DNA metabarcoding of corvid faecal samples
Data files
Jan 16, 2024 version files 534.58 MB
-
All_file_and_sample_details.xlsx
126.61 KB
-
Raw_Fastq_files.zip
534.45 MB
-
README.md
1.65 KB
Abstract
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.
README: DNA metabarcoding of corvid faecal samples
https://doi.org/10.5061/dryad.jh9w0vtj4
The dataset is linked to the article https://doi.org/10.1111/ibi.13294. Here we have uploaded the fastq sequencing data.
Description of the data and file structure
This Dryad dataset contains two main items:
- Excel file with all file and sample details. Description of column headers is included in a second sheet
Zip file with fastq files for:
- Mitochondrial 16S rRNA data against optimised dataset
- 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 (https://doi.org/10.1111/ibi.13294) for further details and references.
Methods
Please refer to the article (https://doi.org/10.1111/ibi.13294) for all details and references.