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553 results for “soil communities”
Unraveling community adaption and survival strategy of soil microbiome under vanadium stress in nationwide mining environments
<p class="Abstract"><span><span>The vanadium (V) smelters soil harbor wide ranges of microorganisms, whose survival relies on their metabolic activities under stress.</span><span> Nonetheless, the characteristics and functions of soil microbiome in V mining environments have not been recognized at a continental scale. This study investigates microbial diversity, community assembly and metabolic traits of soil microbiome across 90 V smelters in China. A decrease in alpha diversity is observed, along with community variation, which is also jointly explained by other environmental, climatic and geographic factors. Null model shows that V promotes homogeneous selection. V also mediates co-occurrence patterns, with increased positive interspecific associations under higher V concentrations (</span><span>></span><span>559.6 mg/kg)</span><span>, e.g., <em>f_Gemmatimonadaceae</em>, <em>Nocardioides</em>, <em>Micromonospora</em>, <em>Rubrobacter</em>.</span><span> In addition, 67 metagenome assembled genomes are retrieved via metagenomic analysis. The metabolic pathways of keystone taxa are disentangled to reveal their putative involvement in the V(V) reduction process. Nitrate and nitrite reductase (<em>nirK</em>, <em>narG</em>), and <em>mtrABC</em> are found to be taxonomically affiliated with <em>Micromonospora</em>. sp, <em>FEN-1250</em>. sp, <em>Nocardioides</em>. sp, etc. Additionally, reverse citric acid cycle (rTCA) serves the main carbon fixation pathway, synthetizing alternative energy for putative V reducers, highlighting a synergistic relationship between autotrophic and heterotrophic processes to support the microbial survival. Our findings comprehensively reveal the driving forces for soil community variation under V stress, suggesting the robust strategies adopted by indigenous microorganisms to alleviate V impact, which can be exploited for bioremediation application.</span></span></p>
Raw sequencing data for studying the colonization of soil communities after glacier retreat
<p>Glaciers show a pattern of retreat at the global scale. Deglaciated areas are exposed and colonized by multiple organisms, but lack of global studies hampers a complete understanding of the future of these ecosystems. Until now, the complete reconstruction of soil communities was hampered by the complex identification of organisms, thus analyses at broad geographical and taxonomic scale have been so far impossible. The dataset used for this study represents the assemblages of Bacteria, Mycota, Eukaryota, Collembola (springtails), Oligochaeta (Earth worms), Insecta, Arthropoda and Vascular Plants obtained using environmental DNA (eDNA) metabarcoding. eDNA was extracted from soil samples collected from multiple glacier forelands representative of some of the main mountain chains of Europe, Asia, the Americas and Oceania. We investigated chronosequences of glacier retreat (i.e., the chronological sequence of specific geomorphological features along deglaciated areas for which the date of glacier retreat is known) ranging from recent years to the Little Ice Age (~1850). We used this newly assembled global DNA metabarcoding dataset to obtain a complete reconstruction of community changes in novel ecosystems after glacier retreat. Information on assemblages can be then combined with analyses of soil, landscape and climate to identify the drivers of community changes.</p>
Drivers of plant diversity, community composition, functional traits and soil processes along an alpine gradient in the central Chilean Andes
<p>The datasets in this repository include plant community surveys, hyperspectral reflectance data at the leaf and canopy level, leaf trait data, and soil chemistry data collected at five sites along an elevation gradient of 2400m-3500m in the Chilean Andes (33°S, 70°W). The purpose of this study was to evaluate the environmental drivers of community assembly processes along the elevation gradient.</p>
Impact of viruses on microbial communities and biogeochemical processes in agricultural soils
<p>The source data of microcosm experiment.</p>
Data from: Metacommunity theory review and its application in community assembly of soil animals
<p>We were interested in community assembly of soil animals and performed a literature study to find out what is known about soil metacommunities. We aimed to study keywords co-occurrence relationships of scientific journal articles in the field of "metacommunity" research from 1992-2017 as a whole. We also investigated the co-occurrence of the top 20 most frequent keywords in five 5-year time periods.</p> <p> </p> <p> </p> <p>In September 2017 we searched the Web of Science with ‘metacommunity’ as the only keyword, and found 1226 English papers published in international journals between January 1992 and September 2017. And we exported these papers from Web of Science as a plain text file (.txt) with the option ‘Full Record and Cited References’, which is archived here. The text file thus contains full records and cited references (in a concise format, so without the titles of the cited papers), and each field is prefaced by a two-character field tag. Afterwards we used the software ‘Citespace’ to create Table 1 and Figure 2 in Guo et al. (2018). We created that Figure 2 using the following settings in Citespace: we choose the “co-occurrence” function and “keyword” as node types, then created keywords co-occurrence relationships (Figure 2). Next, we created lists of the top 20 keywords in different periods, which can reveal the research hotspots (Table 1). The method for extracting the top 20 most frequent keywords was as follows: We use the Web of Science database to retrieve scientific papers from 1992 to 2017, taking into account the relationship between citation and publication time. We calculated the percentage of 200 most cited papers published in each of the 5-year periods. To do so we followed the following steps:</p> <p>Step1: we selected the 200 most cited papers from the entire datset (1226 in the 1992-2017 period).</p> <p>Step 2: we calculated the percentage of those 200 papers published each 5-year period.</p> <p>Step 3: we also calculated an correction coefficient by taking, for each 5-year period, the number of top-200 most cited papers published in that period, and dividing that number by the total number of papers (out of the 1226 selected papers) published in that period.</p> <p>Step 4 Last, we calculate the real keyword frequency by multiplying the keyword frequency in specific 5-year periods with the correction coefficient.</p>
Viroplant Project - Microcosm studies on the effect of bacteriophages used as plant protection products on soil microbial communities
<p>This file contains the description, data and DNA analyses on the effect of bacteriophages with a potential to be used as plant protecction products on the structure and function of soil microbial communities. The objective was to evaluate two different microcsom incubation systems with phages and microbial cells from soil, or soil itself and to analyses in a time dependent manner how the phages affect the natural soil microbiomes. The microbial communities were quantified with qPCR and their diversity analyzed with PCR amplified 16S rRNA gene sequences. Bioinformatic analyses were used to evaluate microbial community responses</p>
Data from: Metabarcoding of soil environmental DNA replicates plant community variation but not specificity
<blockquote> <p>While metabarcoding of plant DNA from their environment is an exciting method that can supplement inventorying of live plant species, the accuracy and specificity has yet to be fully assessed over complex continuous landscapes. In this work, we evaluate plant community profiles produced via metabarcoding of soil by comparing them to a morphological survey. We assessed plant communities by metabarcoding of soil DNA in 130 sites along ecological gradients (nutrients, succession, moisture) in Denmark using chloroplast <i>trn</i>L region (10-143 bp) primer set and compared the resulting communities to communities produced with a longer nuclear ITS2 region (~216 bp) and a morphological survey. We found that the community variation observed within the morphological survey was well represented by molecular surveys, with significant correlation with both community composition and richness using both primer sets. While the majority of the ITS2 sequences could be assigned to species (over 80%), we had less success with the <i>trn</i>L sequences (70%), which was only possible after restricting the reference database to local species. We conclude that the community profiles produced by metabarcoding can be highly effective in performing large-scale macroecological studies. However, the discovery rates and taxonomic assignments produced via metabarcoding remained inferior to morphological surveys, but manual curation of databases improves the <i>specificity</i> of assignments made by the <i>trn</i>L primers, and improves the <i>accuracy</i> of the assignments made with the ITS2 primers. Finally, we suggest that a greater percentage of named diversity would be recovered by increasing soil sampling with the use of additional universal primer sets.</p> </blockquote>
Bacterial community composition of bulk soil from date palm (Phoenix dactylifera) farm depend on irrigation water salinity
<p>Non-saline and saline ground water irrigation is extensively used in the arid regions of United Arab Emirates (UAE) for date palm (<em>Phoenix</em> <em>dactylifera</em>) cultivation without knowing its effect on bulk soil bacterial communities. Bulk soil acts as a supply base for microbes and nutrients that are accessed by date palm roots. We collected soil samples from date farms across UAE and performed V3-V4 16s rRNA metabarcoding analysis to understand how bulk soil bacterial diversity and communities respond to irrigation water sources (non-saline and saline groundwater irrigation). There was no significant variation in bulk bacterial diversity (Shannon diversity, richness as well as evenness). But bulk bacterial communities differed between irrigation water sources and irrigation water electrical conductivity was the significant factor that explained a part of community variation. Out of total 5089 OTUs, saline bulk soil harbored only 21.3% of total OTUs compared to 31.5% OTUs in non-saline bulk soil, while 47.15% OTUs shared between both types of irrigation. Proteobacteria abundance was higher in saline bulk soil, while Actinobacteriota abundance was enhanced in non-saline bulk soil. Similar selection was observed at genus level, wherein saline bulk soil showed increase in abundance of <em>Subgroup_10, Nitrospira </em>and<em> Mycobacterium</em>, whereas <em>Microvirga, Ammoniphilus, Nitrospira</em> and <em>Lysinibacillus </em>were elevated in non-saline bulk soil. Saline (<em>Novibacillus</em> and <em>Bauldea</em>) and non-saline bulk soil (<em>Microvirga</em>, <em>Marmoricola</em>, <em>Domibacillus</em>, <em>Oceanobacillus</em>, <em>Bhargavaea</em> and <em>Solirubrobacter</em>) showed significant selection of indicator taxa (P < 0.05). This indicate that bacterial communities colonizing bulk soil differ depending on irrigation water source and it is affected by irrigation water EC.</p>
Fig. 1 in Species Composition And Structure Of The Communities Of Plant-Parasitic And Free-Living Soil Nematodes In The Greenhouses Of Botanical Gardens Of Ukraine
Fig. 1. Dendrogram of similarity of the nematode communities in the greenhouses of botanical gardens of Ukraine (amalgamation by the method of complete linkage). Explanation of the abbreviations is given in table 2. Рис. 1. Дендрограмма сходства нематодных сообществ в оранжереях ботанических садов Украины (объединение по методу полной связи). Расшифровка сокращений дана в таблице 2.
Fig. 2 in Species Composition And Structure Of The Communities Of Plant-Parasitic And Free-Living Soil Nematodes In The Greenhouses Of Botanical Gardens Of Ukraine
Fig. 2. Dendrogram of similarity of plant-parasitic nematodes' communities in the greenhouses of botanical gardens of Ukraine (amalgamation by the method of complete linkage). Explanation of the abbreviations is given in table 2.
Plant management but not fertilization mediates soil carbon emission and microbial community composition in subtropical Eucalyptus plantations
<p><span>The diversity of </span><span>plant functional group</span><span>s</span><span> in plantations affects soil carbon, but we have limited understanding of the underlying mechanisms for how plant management affects soil carbon dynamics. Here, we conducted a 3-year manipulation experiment of plant functional groups that included understory removal, tree root trenching, and fertilization treatments in 2-year-old and 6-year-old <em>Eucalyptus</em> plantations in the subtropical region. The results showed that soil respiration was significantly suppressed by understory removal (-38%), tree root trenching (-41%), and their interactions (-54%), but that fertilization alone and in interactions had no significant effect. The Chao1 indices for soil bacterial and fungal diversity significantly decreased with understory removal in the 2-year-old plantation and with tree root trenching in the 6-year-old plantation. Soil bacterial and fungal communities were also affected by understory removal and tree root trenching. Soil respiration, physicochemical characteristics, microbial diversity, and community composition were significantly affected by plantation age. Reductions in soil carbon emissions were associated with reductions in plant functional groups and soil microbial groups, while increases in soil respiration were associated with soil physicochemical factors, soil temperature, and plantation age. Our findings highlight that plant managements are of great significance to the soil carbon emission processes in afforested plantations.</span></p>
Data from: Plant community stability is associated with a decoupling of prokaryote and fungal soil networks
<p>Data from the manuscript Plant community stability is associated with a decoupling of prokaryote and fungal soil networks: https://doi.org/10.1101/2022.06.21.496867</p>
Different facets of bacterial and fungal communities drive soil multifunctionality in grasslands spanning a 3,500 km transect
<p>1. Soil microbial communities are essential in regulating ecosystem functions and services. However, the importance of bacterial and fungal communities as predictors of multiple soil functions (i.e., soil multifunctionality) in grassland ecosystems has not been studied systematically.</p> <p>2. Here, we measured soil microbial diversity, community composition, biomass, and multiple soil functions of 41 sites in five grassland ecosystems spanning a 3,500 km northeast–southwest transect. The random forest algorithm was adopted to determine the importance of geographical location, climatic, altitude, edaphic, plant, and microbial predictors in driving a proxy of soil multifunctionality (seven soil functions in this study). Moreover, structural equation models (SEMs) were employed to examine the direct and indirect effects of those predictors on soil multifunctionality.</p> <p>3. Our results demonstrated that soil multifunctionality was positively driven by soil fungal diversity but not by bacterial diversity. Fungal phylogenetic diversity (presence of different evolutionary lineages) showed stronger positive relationships with soil multifunctionality than taxonomic diversity (richness of species). Dominant bacterial taxa, particularly of phyla Actinobacteria and Proteobacteria, were positively associated with soil multifunctionality, while none of the fungal taxa were found to regulate soil multifunctionality. Furthermore, both fungal and bacterial biomass had significant effects on soil multifunctionality, while the effect of microbial biomass was weaker than that of fungal diversity and bacterial taxa. Importantly, the direct positive effects of soil fungal diversity, dominant bacterial taxa, and fungal and bacterial biomass were maintained after accounting for multiple predictors in grassland ecosystems.</p> <p>4. This study provided strong empirical evidence that soil multifunctionality was driven by different facets of the bacterial and fungal communities in the grassland ecosystems. Our results also highlighted that any loss of fungal diversity, dominant bacterial taxa and microbial biomass might reduce soil multifunctionality, exacerbating ecosystem functions and services such as soil fertility, primary production, and climate mitigation in grassland ecosystems. </p>
Data for: Soil legacy effects of plants and drought on aboveground insects in native and range-expanding plant communities
<p><span>Soils contain biotic and abiotic legacies of previous conditions that may influence plant community biomass and associated aboveground biodiversity. However, little is known about the relative strengths and interactions of the various belowground legacies on aboveground plant-insect interactions. We used an outdoor mesocosm experiment to investigate the belowground legacy effects of range-expanding versus native plants, extreme drought, and their interactions on plants, aphids, and pollinators. We show that plant biomass was influenced more strongly by the previous plant community than by a previous summer drought. Plant communities consisted of four congeneric pairs of natives and range expanders, and their responses were not unanimous. </span><span>Legacy effects affected the abundance of aphids more strongly than pollinators</span><span>. We conclude that historical climate warming-induced plant latitudinal range expansion and extreme drought contingencies can be contained as soil 'memories' that influence plant performance and aboveground community interactions in the next growing season.</span></p>
Bacterial Community Weighted rrn Operon Copy Numbers and Enzyme Activity in Costa Rica and Alaska Soils.
<p>Datasets of bacterial community weighted rrn operon copy number and enzyme activity in Alaska and Costa Rica soils. The Alaska soils were collected from across four elevational terraces in a tidal wetland on the western coast. The Costa Rica soils were collected from plots subjected to precipitation manipulation treatments in La Selva Biological Research Station. </p> <p>The variable descriptions for the Alaska dataset are as follows: simple_id: an identifier variable. id: an identifier variable that includes terrace identity. transect_id: a variable that identifies collection transect location. BG_umol_g_h: the activity rate of beta-glucosiadase in micromoles per gram soil per hour. NAG_umol_g_h: the activity rate of N-acetyl-glucosaminidase in micromoles per gram soil per hour. LAP_umol_g_h: activity rate of leucine-aminopeptidase in micromoles per grams soil per hour. AP_umol_g_h: the activity rate of acid phosphatase in micromoles per gram soil per hour. BG_g_C: the activity rate of beta-glucosiadase in micromoles per gram soil carbon per hour. NAG-g_N: activity rate of N-acetyl-glucosaminidase in micromoles per gram soil nitrogen per hour. LAP_g_N: activity rate of leucine-aminopeptidase in micromoles per gram soil nitrogen per hour. AP_mg_P: activity rate of acid-phosphatase in micromoles per gram soil phosphorus per hour. P_mg_kg: soil phosphorus concentration in milligrams phosphorus per kilogram soil. K_mg_kg: soil potassium concentration in milligrams potassium per kilogram soil. C_pct: soil carbon concentration in percent. N_pct: soil nitrogen concentration in percent. pH: soil pH. cn_ratio: the carbon-to-nitrogen ratio of soil. cp_ratio: the carbon-to-phosphorus ratio of soil. np_ratio: the nitrogen to phosphorus ratio of soil. weighted_copy_number: the bacterial community weighted rrn operon copy number.</p> <p>The variable descriptions for the Costa Rica dataset are as follows: ID: a variable that identifies the soil core collected. Plot: a variable that identifies the experimental plot from which soils were collected. Precip: a variable that describes the precipitation manipulation treatment applied. Core: a variable that identifies whether soil cores were enclosed in mesh or not. Moisture: the soil moisture of the collected core in grams water per grams dry soil. weighted copy_number: the bacterial community weighted rrn operon copy number. AP: activity rate of acid-phosphatase in micromoles per gram soil per hour. BG: the activity rate of beta-glucosiadase in micromoles per gram soil per hour.</p> <p>Also included are files of the code used to analyze the data in the R Statistical Computing Environment.</p>
Figure 2 in Changes in galling insect community on Caryocar brasiliense trees mediated by soil chemical and physical attributes
Figure 2. Principal components regressions among: (A) Eurytoma sp. galling insect adults (Eur.) with phosphorus-Mehlich 1 (mg dm-3) contents (P.C.) and silt (dag kg-1) (Si.); (B) numbers of Eurytoma sp. glodoid galls (E.G.G.) with pH in water, capacity of cationic exchange (cmol dm-3) (C.C.E.), and P.C.; (C) area (mm2) of Eurytoma sp. glodoid galls (A.E.G.) with pH and clay (dag kg-1) (Cl.); (D) c length of conglomerate of Eurytoma sp. globoid galls (L.G.E.) with pH and Cl.; (E) width of conglomerate of Eurytoma sp. globoid galls (W.G.E.) with pH and Cl.; and (F) number of Hymenopteran discoid galls (H.D.G.) with aluminum (cmol dm-3) contents (A.C.), C.C.E., c and percentage of soil base saturation of the capacity of cationic exchange to pH 7.0 (S.B.S.) on Caryocar brasiliense trees in three years. The symbols represent the averages and the bars the standard errors. n = 111.
Figure 1 in Changes in galling insect community on Caryocar brasiliense trees mediated by soil chemical and physical attributes
Figure 1. Principal components regressions among: (A) abundance of galling insects (A.G.I.) with phosphorus-Mehlich 1 (mg dm-3) contents (P.C.) and sand (dag kg-1) (Sa.); (B) species richness of galling insects (S.R.G.I.) with capacity of cationic exchange (cmol dm-3) c (C.C.E.); (C) diversity of galling insects (D.G.I.) with C.C.E. and Sa.; (D) percentage of galled leaflet by all galls (P.G.L.) with pH in water and clay (dag kg-1) (Cl.); and (E) percentage of leaflet area taken by all galls (P.L.A.G.) with aluminum (cmol dm-3) contents (A.C.) and c C.C.E. on Caryocar brasiliense trees in three years. The symbols represent the averages and the bars the standard errors. n = 111.
Figure 4 in Changes in galling insect community on Caryocar brasiliense trees mediated by soil chemical and physical attributes
Figure 4. Principal components regressions among: (A) abundance of predators (A.Pr.) with phosphorus-Mehlich 1 (mg dm-3) contents (P.C.) and clay (dag kg-1) (Cl.); (B) species richness of predators (S.R.Pr.) with number of Hymenoptera discoid galls (H.D.G.), species richness of parasitoids (S.R.P.), and Cl.; (C) diversity of predators (D.Pr.) with diversity of parasitoids (D.P.) and Cl.; (D) number of Zelus armillatus (Zar.) with P.C., percentage of soil base saturation of the capacity of cationic exchange to pH 7.0 (S.B.S.), silt (dag kg-1) (Si.), numbers of Eurytoma sp. glodoid galls (E.G.G.), and capacity of cationic exchange (cmol dm-3) (C.C.E.); (E) number of Epipolops sp. (Epi.) c with protocooperanting ants (Ants), Cl., and pH in water; and (F) number of spiders (Spi.) with Ants, P.C., and Si. on Caryocar brasiliense trees in three years. The symbols represent the averages and the bars the standard errors. n = 111.
Data from: Rhizosphere bacterial community composition depends on plant species identity and soil legacy effects
<p>This record contains supplementary information for the article "Rhizosphere bacterial community composition depends on plant diversity legacy in soil and plant species identity".</p> <p><strong>Supplemental Table S1.</strong> The table contains the annotation for all the samples sequenced and analyzed.</p> <p><strong>Supplemental Table S2. </strong>The table contains all primer sequences used in the study.</p> <p><strong>Supplemental Table S3.</strong> The zip-file contains a table with the taxonomic annotation of the operational taxonomic units (OTUs) identified in the study.</p> <p><strong>Supplemental Table S4. </strong> The zip-file contains a table with sequence counts of the operational taxonomic units (OTUs) identified in the study.</p> <p><strong>Supplemental Table S5. </strong>The workbook contains a sheet with the number of operational taxonomic units (OTUs) exhibiting differential abundance in any of the contrasts tested in this study. Note that “down/up” indicates whether the OTU was less (“down”) or more (“up”) abundant in the first group of the contrast. For example, given the contrast “PH_mix_vs_mon_”, “down” corresponds to higher abundance in the pots from the monoculture plant history. Conversely, “up” refers to higher abundance in the pots from the mixed culture plant history. In addition, the workbook contains one sheet per contrast with the logBaseMean (log2 of the average normalized abundance across all samples), the logFC (log2 of the fold-change), the <em>P</em>-value, and the adjusted <em>P</em>-value (FDR). Only OTUs with a <em>P</em>-value <= 0.05 or an adjusted <em>P</em>-value (FDR) <= 0.1 are given.</p> <p><strong>Supplemental Table S6. </strong>The table contains the number of bacterial OTUs annotated with a given bacterial phylum.</p> <p><strong>Supplemental Table S7. </strong>The table contains all phyla tested for enrichment/depletion in the set of OTUs with an increased abundance in monoculture and mixed culture soils respectively. “Total counts (all OTUs)” corresponds to the total number of all OTUs annotated with a given phyla (reference set). “Observed” corresponds to the number of OTUs annotated with a given phyla in the set OTUs with increased abundance in monoculture/mixed culture soils (test set). "Expected" gives the number of OTUs which would be expected to be annotated with a given phyla if the test set were randomly sampled from the reference set.</p> <p><strong>Supplemental File S1.</strong> The zip-file contains a fasta file with the 10'205 OTU sequences identified in the study.</p> <p> </p> <p> </p>
Figure 3 in Organic farming and moderate tillage change the dominance and spatial structure of soil Collembola communities but have little effects on bulk abundance and species richness
Figure 3. Abundance, number of species and Berger-Parker index in samples in different management types and fields. Colors show fields. Boxplots show data distribution (n = 81 per field), horizontal lines represent the medians.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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