DNA metabarcoding of corvid faecal samples

Establishing methods that allow for more focused management of wildlife under predator pressure may increase the efficiency of managing problematic predators. Non-invasive dietary analysis and identification of conservation-sensitive prey in the diet of ‘culprit’ predator individuals could help to facilitate this and is worthy of exploration. Recently on Phillip Island, Australia, Little Ravens Corvus mellori have emerged as a prominent predator on the clutches of burrow-nesting Little Penguins Eudyptula minor. We tested the feasibility of using non-invasive PCR approaches targeting the penguin mitochondrial 16S rRNA marker gene to establish whether penguin DNA could be detected in raven faecal samples, potentially enabling the identification of culprit ravens missed by extensive field observation. Using a metabarcoding approach, we examined the feasibility of non-invasively establishing other dietary items via high throughput amplicon sequencing. We documented components of raven diet ..., Please refer to the article (https://doi.org/10.1111/ibi.13294) for all details and references., , # DNA metabarcoding of corvid faecal samples

The dataset is linked to the article . Here we have uploaded the fastq sequencing data.

## Description of the data and file structure

This Dryad dataset contains two main items:

1. Excel file with all file and sample details. Description of column headers is included in a second sheet
2. Zip file with fastq files for:

1. Mitochondrial 16S rRNA data against optimised dataset
2. CHIAR and ITS2 data against complete dataset

Information about the fastq data and the associated samples from which they were extracted can be found in the Excel file (“All file and sample details.xlsx”) within this Dryad dataset.

## Sharing/Access information

Please contact the corresponding author with any queries.

## Code/Software

**Bioinformatic analyses**
Sequencing reads were trimmed of their PCR primer sequences followed by dereplication, error-correction (denoising) and clustering at 97% similarity using USEARCH v11 (Edgar 2010). To gener...