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223 results for “soil functioning”
Functional environmental genomics of a municipal landfill soil
GEO Series GSE37154. Folsomia candida. 32 samples. Type: Expression profiling by array.
microbial functional genes are fundamentally different from bacterial phylogenetic distributions in arctic soils
GEO Series GSE63927. Bacteria. 72 samples. Type: Genome variation profiling by array.
Soil variation response is mediated by growth trajectories rather than functional traits in a widespread pioneer Neotropical tree
<p>Description of Soil_DataTrees.csv</p> <ul> <li>Tree_label: Label of trees on the field, there are 70 trees</li> <li>Tree_site: Site on which the tree has been sampled; COU: Counami; SPA: Sparouine</li> <li>Descr_date: Date of tree sampling</li> <li>Soil_type: Type of soil; FS: ferralitic soils; WS: white-sand soils</li> <li>Soil_sample: Label of soil sample</li> <li>H2Osoil: Soil water content (g kg<sup>-1</sup>)</li> <li>Clay: Soil clay content (g kg<sup>-1</sup>)</li> <li>SiltTh: Soil thin silt content (g kg<sup>-1</sup>)</li> <li>SiltCo: Soil coarse silt content (g kg<sup>-1</sup>)</li> <li>SandTh: Soil thin sand content (g kg<sup>-1</sup>)</li> <li>SandCo: Soil coarse sand content (g kg<sup>-1</sup>)</li> <li>Csoil: Soil carbon content (g kg<sup>-1</sup>)</li> <li>Nsoil: Soil nitrogen content (g kg<sup>-1</sup>)</li> <li>CNsoil: Soil carbon:nitrogen ratio</li> <li>MOsoil: Soil organic matter content (g kg<sup>-1</sup>)</li> <li>Ptotsoil: Soil total phosphorus content (g 100g<sup>-1</sup>)</li> <li>Kcec: Soil potassium:CEC[cation-exchange capacity] ratio</li> <li>Cacec: Soil calcium:CEC ratio</li> <li>Mgcec: Soil magnesium:CEC ratio</li> <li>Nacec: Soil sodium:CEC ratio</li> <li>Alcec: Soil aluminum:CEC ratio</li> <li>Fecec: Soil iron:CEC ratio</li> <li>Mncec: Soil manganese:CEC ratio</li> <li>Hcec: Soil hydrogen:CEC ratio</li> <li>pHsoil: Soil pH (cmol kg<sup>-1</sup>)</li> <li>CECsoil: Soil cation-exchange capacity (cmol kg<sup>-1</sup>)</li> <li>Indexsoil: Soil index of fertility = (K+Ca+Mg+Na)/CEC</li> </ul> <p>K, Ca, Mg, Na, Al, Fe, Mn, H were initially measured in cmol kg<sup>-1</sup></p> <p> </p> <p>Description of Trait_DataTrees.csv</p> <ul> <li>Tree_label: Label of the tree on the field. There are 70 trees</li> <li>Tree_site: Site of sampling; COU: Counami; SPA: Sparouine</li> <li>Descr_date: Date of tree sampling</li> <li>Calendar_day: Day of the year (between 1 and 365) of tree sampling</li> <li>Soil_type: Type of the soil; FS: ferralitic soils; WS: white-sand soils</li> <li>PCA1_soil: Coordinates of the trees along the first axis of PCA (principal component analysis) with soil data, used as a quantitative soil index on FS-WS soil gradient</li> <li>mesHeight: Measured tree height (m)</li> <li>Height: Tree height based on the sum of all internodes length (m)</li> <li>Dbh: Tree diameter at height breast (cm)</li> <li>Age: Tree age (year)</li> <li>Order: Number of branching order</li> <li>Brtot: Total number of branches branching from the trunk</li> <li>Leaftot: Total number of leaves</li> <li>Fltot: Total number of inflorescences</li> <li>Acrown: Total estimated crown area (m²)</li> <li>INA1: Number of trunk internodes</li> <li>Brbear: Number of A2 bearing branches</li> <li>Brdead: Number of A2 dead branches</li> <li>Br1stH: First branching height</li> <li>Fl1stH: First flowering height</li> <li>Br1stIN: First branching node rank</li> <li>Fl1stIN: First flowering node rank</li> <li>Br1stAge: First branching age</li> <li>Fl1stAge: First flowering age</li> <li>LL: Leaf lifespan (day)</li> <li>Lpet: Petiole length (cm)</li> <li>Apet: Petiole cross-sectional area (mm²)</li> <li>Nlobe: Number of leaf lobes</li> <li>LMA: Leaf mass area (g m<sup>-2</sup>)</li> <li>Thleaf: Leaf thickness (µm)</li> <li>Aleaf: Estimated individual leaf area (cm²)</li> <li>Chlleaf: Leaf chlorophyll content (mg ml<sup>-1</sup>)</li> <li>H20resleaf: Leaf residual water content (%)</li> <li>dC13leaf: δ<sup>13</sup>C content (‰)</li> <li>Cleaf: Leaf carbon content (g kg<sup>-1</sup>)</li> <li>Nleaf: Leaf nitrogen content (g kg<sup>-1</sup>)</li> <li>CNleaf: Leaf carbon:nitrogen ratio</li> <li>Pleaf: Leaf phosphorus content (g kg<sup>-1</sup>)</li> <li>Kleaf: Leaf potassium content (g kg<sup>-1</sup>)</li> <li>WSG: Wood specific gravity (g cm<sup>-3</sup>)</li> </ul> <p> </p> <p> </p> <ul> <li>Tree_label: Label of the tree</li> <li>Soil_type: Type of the soil; FS: ferralitic soils; WS: white-sand soils</li> <li>rank_base: Rank of the internode from the base of the tree</li> <li>rank_top: Rank of the internode from the apex of the tree</li> <li>phyllochron: Phyllochron, number of days for the production of one leaf</li> <li>date: Estimated date of tree germination</li> <li>nb_day_base: Number of days since estimated germination</li> <li>nb_day_top: Age of the internode in days at tree sampling</li> <li>AS_rank_base: Rank of the annual shoot from the base of the tree</li> <li>As_rank_top: Rank of the annual shoot from the apex of the tree</li> <li>AS_nodes_base: Number of internodes per annual shoot</li> <li>AS_length_base: Length of the annual shoot (cm)</li> <li>AS_br_base: Number of A2 branches on the annual shoot</li> <li>AS_flo_base: Number of inflorescences on the annual shoot</li> <li>lg_en: Internode length (cm)</li> <li>ht_en: Cumulated height of the tree based on the sum of internode length (cm)</li> <li>ma_lgen: Moving average of internode length</li> <li>resi_lgen: Residuals of internode length</li> </ul> <p> </p>
Data from: Effects of livestock grazing on soil, plant functional diversity and ecological traits vary between regions with different climate in northeastern Iran
Understanding the responses of vegetation characteristics and soil properties to grazing in different precipitation regimes is useful for the management of rangelands, especially in the arid regions. In northeastern Iran, we studied the responses of vegetation to livestock grazing in three regions with different climate: arid, semi-arid and sub-humid. In each region, we selected 6-7 pairwise sampling areas of high versus low grazing intensity and six traits of the present species were recorded on 1 m2 plots - 5 grazed and 5 ungrazed in each area. The overall fertility was compared using the dissimilarity analysis, linear mixed-effect models were used to compare the individual fertility parameters, functional diversity indices and species traits between the plots with high and low grazing intensity and between the climatic regions. Both climate and grazing, as well as their interaction, affected fertility parameters, functional diversity indices and the representation of species traits. Grazing reduced functional evenness, height of the community, the representation of annuals, but increased the community leaf area. In the sub-humid region, grazing also reduced functional richness. Further, grazing decreased the share of annual species in the semi-arid region and seed mass in the arid region. Larger leaf area and seed mass, smaller height and lower share of annuals were associated with intensive grazing. Species with large LA and seed mass, lower height and perennials can be therefore presumed to tolerate trampling and benefit from high nutrient levels, associated with intensive grazing. By providing a detailed view on the impacts of overgrazing, this study highlights the importance of protection from grazing as an effective management tool for maintaining the pastoral ecosystems. In general, the composition of plant traits across the pastures of northeastern Iran was more affected by intensive grazing compared to climate.
Data from: Effects of rhizoma peanut cultivars (Arachis glabrata Benth.) on the soil bacterial diversity and predicted function in nitrogen fixation
There is a growing awareness of the importance of soil microorganisms in agricultural management practices. Currently, much less is known about whether different crop cultivar has an effect on the taxonomic structure and diversity, and specific functions of soil bacterial communities. Here we examined the changes of the diversity and composition and enzyme-encoding nitrogenase genes in a long-term field experiment with seven different rhizoma peanut cultivars in southeastern USA, coupling high-throughput 16S rRNA gene sequencing and the sequence-based function prediction with Tax4Fun. Of the 32 phyla detected (Proteobacteria class), 13 were dominant: Acidobacteria, Alphaproteobacteria, Actinobacteria, Betaproteobacteria, Bacteroidetes, Verrucomicrobia, Gammaproteobacteria, Deltaproteobacteria, Gemmatimonadetes, Firmicutes, Nitrospirae, Chloroflexi and Planctomycetes (relative abundance >1%). We found no evidence that the diversity and composition of bacterial communities was significantly different among different cultivars, but the abundance of some dominant bacterial groups that have N-fixation potentials (at broad or fine taxonomic level) and predicted abundances of some enzyme-encoding nitrogenase genes showed significant across-cultivar differences. The nitrogenase genes were notably abundant in Florigraze and Latitude soils while remarkably lower in Arbook and UF_TITO soils when compared with other cultivars, indicating different nitrogen fixation potentials among different cultivars. The findings also suggest that the abundance of certain bacterial taxa and the specific function bacteria perform in ecosystems can have an inherent association. Our study is helpful to understand how microbiological responses and feedback to different plant genotypes through the variation in structure and function of their communities in the rhizosphere.
Data from: Top-down control of carbon sequestration: grazing affects microbial structure and function in salt marsh soils
Tidal wetlands have been increasingly recognized as long-term carbon sinks in recent years. Work on carbon sequestration and decomposition processes in tidal wetlands focused so far mainly on effects of global-change factors such as sea-level rise and increasing temperatures. However, little is known about effects of land use, such as livestock grazing, on organic matter decomposition and ultimately carbon sequestration. The present work aims at understanding the mechanisms by which large herbivores can affect organic matter decomposition in tidal wetlands. This was achieved by studying both direct animal-microbe interactions and indirect animal-plant-microbe interactions in grazed and ungrazed areas of two long-term experimental field sites at the German North Sea coast. We assessed bacterial and fungal gene abundance using quantitative PCR, as well as the activity of microbial exo-enzymes by conducting fluorometric assays. We demonstrate that grazing can have a profound impact on the microbial community structure of tidal wetland soils, by consistently increasing the fungi-to-bacteria ratio by 38-42%, and therefore potentially exerts important control over carbon turnover and sequestration. The observed shift in the microbial community was primarily driven by organic matter source, with higher contributions of recalcitrant autochthonous (terrestrial) vs. easily degradable allochthonous (marine) sources in grazed areas favoring relative fungal abundance. We propose a novel and indirect form of animal-plant-microbe interaction: top-down control of aboveground vegetation structure determines the capacity of allochthonous organic matter trapping during flooding and thus the structure of the microbial community. Furthermore, our data provide the first evidence that grazing slows down microbial exo-enzyme activity and thus decomposition through changes in soil redox chemistry. Activities of enzymes involved in C cycling were reduced by 28-40%, while activities of enzymes involved in N cycling were not consistently affected by grazing. It remains unclear if this is a trampling-driven direct grazing effect, as hypothesized in earlier studies, or if the effect on redox chemistry is plant mediated and thus indirect. This study improves our process-level understanding of how grazing can affect the microbial ecology and biogeochemistry of semi-terrestrial ecosystems that may help explain and predict differences in C turnover and sequestration rates between grazed and ungrazed systems.
Soil variation response is mediated by growth trajectories rather than functional traits in a widespread pioneer Neotropical tree
<p>Description of Soil_DataTrees.csv</p> <ul> <li>Tree_label: Label of trees on the field, there are 70 trees</li> <li>Tree_site: Site on which the tree has been sampled; COU: Counami; SPA: Sparouine</li> <li>Descr_date: Date of tree sampling</li> <li>Soil_type: Type of soil; FS: ferralitic soils; WS: white-sand soils</li> <li>Soil_sample: Label of soil sample</li> <li>H2Osoil: Soil water content (g kg<sup>-1</sup>)</li> <li>Clay: Soil clay content (g kg<sup>-1</sup>)</li> <li>SiltTh: Soil thin silt content (g kg<sup>-1</sup>)</li> <li>SiltCo: Soil coarse silt content (g kg<sup>-1</sup>)</li> <li>SandTh: Soil thin sand content (g kg<sup>-1</sup>)</li> <li>SandCo: Soil coarse sand content (g kg<sup>-1</sup>)</li> <li>Csoil: Soil carbon content (g kg<sup>-1</sup>)</li> <li>Nsoil: Soil nitrogen content (g kg<sup>-1</sup>)</li> <li>CNsoil: Soil carbon:nitrogen ratio</li> <li>MOsoil: Soil organic matter content (g kg<sup>-1</sup>)</li> <li>Ptotsoil: Soil total phosphorus content (g 100g<sup>-1</sup>)</li> <li>Kcec: Soil potassium:CEC[cation-exchange capacity] ratio</li> <li>Cacec: Soil calcium:CEC ratio</li> <li>Mgcec: Soil magnesium:CEC ratio</li> <li>Nacec: Soil sodium:CEC ratio</li> <li>Alcec: Soil aluminum:CEC ratio</li> <li>Fecec: Soil iron:CEC ratio</li> <li>Mncec: Soil manganese:CEC ratio</li> <li>Hcec: Soil hydrogen:CEC ratio</li> <li>pHsoil: Soil pH (cmol kg<sup>-1</sup>)</li> <li>CECsoil: Soil cation-exchange capacity (cmol kg<sup>-1</sup>)</li> <li>Indexsoil: Soil index of fertility = (K+Ca+Mg+Na)/CEC</li> </ul> <p>K, Ca, Mg, Na, Al, Fe, Mn, H were initially measured in cmol kg<sup>-1</sup></p> <p> </p> <p>Description of Trait_DataTrees.csv</p> <ul> <li>Tree_label: Label of the tree on the field. There are 70 trees</li> <li>Tree_site: Site of sampling; COU: Counami; SPA: Sparouine</li> <li>Descr_date: Date of tree sampling</li> <li>Calendar_day: Day of the year (between 1 and 365) of tree sampling</li> <li>Soil_type: Type of the soil; FS: ferralitic soils; WS: white-sand soils</li> <li>PCA1_soil: Coordinates of the trees along the first axis of PCA (principal component analysis) with soil data, used as a quantitative soil index on FS-WS soil gradient</li> <li>mesHeight: Measured tree height (m)</li> <li>Height: Tree height based on the sum of all internodes length (m)</li> <li>Dbh: Tree diameter at height breast (cm)</li> <li>Age: Tree age (year)</li> <li>Order: Number of branching order</li> <li>Brtot: Total number of branches branching from the trunk</li> <li>Leaftot: Total number of leaves</li> <li>Fltot: Total number of inflorescences</li> <li>Acrown: Total estimated crown area (m²)</li> <li>INA1: Number of trunk internodes</li> <li>Brbear: Number of A2 bearing branches</li> <li>Brdead: Number of A2 dead branches</li> <li>Br1stH: First branching height</li> <li>Fl1stH: First flowering height</li> <li>Br1stIN: First branching node rank</li> <li>Fl1stIN: First flowering node rank</li> <li>Br1stAge: First branching age</li> <li>Fl1stAge: First flowering age</li> <li>LL: Leaf lifespan (day)</li> <li>Lpet: Petiole length (cm)</li> <li>Apet: Petiole cross-sectional area (mm²)</li> <li>Nlobe: Number of leaf lobes</li> <li>LMA: Leaf mass area (g m<sup>-2</sup>)</li> <li>Thleaf: Leaf thickness (µm)</li> <li>Aleaf: Estimated individual leaf area (cm²)</li> <li>Chlleaf: Leaf chlorophyll content (mg ml<sup>-1</sup>)</li> <li>H20resleaf: Leaf residual water content (%)</li> <li>dC13leaf: δ<sup>13</sup>C content (‰)</li> <li>Cleaf: Leaf carbon content (g kg<sup>-1</sup>)</li> <li>Nleaf: Leaf nitrogen content (g kg<sup>-1</sup>)</li> <li>CNleaf: Leaf carbon:nitrogen ratio</li> <li>Pleaf: Leaf phosphorus content (g kg<sup>-1</sup>)</li> <li>Kleaf: Leaf potassium content (g kg<sup>-1</sup>)</li> <li>WSG: Wood specific gravity (g cm<sup>-3</sup>)</li> </ul> <p> </p> <p> </p> <ul> <li>Tree_label: Label of the tree</li> <li>Soil_type: Type of the soil; FS: ferralitic soils; WS: white-sand soils</li> <li>rank_base: Rank of the internode from the base of the tree</li> <li>rank_top: Rank of the internode from the apex of the tree</li> <li>phyllochron: Phyllochron, number of days for the production of one leaf</li> <li>date: Estimated date of tree germination</li> <li>nb_day_base: Number of days since estimated germination</li> <li>nb_day_top: Age of the internode in days at tree sampling</li> <li>AS_rank_base: Rank of the annual shoot from the base of the tree</li> <li>As_rank_top: Rank of the annual shoot from the apex of the tree</li> <li>AS_nodes_base: Number of internodes per annual shoot</li> <li>AS_length_base: Length of the annual shoot (cm)</li> <li>AS_br_base: Number of A2 branches on the annual shoot</li> <li>AS_flo_base: Number of inflorescences on the annual shoot</li> <li>lg_en: Internode length (cm)</li> <li>ht_en: Cumulated height of the tree based on the sum of internode length (cm)</li> <li>ma_lgen: Moving average of internode length</li> <li>resi_lgen: Residuals of internode length</li> </ul> <p> </p>
Data from: Effects of rhizoma peanut cultivars (Arachis glabrata Benth.) on the soil bacterial diversity and predicted function in nitrogen fixation
Open the record for dataset details and reuse information.
Data from: Top-down control of carbon sequestration: grazing affects microbial structure and function in salt marsh soils
Open the record for dataset details and reuse information.
Data from: Effects of livestock grazing on soil, plant functional diversity and ecological traits vary between regions with different climate in northeastern Iran
Open the record for dataset details and reuse information.
Effect of land use and soil organic matter quality on the structure and function of microbial communities in pastoral soils: implications for disease suppression
GEO Series GSE112489. Archaea; uncultured soil bacterium; Bacteria; Eukaryota. 50 samples. Type: Other.
Warming-induced shifts in Antarctic soil microorganisms disrupt soil functioning
GEO Series GSE22825. environmental samples; environmental samples; Bacteria. 49 samples. Type: Other.
RNA-Seq Analysis to confirm Different Transcriptomic Profiles of Soil-Derived Functional Module Consortia.
GEO Series GSE143587. soil metagenome. 15 samples. Type: Expression profiling by high throughput sequencing.
Environmental functional genes for soil microbes in South East Queensland, Australia
GEO Series GSE23422. synthetic construct; uncultured microorganism. 12 samples. Type: Expression profiling by array.
Functional gene abundance of tundra soil microbial communities sampled at the EML Thaw Gradient site, AK in May 2004
GEO Series GSE97107. Eukaryota; Viruses; synthetic construct; Bacteria; Archaea. 107 samples. Type: Genome variation profiling by array.
Microbial community structure and functions are resilient to metal pollution along two forest soil gradients
GEO Series GSE59620. uncultured bacterium; Bacteria. 12 samples. Type: Genome variation profiling by array.
Effect of biogas slurry combined with biochar on soil microbial function gene
GEO Series GSE133800. Archaea; Bacteria; Eukaryota; Viruses; uncultured soil microorganism. 15 samples. Type: Other.
Survey of Functional Genes in the Arctic Soil of Whapmagoostui-Kuujjuarapik
GEO Series GSE168911. soil metagenome; uncultured soil microorganism. 10 samples. Type: Other.
Survey of Functional Genes in the Arctic Soil of Pond Inlet
GEO Series GSE168985. uncultured soil microorganism; soil metagenome. 10 samples. Type: Expression profiling by array; Other.
soil mcirobial functional communities
GEO Series GSE135153. Archaea; Bacteria; Eukaryota; Viruses; uncultured soil microorganism. 15 samples. Type: Genome variation profiling by array.
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