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The Arctic marine ecosystem is changing fast due to climate change, emphasizing the need for solid ecological baselines and monitoring. The polar cod Boreogadus saida functions as a key species in the Arctic marine food web. We investigated the stomach contents of polar cod from the northern Barents Sea using DNA metabarcoding with the mitochondrial cytochrome c oxidase I gene in parallel with classical visual analysis. Arctic amphipods and krill dominated the diet in both methods. Yet, metabarcoding allowed for the identification of digested and unidentifiable prey and provided higher taxonomic resolution, revealing new and undiscovered prey items of polar cod in the area. Furthermore, molecular results suggest a higher importance of barnacles and fish (presumably eggs and pelagic larvae) in the diet than previously recorded. Parasites and, in 6 cases, other prey items were only visually identified, demonstrating the complementary nature of both approaches. The presence of temperate and boreal prey species such as northern krill and (early life stages of) European flounder and European plaice illustrates the advection of boreal taxa into the polar region or may be indicative of ongoing borealisation in the Barents Sea. We show that a combination of visual analysis and metabarcoding provides complementary and semi-quantitative dietary information and integrative insights to monitor changing marine food webs.

Additional file 1. Samples used for reduced representation sequencing (RRS) optimization. DNA from these samples was used for empirical restriction enzyme digestions with different enzymes (single digest EcoRI, PstI, MspI, or double digest EcoRI-MspI) and for RRS pilot libraries. Some samples were extracted twice as replicates (marked as _rep in sample ID). Three samples per species (family in the case of ostracods) were used for empirical digestions. The amphipod (C. obesa and E. pontomedon) samples and one T. loennbergii were used for empirical digestions, but not included in any RRS library.

Additional file 2. In silico estimates of the number of fragments. Estimates were produced through in silico restriction enzyme digestions for reduced representation sequencing (RRS) optimized for approximately 30× coverage. The number of fragments depends on the restriction enzyme/combination, the size window, the assumed genome size, and the reference genome used for in silico digestion. Reference genomes of related species were used as well as simulated genomes; in this case the size and GC content used to simulate the genomes are listed. The number of fragments were extrapolated to the assumed genome size. Only two different enzyme and size selection setups per target species are listed here (for RRS setups optimized for HiSeq 2500 or HiSeq 4000 sequencing runs, respectively; the same as in Table 4, Table 5, Additional File 4); further estimates can be found in spreadsheets available at https://doi.org/10.5281/zenodo.5045574 .

Additional file 6. Reduced representation sequencing (RRS) laboratory protocol based on the protocol from Elshire et al. (2011) [14]. The protocol is scaled for use with 192 samples and with restriction enzymes PstI or ApeKI; the reagent volumes can be scaled down/up to suit other sample numbers; if other enzymes are used, the respective reaction conditions must be adjusted.

Additional file 5. Reduced representation sequencing (RRS) laboratory protocol based on the protocol from Peterson et al. (2012) [15]. The protocol is scaled for use with 192 samples and with restriction enzymes EcoRI and MspI; the reagent volumes can be scaled down/up to suit other sample numbers; if other enzymes are used, the respective reaction conditions must be adjusted.

Additional file 7. Results from parameter optimization for de novo assembly and genotyping. Eight parameter optimization series were conducted following Rochette & Catchen (2017) [45] to identify optimal parameters to genotype reduced representation sequencing (RRS) data with Stacks v2.4 (Rochette et al. 2019) [21]; one test series for each species/species complex. The Stacks parameter m was kept constant (m = 3), while parameters M and n were varied together from 1 to 9. Subsequently, only loci present in 80% of the samples were retained and for each M = n parameter the number of loci and polymorphic loci was plotted, as well as the proportion of these loci containing 0 to 10 or > 10 SNPs. In ostracods, the library contained DNA from a species-complex, resulting in very few shared loci across 80% of the samples. Therefore, in this case results based on loci shared by 50% of samples are shown. Optimal M = n values were decided in all cases with this information (and reported in Table 5). Note, however, that it is impossible to make absolute calls regarding the ideal value.

Additional file 3. Comparisons of empirical and in silico restriction enzyme digestions. Empirical Bioanalyzer results (left figure panels) with digested DNA are shown as concentration over fragment size and estimated loci numbers over locus size from in silico digestions (right figure panels) for all target taxa except fish (these are shown in Fig. 1).

Additional file 4. Reduced representation sequencing (RRS) setups for seven individually optimized protocols. These setups were optimized in order to be run on a HiSeq 4000 platform (Illumina). The choice of restriction enzyme(s) and size window was optimized to obtain approximately 30× coverage (or half that value in a worst-case scenario) with the assumed genome size (conservatively estimated based on available information, see Table 2). Marker density was estimated as a comparable measure to the metastudy by Lowry et al. (2017) [34].

The development of Genotyping-By-Sequencing (GBS) technologies enables cost-effective analysis of large numbers of Single Nucleotide Polymorphisms (SNPs), especially in “non-model” species. Nevertheless, as such technologies enter a mature phase, biases and errors inherent to GBS are becoming evident. Here, we evaluated the performance of double digest Restriction enzyme Associated DNA (ddRAD) sequencing in SNP genotyping studies including high number of samples. Datasets of sequence data were generated from three marine teleost species (>5500 samples, >2.5 × 1012 bases in total), using a standardized protocol. A common bioinformatics pipeline based on STACKS was established, with and without the use of a reference genome. We performed analyses throughout the production and analysis of ddRAD data in order to explore (i) the loss of information due to heterogeneous raw read number across samples; (ii) the discrepancy between expected and observed tag length and coverage; (iii) the performances of reference based vs. de novo approaches; (iv) the sources of potential genotyping errors of the library preparation/bioinformatics protocol, by comparing technical replicates. Our results showed use of a reference genome and a posteriori genotype correction improved genotyping precision. Individual read coverage was a key variable for reproducibility; variance in sequencing depth between loci in the same individual was also identified as an important factor and found to correlate to tag length. A comparison of downstream analysis carried out with ddRAD vs single SNP allele specific assay genotypes provided information about the levels of genotyping imprecision that can have a significant impact on allele frequency estimations and population assignment. The results and insights presented here will help to select and improve approaches to the analysis of large datasets based on RAD-like methodologies The research leading to these results has received funding from the European Community's Seventh Framework Programme under agreement no. KBBE-311920 (AquaTrace). Samples used in this study were collected by the AquaTrace partners: Laboratory of Biodiversity and Evolutionary Genomics (LBEG), University of Leuven (European sea bass); Departmento de Genética, Universidad de Santiago de Compostela (turbot); Sabina De Innocentiis, ISPRA (gilthead sea bream). JH benefited from a PhD scholarship from Flanders Innovation & Entrepreneurship (IWT). GK was funded by a PhD scholarship of Onassis Foundation SI

• Premise of the study: Nine polymorphic and 12 monomorphic microsatellite loci (simple sequence repeats [SSRs]) were isolated and characterized for the gynodioecious grassland perennial Saxifraga granulata. • Methods and Results: Based on genomic screening of leaf material of four individuals from four populations, a total of 21 microsatellite primer pairs were designed for S. granulata. Nine loci were polymorphic and were optimized into two PCR multiplex reactions and tested on 100 individuals from five riparian populations from central Belgium. The number of alleles of the polymorphic loci ranged from three to 18, and gametic heterozygosity ranged from 0.26 to 0.94. • Conclusions: The markers that are presented here are the first microsatellite markers reported for S. granulata and will be used to assess how river systems shape the spatial genetic structure and diversity of riparian populations of this species.