Unsustainable soil management, climate change, and land degradation jeopardize soil biodiversity and soil\uffe2\uff80\uff90mediated ecosystem functions. Although the transition from conventional to organic agriculture has been proposed as a potential solution to alleviate these pressures, there is limited evidence of its effectiveness in enhancing belowground biodiversity across different biogeographical regions, climates, and land degradation levels. In this study, we holistically assessed the status of soil biodiversity, from microorganisms to meso\uffe2\uff80\uff90 and macrofauna, in agroecosystems distributed across four continents. We identified the primary environmental community composition drivers and assessed the effects of the transition from conventional to organic management (no chemical inputs) on soil ecology. Our findings highlight the mean temperature and precipitation of the warmest and coldest quarters of the year, aridity, pH, and soil texture as the primary drivers of the different soil biodiversity components. Overall, organic farming has a significant but small impact on soil biodiversity compared to the other community drivers. On top of that, the results demonstrate the importance of a regional\uffe2\uff80\uff90specific context for a future generalized transition towards organic soil management. Specifically, under the most arid conditions in our study, organic management showed potential to buffer biodiversity loss in highly degraded soils, with a significant increase in diversity for prokaryotes and protists compared to conventionally managed soils. Therefore, the combination of a global and, simultaneously, regional\uffe2\uff80\uff90specific approach supports the hypothesis that a shift towards organic agriculture would maximize its beneficial impact on belowground diversity in highly degraded soils under arid conditions over the coming years, being a crucial tool to increase resilience and adaptation to global change for agriculture.Environmental DNA (eDNA) metabarcoding is becoming a key tool for biodiversity monitoring over large geographical or taxonomic scales and for elusive taxa such as soil organisms. Increasing sample sizes and interest in remote or extreme areas often require the preservation of soil samples and thus deviations from optimal standardized protocols. However, we still ignore the impact of different methods of soil sample preservation on the results of metabarcoding studies and there is no guideline for best practices so far. Here, we assessed the impact of four methods of soil sample preservation that can be conveniently used also in metabarcoding studies targeting remote or difficult to access areas. Tested methods include: preservation at room temperature for 6\uffc2\uffa0hr, preservation at 4\uffc2\uffb0C for 3\uffc2\uffa0days, desiccation immediately after sampling and preservation for 21\uffc2\uffa0days, and desiccation after 6\uffc2\uffa0hr at room temperature and preservation for 21\uffc2\uffa0days. For each preservation method, we benchmarked resulting estimates of taxon diversity and community composition of three different taxonomic groups (bacteria, fungi and eukaryotes) in three different habitats (forest, river bank and grassland) against results obtained under ideal conditions (i.e., extraction of eDNA immediately after sampling). Overall, the different preservation methods only marginally impaired results and only under certain conditions. When rare taxa were considered, we detected small but significant changes in molecular operational taxonomic units (MOTU) richness of bacteria, fungi and eukaryotes across treatments, but MOTU richness was similar across preservation methods if rare taxa were not considered. All the approaches were able to identify differences in community structure among habitats, and the communities retrieved using the different preservation conditions were extremely similar. We propose guidelines on the selection of the optimal soil sample preservation conditions for metabarcoding studies, depending on the practical constraints, costs and ultimate research goals.
", "keywords": ["0301 basic medicine", "570", "0303 health sciences", "[SDV]Life Sciences [q-bio]", "Biodiversity", "Forests", "15. Life on land", "DNA", " Environmental", "Soil", "03 medical and health sciences", "eDNA metabarcoding; eukaryotes; microbial communities; MOTU richness; sample storage; \u03b1 and \u03b2 diversity", "13. Climate action", "DNA Barcoding", " Taxonomic", "Environmental Monitoring"]}, "links": [{"href": "https://air.unimi.it/bitstream/2434/791337/2/guerrieri%202020%20%20submitted.pdf"}, {"href": "https://air.unimi.it/bitstream/2434/791337/4/mec.15674.pdf"}, {"href": "https://onlinelibrary.wiley.com/doi/pdf/10.1111/mec.15674"}, {"href": "https://doi.org/10.1111/mec.15674"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Molecular%20Ecology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/mec.15674", "name": "item", "description": "10.1111/mec.15674", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/mec.15674"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-05-06T00:00:00Z"}}, {"id": "10.5061/dryad.ffbg79d33", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:22:34Z", "type": "Dataset", "created": "2024-05-08", "title": "Influence of soil type and vertical zonation on soil fungal communities associated with natural jack pine forests", "description": "unspecifiedStudy area The study was conducted in the Abitibi-T\u00e9miscamingue region, Quebec (Canada). The study area is located in northern Quebec\u2019s clay plain, within the balsam fir-paper birch (Abies balsamea (Linnaeus) Miller-Betula papyriferaMarshall) bioclimatic domain (Grondin 1996). Climatic conditions are subpolar, subhumid, and continental with an annual average temperature and precipitation of respectively between 1-2 \u00b0C and 825-975 mm according to the Canadian climate normal 1981-2010 station data (Environment Canada, 2023). Forest vegetation mainly consisted of jack pine stands with interspersed stems of Picea mariana (Miller) Britton, Sterns & Poggenburgh, Populus tremuloides Michaux, Betula papyrifera Marshall, Abies balsamea (Linnaeus) Miller, and Acer rubrum Linnaeus. Specifically, the Abitibi region is characterized by natural gradients of soil physicochemical properties, from sandy eskers to clayey soil types. Indeed, esker soils account for ~3% of the Abitibi\u2019s surface while the remainder of the region\u2019s flat topography (~57%) is dominated by clayey soils (Robitaille and Saucier 1998; Cloutier et al. 2007). Abitibi eskers have mineral surface deposits with an overall thickness of more than 25 cm, an illuvial horizon with coarse texture, and a high stone content. Conversely, glacio-lacustrine clayey soils, which are associated with Luvisols or Gleysols in the Abitibi region (Laverdi\u00e8re and De Kimpe 1984; Bergeron et al. 2007), are characterized by an illuvial horizon with medium texture and a low stone content (Blouin and Berger 2002). Site selection Site selection was based on four main criteria, namely soil type (sandy esker/clayey), dominant vegetation (jack pine, minimum 50-75% of canopy cover), stand age (minimum 50-60 years), stand origin (natural fire-origin, totally or mostly unimpacted by anthropogenic disturbance), and accessibility (maximum 350 m from forest roads). We used satellite images and forest inventory data from the Quebec government\u2019s online platform \u2018For\u00eat Ouverte\u2019 for site selection. We selected 18 natural jack pine stands (12 on sandy eskers, 6 on the clay plain). Only six clayey sites fitting our selection criteria were found within the prospected territory due to the scarcity of unmanaged (natural) jack pine stands on clayey soils. Sites were separated by a minimum distance of 5 km to ensure independence between samples and avoid pseudoreplication. Finally, the study design consisted of a first-level factor (soil type) with two levels (sand and clay), and a second-level factor (soil horizons) with three levels (litter, organic, and mineral). Soil sampling Soil samples were collected during the summer of 2022 to characterize soil fungal communities and obtain the physicochemical composition across the soil profile. A circular plot with a 28 m radius and an area of 2,500 m2 was established at each study site, at least 20 m from the forest edge to avoid related variability bias (Dickie and Reich 2005). Within each plot, five jack pine trees were randomly selected (at least 10 m apart), and two soil sample replicates per tree (2-3 m apart) were collected, following a modified protocol by Tedersoo et al. (2014; 2021). Sampling locations around trees were randomly selected with the restriction criteria of being strictly opposite (angle of 180\u00b0). In total, 180 soil cores (2 replicates \u00d7 5 trees \u00d7 18 sites) were collected. For each sampling spot, forest fallen litter (dead leaves, needles) (hereafter referred to as litter horizon) was collected in plastic bags for DNA and physicochemical analyses, followed by samples from the organic layer (hereafter referred to as organic horizon). The latter represented a dark-colored layer rich in organic matter at various decomposition stages, mainly composed of fallen plant material. These organic samples included both F and H horizons. After the removal of the organic layer, we sampled the mineral soil (hereafter referred to as mineral horizon) using a pedological auger (25 cm in length and 7.5 cm in diameter), capturing both eluvial and illuvial horizons. For esker sites, the mineral soil profile was characterized by the formation and accumulation of organo-metallic assemblages, involving Al and Fe elements, in a leached grey eluvial and rust-brown illuvial horizon, respectively. Due to the variable thickness of the eluvial horizons within both esker and clayey soil profiles, the proportion of this horizon in the mineral soil samples varied among sites. Large roots and coarse woody debris were systematically removed from organic and mineral material while sampling. Samples were pooled per horizon for each site, resulting in one composite sample for each horizon. This represents 18 composite samples per horizon for a total of 54 composite soil samples (3 horizons \u00d7 18 sites). Each composite soil samples were divided into two sub-samples: one for eDNA-based soil fungal community analysis and the other for physicochemical analysis. Composite organic and mineral soil samples for eDNA analysis were sieved with a 6 mm mesh in the field, placed in plastic bags, transported in an ice-filled cooler and stored at \u2013 25\u00b0C in the Ecology Research Group of Abitibi RCM (GREMA) laboratory until further processing. Composite litter samples for eDNA analysis were crushed in liquid nitrogen using a mortar and pestle prior to DNA extraction. Composite soil samples for physicochemical analyses were sieved with a 4 mm mesh using an automatic vibrating sieve AS 200 Control (ATS Care Retsch, Haan, Germany) after being forced-air dried at room temperature for 14 days to facilitate the sieving process. Soil physicochemical analyses Composite soil samples (> 50g) were sent to the organic and inorganic chemistry laboratory of the Forest Research Direction (Quebec, QC, Canada) for physicochemical analysis. Total nitrogen (N) and carbon (C) contents were determined by combustion using a CN 928 elemental analyzer (LECO Corp., St Joseph, MI, USA) with thermal conductivity detection for nitrogen and non-dispersive infra-red (NDIR) cell detection for carbon. Organic matter content (hereafter OM) and percentage of humidity (hereafter humidity) were determined by incineration, a method commonly referred to as the loss on ignition (LOI) method (Davies 1974). Soil pH was measured using 10 g of soil mixed with 20 mL of distilled water with an Orion VersaStar Pro pH meter (Thermo Fisher Scientific Inc., Pittsburgh, PA, USA). Elements (P, K, Ca, Mg, Mn, Zn, Al, Fe) were extracted using the Mehlich-III method (Mehlich 1984) and measured by plasma atomic emission spectroscopy (Optima 8300 model, ICP-OES, Perkin Elmer, Waltham, MA, USA). Particle size distribution and textural class determination were performed on soil samples consisting of fine earth (<2 mm) containing 5% or less carbon using the Bouyoucos method (Bouyoucos 1962). DNA extraction, amplification and library preparation DNA extraction was carried out at the Environmental Genomic Laboratory of the Laurentian Forestry Centre (EGL-CFL) on 150 mg of composite litter and organic samples, and 250 mg of composite mineral samples using a DNeasy powersoil pro kit (Qiagen, Valencia, CA, US) and the QIAcube automated instrument (Qiagen, Hilden, Germany) in accordance with the manufacturer\u2019s instructions. DNA was quantified with the Qubit\u2122 dsDNA BR Assay Kit (Thermo Fisher Scientific Inc, Wilmington, USA). Fungal DNA amplicon libraries were prepared at the EGL-CFL following a previously described procedure (Samad et al. 2023) and sequenced on an Illumina MiSeq platform at the Next Generation Sequencing Platform of the CHU de Qu\u00e9bec-Universit\u00e9 Laval Research Centre using a MiSeq v3 600-cycle Reagent Kit. Even though one ITS region contains less genetic information than the entire ITS (Tedersoo et al. 2022), the ITS2 region of the fungal ribosomal DNA was amplified using the primer set ITS9F (5\u2019-GAACGCAGCRAAIIGYGA-3\u2019) and ITS4R (5\u2019-TCCTCCGCTTATTGATATGC-3\u2019 (White et al. 1990; Ihrmark et al. 2012; Rivers 2016) because of limitations to sequence length that can be obtained with Illumina Sequencing. Negative controls in the DNA extraction and PCR amplification were used to control potential contaminants during the process. Bioinformatic and sequence analyses All bioinformatics analyses were performed in QIIME2 v2023.2.0 (Bolyen et al. 2019). Demultiplexed FASTQ (R1 and R2) files were first imported in QIIME2 using the \u2018qiime import\u2019 command which converts the data into a QZA archive file so it can be processed by the rest of the QIIME2 workflow. First, primers were removed using the QIIME2 implementation of CutAdapt (Martin 2011). Resulting forward and reverse reads went through the DADA2 (Callahan et al. 2016) pipeline for sequence quality control and feature table construction using the \u2018\u2019qiime dada2 denoise-paired\u2019\u2019 command. During this process, low-quality regions of the sequences were trimmed, paired reads assembled, chimeric sequences filtered, remaining high-quality sequences dereplicated, and singletons and very low-frequency abundance ASVs removed using the \u201cqiime feature-table filter-features\u201d command. The output of this step was a feature table (i.e., ASV table) which contains read counts for each unique sequence in each sample of the dataset, and feature data which contains sequences corresponding to each ASV. A taxonomy was assigned to each ASV based on the UNITE reference database (version 9.0) (Abarenkov et al. 2010, 2023; K\u00f5ljalg et al. 2005, 2013; Nilsson et al. 2019b) using the \u2018\u2018classify-sklearn\u2019\u2019 with a Naive Bayes classifier. All ASVs not assigned to the \u2018Fungi\u2019 kingdom were removed from further analyses using the \u201cqiime taxa filter-table\u201d. The resulting fungal ASV table was used to perform downstream statistical analyses. Functional annotation of ASVs was performed for most fungal guilds using FUNGuild version 1.1 (Nguyen et al. 2016).", "keywords": ["Ectomycorrhiza", "eDNA metabarcoding", "Clay belt", "Pinus banksiana", "FOS: Biological sciences", "esker ecosystems", "saprotrophs"], "contacts": [{"organization": "Cazabonne, Jonathan, DesRochers, Annie, Martineau, Christine, Roy, M\u00e9lanie, Girona, Miguel Montoro,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.ffbg79d33"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.ffbg79d33", "name": "item", "description": "10.5061/dryad.ffbg79d33", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.ffbg79d33"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-05-13T00:00:00Z"}}, {"id": "10.5281/zenodo.15720421", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-13T16:24:17Z", "type": "Dataset", "title": "Dataset for paper: Bacterial and Fungal Communities Respond Differently to Changing Soil Properties Along Afforestation Dynamic", "description": "Bacterial and Fungal Communities Respond Differently to Changing Soil Properties Along Afforestation Dynamic. 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