This readme file was generated on 2022-07-04 by J.O. Boyi & E. Heße GENERAL INFORMATION Title of Dataset: Metabarcoding to decipher the fish diet of Eurasian otters and seals Author Name: Joy Ometere Boyi ORCID: 0000-0002-9897-6137 Institution: Institute for Terrestrial and Aquatic Wildlife Research, University of Veterinary Medicine,Hannover Foundation Address: Werftstraße 6, D-25761 Büsum, Germany Email: Joy.Ometere.Boyi@tiho-hannover.de Author Name: Eileen Heße ORCID: 0000-0003-4342-1772 Institution: Institute for Terrestrial and Aquatic Wildlife Research, University of Veterinary Medicine,Hannover Foundation Address: Werftstraße 6, D-25761 Büsum, Germany Email: Eileen.Hesse@tiho-hannover.de Author Name: Anita Gilles ORCID: 0000-0001-7234-8645 Institution: Institute for Terrestrial and Aquatic Wildlife Research, University of Veterinary Medicine,Hannover Foundation Address: Werftstraße 6, D-25761 Büsum, Germany Email: Anita.Gilles@tiho-hannover.de Author Name: Kristina Lehnert ORCID: 0000-0001-8938-3340 Institution: Institute for Terrestrial and Aquatic Wildlife Research, University of Veterinary Medicine,Hannover Foundation Address: Werftstraße 6, D-25761 Büsum, Germany Email: Kristina.Lehnert@tiho-hannover.de Date of data collection: 2014 - 2020 Geographic location of data collection: Germany Information about funding sources that supported the collection of the data: Ministry of Energy, Agriculture, the Environment, Nature and Digitalisation (MELUND); Schleswig-Holstein Agency for Coastal Defence, National Park and Marine Conservation, Germany; BioWeb “Response of biodiversity change in North Sea food webs mediated by environmental drivers and human activities (03F0861D)” funded by the German Federal Ministry of Education and Research (BMBF) within the framework programme “Research for sustainable development - FONA3“. SHARING/ACCESS INFORMATION Recommended citation for this dataset: Boyi, J.O., Heße, E., Rohner, S., Saürich, J., Siebert, U., Gilles, A. And Lehnert, K. (2022). Data from: Deciphering Eurasian otter (Lutra lutra L.) and seal (Phoca vitulina L.; Halichoerus grypus F.) diet: metabarcoding tailored for fresh and saltwater fish species, Dryad, Dataset DATA & FILE OVERVIEW File List: Boyi et al._2022_data_repository.xls. Diet proportions of fish prey species in the diet of Eurasian otter (Lutra lutra L.), harbour seal (Photo vitulina L.) and grey seal (Halichoerus grypus F.) METHODOLOGICAL INFORMATION Description of methods used for collection/generation of data: Paired end reads which passed Illumina’s chastity filter were demultiplexed and Illumina adaptor residuals trimmed with Illumina bcl2fastq software version v2.20.0.422. Quality assessment of reads was performed with the FastQC software version 0.11.8 and sequence reads with an average Q-score below 20 or with uncalled bases (N) were excluded from further analysis (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Cutadapt software v3.2 was used to trim the locus-specific primers from the sequence reads (Martin, 2011). Paired-end reads were discarded if the primer could not be trimmed. The trimmed forward and reverse reads of each paired-end read were merged to in-silico reform the sequenced molecule considering a minimum overlap of 15 bases using USEARCH software version 11.0.667 (Edgar, 2010). Merged sequences were further quality filtered allowing a maximum of one expected erroneous base per merged read. Reads containing ambiguous bases or marked as outliers with regard to amplicon size distribution were also discarded. Operational Taxonomic Units (OTUs) were formed using the UNOISE algorithm implemented in USEARCH, discarding singletons and chimeras in the process (Edgar, 2016b). The resulting OTU abundance table was then filtered for possible barcode bleed-in contaminations using the UNCROSS algorithm (Edgar, 2018). OTU sequences were compared to the manually compiled reference database based on NCBI and MitoFish accessions. Taxonomies were predicted considering a minimum confidence threshold on the species level of 0.0 using the SINTAX algorithm implemented in USEARCH (Edgar, 2016a). The confidence species-level threshold was then used to further classify OTUs and OTUs with a species confidence >0.9 were regarded as sufficiently classified species. DATA-SPECIFIC INFORMATION FOR: Boyi et al._2022_data_repository.xls Number of variables: 4 Number of rows: 102 Variable List: Sample ID, unique ID for each sample; Collection Date: Year, Month and Day on which the samples were collected; Collection Location, where the sample was collected; Fish species, the fish species present in the diet of each individual. Specialized formats or other abbreviations used: Pv = Harbour seals, Hg = Grey seals, Ll = Eurasian otters