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133 results for “Soil sampling”
Data from: Treated like dirt: Robust forensic and ecological inferences from soil eDNA after challenging sample storage
<p>We investigated the effect of storage duration and conditions on the assessment of the soil biota with eDNA metabarcoding. We extracted eDNA from freshly collected soil samples and again from the same samples after storage under contrasting temperature conditions and contrasting exposure (open/closed tubes). We used four different primer sets targeting bacteria, fungi, protists (cercozoans), and general eukaryotes. <span>W</span>e quantified differences in richness, evenness, and community composition. Subsequently, we tested whether we could correctly infer habitat type and original sample identity after storage using a large reference dataset.</p> <p>This repository contains the un-demultiplexed fastq sequences.</p>
Рис. 2. РаспреΔеΛение среΔних почвенных образцов по коΛичеству жизнеспособных цист Heterodera glycines Fig. 2. Distribution of average soil samples by the number of viable cysts of Heterodera glycines in Reproductive potential of Soybean Cyst Nematode Heterodera glycines - quarantine pest of soybean - in Primorsky Region conditions
Рис. 2. РаспреΔеΛение среΔних почвенных образцов по коΛичеству жизнеспособных цист Heterodera glycines Fig. 2. Distribution of average soil samples by the number of viable cysts of Heterodera glycines
Рис. 1. РаспоΛожение стационаров вбΛизи насеΛенных пунктов, в окрестностях которых собираΛись воΑные и почвенные пробы: 1 —ЗакатаΛа (41.755469 N, 46.658248 E; 2 — ИсмаиΛΛы (40.971043 N, 48.133806 E; 3 — ПиргуΛи (40.868328 N, 48.599481 E); 4 — АΛтыагач (40.942707 N, 49.027354 E); 5 — Шемаха (40.742370 N, 48.639842 E); 6 — Куба (41.424798 N, 48.487536 E) Fig 1. Location of Permanent Sampling Points near the settlements in the vicinity of which water and soil samples were collected: 1 — Zagatala (41.755469 N, 46.658248 E; 2 — Ismayilli (40.971043 N, 48.133806 E; 3 — Pirguli (40.868328 N, 48.599481 E); 4 — Altiagach (40.942707 N, 49.027354 E); 5 — Shemakha (40.742370 N, 48.639842 E); 6 — Сuba (41.424798 N, 48.487536 E) in Ciliates of fresh waters and soils of the Greater Caucasus (within Azerbaijan)
Рис. 1. РаспоΛожение стационаров вбΛизи насеΛенных пунктов, в окрестностях которых собираΛись воΑные и почвенные пробы: 1 —ЗакатаΛа (41.755469 N, 46.658248 E; 2 — ИсмаиΛΛы (40.971043 N, 48.133806 E; 3 — ПиргуΛи (40.868328 N, 48.599481 E); 4 — АΛтыагач (40.942707 N, 49.027354 E); 5 — Шемаха (40.742370 N, 48.639842 E); 6 — Куба (41.424798 N, 48.487536 E) Fig 1. Location of Permanent Sampling Points near the settlements in the vicinity of which water and soil samples were collected: 1 — Zagatala (41.755469 N, 46.658248 E; 2 — Ismayilli (40.971043 N, 48.133806 E; 3 — Pirguli (40.868328 N, 48.599481 E); 4 — Altiagach (40.942707 N, 49.027354 E); 5 — Shemakha (40.742370 N, 48.639842 E); 6 — Сuba (41.424798 N, 48.487536 E)
Рис. 1. Точки сбора воΑных и почвенных проб в окрестностях гороΑов Губа (1), Хачмаз (2) и ХуΑат (3) (Северо-Восточный АзербайΑжан) Fig. 1. Sampling points of water and soil samples in the vicinity of the cities of Guba (1), Khachmaz (2) and Khudat (3) (North-East Azerbaijan) in Free-living protozoa of freshwater and soils of the North-East Azerbaijan
Рис. 1. Точки сбора воΑных и почвенных проб в окрестностях гороΑов Губа (1), Хачмаз (2) и ХуΑат (3) (Северо-Восточный АзербайΑжан) Fig. 1. Sampling points of water and soil samples in the vicinity of the cities of Guba (1), Khachmaz (2) and Khudat (3) (North-East Azerbaijan)
Fig. 1 in A report of six unrecorded bacterial species isolated from soil samples in Korea
Fig. 1. Transmission electron micrographs of the strains in this study. Strains: 1, SBS2-5; 2, SRC3-8; 3, SRC1-1; 4, SRC1-3; 5, SRC3-1; 6, SBS9-6.
Fig. 3 in A report of six unrecorded bacterial species isolated from soil samples in Korea
Fig. 3. Neighbor-joining phylogentic tree based on 16S rRNA gene sequences of the isolate in this study and their relatives of the genus Paenibacillus. Strain: SBS9-6. Bootstrap values (>50%) are shown above nodes for the neighbor-joining methods. Bar: 0.01 substitutions per nucleotide position.
Figure 5 in Efficiency of sampling methods for capturing soil-dwelling ants in three landscapes in southern Cameroon
Figure 5: Differentiation coefficient based on species richness (left) and Simpson's index (right) in three sampling methods (A) Bait, (B) Pitfall, (C) Quadrat.
Figure 4 in Efficiency of sampling methods for capturing soil-dwelling ants in three landscapes in southern Cameroon
Figure 4. Differentiation coefficient based on species richness (left) and simpson's index (right) in three habitats: (A) Upland, (B) Littoral, (C) Urban.
Figure 3 in Endogean beetles (Coleoptera) of illustrated DNA barcode library Guatemala: deep soil sampling and
Figure 3. Neighbour Joining DNA barcode tree of 75 endogean beetles from Guatemala. Terminal names consist of the most detailed current taxonomic identification (genus, tribe, or subfamily), followed by specimen number, family name, sample number, length of the DNA barcode fragment [with the number of ambiguously read bases in square brackets], BIN number, and GenBank accession number.
Figure 2 in Endogean beetles (Coleoptera) of illustrated DNA barcode library Guatemala: deep soil sampling and
Figure 2. Sampling methods of the deep soil Guatemala beetles. (A–C) pits producing samples GT12, GT16, and GT25, respectively (note that sample GT16 is from an extremely dry habitat, while sample GT25 is twice as large in volume); (D) a floating soil sample in a barrel with water; (E) scooping floating organic foam containing live beetles on a fine mesh; (F) wet samples prior to specimen extraction; (G) two aluminium thermoeclectors of the novel larger and lighter design; (H) thermoeclectors exposed to the Sun.
Text-fig. 3 Pollen diagram from the locality of Bohutín. 0-0.01 m – sandy soil mixed with humus, slightly clayey, sample B11; 0.05 m – grey-blue strongly sandy clay, sample B10; 0.10-0.25 m – brown-grey sandy clay with plant remains and mixed with a small amount of peat, sample B9, sample B8 (0.15 m), sample B7 (0.20 m), sample B6 (0.25 m); 0.30-0.35 m – dark sandy clay mixed with peat and plant remains, sample B5, sample B4 (0.35 m); 0.40 m – grey strongly sandy clay mixed with peat, sample B3; 0.45-0.50 m – grey-blue strongly sandy clay, sample B2, sample B1 (0.50 m). in Reconstruction Of Vegetation Development On The Floodplain Of The Litavka River In The Holocene (Central Bohemia, Brdy Mts.)
Text-fig. 3 Pollen diagram from the locality of Bohutín. 0-0.01 m – sandy soil mixed with humus, slightly clayey, sample B11; 0.05 m – grey-blue strongly sandy clay, sample B10; 0.10-0.25 m – brown-grey sandy clay with plant remains and mixed with a small amount of peat, sample B9, sample B8 (0.15 m), sample B7 (0.20 m), sample B6 (0.25 m); 0.30-0.35 m – dark sandy clay mixed with peat and plant remains, sample B5, sample B4 (0.35 m); 0.40 m – grey strongly sandy clay mixed with peat, sample B3; 0.45-0.50 m – grey-blue strongly sandy clay, sample B2, sample B1 (0.50 m).
Data of soil infiltration tests and soil samples, Los Arenales MAR Systems, Santiuste and La Laguna del Señor infiltration basins
<p><span>Infiltration test and soil sample data utilised in the article "a nature-based solution to enhance aquifer recharge: combining trees and infiltration basins"</span></p>
Fluxes of the protonated masses from the soil samples collected from two temperate ecosystems detected by PTR-ToF-MS
<p>Volatile organic compounds (VOCs) are reactive gaseous compounds with significant impacts on air quality and the Earth's radiative balance. While natural ecosystems are known to be major sources of VOCs, primarily due to vegetation, soils, an important component of these ecosystems, have received relatively less attention as potential sources and sinks of VOCs.</p> <p>In this study, soil samples were collected from two temperate ecosystems: a beech forest and a heather heath, and then sieved, homogenized, and incubated under various controlled conditions such as different temperatures, oxic <em>vs</em>. anoxic conditions, and different ambient VOC levels. A dynamic flow-through system coupled to a proton transfer reaction-time of flight-mass spectrometry (PTR-ToF-MS) was used to measure production and/or uptake rates of selected VOCs, aiming to explore the processes and their controlling mechanisms.</p> <p>This dataset therefore is collected from these experiments. It includes the raw flux data and figure source data associated with a peer-reviewed publication in Soil Biology & Biochemistry at <a href="https://doi.org/10.1016/j.soilbio.2023.109153">https://doi.org/10.1016/j.soilbio.2023.109153</a>.</p> <p>Overall, our results showed that these soils were natural sources of a variety of VOCs, and the strength and profile of these emissions were influenced by soil biogeochemical properties (e.g. moisture, soil organic matter), oxic/anoxic conditions, and temperature. The soils also acted as sinks for most VOCs when VOC substrates at parts per billions levels (ranging between 0.18-68.65 ppb) were supplied to the headspace of the enclosed soils, and the size of the sink corresponded to the amount of VOCs available in the ambient air. Temperature-controlled incubations and glass bead simulations indicated that the uptake of VOCs by soils was likely driven by microbial metabolism, with a minor contribution from physical adsorption to soil particles. In conclusion, our study suggests that soil uptake of VOCs can mitigate the impact of other significant VOC sources in the near-surface environment and potentially regulate the net exchange of these trace gases in ecosystems.</p> <p>Should you have any questions regarding the dataset, please free feel to contact Yi jiao at yi.jiao@bio.ku.dk or Prof. Rinnan at riikkar@bio.ku.dk</p>
Use of Deep Learning for structural analysis of CT-images of soil samples
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Data from: Treated like dirt: Robust forensic and ecological inferences from soil eDNA after challenging sample storage
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Data from: eDNA metabarcoding of log hollow sediments and soils highlights the importance of substrate type, frequency of sampling and animal size, for vertebrate species detection
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Testing the feasibility of quantifying change in agricultural soil carbon stocks through empirical sampling
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Laboratory-based hyperspectral visible near-infrared reflectance spectral dataset of soil samples across a range of surface orientations
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Characterization of burned and unburned moist acidic tundra soils for estimating C and N loss from the 2007 Anaktuvuk River Fire, sampled in 2008.
This file contains the soil profile data for burned and unburned moist acidic tundra sites used to estimate C and N loss from the Anaktuvuk River Fire (2007). These sites were sampled in summer of 2008. Unburned sites were used to develop a method for estimating soil organic layer depth and plant biomass, and for determining the characteristics of unburned soil organic layers. In burned sites, we characterized residual organic soils and used biometric measurements of tussocks to reconstruct pre-fire soil organic layer depth. Together, these measurements were used to reconstruct pre-fire soil and plant carbon and nitrogen pools and estimate ecosystem losses of these elements during the fire.
Proteolytic enzyme activity of organic and mineral soil core samples collected near Toolik Lake field station, Alaska, July 2001
The original focus of this study was an analysis of proteolytic enzyme activity of Alaskan arctic tundra soils, however initial results raised questions regarding the method (Watanabe and Hayano, 1995). Thus, the goals of the study changed to 1) an investigation of the method, and 2) a comparison of enzyme activities of two different soil layers from the arctic tundra. Methodological examination included the impact of toluene, used to prevent immobilization of the product, and blank correction of enzyme activity, and a search for a true 6-h linear rate of activity during a 48-hour incubation. We measured native and potential, using casein as an artificial substrate, activities as net amino acid production in mineral and organic soil layer samples. Varying toluene concentration had no clear effect on activity; omitting toluene resulted in zero native activity and reduced potential for the organic samples, but not for the mineral. Comparison of activities with and without blank correction indicated, particularly for potential activity of samples with low native rates, that correction was required for accuracy. Native and potential activity of the organic samples, and native of the mineral were linear for the first 6 h of incubation; linearity was observed during the 6 to 24 h incubation for potential activity of the mineral. Soil layer activity data indicated that native activity was higher in organic soils as compared with mineral. The organic layer potential activity was ten-fold greater than the native, suggesting substrate limitation; potential and native activities did not differ in the mineral layer, indicating substrate sufficiency. Casein addition changed the kinetic pattern for both layers from hyperbolic to sigmoidal for the mineral and linear for the organic, implying different enzyme pools or behavioral changes of existing pools. Native activity based on total soluble protein was higher for the mineral samples relative to the organic, reiterating substrate
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