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The information provided here represents the EBV workflow templates collected during the EuropaBON online workshop on Essential Biodiversity Variable (EBV) workflows from 22–24 February 2023. The templates were designed to capture comprehensive descriptions about the three workflow components (data collection and sampling, data integration, and modelling) that are typical for generating EBVs. Recognising the potential value of those EBV templates for European biodiversity monitoring, our objective is to share them for enhancing transparency, knowledge exchange and collaboration, and promoting the operationalisation of EBVs across Europe. EuropaBON (https://europabon.org/) is a Horizon 2020 research and innovation action funded by the European Commission that seeks to co-design a European Biodiversity Observation Network. This network aims to bridge the gap between the biodiversity data needs of policy-makers and authorities on the one hand and the existing reporting streams and available data sources on the other hand, considering both present obligations and forthcoming policy needs. Essential Biodiversity Variables (EBVs) are a central concept of EuropaBON as they provide a standardised framework for biodiversity monitoring and reporting. In 2023, EuropaBON had identified 70 EBVs (Junker et al., 2023) that are policy-relevant for the EU, and measurable with available and existing technologies and with a proven track record of feasibility in ongoing initiatives. EBVs require workflows to process the raw data (primary observations) through data integration and modelling into spatially-explicit EBV data products (Kissling et al., 2018; Schmeller et al., 2017). These workflows can be broken down into three main components (data collection and sampling, data integration, and modelling), with additional aspects of data interoperability and IT infrastructure being recognised as crucial for transnational data streams (Kissling & Lumbierres, 2023). To capture information about the EBV workflows, an online workshop was held on 22–24 February 2023 with 520 registered participants from 49 countries, covering a large range of expertise (Lumbierres & Kissling, 2023). Participants contributed information on EBV workflow components and advanced monitoring techniques, discussed initiatives, and identified tools and requirements for implementing 70 proposed EBVs. The information from the workshop participants was collected through pre-defined EBV workflow templates (provided as Google Docs). Templates were organised into rows representing the workflow components (‘Data collection and sampling’, ‘Data integration’, and ‘Modelling’) and columns reflecting the levels of maturity ('Current initiatives', 'Emerging tools and projects' or 'Future needs'). Prior to the workshop, some information on existing workflows was pre-filled based on previous EuropaBON deliverables, namely an assessment of the current biodiversity monitoring gaps in the EU (Santana et al., 2023) and an assessment of current EU monitoring workflows and bottlenecks (Morán-Ordóñez et al., 2023). After the workshop, the EBV workflow templates were processed to ensure the accuracy and relevance of the information. Each listed initiative was verified to be part of an active biodiversity monitoring scheme and pertinent to the specific EBV under consideration, cross-referencing with the initiative’s websites and other data collected by the EuropaBON deliverables (Morán-Ordóñez et al., 2023; Santana et al., 2023). Moreover, we ensured correct alignment of each initiative and listed requirements and needs with the appropriate workflow components and maturity levels. The EBV workflow templates provide insights into the current biodiversity monitoring landscape in Europe and how EBV production could be operationalized at the EU level. They offer detailed information about ongoing initiatives and projects, methodologies, and technologies that can be used to generate EBVs at a continental scale. Nevertheless, it is important to note that they do not encompass an exhaustive list of all ongoing or proposed initiatives of biodiversity monitoring in all member states of the EU. It is suggested to use them as a starting point and baseline for the further development of EBVs in a European context.

# Software for optimizing treatment to slow the spatial propagation of invasive species: code and results ## ## Description: The complete code and simulation results for finding the optimal treatment of a population front, to slow its propagation to a speed v. The algorithm is described in the paper "Optimizing strategies for slowing the spread of invasive species" by Adam Lampert (PLOS Computational Biology, DOI: 10.1371/journal.pcbi.1011996). Some of the results are demonstrated in Figs. 2-5 in that paper. ## Authorship: The code was written by Adam Lampert, Institute of Environmental Sciences, Robert H. Smith Faculty of Agriculture, Food and Environment, the Hebrew University of Jerusalem, Israel. ## Installation: Running the Matlab code requires the installation of Matlab 2021b for Windows (or a similar version of Matlab). ## Running the code – general model: 1\. Extract all files from "general_model_code.zip" into a single folder. 2\. Open "main.m" and "calc_cost.m" using Matlab. 3\. Change the parameter values, run "main.m," and wait until Matlab completes the execution. ## Running the code – spongy moth model: 1\. Extract all files from "spongy_moth_model_code.zip" into a single folder. 2\. Open "main_F2.m" using Matlab. 3\. Change the parameter values run the code, and wait until Matlab completes the execution. ## Description of the data files: The results for the general model's simulations are given as raw data in the folder "general_model_simulation_results.zip". The data files can be accessed with Matlab. Some of these results are demonstrated in the main article, Fig. 4. The results for the spongy moth model simulations are given as raw data in the folder "spongy_moth_model_simulation_results.zip". The data files can be accessed with Matlab. Some of these results are demonstrated in the main article, Fig. 5. Each data file in "general_model_simulation_results.zip" and in "spongy_moth_model_simulation_results.zip" includes the simulation results for a given set of parameters. The name of the file specifies the parameter values used. Specifically, for the general model, the file name indicates the values of α and v used for the simulation. For the spongy moth model, the file name indicates first the value of (kλ₀) and then the values of v used for the simulation. Each data file includes the following variables: * *n_front:* an array that includes the value of the population front (n-opt) as a function of the location (x). * *treatment:* an array that includes the value of the optimal treatment (A-opt) as a function of the location (x). * *Nx:* size of the n_front and the treatment arrays. The general model data files also cinclude the following parameter value: * *Dt:* time resolution (Δt) Spongy moth model data files also include the following parameter values: * *num_moves:* number of spatial steps the front moves per time unit (equivalent to v). * *delta:* the spatial resolution (σ). * *lambda:* the parameter λ₀. Slowing the spread of invasive species is a major challenge. How can we achieve this goal in the most cost-effective manner? This package includes the complete code and simulation results that help finding the optimal, most cost-effective treatment to slow the spread of a propagating species. This package accompanies the paper "Optimizing strategies for slowing the spread of invasive species" by Adam Lampert (PLOS Computational Biology, DOI: 10.1371/journal.pcbi.1011996). The file general_model_code.zip contains the code for the general model; the file spongy_moth_model_code.zip contains the code for the spongy moth model; and the file general_model_simulation_results.zip contains the results for the general model; and the file spongy_moth_model_simulation_results.zip contains the results for the spongy moth model. The code for the simulations was written in Matlab and the simulation results were obtained by running the code.  Opening the code and results requires an installation of Matlab (2021b for Windows or a similar version).

The silver-cheeked toadfish Lagocephalus sceleratus (Gmelin 1789), and to a lesser degree the orange spotted toadfish Torquigener hypselogeneion (Bleeker, 1852), pose threats to human health from physical attacks and poisonings in the Eastern Mediterranean Sea. This study reviewed human health-related impacts resulting from these pufferfish, compiling and assessing records from online sources, the peer-reviewed literature, medical records, personal interviews, and observations across the Eastern Mediterranean in the years 2004 to 2023. A total of 198 events impacting human health were documented: 28 records of physical attacks, at least 144 non-lethal poisoning episodes, and 27 human fatalities resulting from consumption. The majority of the reported incidences occurred in Syria, Türkiye, and Lebanon. Most physical attacks occurred in summer, while most poisoning events occurred during winter. The number of recorded incidents greatly increased after 2019, especially with regard to poisonings, yet whether this is related to greater media attention, or to increased fish abundance is unclear. This is the first comprehensive study to collate findings on attacks, poisonings and fatalities caused by these pufferfish in the Mediterranean Sea, and may help in improving national health policies. We urge the continuation of national campaigns to caution residents and tourists of these species’ high toxicities and potential aggressiveness.

ABSTRACTStandardised terminology in science is important for clarity of interpretation and communication. In invasion science – a dynamic and rapidly evolving discipline – the proliferation of technical terminology has lacked a standardised framework for its development. The result is a convoluted and inconsistent usage of terminology, with various discrepancies in descriptions of damage and interventions. A standardised framework is therefore needed for a clear, universally applicable, and consistent terminology to promote more effective communication across researchers, stakeholders, and policymakers. Inconsistencies in terminology stem from the exponential increase in scientific publications on the patterns and processes of biological invasions authored by experts from various disciplines and countries since the 1990s, as well as publications by legislators and policymakers focusing on practical applications, regulations, and management of resources. Aligning and standardising terminology across stakeholders remains a challenge in invasion science. Here, we review and evaluate the multiple terms used in invasion science (e.g. ‘non‐native’, ‘alien’, ‘invasive’ or ‘invader’, ‘exotic’, ‘non‐indigenous’, ‘naturalised’, ‘pest’) to propose a more simplified and standardised terminology. The streamlined framework we propose and translate into 28 other languages is based on the terms (i) ‘non‐native’, denoting species transported beyond their natural biogeographic range, (ii) ‘established non‐native’, i.e. those non‐native species that have established self‐sustaining populations in their new location(s) in the wild, and (iii) ‘invasive non‐native’ – populations of established non‐native species that have recently spread or are spreading rapidly in their invaded range actively or passively with or without human mediation. We also highlight the importance of conceptualising ‘spread’ for classifying invasiveness and ‘impact’ for management. Finally, we propose a protocol for classifying populations based on (i) dispersal mechanism, (ii) species origin, (iii) population status, and (iv) impact. Collectively and without introducing new terminology, the framework that we present aims to facilitate effective communication and collaboration in invasion science and management of non‐native species.

Rare-earth monopnictides are a family of materials simultaneously displaying complex magnetism, strong electronic correlation, and topological band structure. The recently discovered emergent arc-like surface states in these materials have been attributed to the multi-wave-vector antiferromagnetic order, yet the direct experimental evidence has been elusive. Here we report the observation of non-collinear antiferromagnetic order with multiple modulations using spin-polarized scanning tunneling microscopy. Moreover, we discover a hidden spin-rotation transition of single-to-multiple modulations 2 K below the Neel temperature. The hidden transition coincides with the onset of the surface state splitting observed by our angle-resolved photoemission spectroscopy measurements. Single modulation gives rise to a band inversion with induced topological surface states in a local momentum region while the full Brillouin zone carries trivial topological indices, and multiple modulation further splits the surface bands via non-collinear spin tilting, as revealed by our calculations. The direct evidence of the non-collinear spin order in NdSb not only clarifies the mechanism of the emergent topological surface states but also opens up a new paradigm of control and manipulation of band topology with magnetism. # Data for: Hidden non-collinear spin-order induced topological surface states ## Description of the data and file structure There are three files in the dataset: Dataset.zip, filter.ipf, and DriftCorrection.ipf. filter.ipf is the IgorPro procedure for filtering out high-frequency noise of topographic images.\ DriftCorrection.ipf is the IgorPro procedure for drift correction of topographic images by the Lawler-Fujita algorithm. The dataset.zip contains folders arranged by the figures in the article "Hidden non-collinear spin-order induced topological surface states" to be published in Nature Communications. Each folder contains the STM raw data for plotting the STM images in the corresponding figure. Please refer to the article for a more detailed description of the data and methods. The STM data was collected by Omicron LT-STM at 4K. MTRX files were exported to the IgorPro files with the software Vernissage. Data analysis was performed on IgorPro. The figures are plotted with Origin and arranged in Adobe Illustrator. Use Vernissage to open the head file ending with "_0001.mtrx" and inspect the data contained within. The data can be exported as IgorPro files and further analyzed in IgorPro. IPF files are IgorPro procedures for data analysis.

Mesocosm core setup and sampling procedure Samples were obtained during the AQUACOSM VIMS-Ehux mesocosm experiment in Raunefjorden near Bergen, Norway (60°16′11N; 5°13′07E), in May 2018. Seven bags were filled with 11m3 water from the fjord, containing natural plankton communities. Algal blooms were induced by nutrient addition and monitored for 24 days, as previously described23. 10 samples were collected from four bags, as follows: From bag 3, on days 15 and 20 (named B3T15, B3T20 correspondingly). From bag 4, on days 13, 15,19, and 20 (named B4T13, B4T15, B4T19, and B4T20, correspondingly). From bag 6, on day 17 (named B6T17). From bag 7, on days 16, 17, and 18 (named B7T16, B7T17, and B7T18, correspondingly). Samples were initially filtered as follows: 2 liters of water were filtered with a 20 µm mesh and collected in a glass bottle. The cells were then concentrated through gentle gravity filtration on a 3 µm polycarbonate filter (Whatman), mounted on a reusable bottle top filter holder (Thermo Fischer). The biomass on the filter was regularly resuspended by gentle pipetting. For samples B7T16, B7T18, B4T15, B3T15, B6T17, B7T17, and B4T19, the 2 liters of seawater were concentrated down to 100 ml, distributed in two 50 ml tubes, which corresponds to a 200 times concentration. For B4T13, the concentration factor was 140 times. For B4T20 and B3T20, the concentration factor was 100 times. The different concentration factors are explained by filter clogging and various field constraints, including processing time. For all samples except B3T20, the 50 ml tubes were centrifuged for 4 min at 2500g, after which the supernatant was discarded. Pellets corresponding to the same day and same bag were pooled and resuspended in a final volume of 200 µl of chilled PBS. 1800 µl of pre-chilled high-performance liquid chromatography (HPLC) grade 100% methanol was added drop by drop to the concentrated biomass. For B3T20, the concentrated biomass was centrifuged for 4 min at 2500g, resuspended in 100 µl of chilled PBS, to which 900 µl of chilled HPLC grade 100% methanol was added. Then, samples were incubated for 15 minutes on ice and stored at -80°C until further analysis. Library preparation and RNA-seq sequencing using 10X Genomics For analysis by 10X Genomics, tubes were defrosted and gently mixed, and 1.7 ml of the samples were transferred into an Eppendorf Lowbind tube and centrifuged at 4°C for 3 min at 3000g. The PBS/methanol mix was discarded and replaced by 400 µl of PBS. Cell concentration was measured using an iCyt Eclipse flow cytometer (SONY) based on forward scatter. Cell concentration ranged from 1044 cells ml-1 to 9855 cells ml-1. All concentrations were brought to 1000 cells ml-1 to target 7000 cells recovery, according to the 10X Genomics Cell Suspension Volume Calculator Table provided in the user guide. The cellular suspension was loaded onto Next GEM Chip G targeting 7000 cells and then ran on a Chromium Controller instrument to generate GEM emulsion (10x Genomics). Single-cell 3' RNA-seq libraries were generated according to the manufacturer's protocol (10x Genomics Chromium Single Cell 3' Reagent Kit User Guide v3/v3.1 Chemistry) on different occasions: B4T19 and B7T17 in January 2020 and B3T15, B3T20, B4T13, B4T15, B4T20, B6T17, B7T16, and B7T18 in August 2020 with 12 cycles for cDNA amplification and 15 cycles for library amplification. Library concentrations and quality were measured using the Qubit dsDNA High Sensitivity Assay kit (Life Technologies, Carlsbad, CA). Libraries were pooled according to targeted cell number, aiming for a minimum of 20,000 reads per cell. Pooled libraries were sequenced using the NextSeq® 500 High Output kit (75 cycles). Bioinformatic pipeline A step-by-step description of the bioinformatic pipeline from this step onward, including all in-house scripts used, is detailed in the GitHub repository under github.com/vardilab/host-virus-pairing. Detection of infected cells in the single-cell RNA-seq data using a custom viral genes database To detect viral transcripts, a reference was built from a database of highly conserved genes6 from all NCLDV in the Giant Virus Database9, such as family B DNA polymerase, RNA polymerase subunits, and the major capsid protein. The genes were clustered using CD-HIT v. 4.6.6 at 90% nucleotide identity To remove redundancy43. From this database of 34866 genes, a reference was created using the 10X Genomics Cell Ranger mkref command. The Cell Ranger Software Suite (v. 5.0.0) was used to perform barcode processing (demultiplexing) and single-cell unique molecular identifier (UMI) counting on the raw reads from 47391 cells using the count script (default parameters), with the deduplicated NCLDV database as a reference. For downstream analysis, 972 cells that highly expressed multiple NCLDV genes and were considered "highly infected" were selected. These 'highly infected' cells were selected based on the following criteria: (a) cell expresses in total ≥10 viral UMIs22,24, (b) expression of more than one viral gene (>1), (c) expression of at least one gene with a UMI count greater than one (>1). Cell selection was wrapped using an in-house script (choose_cells.py). Identifying the taxonomy of individual cells by sequence homology to ribosomal RNA Raw reads from each cell were pulled by the cell's unique barcode identifier using seqtk v. 1.2. Reads were then trimmed (command: trim_galore --phred33 -j 8 --length 36 -q 5 --stringency 1 --fastqc -e 0.1), and poly-A was removed (command: trim_galore --polyA -j 1 --length 36), using TrimGalore (v. 0.6.5), a Cutadapt wrapper 44. Trimmed reads from each cell were assembled using rnaSPAdes 3.1545 with kmer 21,33. Raw reads pulling, trimming, and assembly was wrapped using an in-house script (assemble_cells.sh). To identify the taxonomy of the cells, assembled contigs from each cell were matched against 18S rRNA sequences from the Protist Ribosomal Reference (PR2)46 and metaPR247. To remove redundancy, the sequences in each database were clustered using CD-HIT v. 4.6.6 at 99% identity43. Contigs were filtered using SortMeRNA v. 4.3.648 with default parameters against the PR2 database and then aligned to the PR2 and metaPR2 databases using Blastn49, at 99% identity, E-value ≤ 10-10 and alignment length of at least 100 bp. Contigs were ranked by their bitscore, and only the best hit was kept for each contig. Each contig was assigned to one of the following taxonomic groups that were prevalent in the sample: the classes Bacillariophyta, Prymnesiophyceae, Chrysophyceae, MAST-3, and Katablepharidaceae, the divisions Pseudofungi, Lobosa (Amoebozoa), Ciliphora (Ciliates), Dinoflagellata and Cercozoa. Contigs that matched other groups were assigned as "other eukaryotes". Contigs that matched more than one of these taxonomic groups were considered non-specific or chimeric and were therefore ignored. This downstream analysis of Blast result was wrapped using an in-house script (Sankey_wrapper_extended.ipynb). To avoid detection of doublets and predators, Cells that transcribe 18S rRNA transcripts homologous to more than one taxonomic group were conservatively omitted. Of the 972 infected cells detected, 418 (43%) were omitted because we could not assemble specific 18s rRNA contigs from them or because their identity was ambiguous. None of the cells that were assigned "other eukaryotes" had contigs with conflicting annotations (contigs matching different classes). Identifying the infecting virus using a homology search against a custom protein database To identify transcripts derived from giant viruses, reads from the detected 972 infected cells were compared to a custom protein database using a translated alignment approach. To ensure that as many giant viruses as possible were represented, a database was constructed by combining RefSeq v. 20750 with all predicted proteins in the Giant Virus Database9. The proteins were then masked with tantan51 (using the -p option) and generated the database with the lastdb command (using parameters -c, -p). To identify the infecting virus, the raw sequencing reads in each of the 972 single-cell transcriptomes were compared to the constructed database using LASTAL v. 95952 (parameters -m 100, -F 15, -u 2) with best matches retained. The same procedure was done for the assembled transcripts from each cell to identify viral transcripts. The results were analyzed at different taxonomic levels, consistent with the Giant Virus Database (for giant viruses) or NCBI taxonomy33(everything else). 754 Cells whose best matching virus was coccolithovirus were omitted from the downstream analysis since EhV-infected cells were already reported to be abundant in the algal bloom25, and our analysis aims to explore other host-virus pairs. Plotting host-virus pairs in a Sankey plot for host cells and their infecting giant viruses Of the 218 cells detected as infected by viruses other than EhV, 71 were selected that could be identified using assembled 18S rRNA transcripts and have at least 10 reads aligned to one of the virus families (Supplementary Data 1). Only links representing at least 10% of the aligned reads in each cell are shown in order to highlight the strong links. The Sankey plot was constructed using Holoviews v. 1.15.4; see sankey_wrapper.ipynb in the GitHub repository. Phylogenetic trees of viral and host marker genes For phylogenetic analysis, 31 cells were chosen based on a strong correlation (≥90% of viral reads matched one virus family) between the host and a virus. To obtain reference 18S rRNA sequences to include in a phylogeny, all transcripts assembled from these cells were compared to the PR2 database46 using BLASTN v. 2.9.0+ (parameters -perc_identity 95, -evalue 10-10, -max_target_seqs 20, -max_hsps 1). Sequences shorter than 1000 bp were removed from the reference, and the remainder of the sequences were de-replicated with cd-hit v. 4.743 (-c 0.99) to prevent the inclusion of excessive nearly identical references. Sequences were aligned with Muscle553 (default parameters), and diagnostic trees were created with FastTree 2.1.1054 for quick visualization of trees and for pruning long branches. The final phylogenetic trees were constructed with IQ-TREE v. 2.1.252 (parameters -m GTR+F+G4 -alrt 1000 -T AUTO --runs 10). To identify major capsid protein sequences in the single-cell transcriptomes, proteins were first predicted using FragGeneScanRs v. 1.1.056 (parameters -t, illumina_10). The resulting protein sequences were compared to MCP proteins in the Giant Virus Database with BLASTP v. 2.12.0+ (parameters -evalue 10-3, -max_target_seqs 20, -max_hsps 1) as well as to a custom MCP HMM that were previously designed6 using hmmsearch in the HMMER3 v. 3.3.2 package57 (E-value ≤ 10-3). The results of these searches were manually inspected, and sequences were subsequently aligned with Muscle 5 (default parameters). Similarly, as with the 18S rRNA sequences, diagnostic trees were first made with FastTree 2.1.10 and pruned long branches before making a final tree with IQ-TREE v. 2.1.2 (parameters m LG+F+G4 -alrt 1000 -T AUTO --runs 10). Cells for which transcripts are present in both viral and host trees were denoted (Supplementary Data 4). All the codes used to produce the trees are wrapped in the folder "marker_gene_trees" in the GitHub repository. Single-cell RNA-seq data alignment to a custom reference A new host-virus reference database was curated from the transcriptome of the infected cells (Fig. 2). Repetitive sequences were removed using BBduk (BBtools 38.90)58. An Additional long repetitive sequence was removed manually. A database of E. huxleyi and EhV genes, which were shown to be abundant in the samples25, was also added to this reference to specifically detect E. huxleyi cells and to avoid a non-specific alignment of reads from these cells to other contigs. For EhV, the predicted CDSs in the EhVM1 were used as a reference59. For the host, an integrated transcriptome reference of E. huxleyi was used as a reference60. Viral transcripts in the database were identified using a homology search against a custom protein database as described above. A reference was created from the database using the Cell Ranger mkref command. Raw reads were aligned to this reference database using 10X Genomics Cell Ranger v. 5.0.0 count analysis. Preprocessing of transcript abundance and dimensionality reduction A total of 28,656 cells from the 10 samples were initially aligned to the reference database. Cells with zero UMIs and cells with the lowest 1% number of UMIs, as compared with the distribution of transcripts per cell in the entire dataset, were removed for downstream analyses. To prevent cases of doublet or multiplet cells, which can be biological (cell digestion) or technical (fused cells), cells with the highest 1% number of UMIs were also removed. The raw UMIs of 28,015 cells were further preprocessed using the Python package scprep v. 1.0.10: Low expressing genes were filtered with filter.filter_rare_genes and min_cells=2. This number was chosen because we did not want to include genes mapped to only one cell, but we also did not want to exclude low-expressed genes, as they might represent gene expression of low-abundant organisms. Expression was normalized by cell library size with normalize.library_size_normalize, and the data was scaled with transform.sqrt. Preprocessing was wrapped in an in-house script; see 00.01.filter_normalize_scale_single_cell_data.py in the GitHub repository. To represent the cells in two dimensions based on their gene expression profiles, dimensionality reduction was performed using scprep v. 1.1.0 package PCA (method='svd', eps=0.1) and Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction was conducted using the UMAP method in the manifold package of the Python library scikit-learn v. 0.24.1 (minimum distance=0.4 spread=2, number of neighbors=7). Dimensionality reduction was wrapped in an in-house script (00.02.dimentionality_reduction_single_cell_data.py). Assigning taxonomy to each detected cell using rRNA homology search To identify the taxonomy of each detected cell, reads from each cell were assembled independently. The taxonomy of the cells was determined by 18S rRNA homology to one of the following groups, which were abundant in the population: the classes Bacillariophyta (diatoms), Prymnesiophyceae, Chrysophyceae, MAST-3 and Katablepharidaceae, the divisions, Ciliphora (Ciliates), Dinoflagellata and Cercozoa. Other taxonomic groups were clustered under "Other eukaryotes". 16,358 cells were identified this way, and 11,657 cells that could not be identified were excluded from the plot for convenience. Cells with 18S rRNA contigs homologous to more than one taxonomic group were also conservatively omitted. As described above, cells expressing at least 10 viral UMIs were considered infected1,2. This section was wrapped in a Jupyter notebook (Coexpression_wrapper_extended.ipynb). Identifying the Leucocryptos host and its virus using homology search To better identify the detected Katablepharidaceae cells and to identify their infecting virus, 26 infected Katablepharidaceae cells from bag #4, day 20, were selected. Reads from these cells were retrieved using the unique molecular identifier and then trimmed using TrimGalore v. 0.6.5, a Cutadapt wrapper44. Trimming was wrapped in an in-house script; see pull_trim_clean.sh in the GitHub repository. Trimmed read files from all these cells were concatenated into one file and assembled altogether using rnaSPAdes v. 3.1545. To identify the specific Katablepharidaceae host, assembled contigs were matched against the PR2 rRNA database using blastn at 90% identity, E-value ≤ 10-10, and alignment length ≥ 100bp. Contigs best matched to an unknown Katablepharidaceae (>99% nucleotide identity), but after removing unidentified genera, these contigs best matched (>95% nucleotide identity) the Katablepharidaceae species Leucocryptos marina. Transcripts that matched classes other than Katablepharidaceae were matched against the entire NCBI database using the NCBI web server61. They, too, mostly matched Katablepharidaceae genes, specifically 28S rRNA or internal transcribed spacer (ITS) sequences (Supplementary Data 3). To identify the specific infecting virus, transcripts were matched against an NCLDV gene marker database6 at 90% identity, E-value ≤ 10-10, and alignment length ≥ 100bp. After finding homology to Leucocryptos and the virus GVMAG-M-3300020187-271, gene expression was calculated using RSEM v.1.3.162 (rsem-calculate-expression -p 10 --bowtie2 --fragment-length-mean 58). The genomic features of the virus were taken from Schulz (2020)1, and the viral genome was plotted using ShinyCircos v. 2.063. Gene expression in the plot is measured in expected counts after log 2 transformation. The relative abundance data in Fig. 4 was obtained from an 18S rRNA amplicon sequencing on a size fraction of 2-20µm in bag #4 during the mesocosm experiment23. Days 19, 22, and 23 were sampled twice; all other days were sampled once. In Fig. 4c, relative abundance is calculated per taxa as a fraction of all amplicon sequencing variants (ASV), excluding metazoans. Fig. 4d shows the fraction of Katablepharidaceae out of all ASVs matching Katablepharidaceae (excluding metazoans). E. huxleyi abundance was measured by flow cytometry based on high side scatter and high chlorophyll signals. These data were obtained from the source data of the same study23. Phylogenetic tree of Katablepharidaceae ASVs and 18S rRNA genes To verify the taxonomy of the ASVs, A phylogenetic tree was constructed of 89 ASVs identified as Katablepharidaceae, selected 18S rRNA sequences of Katablepharidaceae and other species from the PR2 database, and the longest single cell assembled contig from the infected Katablepharidaceae cells. Sequences were aligned with ClustalOmega v. 1.2.4 (default parameters)64. A diagnostic tree was first made with FastTree 2.1.1054 for pruning long branches before making the final tree with IQ-TREE55. All but three ASVs and one PR2 sequence clustered together with the assembled Leucocryptos transcript, verifying the taxonomy of 97% of the ASVs used in the relative abundance analysis (Extended Data Fig. 4). Phylogenetic trees of viral heat-shock proteins and metacaspase To examine the evolutionary history of the heat-shock proteins encoded in GVMAG-M-3300020187-27, phylogenetic trees of these proteins were constructed together with homologs present in eukaryotes, bacteria, archaea, and other giant viruses. For this, a custom database of proteins from reference genomes was compiled from EggNOG v. 5.065 (eukaryotes), bacteria and archaea (the Genome Taxonomy Database v. 95)66, and other giant viruses (the Giant Virus Database9). For bacterial and archaeal genomes in the GTDB, proteins were predicted first with Prodigal v. 2.6.367 using default parameters. Proteins were searched against Pfam models for each protein using hmmsearch with the noise cutoff (--cut_nc) and subsequently aligned sequences with ClustalOmega v. 1.2.3 (default parameters). Phylogenetic trees were constructed using IQ-TREE v. 2.1.255 (parameters m TEST -bb 1000 -T 6 --runs 10) using ultrafast bootstraps and with the best model determined with ModelFinder68. Substation matrixes used for the phylogenetic trees: Bax-1 - VT+F+R7; Metacaspase - VT+R7; HSP90 - LG+F+R10; HPS70 - LG+F+R10. # Single-cell RNA-seq of the rare virosphere reveals the native hosts of giant viruses in the marine environment Supplementary Files used in the project. These are the main intermediate files that can help reproduce the data. For a detail description on how to reproduce these files and using them, Go to the GitHub site: [https://github.com/vardilab/host-virus-pairing](https://github.com/vardilab/host-virus-pairing) ## Description of the data and file structure Fromm_2023_Data_Availability Sequences GVDB.markergenes.90.fna # De-duplicated database of NCLDV marker genes Transcripts_Cells_Ehux-EhV.95.fasta # The host-virus reference, based on the single-cell transcriptomes of infected cells, to which we added genes from EhV and E. huxleyi. Transcripts_Katablepharidacea.fasta # Transcripts assembled from a highly infected subpopulation of Katablepharidacea Blast_results first_cells.transcripts.edit.metaPR2.csv # Blastn results of single-cell transcripts assembled from highly infected cells (~970) detected against metapr2 database. first_cells.transcripts.edit.PR2.csv # Blastn results of single-cell transcripts assembled from highly infected cells (~970) detected against pr2 database. all_cells.transcripts.edit.metaPR2.csv # Blastn results of single-cell transcripts assembled from all cells (~28,000) detected against metapr2 database. all_cells.transcripts.edit.PR2.csv # Blastn results of single-cell transcripts assembled from all cells (~28,000) detected against metapr2 database. cells.filtered.blastx.csv # Blastx results of single-cell assembled transcripts against refseq database (to find viral transcripts). DATA-SPECIFIC INFORMATION FOR: all_cells.transcripts.edit.metaPR2.csv 1. Number of variables: 12 2. Number of rows: 1409286 3. Variable List: * qseqid: query or source (gene) sequence id * sseqid: subject or target (reference genome) sequence id * pident: percentage of identical positions * length: alignment length (sequence overlap) * mismatch: number of mismatches * gapopen: number of gap openings * qstart: start of alignment in query * qend: end of alignment in query * sstart: start of alignment in subject * send: end of alignment in subject * evalue: expect value * bitscore: bit score DATA-SPECIFIC INFORMATION FOR: all_cells.transcripts.edit.PR2.csv 1. Number of variables: 12 2. Number of rows: 1549087 3. Variable List: * qseqid: query or source (gene) sequence id * sseqid: subject or target (reference genome) sequence id * pident: percentage of identical positions * length: alignment length (sequence overlap) * mismatch: number of mismatches * gapopen: number of gap openings * qstart: start of alignment in query * qend: end of alignment in query * sstart: start of alignment in subject * send: end of alignment in subject * evalue: expect value * bitscore: bit score DATA-SPECIFIC INFORMATION FOR: first_cells.transcripts.edit.metaPR2.csv 1. Number of variables: 12 2. Number of rows: 56763 3. Variable List: * qseqid: query or source (gene) sequence id * sseqid: subject or target (reference genome) sequence id * pident: percentage of identical positions * length: alignment length (sequence overlap) * mismatch: number of mismatches * gapopen: number of gap openings * qstart: start of alignment in query * qend: end of alignment in query * sstart: start of alignment in subject * send: end of alignment in subject * evalue: expect value * bitscore: bit score DATA-SPECIFIC INFORMATION FOR: first_cells.transcripts.edit.PR2.csv 1. Number of variables: 12 2. Number of rows: 67864 3. Variable List: * qseqid: query or source (gene) sequence id * sseqid: subject or target (reference genome) sequence id * pident: percentage of identical positions * length: alignment length (sequence overlap) * mismatch: number of mismatches * gapopen: number of gap openings * qstart: start of alignment in query * qend: end of alignment in query * sstart: start of alignment in subject * send: end of alignment in subject * evalue: expect value * bitscore: bit score DATA-SPECIFIC INFORMATION FOR: cells.filtered.blastx.csv 1. Number of variables: 10 2. Number of rows: 2021 3. Variable List: * qseqid: query or source (gene) sequence id * sseqid: subject or target (reference genome) sequence id * pident: percentage of identical positions * bitscore: bit score * domain: domain of life the of subject * phylum: phylum the of subject * family: family the of subject * genus: genus the of subject * species: species the of subject * cell barcode: cell barcode of query UMI_tables_first_cells data_raw.pickle.gz # Data combined from all UMI tables of fastq files mapped to the NCLDV marker gene database UMI_tables_scatterplot data_raw.pickle.gz # Data combined from all UMI tables of fastq files mapped to the host-virus reference data.pickle.gz # Preprocessed ata combined from all UMI tables of fastq files mapped to the host-virus reference metadata_dimentionality_reduction_1_1.2_.pickle.gz # Metadata of preprocessed data, after dimensionality reduction. ## Access information The PR2 database can be accessed from here: [https://github.com/pr2database/pr2database](https://github.com/pr2database/pr2database) Giant viruses (phylum Nucleocytoviricota) are globally distributed in aquatic ecosystems. They play significant roles as evolutionary drivers of eukaryotic plankton and regulators of global biogeochemical cycles. However, we lack knowledge about their native hosts, hindering our understanding of their lifecycle and ecological importance. Here, we used single-cell RNAseq and samples from an induced E. huxleyi bloom during a mesocosm experiment to link giant viruses with their protist hosts. We observe active giant virus infections in multiple host lineages, including members of the algal groups Chrysophycae and Prymnesiophycae, as well as heterotrophic flagellates in the class Katablepharidaceae. Katablepharids were infected with a rare Imitevirales-07 giant virus lineage expressing cell fate regulation genes. Analysis of the temporal dynamics of this host-virus interaction indicated a role for the Imitevirales-07 in the collapse of the host Katablepharid population. Our results demonstrate that single-cell RNA-seq can be used to identify previously undescribed host-virus interactions and study their ecological relevance.

The mere presence of predators causes prey organisms to display predation-avoidance strategies. Predator presence is often communicated through predator-released chemical signals. Ovipositing female mosquitoes of several species are repelled by unknown signals released from larvivorous fish. It was previously suggested that in many cases, a predator’s microbiota plays an important role in the release of these signals; however, this mechanism is still poorly understood. In this study, we looked into the effects of the microbiota originating from the larvivorous Gambusia affinis (Baird and Girard) on the oviposition behavior of gravid female mosquitoes. We used fish with altered microbiota and bacterial isolates in a set of outdoor mesocosm experiments to address this aim. We show that interference with the fish microbiota significantly reduces fish’s repellant effect. We further show that the bacterium Pantoea pleuroti, isolated from the skin of the fish, repels oviposition of Culex laticinctus (Edwards) and Culiseta longiareolata (Macquart) mosquitoes similarly to the way in which live fish repel them. Our results highlight the importance of bacteria in the interspecies interactions of their hosts. Furthermore, this finding may lead to the development of an ecologically friendly mosquito repellent, that may reduce the use of larvivorous fish for mosquito control. # Fish microbiota repel ovipositing mosquitoes [https://doi.org/10.5061/dryad.9p8cz8wqf](https://doi.org/10.5061/dryad.9p8cz8wqf) The dataset includes data from 4 field experiments described in Figures 2-5. It includes the distribution of mosquito egg rafts from two species, *Culex laticinctus* and *Culiseta logiareolata*. Egg rafts were collected from pools as described in the method section. ## Description of the data and file structure Data describes the total number of egg rafts collected over all dates in each of the pools. Each pool is a combination of “block” and “treatment” variables. The other columns present the dependent variables, i.e., total number of egg rafts for each mosquito species. Experimental duration is presented at the bottom and consists of beginning and ending dates plus the total day count.

# Rapid and chemically diverse C transfer from trees to mycorrhizal fruit bodies in the forest [https://doi.org/10.5061/dryad.34tmpg4s5](https://doi.org/10.5061/dryad.34tmpg4s5) ## Description of the data and file structure Ectomycorrhizal fungi (EMF) are common belowground tree symbionts, supplying trees with water and nutrients. In return, large amounts of C assimilated by trees can be allocated into EMF. However, the chemical forms in which the C is transferred from trees to fungi under field conditions are mostly unknown. In this study, we aimed to unravel the fate of tree-derived C in EMF. We conducted 13CO2 pulse labeling of *Pinus halepensis* trees in two forest sites with adjacent EMF sporocarps, combined with a non-targeted metabolomics profiling of root and sporocarp tissues. 13C was measured in sporocarps of *Tricholoma terreum* and *Suillus collinitus* up to 3 m from pine stems. Here we provide a table with soil properties (pH, salinity, and mineral composition) under the study trees at the two forest sites, and three tables showing P-values of isotopes of labeled semi-polar metabolites identified in *Pinus* roots, *Suillus* fruit bodies, and *Tricholoma* fruit bodies samples comparing before and after 13C labeling. Below is a description for each of the data tables. #### **Hosted via Dryad** **Rapaport_et_al_Soil_properties.xlsx** Soil properties (pH, salinity, and mineral composition) under the study trees at the two forest sites. Measurements were done on soil at 0-10 cm and 10-20 cm depths (excluding trees 24 and 29 in Charuvit forest, where soil was too shallow). EC, electric conductivity (dS m-1); SOC, soil organic carbon (%); All mineral values (Cl, Ca, Mg, N-NO3, N-NH4, Olsen P) are at mg kg-1. #### **Hosted via Zenodo** **Table S2** Soil properties (pH, salinity, and mineral composition) under the study trees at the two forest sites. Measurements were done on soil at 0-10 cm and 10-20 cm depths (excluding trees 24 and 29 in Charuvit forest, where soil was too shallow). EC, electric conductivity (dS m-1); SOC, soil organic carbon (%); mineral values are at mg kg-1. **Table S6** P-values of isotopes of labeled semi-polar metabolites of roots samples comparing before and after 13C labeling. The isotope shown is the most significant one from each metabolite. FDR correction was performed. TCA, tricarboxylic acid cycle cycle. **Table S7** P-values of isotopes of labeled semi-polar metabolites of samples from *Suillus* comparing before and after 13C labeling. The isotope shown is the most significant one from each metabolite. FDR correction was performed. TCA, tricarboxylic acid cycle cycle. **Table S8** P-values of isotopes of labeled semi-polar metabolites in *Tricholoma* samples comparing before and after 13C labeling. The isotope shown is the most significant one from each metabolite. FDR correction was performed. TCA, tricarboxylic acid cycle cycle. ## Sharing/Access information The data belong to an accepted paper in Functional Ecology. A DOI link will be added. Ectomycorrhizal fungi (EMF) are common belowground tree symbionts, supplying trees with water and nutrients. In return, large amounts of C assimilated by trees can be allocated into EMF. However, the chemical forms in which the C is transferred from trees to fungi under field conditions are mostly unknown. In this study, we aimed to unravel the fate of tree-derived C in EMF. We conducted 13CO2 pulse labeling of Pinus halepensis trees in two forest sites with adjacent EMF sporocarps, combined with a non-targeted metabolomics profiling of root and sporocarp tissues. 13C was measured in sporocarps of Tricholoma terreum and Suillus collinitus up to 3 m from pine stems. C was assimilated in the labeled trees’ needles and transferred to their roots. Starting from day 2 after labeling, the C was transferred to adjacent sporocarps, peaking on day 5. We identified more than 100 different labeled metabolites of different chemical groups present in roots and sporocarps. Of them, 17 were common to pine roots and both EMF species, and an additional 8 were common to roots and one of the two EMFs. The major labeled metabolites in the root tips were amino acids and tricarboxylic acid intermediates. The major labeled metabolites in sporocarps were amino acids, nucleotides, and fatty acids. We also identified labeled carbohydrates in all tissues. Labeling patterns diverged across different tissues, which can hint at how the C was transferred. Considering the young tree as a sole C source for these sporocarps, and with a diurnal assimilation of 5.4 g C, the total monthly C source is ~165 g C. On average, there were 10 sporocarps around each tree, each requiring ~1 g C. Therefore, a 10 g C investment would make 6% of total tree C allocation, and about 12% of net primary productivity. Overall, we found that this significant and ubiquitous transfer of metabolites from tree roots to EMF sporocarps is more rapid and chemically diverse than once thought.

cti_cross_region_taxon This repository provides the data and codes used in the manuscript: Guillem Chust, Ernesto Villarino, Matthew McLean, Nova Mieszkowska, Lisandro Benedetti-Cecchi, Fabio Bulleri, Chiara Ravaglioli, Angel Borja, Iñigo Muxika, José A. Fernandes-Salvador, Leire Ibaibarriaga, Ainhize Uriarte, Marta Revilla, Fernando Villate, Arantza Iriarte, Ibon Uriarte, Soultana Zervoudaki, Jacob Carstensen, Paul J. Somerfield, Ana M. Queirós , Andrea J. McEvoy, Arnaud Auber, Manuel Hidalgo, Marta Coll, Joaquim Garrabou, Daniel Gómez-Gras, Cristina Linares, Francisco Ramírez, Núria Margarit, Mario Lepage, Chloé Dambrine, Jérémy Lobry, Myron A. Peck, Paula de la Barra, Anieke van Leeuwen, Gil Rilov, Erez Yeruham, Anik Brind'Amour, and Martin Lindegren. Cross-basin and cross-taxa patterns of marine community tropicalization and deborealization in warming European seas. Nature Communications Please check the github repo cti_cross_region_taxon for updates. Abstract Ocean warming and acidification, decreases in dissolved oxygen concentrations, and changes in primary production are causing an unprecedented global redistribution of marine life. The identification of underlying ecological processes underpinning marine species turnover, particularly the prevalence of tropicalization over deborealization, has been recently debated in the context of ocean warming. Here, we track changes in the mean thermal affinity of marine communities across European seas by calculating the Community Temperature Index for 65 biodiversity time series collected over four decades and containing 1,817 species from different communities (zooplankton, coastal benthos, pelagic and demersal invertebrates and fish). We show most communities and sites have clearly responded to ongoing ocean warming via abundance increases of warm-water species (tropicalization; 54%) and decreases of cold-water species (deborealization; 18%). Tropicalization dominated Atlantic sites compared to semi-enclosed basins such as the Mediterranean and Baltic Seas, probably due to physical barrier constraints to connectivity and species colonization. Semi-enclosed basins appeared to be particularly vulnerable to ocean warming, experiencing the fastest rates of warming and biodiversity loss through deborealization. Keywords: climate change, community temperature index, CTI, ocean connectivity. Acknowledgements This study has been supported by the European Union's Horizon 2020 research and innovation programme under grant agreement No 869300 (FutureMARES project) (G.C., E.V., J.F-S., M.P., M.Lin., C.D., D.G-G., G.R., L.B., C.R., M.C., F.R., G.R., P.B., M.P., M.Lep., J.L., A.B., N.M., J.G., F.B., L.B.C, A.Q., F.V., A.I., and I.U.), and by the Urban Klima 2050 -- LIFE 18 IPC 000001 project, which has been received funding from European Union's LIFE programme (G.C., E.V., L.I., A.B., A.U., and M.R.). Additional financial support was obtained from the Basque Government (PIBA2020-1-0028 & IT1723-22). We thank the NOAA Climate Prediction Center for providing Sea Temperature data through the NCEP Global Ocean Data Assimilation System (GODAS) www.cpc.ncep.noaa.gov/products/GODAS. We also thank Ocean Biodiversity Information System (OBIS) for providing global occurrences of the biological group studied here. Data from the Basque Country were obtained from the Basque Water Agency (URA) monitoring network, through a Convention with AZTI. M.C., J.G., D.G.-G. and F.R. acknowledge the 'Severo Ochoa Centre of Excellence' accreditation (CEX2019-000928-S). Authors M.H. and M.Lin. are grateful for the support from ICES Working Group on Comparative Ecosystem-based Analyses of Atlantic and Mediterranean marine systems (WGCOMEDA) for this research. This paper is contribution nº xxxx from AZTI, Marine Research, Basque Research and Technology Alliance (BRTA)