Differences in microbial community structure and nitrogen cycling in natural and drained tropical peatland soils

Funding Information: This was supported by the Estonian Research Council (grant IUT2-16); and the EU through the European Regional Development Fund through Centre of Excellence EcolChange and the European Social Fund (Doctoral School of Earth Sciences and Ecology). We would like to thank the PhD students participating in the field works.Peer reviewedPublisher PD

Temporal dynamics of microbial community in soil during phytoremediation field experiment

Oil‐shale chemical industry creates approximately 600 000 tons of thermally processed oil shale solid wastes (semi‐coke) every year in Estonia. A field phytoremediation and bioaugmentation experiment has been monitored for three years in the solid waste depository area of oil‐shale chemical industry. We found enhanced degradation rates of pollutants in plots with vegetation and added bacterial biomass. The concentration of volatile phenols had decreased almost by 100 %, and the concentration of oil products had decreased approximately 3 times in planted plots compared to the control plots. The degradation rates were the highest in the upper soil layer which has the highest root density. Vegetation also changed the microbial community structure in comparison with the control plots. In addition to the vegetation, properties of the substrate had an essential effect on the microbial community. Mikroorganizmų dirvožemyje kaita vykdant fitoatkūrimo lauko eksperimentą Santrauka Kasmet Estijoje naftos skalūnų chemijos pramonėje susidaro apytiksliai 600 000 t termiškai apdorotų naftos skalūnų kietųjų atliekų (pusiau kokso). Fitoatkūrimo ir biopapildymo lauko eksperimentas buvo vykdomas trejus metus naftos skalūnų chemijos pramonės kietųjų atliekų saugojimo zonoje. Pastebėta, kad padidėjo teršalų degradacijos greitis plotuose, kur yra augalijos, ir pridėta bakterinės biomasės. Lakiųjų fenolių koncentracija sumažėjo beveik 100 %, o naftos produktų koncentracija sumažėjo apytiksliai 3 kartus apsodintuose plotuose, palygti su kontroliniais plotais. Degradacijos greitis buvo didžiausias viršutinimame dirvožemio sluoksnyje, kuriame yra didžiausias šaknų tankis. Augalija taip pat pakeitė mikrobiologinės bendrijos struktūrą, palyginti su kontroliniais plotais. Be augalijos, dar ir substrato savybės turėjo didelės įtakos mikrobiologinei bendrijai. Reikšminiai žodžiai: naftos skalūnai, chemijos pramonės kietosios atliekos, biopapildymas, fitoatkūrimas, mikrobiologinė bendrija. Смена микроорганизмов в почве во время полевого эксперимента с применением фиторемедиации Резюме В химической промышленности по обработке нефтяных сланцев Эстонии ежегодно примерно 600 000 т составляют термически обработанные твердые отходы нефтяных сланцев. Эксперимент по полевой фиторемедиации и биодополнению проводился в течение трех лет на территории хранения твердых отходов химической промышленности по обработке нефтяных сланцев. Установлено, что скорость деградации загрязнителей увеличивается на площадях, где имеется растительность и добавлена бактерицидная биомасса. На засаженных площадях концентрация летучих фенолей уменьшилась почти в два раза, а нефтяных продуктов – приблизительно в 3 раза по сравнению с контрольными площадями. Скорость деградации была наибольшей в верхнем слое почвы, в котором больше всего корней. Растительность также изменила структуру микробиологического сообщества по сравнению с контрольными площадями. Кроме растительности, существенное воздействие на микробиологическое сообщество оказали также свойства субстрата. Ключевые слова: нефтяной сланец, твердые отходы химической промышленности, биодополнение, фиторемедиация, микробиологическое сообщество. First Published Online: 14 Oct 201

Soil Bacterial and Archaeal Communities and Their Potential to Perform N-Cycling Processes in Soils of Boreal Forests Growing on Well-Drained Peat

Funding Information: This study was supported by the Estonian Forest Management Centre, the Estonian Research Council grants PRG548, PRG916, and PRG352, WaterJPI-JC-2018_13 project, and Centres of Excellence Environ and EcolChange.Peer reviewedPublisher PD

Microbial community changes in TNT spiked soil bioremediation trial using biostimulation, phytoremediation and bioaugmentation

Trinitrotoluene (TNT), a commonly used explosive for military and industrial applications, can cause serious environmental pollution. 28-day laboratory pot experiment was carried out applying bioaugmentation using laboratory selected bacterial strains as inoculum, biostimulation with molasses and cabbage leaf extract, and phytoremediation using rye and blue fenugreek to study the effect of these treatments on TNT removal and changes in soil microbial community responsible for contaminant degradation. Chemical analyses revealed significant decreases in TNT concentrations, including reduction of some of the TNT to its amino derivates during the 28-day tests. The combination of bioaugmentation-biostimulation approach coupled with rye cultivation had the most profound effect on TNT degradation. Although plants enhanced the total microbial community abundance, blue fenugreek cultivation did not significantly affect the TNT degradation rate. The results from molecular analyses suggested the survival and elevation of the introduced bacterial strains throughout the experiment. First published online: 15 Feb 201

The bacterial community structure and functional profile in the heavy metal contaminated paddy soils, surrounding a nonferrous smelter in South Korea

Funding Information: The authors wish to thank the Basic Science Research Program of the National Research Foundation (NRF) under the Ministry of Education, Science and Technology (2015R1A2A1A05001885), South Korea for providing funding support toward the completion of this study. This study was supported partially by the Estonian Ministry of Education and Research (Grant IUT2–16), and by the European Regional Development Fund through the Centre of Excellence EcolChange. We thank Saale Truu for the assistance in computer graphics. Funding Information: National Research Foundation of Korea, Grant/Award Number: 2015R1A2A1A05001885; Estonian Ministry of Education and Research, Grant/ Award Number: IUT2–16; European Region Development Fund Publisher Copyright: © 2018 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.Peer reviewedPublisher PD

PlutoF—a Web Based Workbench for Ecological and Taxonomic Research, with an Online Implementation for Fungal ITS Sequences

DNA sequences accumulating in the International Nucleotide Sequence Databases (INSD) form a rich source of information for taxonomic and ecological meta-analyses. However, these databases include many erroneous entries, and the data itself is poorly annotated with metadata, making it difficult to target and extract entries of interest with any degree of precision. Here we describe the web-based workbench PlutoF, which is designed to bridge the gap between the needs of contemporary research in biology and the existing software resources and databases. Built on a relational database, PlutoF allows remote-access rapid submission, retrieval, and analysis of study, specimen, and sequence data in INSD as well as for private datasets though web-based thin clients. In contrast to INSD, PlutoF supports internationally standardized terminology to allow very specific annotation and linking of interacting specimens and species. The sequence analysis module is optimized for identification and analysis of environmental ITS sequences of fungi, but it can be modified to operate on any genetic marker and group of organisms. The workbench is available at http://plutof.ut.ee

Severe drought and conventional farming affect detritivore feeding activity and its vertical distribution

11 Pág.Soil invertebrates are key to decomposition, a central ecosystem process related to soil health. In many temperate areas climate change will decrease soil water content, which strongly modulates biological activity. However, data are lacking on how shifts in rainfall patterns affect soil biota and the ecosystem processes they provide. Here, we used the bait-lamina test to experimentally assess how a severe drought event influenced detritivore feeding activity, during a wheat growing season, in soils under long-term organic or conventional farming. Additionally, biotic and abiotic soil parameters were measured. Feeding activity was reduced under extreme drought and conventional management, although no climate-management synergies were found. Vertical migrations of Collembola and Oribatida partially explained the unexpectedly higher bait consumption at shallower depths in response to drought. Exploratory mixed-effects longitudinal random forests (a novel machine learning technique) were used to explore whether the relative abundances of meso‑, microfauna and microbes of the decomposer food web, or abiotic soil parameters, affected the feeding activity of detritivores. The model including meso‑ and microfauna selected four Nematoda taxa and explained higher variance than the model with only microbiota, indicating that detritivore feeding is closely associated with nematodes but not with microbes. Additionally, the model combining fauna and microbiota explained less variance than the faunal model, suggesting that microbe-fauna synergies barely affected detritivore feeding. Moreover, soil water and mineral nitrogen contents were found to strongly determine detritivore feeding, in a positive and negative way, respectively. Hence, our results suggest that severe drought and conventional farming impair the feeding activity of soil detritivores and thus, probably, decomposition and nutrient mineralization in soils. Furthermore, machine learning algorithms arise as a powerful technique to explore the identity of potential key drivers relating biodiversity to ecosystem functioning.This work was financed by the BiodivERsA COFUND (2015–2016 call), in concert with the following national funders: the Swiss National Science Foundation (SNSF), the German Research Foundation (DFG), the Swedish Research Council (Formas), the Estonian Research Council (ETAG), and the Spanish Ministry of Sciences and Innovation (MICINN, ref.: PCIN-2016–045), which also funded the FPI grant of the first author PGC (ref.: PRE2020–095020). The DOK trial is funded through the Swiss Federal Office of Agriculture (FOAG).Peer reviewe

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach