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Experimental exposure of blue mussel beds to soft and rigid macroplastics in the winter reveals litter entrapment but no physiological effects

Luisa Kumpitsch — Mon, 13 Oct 2025 00:00:00 GMT

Abstract of the Journal Article: "Experimental exposure of blue mussel beds to soft and rigid macroplastics in the winter reveals litter entrapment but no physiological effects" Macroplastic items like bags, bottles, and containers dominate marine litter, yet their effects on habitats and ecosystems remain understudied. Blue mussels (Mytilus edulis, Mytilus trossulus) form beds that support biodiversity and provide important ecosystem services. The goal of this work was to investigate in an experiment how planar plastic debris, rigid or soft, influences mussel aggregates with regard to their structure and their physiological performance. Mussel individuals were collected in the Kerteminde Fjord and were transferred to a laboratory where they were allowed to form small aggregates on PVC plates (30 individuals each). During formation, half of the aggregates were polluted with planar plastic litter of a defined type (soft PE bags or rigid fragments of PET bottles) and amount, while the other half remained without incorporated macroplastics. All aggregates were then deployed in the fjord for 14 weeks in the winter 2020/21. Afterwards, we measured the cumulative filtration and respiration rates, filtration-to-respiration ratios, condition indices, growth rates, aggregate rugosities, and byssus strengths. Rigid plastics significantly enhanced aggregate rugosity, while all physiological responses as well as byssus formation remained unchanged. The latter might, at least partly, have been due to the fact that we conducted the experiment in winter, when mussel metabolism is substantially reduced. Notably, soft plastics were often concealed within aggregates, and this was presumably caused by the movements of the mussels. These findings suggest that mussel beds may act as sinks for plastic litter, while soft and film-like litter items can be fully embedded in their three-dimensional matrix. Explanation of the single files: Contrast_Film.csv: Contrast Analysis between blue mussel aggregates with and without macroplastics Controls_Plotting.csv: for Plotting the controls without plastics Hydrography_E1.R: for plotting and analyzing the Hydrography Hydrography.csv: for plotting and analyzing the Hydrography Main Data Analysis.R: all statististical analyses conducted Plotting_R.R: all plotting conducted Summary_R_Contrast Analysis_Contrast1.csv: contrast analysis between control aggregates without plastics and aggregates with plastics Summary_R_Contrast Analysis_Contrast2.csv: contrast analysis between control aggregates without plastics and aggregates with plastics Summary_R_Contrast Analysis_Contrast3.csv: contrast analysis between control aggregates without plastics and aggregates with plastics Summary_R_Contrast Analysis_Contrast4.csv: contrast analysis between control aggregates without plastics and aggregates with plastics Summary_R_Contrast Analysis_Contrast5.csv: contrast analysis between control aggregates without plastics and aggregates with plastics Summary_R_plot_Control: for plotting control aggregates without plastics Summary_R_plot_Plastic: for plotting aggregates with microplastics Summary_R_plot.csv: for plotting Summary_R.csv: for analyses Summary_WO_Control_R.csv: for analyses without control

DNA-based monitoring of bacterial and protist diversity in the Baltic Sea

Krzysztof Jurdzinski — Fri, 11 Apr 2025 00:00:00 GMT

Here we share the code, the sequencing processing output, and the intermediate data files for the work on bacterial and protist diversity patterns in the Baltic Sea area based on 16S and 18S metabarcoding as implemented two times for a year alongside the Swedish coastline monitoring programme. This work is available as a preprint: Distinct bacterial and protist plankton diversity dynamics uncovered through DNA-based monitoring in the Baltic Sea area, Krzysztof T Jurdzinski, Meike AC Latz, Anders Torstensson, Sonia Brugel, Mikael Hedblom, Yue O O Hu, Markus Lindh, Agneta Andersson, Bengt Karlson, Anders F Andersson, bioRxiv 2024.08.14.607742; doi: https://doi.org/10.1101/2024.08.14.607742 Documentation files: README.md - description of the files, including all the files within the zipped folders. environment.yml - conda environment with software/packages needed to run all the included scripts. workflow.sh - a bash script defining the workflow. Zipped folders with data processing documentation and intermediate files ampliseq_16S.zip - this directory includes the scripts used to run the nf-core/ampliseq (https://nf-co.re/ampliseq/2.7.0/) pipeline on the V3-V4 16S metabarcoding samples, as well as output files needed for downstream analysis. ampliseq_18S.zip - same as ampliseq_16S.zip, but for the the V4 18S metabarcoding. taxa_reannotation.zip - each subdirectory contains results of taxonomic re-annotation of the metabarcoding results and the scripts to obtain them. Both 2015-2017 and 2019-2020 datasets were re-annotated with the GTDB corrected for mislabled sequences using SATIVA and with PR2 version 5.0.0 for 16S and 18S respectively. Both 16S datasets were re-annotated using the SILVA database (version 138.1). data_2015_2017.zip -these files correspond to the data for the samples from 2015 to 2017 (+ storage test for some 2019 samples). This is new data, later down the pipeline merged with the 2019-2020 dataset. merged_data.zip - this folder contains merged across the 2015-2017 and the 2019-2020 datasets, based on the files from folders data_2015_2017 and data_2019_2020- GSHHG.zip - Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) version 2.3.7 file needed to plot maps, as downloaded from the NOAA website (https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html) . Herlemann_et_al_2016.zip - data from the transect-based study by Herlemann et al., 2016 (https://doi.org/10.3389/fmicb.2016.01883) . read_downsampling.zip - This folder includes the scripts used to rarefy raw reads and the key output files. It is all based on 16S data. freshwater_marine_matching.zip - this folder includes files and code used for matching the ASVs from this study to database freshwater and marine sequences Zipped folders with key R scripts processing_code.zip - R scripts used for multiple steps of intermediate data table processing. analysis_figures_code.zip - R scripts used to analyze the data and generate the figures.

Phylogenomics of aquatic bacteria

Krzysztof Jurdzinski — Mon, 17 Feb 2025 00:00:00 GMT

Intermediate data files obtained during the work on the manuscript "Phylogenomics of aquatic bacteria reveal molecular mechanisms behind the limits of their adaptation to salinity". The files published here were used at various stages of the analysis (or sum-up the stages) and should allow reproduction of the results as well as expanded investigation of the dataset. These files are: ar_mags_info.txt - information table for collected archeal MAGs. It contains names of the MAGs in format {data source 2-letter code}_{name of the MAG as in ENA}, the biome of origin and taxonomic classification. For the brackish MAGs there is also annotation to the basin (Baltic/Caspian) of origin and additional metadata for the Baltic Sea MAGs. bac_mags_info.txt - information table for collected bacterial MAGs. It contains names of the MAGs in format {data source 2-letter code}_{name of the MAG as in ENA}, the biome of origin and taxonomic classification. For the brackish MAGs there is also annotation to the basin (Baltic/Caspian) of origin and additional metadata for the Baltic Sea MAGs. CheckM_all_MAGs.csv - CheckM results for all the investigated MAGs (completness, contamination, strain heterogeneity). ani_file.txt - average nucleotide indentity between all the pairs of investigated MAGs. MAG-cluster-stats-interbiome-clusters.xlsx - Excel file with a table annotating MAGs to >95% ANI clusters and the represtatives chosen for further analysis marked. Contains also sheets with just the representatives, clusters common between the brackish basins and between the biomes, as well as MSG_table.tsv imported into Excel spreadsheet. The first sheet also contains accession numbers for the bacterial MAGs used in this study. nozero.bifurc.bac.tree.nwk - phylogenetic tree of all the MAGs and GTDB reference genomes. Obtained using GTDB-tk. pruned95.nozero.bifurc.bac.tree.nwk - the phylogenetic tree (nozero.bifurc.bac.tree.nwk) pruned to contain only one represtative for a biome from each >95% ANI cluster. Does not contain GTDB reference genomes. subsampled.pruned95.nozero.bifurc.bac.tree.nwk - the phylogenetic tree with >95% ANI cluster respresntatives further randomly pruned the same number of freshwater and marine representatives. timetree_evo_rate_100.nwk - the full phylogenetic tree (nozero.bifurc.bac.tree.nwk) with branch length adjusted to correspond to estimated times since divergence in mya [million years ago]. time_calibration.txt - constraint file used for estimating time since divergence, input for RelTime (MEGA11). Minimal estimates of time since host species diverged [mya], based on the fossil record, were used to set the constraints MSG_table.tsv - a table (tab-separated) with all the MAGs within identified monobiomic sister groups (MSGs), annotated to appropriate transition_ID, biome and transition type. Taxonomic classification and transition times and directions are also included. make_MSG_table.R - R script used to make MSG_table.tsv. assess_datetree.R - R scirpt used to find the cross-biome transitions on the time-adjusted phylogenetic tree and obtained the information about the estimated time since they occured. transition_directions.R - R script used to estimate the ancestral biome-states of MRCAs (most recent common ancestors) of MSG pair and thus infer the most probable transition directions. all_MSG_ids.txt - a text file with names of all the representative MAGs within all the MSG pairs. filter_MSGs.py - a Python script to extract the MAGs from within the MSGs (given all_MSG_ids.txt) from a folder containing a larger set of sequences. Snakefile_proteins - Snakefile with a pipeline to go from nucleotide MAG sequences to pepstats statistics for inferred proteins. Includes proteome inference step using Prodigal (same procedure was used to infer amino-acid sequences for other purposes, including the taxonomic classification and reconstruction of the phylogenetic tree). MSGs_whole_proteomes.py - a Python script to concatanate inferred proteomes into continous amino acid sequences (for amino acid usage statistics). Snakefile_whole_proteome - Snakefile with a pipeline to obtain amino acid relative frequencies within proteomes. As an input takes proteomes in form of one continous sequence (MSGs_whole_proteomes.py output). MSGs_pI_rel_freq_table.tsv - a table (tab separated) with relative frequencies of proteins with pIs (isoelectric points) within 0.5 pH wide bins. aa_freqs_MSGs_list.json and assessed_aas.tsv - a json file with amino acid relative frequencies for each inferred proteome and a tab-separated list of IUPAC amino acid codes in order corresponding to values in the list. aa_cat_freqs_MSGs_list.json and assessed_aa_cats.tsv - a json file with relative frequencies of amino acid categories for each inferred proteome and a tab-separated list names of the categories ordered accoridngly as in the .json file. pI_aa_statistics.xlsx - statistics (p-values and differences sizes) for pairwise comparisons of inferred proteome properties and composition, i.e. i) relative frequencies of acidic, neutral and basic (isoelectric point (pI) categories) proteins ; ii) genome sizes as defined by number of inferred protein-coding genes; iii) amino acid relative frequencies; iv) relative frequencies of amino acids categories. {transition type}.annotation.gz and MSG_ids_{transition type}_pairs.txt - annotation files (zipped) of inferred genes for random pairs of MAGs from each MSG pair, together with text file with MSG represntatives. Seperate pair of files for each transition type. Used for investigating coannotation. ko_annot_full_everything.tsv - table with multilevel annotation of KEGG orthologs, adpoted from KEGG orthology website ko_anno.rar - compressed table with numbers of genes annotated to respective KEGG orthologs in representative genomes from all the >95% ANI bacterial clusters (used for gene gain/loss analysis). iterate_rarefying.R - R script used to indentify the differentially present genes. gain_loss_tables.xlsx - Sheets 1-3: results of MSG-based (phylogeny-aware) gene content analysis. Tables with all the significant (FDR < 0.1, shaded in orange) differentially present genes across pairs of MSGs. For FB and FM type transitions additional genes were added to the table to show at least the top 25 most significant genes regardless of the FDR values. Sheets 4-6: Biome(s) in which the differentially present KOs were found across the identified transitions (MSG pairs), i.e. the data presented in Fig. 6 in text form and annotated to more specific taxa and single transition events. Includes taxonomic annotation of the transitions and numbers of bacterial species in MSGs from respective biomes. Sheets 7-9: Fraction of cases in which gene A (row) was also annotated as gene B (column), based on {transition type}.annotation.gz files. Sheets 10-12: Results of phylogeny-unaware gene content analysis. Tables with all the significant (FDR < 0.1) differentially present genes from an unpaired comparison of all bacterial species from each biome.

SMHI IFCB Plankton Image Reference Library

Anders Torstensson — Fri, 31 May 2024 00:00:00 GMT

This repository includes manually annotated plankton images by phytoplankton experts at the Swedish Meteorological and Hydrological Institute (SMHI). The images were captured using an Imaging FlowCytobot (IFCB, McLane Research Laboratories (https://mclanelabs.com/imaging-flowcytobot/) ) from different locations and seasons in the Skagerrak, Kattegat, and Baltic Proper. These images can be used for training automatic image classifiers to identify various plankton species. From version 6 onward, the images have been consolidated into a single dataset, combining three previously separate sources: RV Svea (Baltic Proper, 2022–2026), RV Svea (Skagerrak–Kattegat, 2022–2026), and Tångesund (2016). Previous versions are still accessible in this repository. The dataset consists of two ZIP archives. The first, annotated_images, contains .png images organized into class-specific subfolders, along with accompanying .tsv files that store image-level and class metadata. The second, matlab_files, includes raw data files (.roi, .hdr, .adc) as well as .mat files intended for developing a random forest image classifier using MATLAB code from the ifcb-analysis repository. The images in this dataset undergo continuous quality control, and new images are regularly added. Consequently, this dataset will be updated on a regular basis. If you find any mislabeled images, please contact the authors. Version history - Version 6 (2026-03-31): 86,232 annotated images. The three datasets in the previous versions has been merged into a single dataset. - Version 5 (2025-12-19): 82,123 annotated images. - Version 4 (2024-11-04): 76,032 annotated images. Corrected class names to better match WoRMS, and continued quality control of images in the Tångesund dataset. - Version 3 (2024-08-05): 72,086 annotated images. Added iRfcb dataset for user and unit testing. - Version 2 (2024-06-03): 71,525 annotated images. Updated class names and corrected manual files in the Tångesund dataset. Continued quality control of images in the Tångesund dataset. - Version 1 (2024-05-31): 65,435 annotated images