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146 results for “Microbial function”
MarFERReT: an open-source, version-controlled reference library of marine microbial eukaryote functional genes
<p>Metatranscriptomics generates large volumes of sequence data about transcribed genes in natural environments. Taxonomic annotation of these datasets depends on availability of curated reference sequences. For marine microbial eukaryotes, current reference libraries are limited by gaps in sequenced organism diversity and barriers to updating libraries with new sequence data, resulting in taxonomic annotation of only about half of eukaryotic environmental transcripts. Here, we introduce version 1.0 of the Marine Functional EukaRyotic Reference Taxa (MarFERReT), an updated marine microbial eukaryotic sequence library with a version-controlled framework designed for taxonomic annotation of eukaryotic metatranscriptomes. We gathered 902 marine eukaryote genomes and transcriptomes from multiple sources and assessed these candidate entries for sequence quality and cross-contamination issues, selecting 800 validated entries for inclusion in the library. MarFERReT v1 contains reference sequences from 800 marine eukaryotic genomes and transcriptomes, covering 453 species- and strain-level taxa, totaling nearly 28 million protein sequences with associated NCBI and PR2 Taxonomy identifiers and Pfam functional annotations. An accompanying MarFERReT project repository hosts containerized build scripts, documentation on installation and use case examples, and information on new versions of MarFERReT.<br><br>MarFERReT is linked to a code repository hosting containerized build scripts, documentation on installation and use case examples, and information on new versions of MarFERReT here: <a href="https://github.com/armbrustlab/marferret">https://github.com/armbrustlab/marferret</a></p> <p>The raw source data for the 902 candidate entries considered for MarFERReT v1.1.1, including the 800 accepted entries, are available for download from their respective online locations. The source URL for each of the entries is listed here in MarFERReT.v1.1.1.entry_curation.csv, and detailed instructions and code for downloading the raw sequence data from source are available in the MarFERReT code repository (<a href="https://github.com/armbrustlab/marferret/blob/main/docs/process_clean_marmicrodb.log.sh">link</a>). </p> <p>This repository release contains MarFERReT database files from the v1.1.1 MarFERReT release using the following MarFERReT library build scripts: <strong>assemble_marferret.sh</strong>, <strong>pfam_annotate.sh</strong>, and <strong>build_diamond_db.sh</strong><br><br>The following MarFERReT data products are available in this repository:</p> <p><strong>MarFERReT.v1.1.1.metadata.csv</strong><br>This CSV file contains descriptors of each of the 902 database entries, including data source, taxonomy, and sequence descriptors. Data fields are as follows:</p> <ol> <li><strong>entry_id</strong>: Unique MarFERReT sequence entry identifier.</li> <li><strong>accepted: </strong>Acceptance into the final MarFERReT build (Y/N). The Y/N values can be adjusted to customize the final build output according to user-specific needs.</li> <li><strong>marferret_name</strong>: A human and machine friendly string derived from the NCBI Taxonomy organism name; maintaining strain-level designation wherever possible.</li> <li><strong>tax_id</strong>: The NCBI Taxonomy ID (taxID).</li> <li><strong>pr2_accession</strong>: Best-matching PR2 accession ID associated with entry</li> <li><strong>pr2_rank</strong>: The lowest shared rank between the entry and the pr2_accession</li> <li><strong>pr2_taxonomy</strong>: PR2 Taxonomy classification scheme of the pr2_accession</li> <li><strong>data_type</strong>: Type of sequence data; transcriptome shotgun assemblies (TSA), gene models from assembled genomes (genome), and single-cell amplified genomes (SAG) or transcriptomes (SAT).</li> <li><strong>data_source</strong>: Online location of sequence data; the Zenodo data repository (<a href="../">Zenodo</a>), the datadryad.org repository (<a href="http://datadryad.org/">datadryad.org</a>), MMETSP re-assemblies on Zenodo (MMETSP)17, NCBI GenBank (<a href="https://www.ncbi.nlm.nih.gov/genbank/">NCBI</a>), JGI Phycocosm (<a href="https://phycocosm.jgi.doe.gov/phycocosm/home">JGI-Phycocosm</a>), the TARA Oceans portal on Genoscope (<a href="http://www.genoscope.cns.fr/tara/">TARA</a>), or entries from the Roscoff Culture Collection through the METdb database repository (<a href="https://metdb.sb-roscoff.fr/metdb/">METdb</a>).</li> <li><strong>source_link</strong>: URL where the original sequence data and/or metadata was collected.</li> <li><strong>pub_year</strong>: Year of data release or publication of linked reference.</li> <li><strong>ref_link</strong>: Pubmed URL directs to the published reference for entry, if available.</li> <li><strong>ref_doi</strong>: DOI of entry data from source, if available.</li> <li><strong>source_filename</strong>: Name of the original sequence file name from the data source.</li> <li><strong>seq_type</strong>: Entry sequence data retrieved in nucleotide (nt) or amino acid (aa) alphabets.</li> <li><strong>n_seqs_raw</strong>: Number of sequences in the original sequence file.</li> <li><strong>source_name:</strong> Full organism name from entry source</li> <li><strong>original_taxID</strong>: Original NCBI taxID from entry data source metadata, if available</li> <li><strong>alias:</strong> Additional identifiers for the entry, if available</li> </ol> <p><br><strong>MarFERReT.v1.1.1.curation.csv</strong><br>This CSV file contains curation and quality-control information on the 902 candidate entries considered for incorporation into MarFERReT v1, including curated NCBI Taxonomy IDs and entry validation statistics. Data fields are as follows:</p> <ol> <li><strong>entry_id:</strong> Unique MarFERReT sequence entry identifier</li> <li><strong>marferret_name: </strong>Organism name in human and machine friendly format, including additional NCBI taxonomy strain identifiers if available.</li> <li><strong>tax_id</strong>: Verified NCBI taxID used in MarFERReT</li> <li><strong>taxID_status</strong>: Status of the final NCBI taxID (Assigned, Updated, or Unchanged)</li> <li><strong>taxID_notes</strong>: Notes on the original_taxID</li> <li><strong>n_seqs_raw</strong>: Number of sequences in the original sequence file</li> <li><strong>n_pfams</strong>: Number of Pfam domains identified in protein sequences</li> <li><strong>qc_flag</strong>: Early validation quality control flags for the following: LOW_SEQS; less than 1,200 raw sequences; LOW_PFAMS; less than 500 Pfam domain annotations.</li> <li><strong>flag_Lasek</strong>: Flag notes from Lasek-Nesselquist and Johnson (2019); contains the flag 'FLAG_LASEK' indicating ciliate samples reported as contaminated in this study.</li> <li><strong>VV_contam_pct</strong>: Estimated contamination reported for MMETSP entries in Van Vlierberghe et al., (2021).</li> <li><strong>flag_VanVlierberghe: </strong>Flag for a high level of estimated contamination, from 'flag_VanVlierberghe' values over 50%: FLAG_VV.</li> <li><strong>rp63_npfams</strong>: Number of ribosomal protein Pfam domains out of 63 total.</li> <li><strong>rp63_contam_pct</strong>: Percent of total ribosomal protein sequences with an inferred taxonomic identity in any lineage other than the recorded identity, as described in the Technical Validation section from analysis of 63 Pfam ribosomal protein domains.</li> <li><strong>flag_rp63</strong>: Flag for a high level of estimated contamination, from 'rp63_contam_pct' values over 50%: FLAG_RP63.</li> <li><strong>flag_sum: </strong>Count of the number of flag columns (`qc_flag`, `flag_Lasek`, `flag_VanVlierberghe`, and `flag_rp63`). All entries with one or more flag are nominally rejected ('accepted' = N); entries without any flags are validated and accepted ('accepted' = Y).</li> <li><strong>accepted: </strong>Acceptance into the final MarFERReT build (Y or N).</li> </ol> <p> </p> <p><strong>MarFERReT.v1.1.1.proteins.faa.gz</strong><br>This Gzip-compressed FASTA file contains the 27,951,013 final translated and clustered protein sequences for all 800 accepted MarFERReT entries. The sequence defline contains the unique identifier for the sequence and its reference (mftX, where 'X' is a ten-digit integer value). </p> <p> </p> <p><strong>MarFERReT.v1.1.1.taxonomies.tab.gz</strong><br>This Gzip-compressed tab-separated file is formatted for interoperability with the DIAMOND protein alignment tool commonly used for downstream analyses and contains some columns without any data. Each row contains an entry for one of the MarFERReT protein sequences in MarFERReT.v1.proteins.faa.gz. Note that 'accession.version' and 'taxid' are populated columns while 'accession' and 'gi' have NA values; the latter columns are required for back-compatibility as input for the DIAMOND alignment software and LCA analysis. </p> <p>The columns in this file contain the following information:</p> <ol> <li><strong>accession</strong>: (NA)</li> <li><strong>accession.version</strong>: The unique MarFERReT sequence identifier ('mftX').</li> <li><strong>taxid</strong>: The NCBI Taxonomy ID associated with this reference sequence.</li> <li><strong>gi</strong>: (NA).</li> </ol> <p> </p> <p><strong>MarFERReT.v1.1.1.proteins_info.tab.gz</strong><br>This Gzip-compressed tab-separated file contains a row for each final MarFERReT protein sequence with the following columns:</p> <ol> <li><strong>aa_id</strong>: the unique identifier for each MarFERReT protein sequence.</li> <li><strong>entry_id</strong>: The unique numeric identifier for each MarFERReT entry.</li> <li><strong>source_defline</strong>: The original, unformatted sequence identifier</li> </ol> <p> </p> <p><strong>MarFERReT.v1.1.1.best_pfam_annotations.csv.gz<br></strong>This Gzip-compressed CSV file contains the best-scoring Pfam annotation for intra-species clustered protein sequences from the 800 validated MarFERReT entries; derived from the hmmsearch annotations against Pfam 34.0 functional domains. This file contains the following fields:</p> <ol> <li><strong>aa_id</strong>: The unique MarFERReT protein sequence ID ('mftX').</li> <li><strong>pfam_name</strong>: The shorthand Pfam protein family name.</li> <li><strong>pfam_id</strong>: The Pfam identifier.</li> <li><strong>pfam_eval</strong>: hmm profile match e-value score</li> <li><strong>pfam_score:</strong> hmm profile match bitscore</li> </ol> <p><br><strong>MarFERReT.v1.1.1.dmnd</strong><br>This binary file is the indexed database of the MarFERReT protein library with embedded NCBI taxonomic information generated by the DIAMOND makedb tool using the build_diamond_db.sh script from the MarFERReT /scripts/ library. This can be used as the reference DIAMOND database for annotating environment sequences from eukaryotic metatranscriptomes. <br><br></p>
WARM experiment Soil Microbial Function, RMBL Colorado, 2021
We examined how abiotic (warming), and biotic (presence of dominant plant species) factors interact to affect soil microbial processes in montane meadow ecosystems at high and low elevations at the WaRM experimental sites near the Rocky Mountain Biological Laboratory in Colorado in the West Elk range of the southern Rocky Mountains in Colorado, USA, during the summer 2021 growing season. The low elevation site (low site) is at 2740 m elevation (38.715, -106.823) in an open meadow without tree cover, and the dominant plant species is a flowering forb, Wyethia amplexicaulis. The high elevation site (high site) (3460 m, 38.992, -107.067) is also described as open meadow with no tree cover and is dominated by Juncus drummondii, a monocot, grass-like herb. The low and high elevation sites have a mean summertime temperature of 14.9 and 10.9°C respectively, and a mean summertime precipitation of 143 and 151 mm The WaRM experimental design is a 2 × 2 factorial warming × dominant plant species removal experiment deployed at the high elevation site and the low elevation site. Each of the four treatments are replicated 8 times, for total of 32 plots (each of which is 2 × 2 m) at each elevation with warming imposed via transparent hexagonal open-top chambers (OTCs), 1.5 m in diameter, in the center of each warming plot and the dominant plant species (listed above) removed via clipping at soil level within removal plots. Treatments at this site have been deployed each summer (June-August) since 2013. We analyzed multiple soil microbial responses at three times throughout the growing season: pre-growing season [low site; approx. May 26, high site; approx. July 6], peak-growing season [low site; approx. June 23, high site; approx. July 21], and post-growing season [low site; approx. August 18, high site; approx. Sept 14]. We measured edaphic characteristics including volumetric soil water content. We measured soil microbial functions including soil respiration, microbial metabolic
UV radiation accelerates litter decomposition in a valley-type savanna by enhancing microbial community diversity and function
<p><span>We present the data of the study by Gao et al. (202</span><span>4</span><span>): <a name="_Hlk179470571"></a><a name="OLE_LINK42"></a><strong><span>UV radiation accelerates litter decomposition in a valley-type savanna by enhancing microbial community diversity and function</span></strong></span><span>.</span><span> The excel file (Raw Data) includes the following sheets: 1- Radiation (w·m<sup>-2</sup>) variation of UV-A and UV-B during the experimental period. 2- Decay constants (<em>K</em>, yr<sup>−1</sup>) and changes in the mass loss rate of litter under different UV conditions during the experimental period<span>. 3- </span>The content of lignin, cellulose, C, N and P of litter under different UV conditions during the experimental period<span>. 4-</span></span><span> </span><span>16S ASVs under different UV conditions<span>. 5-</span></span><span> </span><span>ITS ASVs under different UV conditions.</span></p>
Sequence-structure-function relationships in the microbial protein universe
<p>The Microbiome Immunity Project (MIP) dataset contains models predicted with both Rosetta and DMPFold (folder `dataset/`). It also contains DeepFRI function predictions for all models. </p> <p>The `metadata` folder contains additional data which may be useful for searching the MIP database (FASTA files, BLAST databases and useful scripts for structure/function search) as well as retrieving the sequence/structural annotations.</p> <p>The `intermediate_data` folder contains preprocessed output for reproducing many of the figures in our manuscript in conjunction with scripts and Juypter notebooks found in our git repository: https://github.com/microbiome-immunity-project/protein_universe .</p> <p>More information about the dataset and associated metadata is provided in the `README.md` file).</p> <p>We are also providing workflows to search the MIP database against a protein sequence or structure or function of interest (see `SEARCHING.md` for more details).</p>
Data collected for: The contrasted impacts of grasshoppers on soil microbial activities in function of ecosystem productivity and herbivore diet
<p>Herbivory can have contrasted impacts on soil microbes and nutrient cycling, which has stimulated the development of conceptual frameworks exploring the links between below- and aboveground processes. The "productivity model" predicts that herbivores stimulate microbial activities and accelerate nutrient mineralization in productive ecosystems, while they have an opposite effect in less productive ecosystems. In parallel, the "diet model" predicts that herbivores feeding on conservative plants accelerate nutrient cycling while those feeding on exploitative plants decelerate nutrient cycling, due to changes in litter inputs. Since these two frameworks can lead to conflicting predictions in some cases, experimental evidence combining herbivore diet and productivity is required.</p> <p>During two consecutive years, we conducted an experiment controlling the presence of three grasshopper species consuming either grasses, forbs or both in twelve natural and managed alpine grasslands of contrasted productivities. In order to assess the effects of herbivory on soil microbes, we measured their enzymatic activities, their biomass and the soil potential nitrogen mineralization (PNM). Soil and vegetation characteristics were also determined in order to test if they modulated the effects of herbivory on microbes.</p> <p>Contrary to the predictions of the diet model, the effects of herbivory on microbial characteristics did not depend on the herbivores diet but relied on ecosystem productivity. The most productive sites were characterized by exploitative plant species which depleted N resources in the soil, and by microbes producing relatively few extracellular enzymes, leading to a lower PNM. Herbivory increased microbial biomass and decreased the production of extracellular enzymes in those sites, possibly through the stimulation of root exudates produced by exploitative species. The least productive sites were characterized by conservative plants, which led to the sequestration of soil C, and by microbes having a resource acquisition strategy (more extracellular enzymes, higher PNM). Herbivory decreased microbial biomass and increased the production of extracellular enzymes in those sites. This pattern can be explained by the loss of carbon associated with insect respiration, which increases the need for microbes to acquire resources and by a lower production of root exudates by conservative species. Therefore, the effects of two years of herbivory on soil microbes were at odds with the productivity model, which focuses instead on longer term effects corresponding to herbivory-induced changes in plant species composition. This highlights the multidimensional feature of the impacts of herbivory on ecosystem functioning, both in space and time.</p>
Fig. 1 in Eukaryotic Microbial Communities Associated with Rock-dwelling Foliose Lichens: A Functional Morphological and Microecological Analysis
Fig. 1. Photograph of a portion of a Flavoparmelia thallus showing an example of a radially oriented lobe with three segments sampled in analyzing the microbial communities: A – inner, B – middle, and C – outer. Scale bar: 5 mm.
Figure 1 in Building a global database of soil microbial biomass and function: a call for collaboration
Figure 1. Sampling locations and underrepresented environmental conditions in the dataset as of 19 November, 2019. For each pixel, we measured the percentage of environmental conditions (e.g. climate, soil characteristics, topographic information, vegetation indices) captured by the current dataset. Hot deserts, tropical rainforests and sub-Arctic regions are the least represented.
Market forces determine the distribution of a leaky function in a simple microbial community
<p>Many biological functions are leaky, and organisms that perform them contribute some of their products to a community "marketplace" where non-performing individuals may compete for them. Leaky functions are partitioned unequally in microbial communities, and the evolutionary forces determining which species perform them and which become beneficiaries are poorly understood. Here we demonstrate that the market principle of comparative advantage determines the distribution of a leaky antibiotic resistance gene in an environment occupied by two "species" - strains of Escherichia coli growing on mutually exclusive resources and thus occupying separate niches. Communities comprised of antibiotic-resistant cells were rapidly invaded by sensitive cells of both types. While the two phenotypes coexisted stably for 500 generations, in 15/18 replicates, antibiotic sensitivity became fixed in one species. Fixation always occurred in the same species, despite both species being genetically identical except for their niche-defining mutation. In the absence of antibiotic, the fitness cost of resistance was identical in both species. However, the intrinsic resistance of the species that ultimately became the sole helper was significantly lower, and thus its reward for expressing the resistance gene was higher. Opportunity cost of resistance, not absolute cost or efficiency of antibiotic removal, determined which species became helper, consistent with the economic theory of comparative advantage. We present a model that suggests that this market-like dynamic is a general property of Black Queen systems, and in communities dependent on multiple leaky functions, could lead to the spontaneous development of an equitable and efficient division of labor.</p>
Microplastics effects on marine microbial communities and their functioning
<p><span>Microplastics pervade ocean ecosystems. Despite their effects on individuals or populations are well documented, the consequences of microplastics on ecosystem functioning, especially regarding lower trophic levels, are still largely unknown. Here we show how microplastics alter the structure and functioning of pelagic microbial ecosystems. Using experimental pelagic mesocosms, we found that microplastics indirectly affect marine productivity by changing the bacterial and phytoplankton assemblages. Specifically, the addition of microplastics increased phytoplankton biomass and shifted bacterial assemblages' composition. Such changes altered the interactions between heterotrophic and autotrophic microbes and the cycling of ammonia in the water column, which ultimately benefited photosynthetic efficiency. The effects of microplastics on marine productivity were consistent for different microplastic types. This study demonstrates that microplastics affect bacteria and phytoplankton communities and influence marine productivity, which ultimately alters the functioning of the whole ocean ecosystem.</span></p>
Market forces determine the distribution of a leaky function in a simple microbial community
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Data collected for: The contrasted impacts of grasshoppers on soil microbial activities in function of ecosystem productivity and herbivore diet
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Shifts and rebound in microbial community function following repeated introduction of a novel species
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Data from: Comparing regression-based approaches for identifying microbial functional groups
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Microplastics effects on marine microbial communities and their functioning
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Data from: Functional diversity enhances, but exploitative traits reduce tree mixture effects on microbial biomass
1. Soil microorganisms play key roles in terrestrial biodiversity and ecosystem functions. Despite recent progress in elucidating the association between plant diversity and soil microorganisms, it remains unclear whether the functional properties of plant mixtures might alter this association. 2. We examined whether the effects of tree species mixtures on soil microbial biomass were impacted by the functional diversity (FD) and community-weighted-mean (CWM) of tree mixtures, by conducting a global meta-analysis involving 123 paired observations of tree mixtures and the corresponding monocultures from 38 studies in forests. 3. We found that the tree mixture effect on microbial biomass increased with the FD of specific leaf area (SLA), leaf N and P content, as well as the FD based on all of these traits plus leaf dry matter content. Meanwhile, the responses of microbial biomass to tree mixtures decreased with the CWM of SLA, leaf N, and P content. The effects of FD and CWM remained consistent, despite variable tree species richness, stand age and climatic factors. 4. Our results provide a new insight that the functional properties of plants may alter the magnitude of the association between plant diversity and soil microorganisms.
Data from: Do temperate tree species diversity and identity influence soil microbial community function and composition?
Studies of biodiversity-ecosystem function in treed ecosystems have generally focused on aboveground functions. The present study investigates inter-trophic links between tree diversity and soil microbial community function and composition.We examined how microbial communities in surface mineral soil responded to experimental gradients of tree species richness (SR), functional diversity (FD), community-weighted mean trait value (CWM) and tree identity. The site was a 4-yr-old common garden experiment near Montreal, Canada, consisting of deciduous and evergreen tree species mixtures. Microbial community composition, community-level physiological profiles (CLPP) and respiration were evaluated using phospholipid fatty acid (PLFA) analysis and the MicroRespTM system, respectively. The relationship between tree species richness and glucose induced respiration (GIR), basal respiration (BR), metabolic quotient (qCO2) followed a positive but saturating shape. Microbial communities associated with species mixtures were more active (basal respiration (BR)), with higher biomass (glucose induced respiration (GIR)), and used a greater number of carbon sources than monocultures. Communities associated with deciduous tree species used a greater number of carbon sources than those associated with evergreen species, suggesting a greater soil carbon storage capacity. There were no differences in microbial composition (PLFA) between monocultures and SR mixtures. The FD and the CWM of several functional traits affected both BR and GIR. In general, the CWM of traits had stronger effects than did FD, suggesting that certain traits of dominant species have more effect on ecosystem processes than does FD. Both the functions of GIR and BR were positively related to aboveground tree community productivity. Both tree diversity (SR) and identity (species and functional identity – leaf habit) affected soil microbial community respiration, biomass and composition. For the first time, we identified functional traits related to life history strategy, as well as root traits that influence another trophic level, soil microbial community function, via effects on BR and GIR.
Expanding the microbial biosynthetic landscape using genes with unknown function
<p>Code for during the realization of the MSc Thesis project: Expanding the microbial biosynthetic landscape using genes with unknown function. For a detail description of the project, datasets and code visit the following GitHub repository: https://github.com/carcarmor/master_thesis/tree/main/package/msc_thesis</p>
Data and metadata of soil microbial community structure, enzyme activities, functional genes and earthworms derived from H2020 Diverfarming project
<p>Soil data and metadata of soil microbial community structure, enzyme activities (dehydrogenase, β-glucosidase, leucine-aminopeptidase, alkaline phosphatase and arylsusfatase activities), N functional genes and earthworms from the different cases studies and long terms from WP4 "Impact of crop diversification on biodiversity", derived from H2020 Diverfarming project. The main objective of workpackage is to provide a scientific understanding of the link between diversified cropping systems, above- and belowground biodiversity, and the resulting ecosystem services provided by soil microorganisms, soil invertebrates and vegetation in agro-ecosystems. Soil organisms contribute to all biogeochemical cycles, Soil organic matter mineralization and stabilization, shape soil structure and have associations with plant species promoting growth and development. http://www.diverfarming.eu.</p>
Deciphering microbial gene function using natural language processing
<p>Dataset for the support of a journal publication. </p> <p>The data include both computed models presented in the paper and all data used for analysis.</p>
Climate regulates the effect of land-use change on the diversity of soil microbial functional groups and soil multifunctionality
<p>Although studies have explored how soil microbial diversity and soil multifunctionality respond to land-use change at local scales, they have rarely been explored at larger scales and across different climatic and soil environmental conditions.</p> <p>By sampling 40 paired sites of land-use change from natural forests to agricultural lands (including croplands and orchards) along the middle and lower Yangtze River, combined with a global meta-analysis, we investigated the effects of land-use change and climate on the alpha and beta diversity of soil bacterial and fungal functional groups (FGs) and their associated soil multifunctionality at a regional scale.</p> <p>Our results showed that land-use change strongly changed the diversity of soil bacterial and fungal FGs and decreased multifunctionality, which was supported by our meta-analysis at a global scale. Direct effects of land-use change and climate and their interaction, together with changes in soil environmental variables, were the main determinants of the land-use change-induced changes in the diversity of soil bacterial or fungal FGs. The land-use change-induced decrease in multifunctionality was mainly associated with the direct effect of forest conversion, soil fertility, and diversity of fungal FGs. Furthermore, climate also regulated the effects of land-use change on multifunctionality by affecting soil fertility and fungal FGs diversity along the Yangtze River.</p> <p><em>Synthesis and applications</em>. Taken together, our findings highlight the important effects of land-use change, climate, and their interactions on microbial diversity and multifunctionality, and suggest that effective land-use management and climate change mitigation strategies should be adopted to protect biodiversity and ecosystem function in the Yangtze River Basin.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
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