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opencc-zeroJun 2024View details →

Slope position affects growth and allometry of the endangered conifer Calocedrus macrolepis by mediating soil properties and microbial communities

Premise: The allometric relationships among growth traits are highly relevant for a tree's fitness, however, the mechanism of how the slope position affects the plant growth and allometry remains poorly understood, hindering our understanding of the variation in allometry of trees along slope position in mountainous areas. Methods: A typical slope of Calocedrus macrolepis plantation in southwest China was chosen to measure growth traits and their allometric relationships. In addition, spatial variations in soil properties and microbial communities were also investigated. Results: Slope position altered the allometric growth pattern with the larger allometric exponents of the tree height, diameter and wood volume relative to the crown size and height under the branch for the downslope. Additionally, most of soil nutrients, microbial diversity and abundances were greater at mesoslope and downslope, especially at the surface soil layer. The relative abundance both in Chloroflexi and Actinobacteria differed significantly among slope positions, while fungal dominant phyla abundances varied little across slope positions, indicating that bacterial community was more sensitive to slope position than fungal community. The growth traits and allometry were affected by the slope position, which is mainly caused by the variations of soil properties and microbial communities, and bacteria were more important than fungi in their relationships to growth traits and allometry. Conclusions: Together, results emphasized that slope position indirectly influences the growth traits and allometry of C. macrolepis by regulating soil nutrients and microbial communities, which will provide important theoretical basis for the plantation management of C. macrolepis.

opencc-zeroJun 2024View details →

Response of soil fungal communities and their co-occurrence patterns to grazing exclusion in different grassland types

Overgrazing and climate change are the main causes of grassland degradation, and grazing exclusion is one of the most common measures for restoring degraded grasslands worldwide. Soil fungi can respond rapidly to environmental stresses, but the response of different grassland types to grazing control has not been uniformly determined. Three grassland types (temperate desert, temperate steppe grassland, and mountain meadow) that were closed for grazing exclusion for nine years were used to study the effects of grazing exclusion on soil nutrients as well as fungal community structure in the three grassland types. The results showed that (1) in the 0–5 cm soil layer, grazing exclusion significantly affected the soil water content of the three grassland types (P<0.05), and the pH, total phosphorous (TP) and nitrogen-to-phosphorous ratio (N/P) changed significantly in all three grassland types (P<0.05). Significant changes in soil nutrients in the 5–10 cm soil layer after grazing exclusion occurred in the mountain meadow grasslands (P<0.05), but not in the temperate desert and temperate steppe grasslands. (2) For the different grassland types, Archaeorhizomycetes was most abundant in the montane meadows, and Dothideomycetes was most abundant in the temperate desert grasslands and was significantly more abundant than in the remaining two grassland types (P<0.05). Grazing exclusion let to insignificant changes in the dominant soil fungal phyla and in α diversity but significant changes in the β diversity of soil fungi (P<0.05). (3) Grazing exclusion areas have higher mean clustering coefficients and modularity classes than grazing areas. In particular, the highest modularity class is found in temperate steppe grassland grazing exclusion areas. (4) We also found that pH is the main driving factor affecting soil fungal community structure, that plant coverage is a key environmental factor affecting soil community composition, and that grazing exclusion indirectly affects soil fungal communities by affecting soil nutrients. The above results suggest that grazing exclusion may regulate microbial ecological processes by changing the soil fungal β diversity in the three grassland types. Grazing exclusion is not conducive to the recovery of soil nutrients in areas with mountain meadow but improves the stability of soil fungi in temperate steppe grassland. Therefore, the type of degraded grassland should be considered when formulating suitable restoration programmes when grazing exclusion measures are implemented. The results of this study provide new insights into the response of soil fungal communities to grazing exclusion, providing a theoretical basis for the management of degraded grassland restoration.

opencc-zeroJun 2024View details →

Data from: More diverse rhizobial communities can lead to higher symbiotic nitrogen fixation rates, even in nitrogen-rich soils

Symbiotic nitrogen fixation (SNF) by legumes and their rhizobial partners is one of the most important sources of bioavailable nitrogen to terrestrial ecosystems. While most work on the regulation of SNF has focused on abiotic drivers such as light, water, and soil nutrients, the diversity of rhizobia with which an individual legume partners may play an important but under-recognized role in regulating nitrogen inputs from SNF. By experimentally manipulating the diversity of rhizobia available to legumes, we demonstrate that rhizobial diversity can increase average SNF rates by more than 90%, and that high rhizobial diversity can induce increased SNF even under conditions of high soil N fertilization. However, the effects of rhizobial diversity, the conditions under which diversity effects were strongest, and the likely mechanisms driving these diversity effects differed between the two legume species we assessed. These results provide evidence that biodiversity-ecosystem function relationships can occur at scales of an individual plant, and that the effects of rhizobial diversity may be as important as long-established abiotic factors, such as nitrogen availability, in driving terrestrial nitrogen inputs via SNF.

opencc-by-4.0Dec 2018View details →

Agriculture erases climate constraints on soil nematode communities across large spatial scales

Data supporting "Agriculture erases climate constraints on soil nematode communities across large spatial scales".

opencc-by-4.0Aug 2024View details →

Figure 2 in sOilFauna - a global synthesis effort on the drivers of soil macrofauna communities and functioning

Figure 2. Main hypotheses of soil biodiversity drivers. Adapted from (Calderón-Sanou et al. 2022).

opencc-by-4.0Dec 2022View details →

Figure 2 in Community analysis of soil-inhabiting nematodes in natural vegetations of Singalila National Park, West Bengal (India)

Figure 2. Taxonomic diversity (abundance and genera) of nematode orders in Singalila National Park.

opencc-by-4.0Nov 2015View details →

Figure 1 in Impact of dike age on biodiversity and functional composition of soil macrofaunal communities in poplar forests in a reclaimed coastal area

Figure 1. Distribution of sample sites on the reclaimed coast.

opencc-by-4.0May 2019View details →

Figure 5 in Structural characteristics of the soil fauna community in beach wetlands of the Poyang Lake region

Figure 5. Margalef index (D) of soil fauna community at different sampling sites.

opencc-by-4.0Aug 2024View details →

Soil Properties and Soil Macroinvertebrate Communities in Amazonian Anthropogenic Soils

<h3>About</h3> This dataset is an update of the dataset 'A “Dirty” Footprint: Soil macrofauna biodiversity and fertility in Amazonian Dark Earths and adjacent soils' previously published in Dryad repository (https://doi.org/10.5061/dryad.3tx95x6cc). This dataset now include information on soil humification index, soil carbon related to different soil minerals and soil fatty acids characterization. <h3>Description</h3> Soils were sampled in Brazilian Amazonia in the municipalities of Iranduba-AM, Belterra-PA and Porto Velho-RO. In each region, paired sites with anthropogenic dark earths (ADE) and nearby reference (REF) non-anthropogenic soils were sampled under three different land-use systems: native secondary vegetation (dense ombrophilous forest) classified as old secondary forest when >20 years old, or young regeneration forest when <20 years old, and agricultural systems (maize in Iranduba, soybean in Belterra, and introduced pasture in Porto Velho). At each site, soil and litter macrofauna were collected using the Tropical Soil Biology and Fertility (TSBF) method (Anderson and Ingram, 1993) at five sampling points (soil monoliths 25x25 cm up to 30 cm depth) within a 1 ha plot, four at the corners and one on the center of a 60 x 60 m square, resulting in an “X” shaped sampling design. Each soil monolith was divided into surface litter and three 10 cm-thick soil layers (0-10, 10-20, 20-30 cm). Macroinvertebrates (animals with >2 mm body width) were manually hand-sorted and fixed in 92% ethanol. Earthworms, ants and termites were identified to species or genus level and other macroinvertebrates were sorted into morphospecies with higher taxonomic level assignations. Density (number of individuals) and biomass of the soil macrofauna surveyed using the TSBF method were extrapolated per square meter. For the earthworms, ants and termites (ecosystem engineers) additional samples were performed, especially in forest sites to better estimate species richness of these taxa. Earthworms were collected at four additional cardinal points of the grid at all sites, and hand-sorted from holes of similar dimensions as the TSBF monoliths. Termites were sampled in the forest sites only forests (except one of the REF young forests at Porto Velho), in five 20 m2 (2 x 10 m) plots (close to the five main soil monoliths) by manually digging the soil and looking for termitaria in the soil, as well as in the litter and on trees using a modification of the transect method (Jones and Eggleton, 2000). Ants were sampled in 10 pitfall traps (300 ml plastic cups) set up as two 5-trap transects on the sides of each 1 ha plot, as well as in two traps to the side of each TSBF monolith (distant ~5 m) only in the forest systems of Iranduba and Belterra (not Porto Velho). Each cup was filled to a third of its volume with water, salt and detergent solution. Termites and ants were preserved in 80% ethanol and earthworms in 96% ethanol and the alcohol changed after cleaning the samples within 24 h. All the animals (earthworms, ants, termites) were identified to species level or morphospecies level (with genus assignations) by co-authors SWJ/MLCB (earthworms), AA (termites) and ACF/RMF (ants). Soil samples for chemical and particle size analysis were collected from each TSBF monolith after the soil fauna hand-sorting. Around 2 to 3 kg soil from each depth (0-10, 10-20, 20-30 cm) and the following soil properties were evaluated according to standard methodologies (Teixeira et al., 2017): pH (CaCl2); Ca2+, Mg2+, Al3+ (KCl 1 mol L-1); K+, P, Fe, Zn, Mn, Cu and Ni (Mehlich-1); Pseudo-total contents of trace elements (Ba, Cd, Co, Cu, Ni, Pb, Se and ZN) were determined by acid digestion (HNO3 + HCl); Fe (sulfuric extract); total nitrogen (TN) and carbon (TC) using an element analyzer (CNHS). Base saturation and cation exchange capacity (CEC) were calculated using standard formulae (Teixeira et al., 2017) and particle size fractions (% sand, silt, clay) were obtained following standard methodologies (Teixeira et al., 2017). Soil magnetic susceptibility (MS) and apparent electrical conductivity (ECa) (Siemens per meter – S m-1) were obtained using a KT-10 S/C magnetic susceptibility/conductivity meter (Terraplus) with 10 Hz of operating frequency. Soil macromorphology samples were taken close to the TSBF monolith (~2 m) using a 10 x 10 x 10 cm metal frame. The collected material was separated into different fractions including: living invertebrates, litter, roots, pebbles, pottery sherds, charcoal (biochar), non-aggregated/loose soil (NA), physical aggregates (PA), root-associated aggregates (RA), and fauna-produced aggregates (FA) using the methodology proposed by Velasquez et al. (2007). Laser-induced fluorescence spectroscopy analysis (LIFS) was performed on soil macroaggregate fraction (FA, PA, RA and NAS) from both YF and the pasture from Porto Velho to obtain the humification index of soil organic matter according to Milori et al. (2006). Were analysed fatty acids in soil macroaggregates (PA, RA and FA) from one site in Teotônio. The process involved extracting 2 g of each sample with a chloroform: methanol solution and a surrogate compound, 5α-cholestane. The extract was centrifuged, combined, and the solvent removed using a rotary evaporator and nitrogen. Extracts were stored at -20°C until analyzed by GC-MS. Samples were silylated, with excess silylating agent removed, followed by the addition of hexane and vortexing for GC-Q-MS analysis. The equipment used included an Agilent Technologies GC (7890B) and MS (5977A), with an autosampler and HP-5ms column. MassHunter and MSD ChemStation software facilitated analysis and quantification, respectively. Deconvolution and retention index calculations were performed using AMDIS software. Compounds were identified using NIST MS software, requiring at least three specific mass fragments per compound and a retention index deviation of less than 1.5%. Analyte intensities were normalized by dried soil sample weights and the internal standard. Soil samples from TSBF monoliths were fractionated by dry sieving into small (<500 µm) and large (>500 µm) aggregate size classes. These were further fractionated into sand-particulate organic matter (sand-MOP) (>53 µm), silt-organic matter associated with minerals (MOM) (53-2 µm), and clay-MOM (<2 µm). Total organic carbon and nitrogen in these fractions were measured using a Vario EL III elemental analyzer. Clay-MOM samples underwent a four-step sequential extraction with hydroxylamine, sodium dithionite, sodium pyrophosphate, and sodium hydroxide to determine carbon, silicon, iron, and aluminum contents associated with different soil components. For further details see Ramalho (2020). Soil bulk density and total porosity were determined using undisturbed core samples (0.05 m diameter, 0.05 m depth) collected at ~2 m from the TSBF samples following the method proposed by Teixeira et al. (2017). All data is provided in excel format, and includes 14 tabs in the data file: Metadata and legend, Site description, Soil chem, BD+POR, Macromorph, Seq_ext, Biomark, HLIF, Macro_den, Macro_bio, Morpho_TSBF, Add_worm, Add_ants, Add_termites. The Metadata and legend tab provides a detailed explanation for each variable included in each table, including the units used for each. The Site description tab include a brief description of the sites sampled. Soil chem, BD+POR, Macromorph, Seq_ext, Biomark and HLIF tables contain the data on soil chemical, physical, macromorphological, organic matter related to soil minerals, fatty acids and humidification index variables, respectively. The Macro_den and Macro_bio contain the data about density and biomass on all the soil invertebrate taxa found, respectively. The Morphosp_TSBF, Add_worm, Add_ants and Add_termites tables contain the invertebrate species/morphospecies occurrence in TBSF and extra samples for earthworms, ants and termites, respectively. <h3>References used in methods section</h3> Anderson, J.M., Ingram, J.S.I., 1993. Tropical Soil Biology and Fertility: A handbook of methods, 2 edition. ed. Oxford University Press, Oxford. https://doi.org/10.2307/2261129 Jones, D.T., Eggleton, P., 2000. Sampling termite assemblages in tropical forests: testing a rapid biodiversity assessment protocol. Journal of Animal Ecology 37, 191–203. https://doi.org/10.1046/j.1365-2664.2000.00464.x Milori, D.M.B.P., Galeti, H.V.A., Martin-Neto, L., Dieckow, J., González-Pérez, M., Bayer, C., Salton, J., 2006. Organic Matter Study of Whole Soil Samples Using Laser-Induced Fluorescence Spectroscopy. Soil Science Society of America Journal. 70, 57. https://doi.org/10.2136/sssaj2004.0270 Teixeira, P.C., Donagemma, G.K., Fontana, A., Teixeira, W.G., 2017. Manual de métodos de análise de solo, 3o. ed. Embrapa, Brasília. Ramalho. B., 2020. Caracterização das interações organo-mineral em Terra Preta de Índio. Thesis. Universidade Federal do Paraná. 113p. Velasquez, E., Pelosi, C., Brunet, D., Grimaldi, M., Martins, M., Rendeiro, A.C., Barrios, E., Lavelle, P., 2007. This ped is my ped: Visual separation and near infrared spectra allow determination of the origins of soil macroaggregates. Pedobiologia 51, 75–87. https://doi.org/10.1016/j.pedobi.2007.01.002 <h3>Funding</h3> The study was supported by the Newton Fund and Fundação Araucária (grant Nos. 45166.460.32093.02022015, NE/N000323/1), Natural Environment Research Council (NERC) UK (grant No. NE/M017656/1), a European Union Horizon 2020 Marie-Curie fellowship to LC (MSCA-IF-2014-GF-660378) and another to DWGS (No. 796877), by CAPES scholarships to WCD, ACC, TF, RFS, AF, LM, HSN, TS, AM and RSM (PVE A115/2013), Araucaria Foundation scholarships to LB, AS, ACC and ES, Post-doctoral fellowships to DWGS (NERC grant NE/M017656/1) and ES (CNPq No. 150748/2014-0), PEER (Partnerships for Enhanced Engagement in Research Science Program) NAS/USAID award number AID-OAA-A-11-0001 - project 3-188 to RMF, and by CNPq grants, scholarships and fellowships to ACF, GGB, RF, SWJ, EGN and PL (Nos. <a>140260/2016-1</a>, 307486/2013-3, 302462/2016-3, <a>401824/2013-6</a>, 307179/2013-3, 400533/2014-6). We thank INPA, UFOPA, Embrapa Rondônia, Embrapa Amazônia Ocidental and Embrapa Amazônia Oriental and their staff for logistical support, and the farmers for access to and permission to sample on their properties. Sampling permit for Tapajós National Forest was granted by ICMBio.

Table 2 in Changes in a soil microarthropod community in the vicinity of dominant tree species under trampling management at the Safari Zoological Center, Israel

Table 2 Mean values (± SD) of soil physical and chemical parameters at different treatment sites at the Safari Zoological Center, Israel, December 2013. SM = soil moisture, OM = organic matter, pH = soil pH, SEC = soil electrical conductivity, SD = soil density, WHC = water-holding capacity. OE = open places under enclosure conditions, OT = open places under trampling conditions; EE E. = camaldulensis canopy habitat under enclosure conditions, ET = E. camaldulensis canopy habitat under trampling conditions, TE T =. aphylla canopy habitat under enclosure conditions, TT = T. aphylla canopy habitat under trampling conditions, CE = C. sempervirens canopy habitat under enclosure conditions, CT = C. sempervirens canopy habitat under trampling conditions. Different letters in the same column represent significant difference p at <0.05.<table><tbody><tr><th></th><th>SM (%)</th><th>OM (%)</th><th>pH</th><th>SEC (µ -1) cm</th><th>SD (g -3) cm</th><th>WHC (%)</th></tr></tbody><tbody><tr><th>OE</th><td>25.6±3.4a</td><td>1.1±0.2b</td><td>7.5±0.2b</td><td>87.6±17.5d</td><td>1.1±0.0b</td><td>53.6±0.9ab</td></tr><tr><th>OT</th><td>7.8±1.7c</td><td>0.2±0.0e</td><td>7.6±0.0b</td><td>150.3±51.3c</td><td>1.6±0.0a</td><td>25.6±1.0c</td></tr><tr><th>EE</th><td>16.6±1.6b</td><td>1.3±0.2b</td><td>7.6±0.0b</td><td>130.2±9.4cd</td><td>1.0±0.1c</td><td>52.6±11.1ab</td></tr><tr><th>ET</th><td>23.2±3.8a</td><td>2.0±0.3a</td><td>7.6±0.0b</td><td>255.3±39.6ab</td><td>1.1±0.0bc</td><td>31.9±3.5c</td></tr><tr><th>TE</th><td>21.9±4.4ab</td><td>0.4±0.1d</td><td>7.9±0.0a</td><td>152.0±17.8c</td><td>1.0±0.1c</td><td>52.1±14.6ab</td></tr><tr><th>TT</th><td>14.1±5.2b</td><td>1.1±0.1b</td><td>7.6±0.1b</td><td>254.1±48.0ab</td><td>1.0±0.1c</td><td>43.3±6.2b</td></tr><tr><th>CE</th><td>26.9±3.4a</td><td>0.8±0.3c</td><td>7.6±0.1b</td><td>209.9±22.5b</td><td>1.0±0.1c</td><td>56.3±5.8a</td></tr><tr><th>CT</th><td>22.0±6.0ab</td><td>0.5±0.2d</td><td>7.8±0.1a</td><td>275.9±21.9a</td><td>1.0±0.0c</td><td>43.0±3.9b</td></tr></tbody></table>

Table 3 in Changes in a soil microarthropod community in the vicinity of dominant tree species under trampling management at the Safari Zoological Center, Israel

Table 3 Effects of sampling habitat (“Habitat”), trampling management (“Trampling”), and their interaction on soil parameters, abundance of soil microarthropods, and diversity indices of soil Acari at the Safari Zoological Center, Israel, December 2013 (General linear model, α = 0.05). * p <0.05, ** p <0.01, *** p <0.001.<table><tbody><tr><th>Microarthropods</th><th>d f</th><th>F</th><th>Soil parameters</th><th>d f</th><th>F</th></tr></tbody><tbody><tr><th>Total microarthropod abundance</th><td></td><td></td><td>Soil moisture</td><td></td><td></td></tr><tr><th>Model</th><td>8</td><td>15.85***</td><td>Model</td><td>8</td><td>109.38***</td></tr><tr><th>Trampling</th><td>1</td><td>39.13***</td><td>Trampling</td><td>1</td><td>18.46***</td></tr><tr><th>Habitat</th><td>3</td><td>1.65</td><td>Habitat</td><td>3</td><td>5.90**</td></tr><tr><th>Trampling * Habitat</th><td>3</td><td>4.89**</td><td>Trampling * Habitat</td><td>3</td><td>12.84***</td></tr><tr><th>Collembola abundance</th><td></td><td></td><td>Organic matter</td><td></td><td></td></tr><tr><th>Model</th><td>8</td><td>4.90**</td><td>Model</td><td>8</td><td>116.32***</td></tr><tr><th>Trampling</th><td>1</td><td>11.18**</td><td>Trampling</td><td>1</td><td>0.05</td></tr><tr><th>Habitat</th><td>3</td><td>2.29</td><td>Habitat</td><td>3</td><td>48.99***</td></tr><tr><th>Trampling * Habitat</th><td>3</td><td>2.56</td><td>Trampling * Habitat</td><td>3</td><td>32.99***</td></tr><tr><th>Other arthropod abundance</th><td></td><td></td><td>Soil pH</td><td></td><td></td></tr><tr><th>Model</th><td>8</td><td>1</td><td>Model</td><td>8</td><td>35120.21***</td></tr><tr><th>Trampling</th><td>1</td><td>1.8</td><td>Trampling</td><td>1</td><td>0.5</td></tr><tr><th>Habitat</th><td>3</td><td>0.73</td><td>Habitat</td><td>3</td><td>4.69*</td></tr><tr><th>Trampling * Habitat</th><td>3</td><td>0.73</td><td>Trampling * Habitat</td><td>3</td><td>13.93***</td></tr><tr><th>Soil Acari abundance</th><td></td><td></td><td>Electrical conductivity</td><td></td><td></td></tr><tr><th>Model</th><td>8</td><td>18.85***</td><td>Model</td><td>8</td><td>156.31***</td></tr><tr><th>Trampling</th><td>1</td><td>43.09***</td><td>Trampling</td><td>1</td><td>61.80***</td></tr><tr><th>Habitat</th><td>3</td><td>0.88</td><td>Habitat</td><td>3</td><td>20.89***</td></tr><tr><th>Trampling * Habitat</th><td>3</td><td>3.60*</td><td>Trampling * Habitat</td><td>3</td><td>1.76</td></tr><tr><th>Taxon richness of soil Acari</th><td></td><td></td><td>Soil density</td><td></td><td></td></tr><tr><th>Model</th><td>8</td><td>16.11***</td><td>Model</td><td>8</td><td>1769.86***</td></tr><tr><th>Trampling</th><td>1</td><td>34.68***</td><td>Trampling</td><td>1</td><td>57.12***</td></tr><tr><th>Habitat</th><td>3</td><td>3.17*</td><td>Habitat</td><td>3</td><td>82.99***</td></tr><tr><th>Trampling * Habitat</th><td>3</td><td>1.23</td><td>Trampling * Habitat</td><td>3</td><td>36.99***</td></tr><tr><th>Shannon index of soil Acari</th><td></td><td></td><td>Water-holding capacity</td><td></td><td></td></tr><tr><th>Model</th><td>8</td><td>18.88***</td><td>Model</td><td>8</td><td>155.49***</td></tr><tr><th>Trampling</th><td>1</td><td>51.07***</td><td>Trampling</td><td>1</td><td>45.98***</td></tr><tr><th>Habitat</th><td>3</td><td>4.03*</td><td>Habitat</td><td>3</td><td>3.22*</td></tr><tr><th>Trampling * Habitat</th><td>3</td><td>0.78</td><td>Trampling * Habitat</td><td>3</td><td>2.6</td></tr><tr><th>Simpson index of soil Acari</th></tr><tr><th>Model</th><td>8</td><td>13.42***</td><td></td><td></td><td></td></tr><tr><th>Trampling</th><td>1</td><td>0</td><td></td><td></td><td></td></tr><tr><th>Habitat</th><td>3</td><td>7.47**</td><td></td><td></td><td></td></tr><tr><th>Trampling * Habitat</th><td>3</td><td>5.22**</td><td></td><td></td><td></td></tr><tr><th>Evenness index of soil Acari</th></tr><tr><th>Model</th><td>8</td><td>42.11***</td><td></td><td></td><td></td></tr><tr><th>Trampling</th><td>1</td><td>120.61***</td><td></td><td></td><td></td></tr><tr><th>Habitat</th><td>3</td><td>3.68*</td><td></td><td></td><td></td></tr><tr><th>Trampling * Habitat</th><td>3</td><td>1.82</td><td></td><td></td><td></td></tr></tbody></table>

Table 1 in Changes in a soil microarthropod community in the vicinity of dominant tree species under trampling management at the Safari Zoological Center, Israel

Table 1 Sampling design (replication = 4) for sites at the Safari Zoological Center, Israel, December 2013. Herbaceous ground cover: +++ patchy; + a few plants, – no plants. OE = open places under enclosure conditions, OT = open places under trampling conditions; EE E =. camaldulensis canopy habitat under enclosure conditions, ET = E. camaldulensis canopy habitat under trampling conditions, TE T =. aphylla canopy habitat under enclosure conditions, TT T =. aphylla canopy habitat under trampling conditions, CE = C. sempervirens canopy habitat under enclosure conditions, CT = C. sempervirens canopy habitat under trampling conditions.<table><tbody><tr><th>Habitat</th><th>Code</th><th>Treatment</th><th>Tree height (m)</th><th>Tree canopy crown (m2)</th><th>Herbaceous vegetation</th><th>Soil physical/biological top layer</th><th>Litter layer (cm)</th></tr></tbody><tbody><tr><th>Open spaces</th><td>OT OE</td><td>Trampling Enclosure</td><td>- -</td><td>- -</td><td>No +++</td><td>No Physical top layer</td><td>No No</td></tr><tr><th>E. camaldulensis</th><td>ET EE</td><td>Trampling Enclosure</td><td>10-13</td><td>6×8</td><td>No +</td><td>No Biological top layer</td><td>No 2-3</td></tr><tr><th>T. aphylla</th><td>TT TE</td><td>Trampling Enclosure</td><td>14-16</td><td>8×8</td><td>No +++</td><td>No Physical top layer</td><td>No Few</td></tr><tr><th>C. sempervirens</th><td>CT CE</td><td>Trampling Enclosure</td><td>14-16</td><td>7×9</td><td>No +</td><td>No Biological layer</td><td>No 1-2</td></tr></tbody></table>

Table 4 in Changes in a soil microarthropod community in the vicinity of dominant tree species under trampling management at the Safari Zoological Center, Israel

Table 4 Correlation coefficients (Pearson correlation, r) between the abundance of microarthropods, diversity indices of soil Acari, and soil parameters at the Safari Zoological Center, Israel, December 2013. SM = soil moisture, OM = organic matter, pH = soil pH, SEC = soil electrical conductivity, SD = soil density, WHC = water-holding capacity p. <*0.05, ** p <0.01, *** p <0.001.<table><tbody><tr><th>Index</th><th></th><th>SM</th><th>OM</th><th>pH</th><th>SEC</th><th>SD</th><th>WHC</th></tr></tbody><tbody><tr><th></th><td>Acari</td><td>0.349*</td><td>0.068</td><td>-0.198</td><td>-0.506**</td><td>-0.299</td><td>0.571***</td></tr><tr><th>Abundance</th><td>Collembola Other soil arthropods</td><td>0.153 0.202</td><td>0.210 0.098</td><td>-0.486** -0.294</td><td>-0.506** -0.209</td><td>-0.107 -0.065</td><td>0.207 0.245</td></tr><tr><th></th><td>Total microarthropod</td><td>0.292</td><td>0.158</td><td>-0.403*</td><td>-0.574***</td><td>-0.233</td><td>0.445*</td></tr><tr><th>Diversity indices of Acari</th><td>Taxon richness Shannon index Simpson index</td><td>0.253 0.285 -0.006</td><td>0.023 0.032 -0.175</td><td>-0.098 -0.120 0.165</td><td>-0.392* -0.455** 0.240</td><td>-0.316 -0.293 -0.475**</td><td>0.561*** 0.585*** 0.336</td></tr><tr><th></th><td>Evenness index</td><td>0.387*</td><td>0.009</td><td>-0.203</td><td>-0.466**</td><td>-0.411*</td><td>0.739***</td></tr></tbody></table>

opencc-by-4.0Apr 2024View details →

Data and R code used in Hennecke et al. "Plant species richness and the root economics space drive soil fungal communities"

To investigate how plant diversity and root traits relate to soil fungal communities, in 2021 we collected trait data from plots in the Jena Experiment (https://the-jena-experiment.de; funded by the DFG FOR 5000) and characterized fungal communities by sequencing, respiration and lipid fatty acid quantification.

opencc-zeroDec 2018View details →

Data from: Non-linear responses of soil nematode community composition to increasing aridity

Aim: Increasing aridity under global change is predicted to have a profound impact on the structure and functioning of terrestrial ecosystems, yet we have poor understanding of how belowground communities respond. In order to understand the longer-term responses of different trophic levels in the soil food web to increasing aridity, we investigated the abundance, richness and community similarity of the soil nematode community along a 3200-km aridity gradient. Location: A transect across semi-arid and arid grasslands in Northern China, where the aridity ranges from 0.43 to 0.97. Time period: July and August 2012. Major taxa studied: Soil-borne Nematoda. Methods: We used Generalized Additive (Mixed) Models to analyze the abundance, richness and community similarity patterns of soil nematodes. We used Structural Equation Modelling (SEM) to disentangle the direct and indirect environmental drivers (aridity, soil and plant variables) of the nematode community. Results: The abundance, richness and similarity of nematode communities declined non-linearly with increasing aridity. The most pronounced decline in nematode richness and community similarity occurred under arid conditions (aridity > 0.80). However, the shape of response to aridity differed among nematode feeding groups. Under arid conditions, the abundance and richness of bacterial feeders were less sensitive to changes in aridity than fungal feeders. The SEM analysis revealed that nematode community responses to aridity were not mediated via changes in plant and soil variables, but rather were directly affected by aridity. Main conclusions: Our results show that in mesic grasslands increasing aridity primarily caused decline in nematode abundance, whereas increasing aridity in xeric grasslands led to loss of nematode diversity. The non-linear responses of nematodes to aridity could result in non-linear shifts in ecosystem functioning as well, because soil nematodes operate at various trophic levels in the soil food web, thereby influencing the performance of plants, soil biodiversity and biogeochemical cycling.

opencc-zeroAug 2021View details →

Tree communities and soil properties influence fungal community assembly in neotropical forests

The influence exerted by tree communities, topography and soil chemistry on the assembly of macrofungal communities remains poorly understood, especially in highly diverse tropical forests. Here, we used a large dataset that combines inventories of macrofungal Basidiomycetes fruiting bodies, tree species composition and measurements for 16 soil physico-chemical parameters, collected in 34 plots located in four sites of lowland rainforests in French Guiana. Plots were established on three different topographical conditions: hilltop, slope and seasonally flooded soils. We found hyperdiverse Basidiomycetes communities, mainly comprising members of Agaricales and Polyporales. Phosphorus, clay contents and base saturation in soils strongly varied across plots and shaped the richness and composition of tree communities. The latter composition explained 23% of the variation in the composition of macrofungal communities, probably through high heterogeneity of the litter chemistry and selective effects of biotic interactions. The high local heterogeneity of habitats influenced the distribution of both macrofungi and trees, as a result of diversed local soil hydromorphic conditions associated to contrasting soil chemistry. This first regional study across habitats of French Guiana forests revealed new niches for macrofungi, such as ectomycorrhizal ones, and illustrate how macrofungi inventories are still paramount to can be to understand the processes at work in the tropics.

opencc-zeroSep 2021View details →

European soil seed bank communities across a climate and land-cover gradient

This is the data set used for the publication Buffering effects of soil seed banks on plant community composition in response to land use and climate, published in the journal Global Ecology and Biogeography. Aim. Climate and land use are key determinants of biodiversity, with past and ongoing changes posing serious threats to global ecosystems. Unlike most other organism groups, plant species can possess dormant life-history stages such as soil seed banks, which may help plant communities to resist or at least postpone the detrimental impact of global changes. This study investigates the potential for soil seed banks to achieve this. Location. Europe Time period. 1978 – 2014 Major taxa studied. Flowering plants Methods. Using a space-for-time/warming approach, we study plant species richness and composition in the herb layer and the soil seed bank in 2796 community plots from 54 datasets in managed grasslands, forests and intermediate, successional habitats across a climate gradient. Results. Soil seed banks held more species than the herb layer, being compositionally similar across habitats. Species richness was lower in forests and successional habitats compared to grasslands, with annual temperature range more important than mean annual temperature for determining richness. Climate and land use effects were generally less pronounced when plant community richness included seed bank species richness, while there was no clear effect of land use and climate on compositional similarity between the seed bank and the herb layer. Main conclusions. High seed bank diversity and compositional similarity between the herb layer and seed bank plant communities may provide a potentially important functional buffer against the impact of ongoing environmental changes on plant communities. This capacity could, however, be threatened by climate warming. Dormant life-history stages can therefore be important sources of diversity in changing environments, potentially underpinning already observed time-lags in plant community responses to global change. However, as soil seed banks themselves appear, albeit less, vulnerable to the same changes, their potential to buffer change can only be temporary, and major community shifts may still be expected.

opencc-zeroNov 2022View details →

Data from: Arbuscular mycorrhizal fungi communities shaped by host-plant affect the outcome of plant-soil feedback in dryland restoration

1. Plant inoculation with Arbuscular mycorrhizal fungi (AMF) can be a useful tool to overcome challenges in dry forest restoration. However, advances are still needed to guide choices regarding soil origin and inoculum production methods, since outcomes can vary due to plant-soil feedbacks (PSF). We evaluate how soil origin and host plant used for inoculum production affect AMF community and therefore the plant biomass accumulation and functional traits. 2. In the conditioning phase, we investigated whether soils originating from a recovered area (Quarry) and a vegetation fragment (Caatinga) would have their AMF communities modified due to the growth of Sorghum bicolor (used for inoculum production) and Senna uniflora (used in Brazilian semiarid restoration). In the feedback phase, we compared the performance of four plants species growing on a degraded soil and inoculated or not by a mixture of AMF isolates in comparison to soil inoculum prepared from the conditioning phase. 3. The inoculum from Caatinga presented seven times more AMF species compared to that from the Quarry, which presented ruderal and stress tolerant species. The soil inoculum conditioned by S. uniflora, regardless of origin, presented greater evenness compared to the soil inoculum produced with S. bicolor and promoted 33% more plant biomass compared to the control without inoculation. Root colonization by AMF increased PSF and decreased plant investment in functional traits such as specific root length (SRL) and specific leaf area (SLA). 4. Our results demonstrate the importance of adopting strategies that preserve local adaptation of inoculants produced. The use of native plant for propagation of native AMF in the conditioning phase provided more positive responses for Mesosphaerum suaveolens and Rhaphiodon echinus than inoculated with introduced AMF isolates. This is probably due to the interaction of inoculated plants with responsive AMF present in the soil. 5. Synthesis and applications: Our study shows that conditioning field-collected soil with S. uniflora and using it for inoculation can be a simple technique to promote biomass accumulation for other native herbaceous species. This preserves the compatibility between the soil inoculum produced with native AMF and native plants, representing an important tool for restoration programs.

opencc-zeroDec 2022View details →

eDNA metabarcoding reveals high soil fungal diversity and variation in community composition among Spanish cliffs

Environments characterized by physical extremes harbor unique species diversity with particular adaptations. Cliffs are harsh environments for organisms but host a great diversity of specialized plants with many endemics, rare and even endangered species. It is, however, less known which fungal diversity the cliff habitats contain and whether it differs among different cliff locations. We thus sampled soil from three separate cliff locations in the North, Centre and South of Spain and used eDNA metabarcoding to determine fungal diversity. To better understand whether cliff specialist plants may promote particular fungal communities, we have sampled soil from crevices with cliff specialist plants and no apparent plants as controls. Major lifestyles found in cliff soils were saprotrophs, and major fungal orders were Dothideomycetes, Sordariomycetes, and Eurotiomycetes, while the amount of symbiotrophic fungi was relatively low. We found no significant differences in fungal amplicon sequence variant (ASV) richness among the three sampled locations, but the sites were significantly different in their community composition and their main indicator species. Overall, there were no significant differences in fungal ASV richness or composition between soils from cliff specialist plants and soils without plants, suggesting a unique fungal diversity in cliff soils independent from specialized plants. However, preliminary findings on soils of the specialist cliff plant Sedum dasyphyllum against control soils suggest that the presence of a specialist plant may be a relevant factor affecting the specificity of the fungal community in cliff soils. Our results indicate the existence of particular cliff fungal communities in each location, and that, despite limited and poorly developed soils and harsh conditions, cliffs can harbor a great diversity of fungal species, comparable to other ecosystems of Spain. This study points out that some fungi may be cliff-specific, shaping particular communities that mediate plant adaptations to cliffs' extreme conditions.