DOI: 10.5061/dryad.jh9w0vtj4
title: DNA metabarcoding of corvid faecal samples

description: 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 using the universal mitochondrial 16S rRNA, insect-specific ‘Chiar’
 16S rRNA, and plant ITS2. The assemblage of dietary items did not differ
 with raven culprit status (i.e. a raven previously observed preying upon
 penguin), sex, or date. Penguin was detected in the diet of some
 individuals classified observationally as non-culprits. While some cases
 may conceivably have been false detections, other explanations include
 missed depredation events, consumption via scavenging, or consumption
 through secondary consumption (e.g. eating invertebrates that have
 consumed penguin). While this study found metabarcoding unreliable for
 unambiguous assigning of raven culprit status, at least as we implemented
 it, it may hold promise complementing observations if consumption via
 scavenging can be distinguished from direct depredation.
description: Please refer to the article (https://doi.org/10.1111/ibi.13294) for all details and references.
description: # 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 generate an Operational Taxonomic Units (OTU) count table, the
 trimmed reads were aligned back to the OTUs using the ‘Usearch_global’
 command. Taxonomic assignment of the OTUs used the ‘sintax’ command with a
 confidence score cut-off of 0.8 based on the MIDORI reference database
 (Banchi et al.2020, Leray et al. 2018). The OTU table, taxonomic
 assignment data, and sample metadata were submitted to MicrobiomeAnalyst
 for data visualisation (Dhariwal et al. 2017). Please see the article
 () for further details and
 references.