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553 results for “soil communities”
Figure 1 in sOilFauna - a global synthesis effort on the drivers of soil macrofauna communities and functioning
Figure 1. Location of the transects in the present version of the MACROFAUNA database (A) Map of the data (B) Location of the data in relation to the biomes.
Figure 3 in Comparison of soil invertebrate communities in organic and conventional production systems in Southern Brazil
Figure 3. Non-metric multidimensional scaling (NMDS) plot showing the relationship between macrofauna taxa (black text) and soil chemical and physical properties (red text) of samples taken in four land-use system in Quitandinha, Brazil. NF = Native forest, OH = Organic horticulture, RT = Reduced tillage, CH = Conventional horticulture.
Figure 3 in Soil Oligochaeta communities after 9 decades of continuous fertilization in a bare fallow experiment
Figure 3. Feeding activity (mean of 16 bait-lamina sticks, N plots per treatment), in % of the total number of baits consumed.
Figure 3 in Effect of land cover on biodiversity and composition of a soil macrofauna community in a reclaimed coastal area at Yancheng, China
Figure 3. The dendrogram of cluster analysis on soil macrofauna from different habitats with Bray–Curtis similarity by paired groups method (A: Uncultivated land; B: Bulrush land; C: Wheat farm; D: Poplar forest; E: Metasequoia forest).
Figure 2 in Effect of land cover on biodiversity and composition of a soil macrofauna community in a reclaimed coastal area at Yancheng, China
Figure 2. One-way ANOVA on taxonomic richness and abundance, Margalef 's richness index (R) and Shannon-Weaver diversity index (H') among different habitats (Mean ± SE). The means with different scripts are significantly different by SNK test, α = 0.05.
Figure 3 in Impact of dike age on biodiversity and functional composition of soil macrofaunal communities in poplar forests in a reclaimed coastal area
Figure 3. PCoA ordinal configuration of soil macrofaunal communities from different habitats by Euclidean distance similarity index. In the code of the samples, the prefix means the code of the habitat, and the suffix means the number of the sample.
Figure 2 in Impact of dike age on biodiversity and functional composition of soil macrofaunal communities in poplar forests in a reclaimed coastal area
Figure 2. One-way ANOVA of taxonomic richness and abundance (A) and Margalef 's richness index R and Shannon– Weaver diversity index H' (B) across different habitats (mean ± SE). Means with different scripts are significantly different by Dunnett's T3 test (A) and LSD test (B), α = 0.05.
Figure 4 in Composition and structure of soil fauna community in the Dexing Copper Mine tailings pool after revegetation
Figure 4. One-way ANOVA of Shannon–Wiener diversity index (H'), Pielou index (Js), and Margalef index (D) across the different samples (mean ± SE). Means with different letters are significantly different as assessed by LSD test, α = 0.05.
Figure 3 in Composition and structure of soil fauna community in the Dexing Copper Mine tailings pool after revegetation
Figure 3. One-way ANOVA of the functional groups across the different samples (mean ± SE). Means with different letters are significantly different as assessed by LSD test, α = 0.05.
Figure 5 in Composition and structure of soil fauna community in the Dexing Copper Mine tailings pool after revegetation
Figure 5. Results of the principal components analysis (PCA) of the soil fauna data. The values are means (n = 3) with bidirectional error bars of axis 1 and 2. For all the PCA plots, the values on the x- and y-axes represent the percent variation explained by axis 1 and axis 2, respectively. I, II, III, and IV refer to the sample I, sample II, sample III, and sample IV, respectively.
Figure 2 in Composition and structure of soil fauna community in the Dexing Copper Mine tailings pool after revegetation
Figure 2. One-way ANOVA of the abundance (A) and taxonomic richness (B) across different samples (mean ± SE). Means with different letters are significantly different as assessed by LSD test, α = 0.05.
Figure 4 in Structural characteristics of the soil fauna community in beach wetlands of the Poyang Lake region
Figure 4. Shannon–Weiner index (H') and Pielou index (Js) of soil fauna community at different sampling sites in the Poyang Lake region.
Figure 1 in Structural characteristics of the soil fauna community in beach wetlands of the Poyang Lake region
Figure 1. Schematic diagram of soil fauna sampling locations in Poyang Lake beach wetlands. 1: Henghu Reclamation Farm, Xinjian district, Nanchang city (HH); 2: Nanji township, Xinjian district, Nanchang city (Nanji Wetland National Natural Reserve) (NJ); 3: Lianhu township, Poyang county, Shangrao city (LH).
Soil microbial communities from tropical forest and oil palm
<div> <h1>Description</h1> <p>A study examining the impact of selective logging and forest conversion to oil palm on soil microbial community composition. Soil samples were collected from old growth forest, selectively logged forest and oil palm plantations. Soil bacterial, protistan and fungal community composition were measured and summarised by calculating richness. </p> <h1>Projects</h1> <p> This dataset was collected as part of the following projects: </p><ul> <li><a href="https://safeproject.net/projects/project_view/124">https://safeproject.net/projects/project_view/124</a> </li> </ul> <p></p> <h1>Funding</h1> <p> These data were collected as part of research funded by: </p> <ul> <li>UK NERC-funded Biodiversity And Land-use Impacts on Tropical Ecosystem Function (BALI) consortium (Standard grant , NE/K016377/1 ) </li> </ul> <p></p> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <h1>Permits</h1> <p>These data were collected under permit from the following authorities:</p> <ul> <li>Sabah Biodiversity Centre ( Research licence JKM/MBS.1000-2/2 JLD.5 (20))</li> <li>Sabah Biodiversity Centre ( Export licence JKM/MBS.1000-2/3 JLD.2 (70))</li> </ul> <p></p> <h1>Files</h1> <p>This dataset consists of 1 file: SAFE_Dataset_Richness.xlsx</p> <h2>SAFE_Dataset_Richness.xlsx</h2> <p>This file contains dataset metadata and 1 data tables:</p> <h3>Soil_Microbial_Communities</h3> <ul> <li>Worksheet: Soil_Microbial_Communities</li> <li>Description: Summary richness statistics from bacterial 16S, protistan 18S and fungal ITS biomarker microbial sequencing from DNA extracted from soils</li> <li>Number of fields: 10</li> <li>Number of data rows: 225</li> <ul> <li>PlotName: Plot name corresponding to the GEM Carbon plot where soils were sampled (type: id)</li> <li>ForestType: Old-growth, Logged or Oil palm (type: categorical)</li> <li>ForestPlotsCode: Plot name as listed in ForestPlots database (type: id)</li> <li>Replicate: replicate identifier for which soil core taken from each subplot (type: replicate)</li> <li>location_name: Name of subplot where soils were collected (type: location)</li> <li>Bacteria_Richness: Number of observed bacterial taxa from sequencing of 16S marker genes from soil samples (type: numeric)</li> <li>Protist_Richness: Number of observed protistan taxa from sequencing of 18S marker genes from soil samples (type: numeric)</li> <li>Fungal_Richness: Number of observed fungal taxa from sequencing of 16S marker genes from soil samples (type: numeric)</li> <li>EcM_Fungal_Richness: Number of observed Ectomycorrhizal fungal taxa from sequencing of ITS marker genes from soil samples (type: numeric)</li> <li>AMF_Fungal_Richness: Number of observed Arbuscular Mycorrhizal fungal taxa from sequencing of ITS marker genes from soil samples (type: numeric)</li> </ul> </ul> <h1>Extents</h1> <ul> <li>Date range: 2014-10-01 to 2018-09-01</li> <li>Latitudinal extent: 4.64° to 4.954°</li> <li>Longitudinal extent: 116.95° to 117.796°</li> </ul> </div>
Saline groundwater irrigation affects the date palm bulk soil associated fungal guild community and enhances the pathotroph abundance
<p><strong><em> Exploring the Influence of Saline Groundwater Irrigation on Soil Fungal Biodiversity in Date Palm (Phoenix dactylifera) Bulk Soil</em></strong></p>
Fig. 3 in Recovery Of Litter And Soil Invertebrate Communities Following Swidden Cultivation In Sarawak, Malaysia
Fig. 3. Nonmetric multidimensional scaling (NMDS) analysis of litter (A) and soil (B) invertebrates and relationship (P<0.05) with microhabitat environmental factors in young (Y) and old (O) fallows and primary forest (P). BA: tree basal area; CanopyOpen: canopy openness; MiniDis: the shortest straight distance from each plot to the primary forest of LHNP; TreeSp: observed number of tree species.
Fig. 4 in Recovery Of Litter And Soil Invertebrate Communities Following Swidden Cultivation In Sarawak, Malaysia
Fig. 4. Nonmetric multidimensional scaling (NMDS) analysis of litter (A) and soil (B) termites and relationship (P<0.05) with microhabitat environmental factors in young (Y) and old (O) fallows and primary forest (P). BA: tree basal area; TreeSp: observed number of tree species.
Fig. 1 in Recovery Of Litter And Soil Invertebrate Communities Following Swidden Cultivation In Sarawak, Malaysia
Fig. 1. Individual density distributions of different functional groups in litter (A) and soil (B) within the three forest types. a Significant difference was detected among forest types in litter (P<0.01) and soil (P<0.05).
Fig. 2 in Recovery Of Litter And Soil Invertebrate Communities Following Swidden Cultivation In Sarawak, Malaysia
Fig. 2. Termite frequency distribution of different feeding guilds in litter (A) and soil (B) within the three forest types. a Significant difference was detected among forest types in soil (P<0.05).
Figure 2 in Edaphic characteristics and environmental impact of rubber tree plantations on soil mite (Acari) communities
Figure 2 Abundance logarithmic transformation – logx (+1) of Gamasida (A) and Oribatida (B) major groups across the land use types. SF: secondary forests, R7: 7-year-old rubber plantations, R12: 12- year-old rubber plantations, R25: 25-year-old rubber plantations. N = 120; one-way ANOVA test,p
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OpenNeuro
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