The soil microbiome determines the fate of plant-fixed carbon. The shifts in soil properties caused by land use change leads to modifications in microbiome function, resulting in either loss or gain of soil organic carbon (SOC). Soil pH is the primary factor regulating microbiome characteristics leading to distinct pathways of microbial carbon cycling, but the underlying mechanisms remain understudied. Here, the taxa-trait relationships behind the variable fate of SOC were investigated using metaproteomics, metabarcoding, and a 13C-labeled litter decomposition experiment across two temperate sites with differing soil pH each with a paired land use intensity contrast. 13C incorporation into microbial biomass increased with land use intensification in low-pH soil but decreased in high-pH soil, with potential impact on carbon use efficiency in opposing directions. Reduction in biosynthesis traits was due to increased abundance of proteins linked to resource acquisition and stress tolerance. These trait trade-offs were underpinned by land use intensification-induced changes in dominant taxa with distinct traits. We observed divergent pH-controlled pathways of SOC cycling. In low-pH soil, land use intensification alleviates microbial abiotic stress resulting in increased biomass production but promotes decomposition and SOC loss. In contrast, in high-pH soil, land use intensification increases microbial physiological constraints and decreases biomass production, leading to reduced necromass build-up and SOC stabilization. We demonstrate how microbial biomass production and respiration dynamics and therefore carbon use efficiency can be decoupled from SOC highlighting the need for its careful consideration in managing SOC storage for soil health and climate change mitigation.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.Soils harbour a rich arthropod fauna, but many species are still not formally described (Linnaean shortfall) and the distribution of those already described is poorly understood (Wallacean shortfall). Metabarcoding holds much promise to fill this gap, however, nuclear copies of mitochondrial genes, and other artefacts lead to taxonomic inflation, which compromise the reliability of biodiversity inventories. Here, we explore the potential of a bioinformatic approach to jointly \uffe2\uff80\uff9cdenoise\uffe2\uff80\uff9d and filter nonauthentic mitochondrial sequences from metabarcode reads to obtain reliable soil beetle inventories and address open questions in soil biodiversity research, such as the scale of dispersal constraints in different soil layers. We sampled cloud forest arthropod communities from 49 sites in the Anaga peninsula of Tenerife (Canary Islands). We performed whole organism community DNA (wocDNA) metabarcoding, and built a local reference database with COI barcode sequences of 310 species of Coleoptera for filtering reads and the identification of metabarcoded species. This resulted in reliable haplotype data after considerably reducing nuclear mitochondrial copies and other artefacts. Comparing our results with previous beetle inventories, we found: (i) new species records, potentially representing undescribed species; (ii) new distribution records, and (iii) validated phylogeographic structure when compared with traditional sequencing approaches. Analyses also revealed evidence for higher dispersal constraint within deeper soil beetle communities, compared to those closer to the surface. The combined power of barcoding and metabarcoding contribute to mitigate the important shortfalls associated with soil arthropod diversity data, and thus address unresolved questions for this vast biodiversity fraction.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": "10.2478/jofnem-2023-0006", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:06Z", "type": "Journal Article", "created": "2023-04-22", "title": "18S-NemaBase: Curated 18S rRNA Database of Nematode Sequences", "description": "AbstractNematodes are the most abundant and diverse animals on the planet but lack representation in biodiversity research. This presents a problem for studying nematode diversity, particularly when molecular tools (i.e., barcoding and metabarcoding) rely on well-populated and curated reference databases, which are absent for nematodes. To improve molecular identification and the assessment of nematode diversity, we created and curated an 18S rRNA database specific to nematodes (18S-NemaBase) using sequences sourced from the most recent publicly available 18S rRNA SILVA v138 database. As part of the curation process, taxonomic strings were standardized to contain a fixed number of taxonomic ranks relevant to nematology and updated for the most recent accepted nematode classifications. In addition, apparent erroneous sequences were removed. To test the efficacy and accuracy of 18S-NemaBase, we compared it to an older but also curated SILVA v111 and the newest SILVA v138 by assigning taxonomies and analyzing the diversity of a nematode dataset from the Western Nebraska Sandhills. We showed that 18S-NemaBase provided more accurate taxonomic assignments and diversity assessments than either version of SILVA, with a much easier workflow and no need for manual corrections. Additionally, observed diversity further improved when 18S-NemaBase was supplemented with reference sequences from nematodes present in the study site. Although the 18S-NemaBase is a step in the right direction, a concerted effort to increase the number of high-quality, accessible, full-length nematode reference sequences is more important now than ever.Study 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": "10.5061/dryad.v6wwpzh2j", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:21:02Z", "type": "Dataset", "created": "2023-09-07", "title": "Data from: Unravelling large-scale patterns and drivers of biodiversity in dry rivers", "description": "unspecified# Data from: Unravelling large-scale patterns and drivers of biodiversity in dry rivers [https://doi.org/10.5061/dryad.v6wwpzh2j](https://doi.org/10.5061/dryad.v6wwpzh2j) Sediment samples were collected by an international consortium ([http://1000_intermittent_rivers_project.irstea.fr](http://1000_intermittent_rivers_project.irstea.fr)) following a standardized protocol during dry phases in the years 2015-2016. We conducted a metabarcoding approach on environmental DNA targeting multiple taxa (i.e. Archaea, Bacteria, Fungi, Algae, Protozoa, Nematoda, Arthropoda and Streptophyta). ## Description of the data and file structure * DRIME_bact02.filtered.uniq.fasta : de-replicated and de-multiplexed sequencing data for the barcode Bact02 targetting Bacteria and Archaea * Metabarcoding DRIME workflow Bact02.Rmd : R markdown file describing the bioinformatic processing and the statistical analyses conducted on the Bact02 barcode * bact_OTU_097_DRIME_agg_ORDER.txt : curated OTU table obtained for the Bact02 barcode * CLASSIFICATION_clean_trimmed.txt : taxonomic assignation of Bact02 OTUs * environment_DRIME_ORDER_Bact02_NC.txt : environmental data used in statistical analyses for the Bact02 barcode Euka02 : folder for the barcode Euka02 targetting Eukaryotes * DRIME_euka2.filtered.uniq.fasta : de-replicated and de-multiplexed sequencing data for the barcode Euka02 targetting Eukaryotes * Metabarcoding DRIME workflow Euka02.Rmd : R markdown file describing the bioinformatic processing and the statistical analyses conducted on the Euka02 barcode * euka_OTU_097_DRIME_agg_ORDER.txt : curated OTU table obtained for the Euka02 barcode * CLASSIFICATION_clean_curated.txt : taxonomic assignation of Euka02 OTUs * environment_DRIME_ORDER_Euka02_NC.txt : environmental data used in statistical analyses for the Euka02 barcode * environmental_variables_description.xlsx: environmental data name, description and units ## Code/Software Code can be run using the OBITools software package and R.", "keywords": ["metabarcoding", "FOS: Earth and related environmental sciences", "Biodiversity", "eDNA", "intermittent rivers"], "contacts": [{"organization": "Foulquier, Arnaud", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.v6wwpzh2j"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.v6wwpzh2j", "name": "item", "description": "10.5061/dryad.v6wwpzh2j", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.v6wwpzh2j"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-07-05T00:00:00Z"}}, {"id": "10449/91579", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:23:56Z", "type": "Journal Article", "created": "2025-07-22", "title": "The blueberry phyllosphere microbiota: tissue-specific core communities and their stability across cultivars and years", "description": "Blueberries are critical for food production due to their widespread consumption and nutritional value. Beyond agriculture, wild Vaccinium species play essential ecological roles, including supporting pollinators and enhancing soil health. This dual importance underscores their relevance to both food security and ecosystem sustainability. The fruit-associated microbiome, both internal and surface-dwelling, includes a wide range of microorganisms. These microbial communities play a dual role: they influence fruit quality (e.g., taste, texture, shelf life) and are also involved in the degradation processes that occur during fruit senescence or postharvest storage.\u201d. Despite their importance, the specific factors shaping the microbiomes of blueberry fruits, as well as their relationship with other above-ground parts of the plant and their stability over different years, remain poorly understood. We conducted a field experiment to characterize the taxonomic composition of fungal and bacterial communities colonizing the leaves and the surface and pulp of fruits on a collection of 10 different cultivars of blueberry over two years. Independently from the sampling time, pulp of the fruit, surface and leaves harbor specific and distinct microbiomes. A major factor determining the microbiome of blueberry fruits and leaves was plant cultivar, followed by tissue. We further identified the core microbiome for each plant tissue and demonstrated that core taxa account for the dominant fraction of the microbiota of each plant. As trade and production of blueberries is expanding, our results provide a foundation for advancing the development of targeted microbiome management strategies, with potential applications in enhancing plant health and productivity. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12870-025-06871-6.", "keywords": ["Bacteria", "Plant microbiome", "Research", "Metabarcoding", "Fungi", "Climate change", "Network analysis", "Core microbiome", "Biodiversity"]}, "links": [{"href": "https://openpub.fmach.it/bitstream/10449/91579/1/2025%20BMC%20PB%20Donati.pdf"}, {"href": "https://doi.org/10449/91579"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/BMC%20Plant%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10449/91579", "name": "item", "description": "10449/91579", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10449/91579"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-22T00:00:00Z"}}, {"id": "10.5281/zenodo.15720421", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:22:25Z", "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. New collected data at HIS below.Corresponding author: Speranza Panico - speranza.panico@uniud.it", "keywords": ["climate change", "Soil organic carbon", "space-for-time approach", "soil microbiota", "alpha-diversity", "DNA metabarcoding"], "contacts": [{"organization": "Panico, Speranza Claudia, Alberti, Giorgio, Foscari, Alessandro, Tomao, Antonio, Incerti, Guido, Sciabbarrasi, Giovanni Luca,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15720421"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15720421", "name": "item", "description": "10.5281/zenodo.15720421", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15720421"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-06-23T00:00:00Z"}}, {"id": "10.5281/zenodo.14845589", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:22:07Z", "type": "Dataset", "title": "Data from: Comparison and evaluation of sampling and eDNA metabarcoding protocols to assess soil biodiversity in Belgian LUCAS Biopoints", "description": "Environmental DNA (eDNA) metabarcoding is emerging as a novel tool for monitoring soil biodiversity. Soil biodiversity, critical for soil health and ecosystem services, is currently under-monitored due to the lack of standardized and efficient methods. We assessed whether refinements to sampling and molecular protocols could improve soil biodiversity detection and monitoring.\u00a0Comparing the 2018 LUCAS soil biodiversity protocols with newly developed national methods, we tested sampling topsoil (0-10 cm) versus deeper layers, larger soil sample sizes for DNA-extraction, taking more subsamples for composite soil samples, and alternative primer sets across 9 Belgian Biopoints included in the LUCAS 2022 survey. The results suggest that significantly more species can be detected in upper soil layers, including the forest floor, while the diversity of taxa and eDNA in the 10\u201330 cm soil layer is insufficient for annelids and arthropods to serve as indicators of ecological change. Additionally, comparison of the universal eukaryotic primers (18S) with primer sets tailored to soil mesofauna and macrofauna, showed that universal 18S primers provide limited resolution for Collembola and Annelida. Overall, the analyses suggest that vertical soil stratification (with two sampling depths) has a greater influence on the captured diversity of soil mesofauna and macrofauna than the number of subsamples, and that the highest diversity is recovered when surface sampling (0\u201310 cm topsoil and forest floor) is combined with a greater number of subsamples and a larger sampled area. With refinement and standardization, eDNA metabarcoding, combined with optimized sampling protocols, could become a powerful and efficient tool for monitoring soil biodiversity in European soils. Description of the files This dataset includes interactive Krona taxonomy charts to visually summarize the diversity and relative read abundance of detected taxa across sampling locations and protocols. Each ring in the chart represents a taxonomic level, with the relative width of segments reflecting the proportion of reads assigned to specific taxa at that level. These charts enable exploration of taxonomic composition and allow for comparisons between the different sampled locations, sampling protocols tested, and primer sets tested. All krona charts were made in R using psadd::plot_krona. To correct for uneven sequencing depth per sample, datasets were rarefied using a random subsampling method to 27913, 31655, 1856, 19728, and 19632 reads for Annelida (Olig01), Collembola (Coll01), Fungi (ITS9mun/ITS4ngsUni), protists (18S), and Archaea (SSU1ArF/SSU1000ArR) respectively. Fauna datasets that are subsets of the total data recovered by a primer set designed to target many different phyla (e.g. 18S) were not rarefied prior to generating the krona plots. ejp_soil_annelida_olig01_27913.html contains the interactive taxonomy charts for Annelida. The data was generated using the group-specific Olig01 primer set and rarefied to 27,913 reads per sample. ejp_soil_collembola_coll01_31655.html contains the interactive taxonomy charts for Collembola. The data was generated using the group-specific Coll01 primer set and rarefied to 31,655 reads per sample. ejp_soil_arthropoda_inse01.html contains the interactive taxonomy charts for Arthropoda (Insecta, Arachnida, Chilopoda, Diplura, and Malacostraca). The data was generated using the Inse01 primer set. ejp_soil_fungi_its9mun_its4ngsuni_1856.html contains the interactive taxonomy charts for Fungi. The data was generated using the ITS9mun and ITS4ngsUni primer set and rarefied to 1,856 reads per sample. ejp_soil_protists_18s_19728.html contains the interactive taxonomy charts for protists. The data was generated using the eukaryotic 18S primer set and rarefied to 19,728 reads per sample. ejp_soil_archaea_ssu1arf_ssu1000arr_19632.html contains the interactive taxonomy charts for Archaea. The data was generated using the SSU1ArF and SSU1000ArR primer set and rarefied to 19,632 reads per sample. ejp_soil_annelida_18s.html contains the interactive taxonomy charts for Annelida. The data was generated using the eukaryotic 18S primer set. ejp_soil_collembola_18s.html contains the interactive taxonomy charts for Collembola. The data was generated using the eukaryotic 18S primer set. ejp_soil_arthropoda_18s.html contains the interactive taxonomy charts for Arthropoda. The data was generated using the eukaryotic 18S primer set. ejp_soil_metadata.csv contains metadata for the samples in this study. It includes information about the sampling locations, the sampling protocols used, the sampling depth (cm), land use type, EUNIS habitat classification, and the LUCAS-ID for each sample.", "keywords": ["soil monitoring", "metabarcoding", "LUCAS", "soil biodiversity", "eDNA"], "contacts": [{"organization": "Lambrechts, Sam, Deflem, Io Sarah, Sensalari, Cecilia, De Backer, Silke, De Beer, Berdien, Neyrinck, Sabrina, De Vos, Bruno,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14845589"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14845589", "name": "item", "description": "10.5281/zenodo.14845589", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14845589"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-02-10T00:00:00Z"}}, {"id": "10.5281/zenodo.15046019", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:22:12Z", "type": "Report", "title": "Comparison and evaluation of sampling and eDNA metabarcoding protocols to assess soil biodiversity in Belgian LUCAS Biopoints", "description": "Environmental DNA (eDNA) metabarcoding is emerging as a novel tool for monitoring soil biodiversity. Soil biodiversity, critical to soil health and ecosystem services, remains under-monitored due to the lack of standardized and efficient methods. We evaluated whether refinements to sampling protocols (for soil invertebrates and Fungi) and molecular protocols (for soil invertebrates) could improve biodiversity detection. Comparing the 2018 LUCAS soil biodiversity protocol with newly developed national methods, we tested sampling and sequencing surface layers (0-10 cm and forest floor) versus deeper layers, larger soil sample sizes for DNA-extraction, taking more subsamples for composite soil samples, and alternative primer sets across 9 Belgian Biopoints included in the LUCAS 2022 survey. We show that the choice of sampling protocol significantly influences soil biodiversity assessments. The results show that, based on eDNA, we are able to detect significantly more species when sampling and sequencing the upper soil layers separately, while the diversity in the 10\u201330 cm soil layer is insufficient for annelids and arthropods to serve as indicators of ecological changes. Collembola and Arthropoda richness and diversity generally increased towards less intensely managed soils, when using the national (Cmon), and to a lesser extent the European (LUCAS) sampling protocols. In contrast, sampling and sequencing the 10-30 cm layer failed to capture such a pattern. Overall, the analyses suggest that soil depth has a greater influence on the soil invertebrate diversity captured than sampling intensity, and that the highest diversity is recovered when surface sampling (0\u201310 cm topsoil and forest floor) is combined with a greater number of subsamples (16 compared to 5 in LUCAS) and a larger sampled area. Additionally, comparison of the universal eukaryotic primers (18S) with primer sets tailored to important soil invertebrate groups, showed that universal 18S primers provide limited resolution for Collembola and Annelida, making them less suitable for accurately assessing the diversity of these groups as a response variable in monitoring ecological changes and biological soil health. With refinement and standardization, eDNA metabarcoding, combined with optimized sampling protocols, could become a powerful and efficient tool for monitoring soil biodiversity in European soils.", "keywords": ["EJP SOIL", "soil monitoring", "metabarcoding", "LUCAS", "soil biodiversity", "eDNA"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15046019"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15046019", "name": "item", "description": "10.5281/zenodo.15046019", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15046019"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-03-18T00:00:00Z"}}, {"id": "10261/295770", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:23:49Z", "type": "Journal Article", "created": "2022-10-05", "title": "Metabarcoding for biodiversity inventory blind spots: A test case using the beetle fauna of an insular cloud forest", "description": "Abstract
Soils harbour a rich arthropod fauna, but many species are still not formally described (Linnaean shortfall) and the distribution of those already described is poorly understood (Wallacean shortfall). Metabarcoding holds much promise to fill this gap, however, nuclear copies of mitochondrial genes, and other artefacts lead to taxonomic inflation, which compromise the reliability of biodiversity inventories. Here, we explore the potential of a bioinformatic approach to jointly \uffe2\uff80\uff9cdenoise\uffe2\uff80\uff9d and filter nonauthentic mitochondrial sequences from metabarcode reads to obtain reliable soil beetle inventories and address open questions in soil biodiversity research, such as the scale of dispersal constraints in different soil layers. We sampled cloud forest arthropod communities from 49 sites in the Anaga peninsula of Tenerife (Canary Islands). We performed whole organism community DNA (wocDNA) metabarcoding, and built a local reference database with COI barcode sequences of 310 species of Coleoptera for filtering reads and the identification of metabarcoded species. This resulted in reliable haplotype data after considerably reducing nuclear mitochondrial copies and other artefacts. Comparing our results with previous beetle inventories, we found: (i) new species records, potentially representing undescribed species; (ii) new distribution records, and (iii) validated phylogeographic structure when compared with traditional sequencing approaches. Analyses also revealed evidence for higher dispersal constraint within deeper soil beetle communities, compared to those closer to the surface. The combined power of barcoding and metabarcoding contribute to mitigate the important shortfalls associated with soil arthropod diversity data, and thus address unresolved questions for this vast biodiversity fraction.The soil microbiome determines the fate of plant-fixed carbon. The shifts in soil properties caused by land use change leads to modifications in microbiome function, resulting in either loss or gain of soil organic carbon (SOC). Soil pH is the primary factor regulating microbiome characteristics leading to distinct pathways of microbial carbon cycling, but the underlying mechanisms remain understudied. Here, the taxa-trait relationships behind the variable fate of SOC were investigated using metaproteomics, metabarcoding, and a 13C-labeled litter decomposition experiment across two temperate sites with differing soil pH each with a paired land use intensity contrast. 13C incorporation into microbial biomass increased with land use intensification in low-pH soil but decreased in high-pH soil, with potential impact on carbon use efficiency in opposing directions. Reduction in biosynthesis traits was due to increased abundance of proteins linked to resource acquisition and stress tolerance. These trait trade-offs were underpinned by land use intensification-induced changes in dominant taxa with distinct traits. We observed divergent pH-controlled pathways of SOC cycling. In low-pH soil, land use intensification alleviates microbial abiotic stress resulting in increased biomass production but promotes decomposition and SOC loss. In contrast, in high-pH soil, land use intensification increases microbial physiological constraints and decreases biomass production, leading to reduced necromass build-up and SOC stabilization. We demonstrate how microbial biomass production and respiration dynamics and therefore carbon use efficiency can be decoupled from SOC highlighting the need for its careful consideration in managing SOC storage for soil health and climate change mitigation.Nematodes are the most abundant and diverse animals on the planet but lack representation in biodiversity research. This presents a problem for studying nematode diversity, particularly when molecular tools (i.e., barcoding and metabarcoding) rely on well-populated and curated reference databases, which are absent for nematodes. To improve molecular identification and the assessment of nematode diversity, we created and curated an 18S rRNA database specific to nematodes (18S-NemaBase) using sequences sourced from the most recent publicly available 18S rRNA SILVA v138 database. As part of the curation process, taxonomic strings were standardized to contain a fixed number of taxonomic ranks relevant to nematology and updated for the most recent accepted nematode classifications. In addition, apparent erroneous sequences were removed. To test the efficacy and accuracy of 18S-NemaBase, we compared it to an older but also curated SILVA v111 and the newest SILVA v138 by assigning taxonomies and analyzing the diversity of a nematode dataset from the Western Nebraska Sandhills. We showed that 18S-NemaBase provided more accurate taxonomic assignments and diversity assessments than either version of SILVA, with a much easier workflow and no need for manual corrections. Additionally, observed diversity further improved when 18S-NemaBase was supplemented with reference sequences from nematodes present in the study site. Although the 18S-NemaBase is a step in the right direction, a concerted effort to increase the number of high-quality, accessible, full-length nematode reference sequences is more important now than ever.