Find research datasets worth reusing

opencc-by-4.0Dec 2018View details →

Isotopes and related data associated with water tracing with environmental DNA in a high-Alpine catchment

Isotopes and related data associated with water tracing with environmental DNA in a high-Alpine catchment Prepared by Natalie Ceperley, February 2020.   All methods associated with this data are available in the manuscript: Elvira Mächler, Anham Salyani, Jean-Claude Walser, Annegret Larsen, Bettina Schaefli, Florian Altermatt, and Natalie Ceperley.  2019.  Water tracing with environmental DNA in a high-Alpine catchment, Hydrology and Earth System Sciences. https://doi.org/10.5194/hess-2019-551.  Related data sets are and will be published in the Vallon de Nant Community on Zenodo. Associated sequencing data are publicly available on European Nucleotide Archive (Mächler et al., 2020).  All isotope data analyzed in the laboratory of Torsten W. Vennemann at the University of Lausanne.    All Files:     ▪    NaN - No measurement or sample     ▪    Details regarding measurement are available in paper or supplement.   Files:  1)    climate_hydro_2017_daily.csv      ⁃    16 columns:          ⁃    1. day of year with January 1, 2017 = 1         ⁃    2-5. Q: daily mean, min, max, and baseflow discharge as measured at outlet (location ER/MR), in liters / day          ⁃    6. P: mean mm of rain across catchment per day         ⁃    7. SR: total solar radiation per day in W/hr/m2 as median of 4 meteorological stations         ⁃    8-10. SCA: mean, min, and max snow covered area on days with satellite imagery available for whole catchment area, in %         ⁃    11-13. water temperature, mean, min, and max, at outlet (location ER/MR), in degrees C         ⁃    14-16. air temperature, mean, min, and max at 4 meteorological stations, in degrees C 2)    delta-18-O_permil.csv      ⁃    stable isotopes of water (delta 18-O) in per mil     ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv) 3)    delta-2-H_permil.csv      ⁃    stable isotopes of water (delta 2-H) in per mil     ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv) 4)    dqdt_outlet_prev48hrs.csv     -    dq/dt determined at the outlet for the previous 48 hours at sampling moment (TimeOfSamples_HR.csv) for each sampling site     ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv) 5)    ednasamplecount.csv      -    this is the tally of samples (1 sample includes 4 replicates)     ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv) 6)    electricalconductivity_instrument.csv      ⁃    Code:              108 - post-analyzed using a glass bodied 6 mm probe in the laboratory (Jenway  4510, Staffordshire, UK).              102 - hand measurement with WTW (multi-3510 with a  IDS-tetracon-925, Xylem Analytics, Germany)     ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv) 7)    electricalconductivity_uScm.csv      -    this is the electrical conductivity in micro siemens per cm, according to the instruments coded in electricalconductivity_instrument.csv     ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv) 8)    LC-excess.csv      -     this is the line control execss from the meteoric water line as determined by the samples in the file: precipitationistopemetadata.csv     ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv) 9)    locations.csv      ⁃    Location codes used in other files.      -    Coordinates in CH1903 / LV03 and WGS 84 (lat/lon). Elevation in m. asl.  10)    precipitationisotopemetadata.csv      -     This is the sampling information for the isotope data that was used to calculate the meteoric water line.      -    The full data set will become available in a subsequent publication on Zenodo linked to the same community.      -    4 columns:          -    1. code: rain (1) or snow (2)         -    2. collection date and time         -    3. elevation in m. asl.          -    4. in the case of rain, this is the depth of collection in mm (area normalized volume), in the case of snow, this is the mean depth below the surface that the sample was taken from in cm.      ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv) 11)    sampledates.csv      -    These are the sample dates in day, month, year and day of year corresponding to the rows in other files 12)    stationlocations.csv     -     These are the locations of four meteorological stations and discharge measurement station.      -    Coordinates in CH1903 / LV03 and WGS 84 (lat/lon). Elevation in m. asl.  13)    TimeOfSamples_HR.csv      -    This is the time of the sample in hours and decimals correspond to minutes past hour     ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv) 14)    watertemperature_degC.csv      ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)     -     measure in degrees C     -     instrument in watertemperature_instrument.csv 15)    watertemperature_instrument.csv      ⁃    Code:              1 = hand measurement with WTW (multi-3510 with a  IDS-tetracon-925, Xylem Analytics, Germany)             2 = HOBO Pendant Temperature/Light Data Logger 64K - UA-002-64", Onset (Bourne, MA, USA)             3 = Continually logging WTW (IDS-tetracon-325, Xylem Analytics, Germany)             4 = Continually logging (10min) HOBO U24-001 Conductivity, Onset (Bourne, MA, USA)      ⁃    columns correspond to sampling locations (locations.csv), rows correspond to sampling days (sampledates.csv)

opencc-by-4.0Nov 2023View details →

Environmental DNA captures signals of the internal structure of a pond metacommunity

R Code for the study of "Environmental DNA captures signals of the internal structure of a pond metacommunity"

opencc-by-4.0Nov 2024View details →

Temporal study of Santa Cruz Mountain bats using environmental DNA and acoustic data

Data and R scripts for a study of niche partitioning in a bat community in California's Santa Cruz Mountains using environmental DNA and bioacoustic data collected over a roosting season. Associated with the publication "Temporal study of environmental DNA and acoustic data reveals coexistence of sympatric bat species in a North American ecosystem" in Environmental DNA.

opencc-by-4.0Nov 2022View details →

Sampling metadata for the publication: "Deep-sea sponge derived environmental DNA analysis reveals demersal fish biodiversity of a remote Arctic ecosystem "

Meta data of sampling location, time and depth of eDNA samples used in the study: "Deep-sea sponge derived environmental DNA analysis reveals demersal fish biodiversity of a remote Arctic ecosystem". As well as taxonomic identification of sponges, their microbial abundance and growth form.

opencc-by-4.0Jan 2023View details →

The raw data of Souma, Katano, Doi et al. "Comparing environmental DNA with whole pond survey to estimate the total biomass of fish species in ponds" in Freshwater Biology

The raw data of Souma, Katano, Doi, Takahara, and Minamoto. "Comparing environmental DNA with whole pond survey to estimate the total biomass of fish species in ponds" in Freshwater Biology.

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Kelp forest fish communities environmental DNA samples from Santa Barbara Channel

The dataset in this package is the processed fish community structure inferred from 12S eDNA metabarcoding in the Santa Barbara Channel. 49 water samples were collected across 11 sites in 2017 and the taxa were identified to the highest resolution possible. The raw DNA sequence has been archived in the Sequence Read Archive (SRA) database (https://www.ncbi.nlm.nih.gov/sra) under the accession number PRJNA667508. This dataset is used to support manuscript: Lamy, T., Pitz, K.J., Chavez, F.P. et al. Environmental DNA reveals the fine-grained and hierarchical spatial structure of kelp forest fish communities. Sci Rep 11, 14439 (2021). https://doi.org/10.1038/s41598-021-93859-5

openCC (other)May 2022View details →

opencc-zeroMar 2024View details →

Environmental DNA metabarcoding differentiates between micro-habitats within the rocky intertidal

While the utility of environmental DNA (eDNA) metabarcoding surveys for biodiversity monitoring continues to be demonstrated, the spatial and temporal variability of eDNA, and thus the limits of the differentiability of an eDNA signal, remains under-characterized. In this study, we collected eDNA samples from distinct micro-habitats (~40 m apart) in a rocky intertidal ecosystem over their exposure period in a tidal cycle. During this period, the micro-habitats transitioned from being interconnected, to physically isolated, to interconnected again. Using a well-established eukaryotic (cytochrome oxidase subunit I) metabarcoding assay, we detected 415 species across 28 phyla. Across a variety of univariate and multivariate analyses, using exclusively taxonomically assigned data as well as all detected amplicon sequence variants (ASVs), we identified unique eDNA signals from the different micro-habitats sampled. This difference paralleled expected ecological gradients and increased as the sites became more physically disconnected. Our results demonstrate that eDNA biomonitoring can differentiate micro-habitats in the rocky intertidal only 40 m apart, that these differences reflect known ecology in the area, and that physical connectivity informs the degree of differentiation possible. These findings showcase the potential power of eDNA biomonitoring to increase the spatial and temporal resolution of marine biodiversity data, aiding research, conservation, and management efforts.

opencc-zeroApr 2024View details →

Data from: Sorting states of environmental DNA: Effects of isolation method and water matrix on recovery of membrane-bound, dissolved, and adsorbed states of eDNA

Environmental DNA (eDNA) once shed can exist in numerous states with varying behaviors including degradation rates and transport potential. In this study we consider three states of eDNA: 1) a membrane-bound state referring to DNA enveloped in a cellular or organellar membrane, 2) a dissolved state defined as the extracellular DNA molecule in the environment without any interaction with other particles, and 3) an adsorbed state defined as extracellular DNA adsorbed to a particle surface in the environment. Capturing, isolating, and analyzing a target state of eDNA provides utility for better interpretation of eDNA degradation rates and transport potential. While methods for separating different states of DNA have been developed, they remain poorly evaluated due to the lack of state-controlled experimentation. We evaluated the methods for separating states of eDNA from a single sample by spiking DNA from three different species to represent the three states of eDNA as state-specific controls. We used chicken DNA to represent the dissolved state, cultured mouse cells for the membrane-bound state, and salmon DNA adsorbed to clay particles as the adsorbed state. We performed the separation in three water matrices, two environmental and one synthetic, spiked with the three eDNA states. The membrane-bound state was the only state that was isolated with minimal contamination from non-target states. The membrane-bound state also had the highest recovery (54.11 ± 19.24 %), followed by the adsorbed state (5.08 ± 2.28 %), and the dissolved state had the lowest total recovery (2.21 ± 2.36 %). This study highlights the potential to sort the states of eDNA from a single sample and independently analyze them for more informed biodiversity assessments. However, further method development is needed to improve recovery and reduce cross-contamination.

opencc-by-4.0Dec 2021View details →

DNA methylation in clonal Duckweed lineages (Lemna minor L.) reflects current and historical environmental exposures.

The following depository contains raw phenotypic data and intermediate DNA methylation data presented in the article "DNA methylation in clonal Duckweed lineages (Lemna minor L.) reflects current and historical environmental exposures." : 1) Raw phenotypic data - Frond_area_Phase1_Phase2 -> Frond area measured at different time points (week 1, 6, 7 and 8) during the experiment. - Frond_number_Phase1_Phase2 -> Frond number measured at different time points (week 1, 6, 7 and 8) during the experiment. 2) Intermediate files obtained from running the epiGBS2 pipeline. The following files are available: - consensus_cluster.renamed.fa ->  epiGBS de novo loci. This file consists of the de novo epiGBS reference sequence file obtained during the de novo reference creation. - methylation.filtMETH -> The filtered DNA methylation data. This data was obtained after filtering the raw DNA methylation data. Cytosines which had a 10X coverage or higher and which were present in 80% of all samples were kept for further analysis. Demultiplexed and raw data were deposited at NCBI: BioProject: PRJNA883550

opencc-zeroFeb 2022View details →

Data from: Metabarcoding of soil environmental DNA replicates plant community variation but not specificity

<blockquote> While metabarcoding of plant DNA from their environment is an exciting method that can supplement inventorying of live plant species, the accuracy and specificity has yet to be fully assessed over complex continuous landscapes. In this work, we evaluate plant community profiles produced via metabarcoding of soil by comparing them to a morphological survey. We assessed plant communities by metabarcoding of soil DNA in 130 sites along ecological gradients (nutrients, succession, moisture) in Denmark using chloroplast trnL region (10-143 bp) primer set and compared the resulting communities to communities produced with a longer nuclear ITS2 region (~216 bp) and a morphological survey. We found that the community variation observed within the morphological survey was well represented by molecular surveys, with significant correlation with both community composition and richness using both primer sets. While the majority of the ITS2 sequences could be assigned to species (over 80%), we had less success with the trnL sequences (70%), which was only possible after restricting the reference database to local species. We conclude that the community profiles produced by metabarcoding can be highly effective in performing large-scale macroecological studies. However, the discovery rates and taxonomic assignments produced via metabarcoding remained inferior to morphological surveys, but manual curation of databases improves the specificity of assignments made by the trnL primers, and improves the accuracy of the assignments made with the ITS2 primers. Finally, we suggest that a greater percentage of named diversity would be recovered by increasing soil sampling with the use of additional universal primer sets. </blockquote>

opencc-zeroMar 2022View details →

Data for fitness analyses used in: Environmentally-induced DNA methylation is inherited across generations in water fleas (Daphnia magna)

Data of fitness effects of environmental stressors on Daphnia magna over multiple generations. Ages of first and second reproduction, and sizes of first and second brood were measured and used to calculate replacement rate. This data is part of a study on whole-genome bisulphate sequencing on individual Daphnia magna to assess whether environmentally-induced DNA methylation can persist for up to four generations.

opencc-by-4.0Mar 2022View details →

Vegetation changes over the last centuries in the Lower Lake Constance region reconstructed from sediment-core environmental DNA

Many European lake ecosystems, including their respective catchment areas, underwent anthropogenic environmental changes over the last centuries. This has resulted in changes in the aquatic and terrestrial vegetation, but historical records on the composition of the past vegetation on centennial scale are scarce. In this study, we examined changes in the terrestrial and aquatic plant communities in and around Lower Lake Constance using metabarcoding of sedimentary DNA (sedDNA) of three cores from different sub- basins covering the past, up to 300 years. We successfully identified an average of c. 3000 sequence variants (molecular operational taxonomic units - MOTUs) and obtained a taxonomically annotated dataset of 127 species, 104 genera and 72 families. We could detect major changes in the terrestrial and aquatic vegetation of the Lower Lake Constance region by examining the cores. For example, alpha diversity decreased in the last c. 100 years, and this decrease was more pronounced in the terrestrial than in the aquatic plant community. Unlike the terrestrial plant-community, the current aquatic plant- community composition partially resembles the community from before the 20th-century eutrophication phase of the lake. In addition to changes that can be attributed to anthropogenic impacts, we also captured the effect of DNA sedimentation on the terrestrial DNA diversity representation in sediments during periods of extensive flooding and potentially as a consequence of extremely cold winters. With 1sedDNA from Lower Lake Constance, we provide a new local dataset to investigate and extend the historical changes of different shoreline habitats and to identify characteristic and invasive plant species. Such highly-resolved datasets spanning the past centuries can provide detailed information on human environmental history in densely populated regions that have undergone severe changes in the recent past.

opencc-by-4.0Jul 2021View details →

Multi-taxa environmental DNA inventories reveal distinct taxonomic and functional diversity in urban tropical forest fragments

Urban expansion and associated habitat transformation drives shifts in biodiversity, with declines in taxonomic and functional diversity. Forests fragments within urban landscapes offer a number of ecosystem services, and help to maintain biodiversity and ecosystem functions. Here, we focus on a tropical forest environment, and on the soil biota. Using eDNA metabarcoding, we compare forest fragments within the city of Cayenne, French Guiana, with a neighbouring continuous undisturbed forest. We wished to determine if urban forest fragments conserve high levels of alpha and beta diversity as well as similar functional composition for plants, soil animals, fungi and bacteria. We found that alpha diversity is similar across habitats for plants and fungi, lower in urban forests for metazoans and higher for bacteria. We also found that urban forests communities differ from undisturbed forests in their taxonomic composition, with urban forests exhibiting greater turnover between fragments potentially caused by ecological drift and limited dispersal. However, their functional composition exhibited limited differences, with an enrichment of palms, arbuscular mycorrhizal fungi and bacteria and a depletion of climber plants and termites. Thus, although urban forest fragments do shelter soil biodiversity that differs from native forests, the losses of soil functions may be relatively limited. This study demonstrates the strong potential of a multi-taxa eDNA approach for rapid inventories across taxonomic kingdoms, in particular for cryptic soil diversity. It also demonstrates the key role of urban forest fragments in conserving biodiversity and ecosystem function, and points to a need for more systematic monitoring of these areas in urban management plans. For each of the 16 samples per plot, 15 g of soil was used for eDNA analyses. Extracellular DNA was extracted as described previously (Zinger et al., 2016; 2019), where each soil sample is added to 15ml of saturated phosphate buffer (Na2HPO4; 0.12m; pH ≈8) in 50ml Falcon tubes. This is placed in an agitator for 15 minutes, before a 2ml aliquot of the soil/phosphate buffer mixture is pipetted into an Eppendorf tube and centrifuged for five minutes at 13000 rcf. 500μL of the resulting supernatant is then recovered and used for the next extraction steps that are carried out with a commercial kit for soil DNA (NucleoSpin® Soil; Macherey-Nagel, Düren, Germany), skipping the lysis step and following manufacturer’s instructions. The DNA extract was recovered in 100 μL and diluted 10 times before being used as PCR template.  For each plot one DNA extraction negative control was performed adding up 17 extractions per plot. PCR amplifications were then conducted for four DNA molecular markers, with primers targeting either Viridiplantae (subsequently referred to as plants), Eukaryotes, Fungi or Bacteria (Table 1). For each marker, PCR amplification of samples occurred across 12 plates. Each PCR reaction was performed in a total volume of 20 μl and comprised 10 μl of AmpliTaq Gold Master Mix (Life Technologies, Carlsbad, CA, USA), 5.84 μl of Nuclease-Free Ambion Water (Thermo Fisher Scientific, Massachusetts, USA), 0.25 μM of each primer, 3.2 μg of BSA (Roche Diagnostic, Basel, Switzerland), and 2 μl of DNA template that was before 10-fold diluted to reduce the amounts of PCR inhibitors. Thermocycling conditions for each primer pair are indicated in Table 1.  A negative extraction control per site and a negative PCR control per PCR plate were amplified and sequenced in parallel with the regular samples. Positive controls were also included and consisted of mock communities of plants and fungi DNA (no mock communities were built for bacteria or eukaryotes here), which were used to guide choices in our data curation process. Two PCR replicates were performed for each sample and control. Amplification was conducted using a double indexing system strategy (Binladen et al. 2007) using a system of 32 by 36 octamers with at least five differences between them located at the 5’ end of each primer (Coissac 2012). In doing so, each PCR product had a unique combination of tags for both forward and reverse primers, allowing for the retrieval of sequence data for each sample. Ten wells per PCR plate were left empty to act as sequencing controls (non-used tag combinations) for downstream data curation (see below). PCR products were pooled and sequencing libraries were constructed using the Illumina TruSeq NanoPCRFree kit following the supplier’s instructions (Illumina Inc., San Diego, California, USA), except that the ligation product was not PCR amplified to limit tag-jump biases (Taberlet et al 2018). The libraries were then sequenced on different Illumina platforms (San Diego, CA, USA) depending on the marker considered (Table S1), using the paired-end technology. Bioinformatic analyses were performed on the GenoToul bioinformatics platform (Toulouse, France), with the OBITOOLS package (Boyer et al. 2016). First, ‘illuminapairedend’ was used to assemble paired-end reads. This algorithm is based on an exact alignment algorithm that considers the quality scores at all positions during the assembly process. Subsequently, we used the ‘ngsfilter’ command to identify and remove the primers and tags on each read, and assign reads to their respective samples. This program was used with its default parameters tolerating two mismatches for each of the two primers and no mismatch for the tags. Following this, sequencing reads were dereplicated using the ‘obiuniq’ command. Sequences of low quality (containing Ns or with paired-end alignment scores below 50) were excluded using the ‘obigrep’ command. The same command was used to exclude sequences represented by only one read (singletons) as they are more likely to be molecular artefacts (Taberlet et al. 2018). Sequences outside of the preset range were also discarded (Table 1). To remove PCR/sequencing errors as well as intraspecific variability, we built OTUs (Operational Taxonomic Units) using the ‘sumaclust’ clustering algorithm (Mercier et al. 2013), which considers the most abundant sequence of each cluster as the cluster representative.  OTUs were set at a sequence similarity threshold of 97% for eukaryotes, fungi and bacteria following the standards in microbial ecology, but this was lowered to 95% for plants since the eDNA target region is shorter (typically around 50 base pairs), where one mismatch inherently results in a lower percentage of similarity. To assign a taxon to plant and fungal OTUs, we built two reference sequence databases, one global, using the ecoPCR programme (Ficetola et al. 2010) and the plant / fungi specific markers on the European Molecular Biology Laboratory (EMBL; release 141), a second local, generated from specimens of fungi (Jaouen et al. 2019) and plants (see Zinger et al. 2019) collected in French Guiana. OTUs were then assigned a taxonomy, using OBITOOL’s ecotag programme (Boyer et al. 2016), which performs a global alignment of each OTU sequence (the query) against each reference. The reference taxon assigned to each OTU corresponds to the Last Common Ancestor of all the best-match sequences for the query. For taxonomic assignment of bacteria and eukaryote OTUs, the SILVA taxonomic database was used (version 1.3; Quast et al., 2012). Classification was performed by a local nucleotide BLAST search against the non-redundant version of the SILVA SSU Ref dataset (release 132; http://www.arb-silva.de) using blastn (version 2.2.30+; http://blast.ncbi.nlm.nih.gov/Blast.cgi) with standard settings (Camacho et al., 2009).  Eukaryote derived metazoan OTUs were then further assigned a taxonomy for Phyla identified at the Arthropoda, Annelida and Nematoda level using reference sequence databases built as above for these groups using the ecoPCR programme on EMBL release 141. Datasets were subsequently filtered to remove contaminants as well as artefacts such as PCR chimeras and remaining sequencing errors, following Zinger et al. (2019) and using routines now implemented in the metabaR R package (Zinger et al 2020b), in R version 3.6.1 (R Development Core Team, 2013). The filtering process consisted of four steps: (i) a negative control-based filtering. OTUs whose maximum abundance was found in extraction/PCR negative controls were removed from the dataset, as they were likely to be reagent/aerosol contaminants, better amplified in the absence of competing DNA fragments as it is the case in biological samples. (ii) a reference-based filtering. OTUs which are too dissimilar from sequences available in reference databases are potential chimeras generated during sequencing and amplification. In this study, we chose to set similarity thresholds at 95% for plants, 80% for bacteria and eukaryotes and due to the marker being more polymorphic, 65% for fungi. For plants and fungi, the remaining assignment was then verified with the local database, to confirm if assigned taxa also occurred in the local dataset, with preference given to local assignment. In addition, we removed all taxa that are not targeted by the primer used. (iii) an abundance-based filtering. This procedure targets incorrect assignment of a few numbers of sequences corresponding to true OTUs occurring to the wrong sample, a phenomenon called “tag-switching” (Esling et al. 2015), “tag jumps” (Schnell et al. 2015) or “cross-talk” (Edgar 2018). It consists in setting OTUs abundances to 0 in samples where their abundance represents < 0.03% of the total OTU abundance in the entire dataset. (iv) Finally, we conducted a PCR-based filtering by considering any PCR reaction that yielded less than 100 reads for plants, 1000 reads for fungi, bacteria and eukaryotes as non-functional, and removed them from the dataset. Data provided consists of 4 x OTU tables for each of the markers used to target different components of the soil biota, with rows representing each OTU, and columns the features of the OTU within the dataset, namely their id code, the number of read counts in the analysed dataset, their similarity score against the taxonomic dataset used to identify them, and when possible, a functional group assignment used in the manuscript. Details of these can be found above and in the manuscript and supplementary information. For each of the four datasets, we also provide a .rds file, corresponding to the processed dataset used in manuscript preparation. This is in the format of a metabaR list which includes PCR, Sample, Read count and the seperately provided OTU datasets. To facilitate interpretation, please refer to Zinger, L., Lionnet, C., Benoiston, A.S., Donald, J., Mercier, C. and Boyer, F., 2021. metabaR: an R package for the evaluation and improvement of DNA metabarcoding data quality. Methods in Ecology and Evolution, 12(4), pp.586-592. For the fungal (ITS) data, we also provide :  - the R1/R2 raw fastq files of the samples used in the paper + experimental controls - a tsv file containing the tag combinations corresponding to the samples/PCR replicates, to enable demultiplexing of data. - a csv file containing the description of each sample.

opencc-by-4.0Jun 2022View details →

All raw data for Fukuzawa, T et al. "Environmental DNA extraction method for a high and stable DNA yield"

The all raw data of quantitative PCR for environmental DNA in Fukuzawa, T et al. "Environmental DNA extraction method for a high and stable DNA yield".

opencc-by-4.0Nov 2021View details →

BeeDNA: microfluidic environmental DNA metabarcoding as a tool for connecting plant and pollinator communities

Data repository accompanying the paper 'BeeDNA: microfluidic environmental DNA metabarcoding as a tool for connecting plant and pollinator communities' by Harper et al. (2021). 1_Raw_Data.zip This zipped folder contains the raw sequence data (sorted by primer set and demultiplexed) for both sequencing runs (2019-10-24 and 2019-11-11). To decompress each file, run:  <pre><code>tar -xvf filename.bz2</code></pre> This will create a folder for each primer set containing the raw reads for each sample/control. 2_Anacapa_Bioinformatic_Processing.zip This zipped folder contains all files needed to perform bioinformatic processing with Anacapa. Please process sequence data belonging to each primer set individually (i.e. do not process sequence data belonging to different primer sets together). 3_metaBEAT_Bioinformatic_Processing.zip  This zipped folder contains the scripts and files needed to perform bioinformatic processing with metaBEAT. Before running the scripts, move the raw reads for each sample belonging to each primer set into the dedicated folder within metaBEAT_Bioinformatic_Processing, e.g. all .fastq files in Raw_Data > BF1_BR1 should be moved to metaBEAT_Bioinformatic_Processing > BF1-BR1 > raw_reads. To run metaBEAT, you will have to install Docker on your computer. Docker is compatible with all major operating systems, but see the Docker documentation for details. On Ubuntu, installing Docker should be as easy as: <pre><code>sudo apt-get install docker.io</code></pre> Once Docker is installed, you can enter the environment by typing: <pre><code>sudo docker run -i -t --net=host --name metaBEAT -v $(pwd):/home/working chrishah/metabeat /bin/bash</code></pre> This will download the metaBEAT image (if not yet present on your computer) and enter the 'container', i.e. the self contained environment (NB: sudo may be necessary in some cases). With the above command, the container's directory /home/working will be mounted to your current working directory (as instructed by $(pwd)). In other words, anything you do in the container's /home/working directory will be synced with your current working directory on your local machine. Please process sequence data belonging to each primer set individually (i.e. do not process sequence data belonging to different primer sets together). An example of expected outputs can be seen in the Jupyter Notebook for the BF1/BR1 primer set from the 2019-11-11 sequencing run. 4_Illinois_Invert_Reference_Database.zip This zipped folder contains all files that were used to generate the custom COI and 16S reference databases for invertebrates that occur in Illinois, U.S. You will need to have metaBEAT installed (see above) before you try to run any Jupyter Notebooks (.ipynb files). 5_ecoPCR.zip This zipped folder contains all files used to perform ecoPCR for each primer set evaluated for microfluidic eDNA metabarcoding. You will need to <a href="https://git.metabarcoding.org/obitools/ecopcr/wikis/home">install ecoPCR</a> before running any shell scripts. 6_Tidied_Data.zip This zipped folder contains the taxonomically assigned data for both sequencing runs produced by metaBEAT and Anacapa. These were copied over from the folders 2_Anacapa_Bioinformatic_Processing and 3_metaBEAT_Bioinformatic_Processing and rearranged into a more logical order. These files are used as the input for data analysis using R. 7_Data_Analysis.zip This zipped folder contains all scripts and metadata required to summarise and statistically analyse data in R.   Please contact Dr Lynsey Harper (lynsey.harper2@gmail.com) or Dr Mark Davis (davis63@illinois.edu) if you encounter any issues!

opencc-zeroSep 2022View details →

Environmental DNA reveals fine-scale habitat associations for sedentary and resident marine species across a coastal mosaic of soft and hard-bottom habitats

Accurate knowledge on spatiotemporal distributions of marine species and their association with surrounding habitats is crucial to inform adaptive management actions responding to coastal degradation across the globe. Here, we investigate the potential use of environmental DNA (eDNA) to detect species-habitat associations in a patchy coastal area of the Baltic Sea. We directly compare species-specific qPCR analysis of eDNA with baited remote underwater video systems (BRUVS), two non-invasive methods widely used to monitor marine habitats. Four focal species (cod Gadus morhua, flounder Platichthys flesus, plaice Pleuronectes platessa and goldsinny wrasse Ctenolabrus rupestris) were selected based on contrasting habitat associations (reef- vs. sand-associated species), as well as differential levels of mobility and residency, to investigate whether these factors affected the detection of species-habitat associations from eDNA. To this end, a species-specific qPCR assay for goldsinny wrasse is developed and made available herein. In addition, potential correlations between eDNA signals and abundance counts (MaxN) from videos were assessed. Results from Bayesian multi-level models revealed strong evidence for a sand association for sedentary flounder (98% posterior probability) and a reef association for highly resident wrasse (99% posterior probability) using eDNA, in agreement with BRUVS. However, contrary to BRUVS, eDNA sampling did not detect habitat associations for cod or plaice. We found a positive correlation between eDNA detection and MaxN for wrasse (posterior probability 95%), but not for the remaining species and explanatory power of all relationships was generally limited. Our results indicate that eDNA sampling can detect species-habitat associations on a fine spatial scale, yet this ability likely depends on the mobility and residency of the target organism, with associations for sedentary or resident species most likely to be detected. Combined sampling with conventional non-invasive methods is advised to improve detection of habitat associations for mobile and transient species, or for species with low eDNA concentrations.

opencc-by-4.0Sep 2022View details →

Figure 3 in Elasmobranch diversity across a remote coral reef atoll revealed through environmental DNA metabarcoding

Figure 3. Spatial variation in elasmobranch abundance and diversity inferred from eDNA metabarcoding of surface (A) and deep (40 m) (B) water samples collected around Diego Garcia. Negaprion acutidens is not visible in the charts as a result of low copy number, but was detected at site 8 in surface samples. Numbers correspond to the site numbers detailed in Figure 1.