Microplastics (MPs) have been found everywhere as they are easily transported between environmental compartments. Through their transport, MPs are quickly colonized by microorganisms; this microbial community is known as the plastisphere. Here, we characterized the plastisphere of three MPs, one biodegradable (PHB) and two non-biodegradables (HDPE and LDPE), deployed in an Arctic freshwater lake for eleven days. The plastisphere was found to be complex, confirming that about a third of microbial colonizers were viable. Plastisphere was compared to microbial communities on the surrounding water and microbial mats on rocks at the bottom of the lake. Microbial mats followed by MPs showed the highest diversity regarding both prokaryotes and eukaryotes as compared to water samples; however, for fungi, MPs showed the highest diversity of the tested substrates. Significant differences on microbial assemblages on the three tested substrates were found; regarding microbial assemblages on MPs, bacterial genera found in polar environments such as Mycoplana, Erythromicrobium and Rhodoferax with species able to metabolize recalcitrant chemicals were abundant. Eukaryotic communities on MPs were characterized by the presence of ciliates of the genera Stentor, Vorticella and Uroleptus and the algae Cryptomonas, Chlamydomonas, Tetraselmis and Epipyxis. These ciliates normally feed on algae so that the complexity of these assemblages may serve to unravel trophic relationships between co-existing taxa. Regarding fungal communities on MPs, the most abundant genera were Betamyces, Cryptococcus, Arrhenia and Paranamyces. MPs, particularly HDPE, were enriched in the sulI and ermB antibiotic resistance genes (ARGs) which may raise concerns about human health-related issues as ARGs may be transferred horizontally between bacteria. This study highlights the importance of proper waste management and clean-up protocols to protect the environmental health of pristine environments such as polar regions in a context of global dissemination of MPs which may co-transport microorganisms, some of them including ARGs.

Electrocatalytic syngas generation with a redox non-innocent cobalt 2-phosphinobenzenethiolate complex.

A cobalt complex supported by the 2-(diisopropylphosphaneyl)benzenethiol ligand was synthesized and its electronic structure and reactivity were explored. X-ray diffraction studies indicate a square planar geometry around the cobalt center with a trans arrangement of the phosphine ligands. Density functional theory calculations and electronic spectroscopy measurements suggest a mixed metal-ligand orbital character, in analogy to previously studied dithiolene and diselenolene systems. Electrochemical studies in the presence of 1 atm of CO2 and Brønsted acid additives indicate that the cobalt complex generates syngas, a mixture of H2 and CO, with faradaic efficiencies up to >99%. The ratios of H2 : CO generated vary based on the additive. A H2 : CO ratio of ∼3 : 1 is generated when H2O is used as the Brønsted acid additive. Chemical reduction of the complex indicates a distortion towards a tetrahedral geometry, which is rationalized with DFT predictions as attributable to the populations of orbitals with σ*(Co-S) character. A mechanistic scheme is proposed whereby competitive binding between a proton and CO2 dictates selectivity. This study provides insight into the development of a catalytic system incorporating non-innocent ligands with pendant base moieties for electrochemical syngas production.

Assessing the impact of climate change - and its uncertainty - on snow cover areas by using cellular automata models and stochastic weather generators.

Climate change will modify the spatiotemporal distribution of water resources in the future. Snow availability in alpine systems plays an important role for water dependent ecosystems, water demand supply, tourism, and hydropower. The assessment of the impact of climate change (and its uncertainty) on snow is a key subject in determining suitable adaptation strategies in these systems. In this paper, we propose a new methodology for assessing the impact of climate change on snow cover areas (SCAs). We have developed the Monte Carlo method analysis to combine several approaches to generate multiple input series and propagate them within a previously calibrated SCA cellular automata model. This generates potential future local scenarios from regional climate models. These scenarios are used to generate multiple series by using a stochastic weather generator. The methodology also includes an approach to correct the outputs bias of the stochastic weather generators when it is needed. Finally, the historical and the corrected multiple future weather series are used to simulate the impact on the SCA by using a cellular automata model. It is a novel approach that allows us to quantify the impact and uncertainty of climate change on the SCA. The methodology has been applied to the Sierra Nevada (southern Spain), which is the most southern alpine mountain range in Europe. In the horizon 2071-2100, under the RCP 8.5 emission scenario, we estimate mean reductions of SCA that will move from 42 to 66% from December to February. The reductions are higher for the rest of the year (from March to May reductions of between 47 and 95% and from September to November reductions of between 54 and 100%). These SCA changes may be roughly equivalent to an elevation shift of snow of around 400 m.

First detection of microplastics in the freshwater of an Antarctic Specially Protected Area.

Microplastics have been found in all environmental compartments investigated so far, even reaching remote areas. However, their presence in Antarctic freshwaters has not been yet reported. Here, we investigated the occurrence of microplastics in a stream from an Antarctic Specially Protected Area (Byers Peninsula, Livingston Island - ASPA No. 126), which is subject to stringent environmental protection measures as a result of which it is considered a pristine international reference site for inland waters research. Our results showed the presence of three types of microplastics in a freshwater seasonal stream, namely four polyester fibers, one black and three transparent; two acrylic fibers, one transparent and one red; and two transparent polytetrafluoroethylene films. The length and width of these fibers and films were in the 400-3546 µm (average 1118 µm), and 10-1026 µm (average 199 µm) ranges respectively. The concentration of MP was 0.95 items/1000 m3 with estimated variability in the 0.47-1.43 items/1000 m3 range. This is the first report of the presence of microplastics in Antarctic freshwater with the uniqueness that it is an Antarctic Specially Protected Area, meaning that plastic pollution reached even the most remote and pristine environments in the planet.

Estimation of the spatiotemporal dynamic of snow water equivalent at mountain range scale under data scarcity.

The snow dynamics in alpine systems play a significant role in the hydrosphere, biosphere, and anthroposphere interfaces of these regions. The storage of water resources as snow is essential for ecosystems, human consumption, tourism, and hydropower in many areas. However, snow data are usually scarce due to poor accessibility, difficulties to maintain monitoring system under harsh climatic conditions and limited economic funds. Most of the scientific studies aimed to quantify water stored as snow are carried out at small or medium spatial scales, but few analyses are done for the whole mountain ranges. The main goal of this work is to propose a general parsimonious methodology to estimate snow water equivalent under data scarcity for the Sierra Nevada mountain range (Spain). The methodology is easily transferable to any other study areas. It combines a dynamic regression approach of snow depth from punctual data, snow cover area data from the MODIS satellite and simulations of snow density from a coupled mass and energy balance model. The regression model includes two kinds of explanatory variables (steady and non-steady) to assess the snow depth dynamics. The dynamic of the snow density in the mountain range has been obtained using a physically based simulation driven by climate model data for the Iberian Peninsula. These three variables (snow depth, snow cover area and snow density) have been used to obtain spatially distributed series of snow water equivalent for the whole mountain range. The proposed solution allows studying the snow water equivalent distribution, duration of the snow cover and number of accumulation and melting days for different snow seasons. The mean accumulated snow water equivalent per season in the historical period is 330 Hm3 and the maximum of 480 Hm3, which is a significant amount of resources in an area characterized by limited water availability.

Fibers spreading worldwide: Microplastics and other anthropogenic litter in an Arctic freshwater lake.

We investigated the presence of microplastics and other anthropogenic litter in the sediments adhered to rocks of an Arctic freshwater lake at Ny-Ålesund (Svalbard Archipelago, 78°N; 11°E). Most of the sampled microparticles were fibers (>90%). The identification of polymer types and additives was performed by combining three spectroscopic techniques, namely Raman Microscopy, Fourier-Transform Infrared microspectroscopy (µFTIR) and Synchrotron Radiation µFTIR (SR-FTIR). SR-FTIR confirmed the presence of poly(ethylene terephthalate) fibers, while RAMAN spectroscopy provided evidence of fibers containing industrial additives. Our results estimated an average concentration of 400 microparticles/m2 of rocks identified as anthropogenic litter, which included an estimation of 90 microplastics/m2 identified as polyester fibers; the rest are mostly natural fibers with evidence of anthropogenic origin. Taken together, the results proved the occurrence of anthropogenic pollutants in remote polar areas. Their probable origin is the long range atmospheric transport.

Bacterioplankton Community Composition Along Environmental Gradients in Lakes From Byers Peninsula (Maritime Antarctica) as Determined by Next-Generation Sequencing.

This study comprises the first attempt to describe the planktonic bacterial communities of lakes from Byers Peninsula, one of the most significant limnological districts in the Maritime Antarctica, leveraging next-generation sequencing (NGS) technologies. For the survey, we selected 7 lakes covering the environmental gradient from inland to coastal lakes, some of them sampled both in surface and deep waters. Analysis provided just over 85,000 high quality sequences that were clustered into 864 unique Zero-radius Operational Taxonomic Units (ZOTUs) (i.e., 100% sequence similarity). Yet, several taxonomic uncertainties remained in the analysis likely suggesting the occurrence of local bacterial adaptations. The survey showed the dominance of the phyla Proteobacteria and Bacteroidetes. Among the former, the Gammaproteobacteria class, more specifically the order Betaproteobacteriales, was the dominant group, which seems to be a common trend in nutrient-limited Antarctic lakes. Most of the families and genera ubiquitously detected belonging to this class are indeed typical from ultra-oligotrophic environments, and commonly described as diazotrophs. On the other hand, among the members of the phylum Bacteroidetes, genera such as Flavobacterium were abundant in some of the shallowest lakes, thus demonstrating that also benthic and sediment-associated bacteria contributed to water bacterial assemblages. Ordination analyses sorted bacterial assemblages mainly based on the environmental gradients of nutrient availability and conductivity i.e., salinity. However, transient bacterial associations, that included the groups Clostridiaceae and Chloroflexi, also occurred as being forced by other drivers such as the influence of the nearby fauna and by the airborne microorganisms. As we intended, our NGS-based approach has provided a much greater resolution compared to the previous studies conducted in the area and confirmed to a large extent the previously obtained patterns, thus reinforcing the view of Byers as a hotspot of microbial biodiversity within Antarctica. This high microbial diversity allows the use of these aquatic ecosystems and their bacterial assemblages as sentinels for the monitoring of adaptive responses to climate change in this rapidly warming area.

Carbon Pathways Through the Food Web of a Microbial Mat From Byers Peninsula, Antarctica.

Microbial mats are complex communities that represent a large biomass fraction in non-marine Antarctic ecosystems. They confer structure to soils and constitute, by themselves, intricate microecosystems, where a great variety of microorganisms and microfauna contributes to the ecosystem functions. Although in recent years Antarctic microbial mats have been thoroughly investigated, trophic relationships within the communities remain unresolved. We therefore conducted a study of the trophic relationships of a microbial mat from Byers Peninsula, Antarctica, using DNA analysis and stable isotopes as trophic tracers. Our results suggested, based on a Bayesian mixing model, that at least four trophic levels are present within this microecosystem: primary producers (cyanobacteria and diatoms), primary consumers (rotifers and tardigrades), secondary consumers (nematodes) and decomposers (fungi). Nematodes would play a key role as top consumers of the community, connecting the two carbon inputs described into the system, as omnivores at the secondary trophic level. In addition, carbon pathways from primary trophic level to consumers take place quickly during the first 24 h after its incorporation in the primary producers, dispersing across all the trophic levels and reaching secondary consumers in less than 11 days. This suggests that, given the changing physical conditions and presumably short periods of activity, there is a fine temporal coupling among the organisms in the community, minimizing the redundancy in function performance among trophic levels.