Metabarcoding
We followed the methodological
approach described in Scholz and Voigt (2022). In brief, DNA was
extracted from the frozen fish gut content by applying NucleoSpin© Food
and NucleoSpin© Soil Kit (Macherey-Nagel GmbH & KG, Düren, Germany) as
outlined in the manufacturer’s instructions. We performed two DNA
extractions for each gut content sample. The concentration of the
extracts was determined by fluorometric quantification in a Qubit
Fluorometer (Qubit fluorometric quantification dsDNA High Sensitivity
Kit, ThermoFisher Scientific, Walham, USA). Some of the DNAs had to be
cleaned and concentrated using a DNA Clean and Concentrator Kit (Zymo
Research, 17062 Murphy Ave, Irvine, CA 92614, USA) to get rid of
PCR-inhibitors. Throughout the laboratory work, we strictly applied
protocols to prevent contaminations by alien DNA or PCR products. The
presence of contaminations was checked through all laboratory steps
using different negative controls.
We performed a double-PCR strategy with dual indexing. The first PCR
amplified the target region CO1 (Cytochrome oxidase subunit 1) region
(Galan et al. 2018), the second PCR added the indices to the target
region. Products were checked with an agarose gel and cleaned twice with
magnetic beads (CleanNGS, GC biotech, Waddinxveen, Niederlande). All
products were quantified by fluorometric quantification in the plate
reader (Quant-iT™ dsDNA Assay Kit, high sensitivity, ThermoFisher
Scientific, Walham, USA) and pooled in equimolar concentration. If
necessary, the final library was purified and concentrated by using
CleanNGS beads. The quality and integrity of the library were confirmed
using the Agilent 2200 TapeStation with D1000 ScreenTapes (Agilent
Technologies, Santa Clara, California, USA).
Sequences were generated at the Berlin Centre for Genomics in
Biodiversity Research (BeGenDiv) in two runs on the Illumina MiSeq
platform (Illumina, San Diego, California, USA) using v3 chemistry with
600 cycles. The quality of the generated reads was evaluated using
FastQC v.0.11.9 and multiqc. The remaining adapter sequences were
removed using cutadapt (version 2.8).
Sequencing reads processing from quality control to taxonomic assignment
was performed using the R package “dada2” (Callahan et al. 2016). We
assigned taxonomy to the inferred Amplified Sequence Variants (ASVs) up
to species level based on the reference database provided by the
BeGenDiv (Heller et al. 2018). Taxonomy was assigned based on the single
best hit or a last common ancestor (in case of multiple best hits) with
50 out of 100 bootstrap replicates as minimum bootstrap confidence for
assigning a taxonomic level. For post-sequencing removal of reads caused
by contamination, we used the R package “microDecon” (McKnight et al.
2019) which uses the proportions of ASVs in blank samples (negative
controls) to systematically identify and remove contaminant reads from
the metabarcoding data set. Afterwards, we summed up reads for
pseudo-biological replicates and removed reads which were only present
in one of two technical replicates to further increase the power and
quality of our data set.
We restricted our dataset to results of sequences on the species level
and deleted the finding of bat sequences (Myotis daubentoniid andPipistrellus pygmaeus ) in the gut content of seven fish
individuals, as we assumed that this was not the results of selective
feeding on the bats, but instead the incorporation of bat feces. All
species identified in the gut content were classified into the
categories “aquatic” and “terrestrial”, corresponding to their
dominant life phases and we counted the number of ingested terrestrial
species for each fish individual.