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110 results for “Soil structure”
Soil and plant biogeochemical and soil temperature variables collected at brown lemming (Lemmus trimucronatus) and tundra vole (Microtus oeconomus) structure sties near Nome, Toolik Lake, and Utqaigvik, Alaska, summer 2018-2020
Soil and plant sampling analysis under small mammal-built structures and controls sites from near the Team Vole fences: Nome, Toolik, Utqiagvik, AK 2018-2020.
RCS01 Recovery and relative influence of root, microbial, and structural properties of soil on physically sequestered carbon stocks in restored grassland at Konza Prairie
Managing soil to sequester C can help mitigate increasing CO2 in the atmosphere. To maximize this ecosystem service, more knowledge of factors influencing C sequestration is needed. The objectives of this study were to (i) quantify recovery of the roots, microbial biomass and composition, and soil structure across a chronosequence of grassland restorations and (ii) use a structural equation model to develop a data-based hypothesis on the relative influence of physical and biological soil properties on the soil C aggregate fraction diagnostic of sequestered C. We hypothesized measured variables would recover with restoration age. Belowground plant biomass and tissue quality (C/N ratio), soil microbial biomass C, phospholipid fatty acid (PLFA) concentrations, soil structure, and soil C stocks in the bulk soil and each aggregate fraction were quantified from a cultivated field, prairies restored for 1 to 35-yr (n = 6), and a never-cultivated (native) prairie. Root biomass, microbial biomass C, arbuscular mycorrhizal fungi (AMF) PLFA biomass across the chronosequence increase to resemble native prairie following 35 yr of restoration. Many aspects of soil structure (i.e., bulk density, proportional mass of aggre- gate fractions, and aggregate mean weighted diameter) and the distribution C among soil fractions, including C in the micro-within-macro aggregate fraction (sequestered C), also became representative of native prairie within 35 yr of restoration. Total soil C stock and physically protected C increased at a similar rate (23 and 27 g C m-2 yr-1) respectively, across the chronosequence. After 35 yr of restoration, 50% of the total C pool was physically protected. The structural equation modeling developed by these data hypothesizes that microbial biomass C and AMF biomass (microbial composition) have the strongest causal influence on physically protected C. This model needs to be tested using independent sites to achieve greater inference.
Dataset Changes in structure and assembly of a species-rich soil natural community with contrasting nutrient availability upon establishment of a plant-beneficial Pseudomonas in the wheat rhizosphere
<p>This dataset is related to the paper "<strong>Changes in structure and assembly of a species-rich soil natural community with contrasting nutrient availability upon establishment of a plant-beneficial <em>Pseudomonas </em>in the wheat rhizosphere</strong>" (Garrido-Sanz et al., 2023, doi: 10.1186/s40168-023-01660-5) and contains the data obtained from bacterial competition asays and plant-growth measurements.</p> <p>Sequencing data used in this study has been deposited in the NCBI Sequence Read Archive (RSA) under the BioProject accession number <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA948847">PRJNA948847</a>.</p> <p>The R script used to analyze the data generated in the paper is available at <a href="https://github.com/dgarrs/Pprotegens_proliferation_NatComs">GitHub </a>and <a href="https://doi.org/10.5281/zenodo.8322086">Zenodo</a>.</p>
Soil redox potential vs. soil microbial activity and structure
<p>The dataset was collected as a part of the Carbon Action field experiment in 2021. The experiment had 100 farms testing carbon farming practices 2019-2023. These samples were from a subset of 20 farms, which were routinely sampled every year.</p> <p>I measured the following parameters</p> <ul> <li>redox potential Eh, with an ORP meter + electrode reference voltage at 50% pore filled moisture</li> <li> pH in distilled water 10:1 water:soil mixture</li> <li>VESS soil structure on the field</li> <li>CO2burst by rewetting dried soil and measuring how much CO2 was respired in 24 hours</li> </ul> <p>pH and Eh were used to calculate a rH2 hydrogen potential, which combines Eh and pH</p> <p>Background information for clay and organic matter content was determined separately</p> <p>the clay content was defined by sieving and the organic matter content by loss of ignition.</p> <p>The analysis of the results is published in Plant and Soil journal in 2023. </p> <p>Mattila, T.J., 2023. Redox potential as a soil health indicator - how does it compare to microbial activity and soil structure?</p> <p><a href="https://doi.org/10.1007/s11104-023-06305-y">https://doi.org/10.1007/s11104-023-06305-y</a></p>
Fig. 1 in Changes in soil moisture and riparian forest structure after a dam construction
Fig. 1. Satellite image of a riparian forest on southern Brazil. Study area image with square showing plots locations. A = Spillway and the beginning of Reduced Outflow Stretch, A' = end of Reduced Outflow Stretch, B = hydroeletric dam, B' = end of hydroelectric dam, C = artificial lake created by dam, D = river patch returns to normal flow. The square ilustrates the study area.
Fig. 3 in Changes in soil moisture and riparian forest structure after a dam construction
Fig. 3. Major changes that drives the community changes. Before river diversion, the sectors near the river had greater basal areas because they had many thick trees while distant sectors had thin trees (the density was statistically similar). After four years of river diversion, there were many trunks of still alive trees and dead trees in the sector closer to the river. Even with high growth, the basal area in this sector was severely reduced and became similar to the distant sector (which already has small basal area).
Fig. 2 in Changes in soil moisture and riparian forest structure after a dam construction
Fig. 2. Soil moisture changes that occurred due to construction of the dams. A and C represent soil moisture in dry forests before damming, and B and D represent soil moisture after damming construction. The continuous line represents soil surface; vertical black bars represent soil sampling sites; blue bars represent soil moisture and their thickness illustrates soil moisture; and thicker bars represent more moisture. After dam influence, soil moisture increased mainly in the dry season and mainly near the lakeshore.
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.
Spatial structure within root systems moderates stability of Arbuscular Mycorrhizal mutualism and plant-soil feedbacks
<p class="RealLife">The persistence of mutualisms is paradoxical, as there are fitness incentives for exploitation. This is particularly true for plant-microbe mutualisms like arbuscular mycorrhizae (AM), which are promiscuously, horizontally-transmitted. Preferential allocation by hosts to the best mutualist can stabilize horizontal mutualisms, however preferential allocation is imperfect, with its fidelity likely depending upon the spatial structure of symbionts in plant roots. In this study, we tested AM mutualisms' dependence on two dimensions of spatial structure: the initial dispersion of fungi and the ease of fungal dispersal, through three complementary experiments. We found that fitness of the beneficial AM fungus increased when fungi were initially separate, while initial spatial mixing benefited the fitness of the non-beneficial fungus. These effects were strongest when dispersal was limited, and hosts could discriminate. Additionally, we found that spatial structure moderated changes in AM fungal composition produced differential feedbacks on plant growth. Our results identify symbiont spatial structure within plant roots as an important modifier of plant preferential allocation and the dynamics of mycorrhizal mutualisms, with cascading effects on plant communities.</p>
Frontiers in Ecology and Evolution 01 frontiersin.org Why grazing and soil matter for dry grassland diversity: New insights from multigroup structural equation modeling of micro-patterns
<p>Grazing is recognized as a major process driving the composition of plant<br> communities in grasslands, mostly due to the heterogeneous removal of<br> plant species and soil compaction that results in a mosaic of small patches<br> called micro-patterns. To date, no study has investigated the differences in<br> composition and functioning among these micro-patterns in grasslands in<br> relation to grazing and soil environmental variables at the micro-local scale.<br> In this study, we ask (1) To what extent are micro-patterns different from each<br> other in terms of species composition, species richness, vegetation volume,<br> evenness, and functioning? and (2) based on multigroup structural equation<br> modeling, are those differences directly or indirectly driven by grazing and soil<br> characteristics? We focused on three micro-patterns of the Mediterranean dry<br> grassland of the Crau area, a protected area traditionally grazed in the South-<br> East of France. From 70 plant community relevés carried out in three micro-<br> patterns located in four sites with different soil and grazing characteristics,<br> we performed univariate, multivariate analyses and applied structural equation<br> modeling for the first time to this type of data. Our results show evidence<br> of clear differences among micro-pattern patches in terms of species<br> composition, vegetation volume, species richness, evenness, and functioning<br> at the micro-local scale. These differences are maintained not only by direct<br> and indirect effects of grazing but also by several soil variables such as fine<br> granulometry. Biological crusts appeared mostly driven by these soil variables,<br> whereas reference and edge communities are mostly the result of different<br> levels of grazing pressure revealing three distinct functioning specific to each<br> micro-pattern, all of them coexisting at the micro-local scale in the studied<br> Mediterranean dry grassland. This first overview of the multiple effects of<br> grazing and soil characteristics on communities in micro-patterns is discussed<br> within the scope of the conservation of dry grasslands plant diversity.</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.
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).
Figure 1 in Soil preference and burrow structure of an endangered tarantula, Brachypelma vagans (Mygalomorphae: Theraphosidae)
Figure 1. Study sites of Brachypelma vagans in the Greater Calakmul Region in southern Mexico. See ''Methods'' for codes.
Figure 2. Principal components 1 and 2 in Soil preference and burrow structure of an endangered tarantula, Brachypelma vagans (Mygalomorphae: Theraphosidae)
Figure 2. Principal components 1 and 2 extracted from the analysis of the tarantula densities and soil characteristics (squares) at seven sites (circles). See ''Methods'' for codes.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.