Data from: Metabarcoding analysis provides insight into the link between prey and plant intake in a large alpine cat carnivore, the snow leopard
Cite this dataset
Yoshimura, Hiroto et al. (2024). Data from: Metabarcoding analysis provides insight into the link between prey and plant intake in a large alpine cat carnivore, the snow leopard [Dataset]. Dryad. https://doi.org/10.5061/dryad.j9kd51cjp
Abstract
Species of the family Felidae (a group represented by cats) are thought to be obligate carnivores, specialized for hunting and consuming other animals. However, the detection of plants in the feces of felids raises questions about the role of plants in their diet. This is particularly true for the snow leopard (Panthera uncia), a big cat native to central and South Asia's high mountains. Our study aimed to comprehensively identify the prey and plants consumed by snow leopards as well as six other sympatric mammals. We applied DNA metabarcoding methods on 126 fecal samples collected from the Sarychat-Ertash Nature Reserve in Kyrgyzstan. We found that among the three most common plant families in snow leopard feces, Tamaricaceae (genus Myricaraia) was consumed often by snow leopards. The genus Myricaria frequently appeared in samples lacking any animal prey DNA, indicating that snow leopards might have consumed this plant especially when their digestive tracts were empty. We also observed a significant difference in plant composition between male and female snow leopards, and potentially between sampling seasons. We provide a comprehensive overview of the prey and plants detected in the feces of snow leopards and sympatric mammals. We believe our findings will help in formulating hypotheses and guiding future research to understand the adaptive significance of plant-eating behavior in felids and animal-plant relationships in the ecosystem.
README: Metabarcoding analysis insights into the link between prey and plant intake in the alpine large cat carnivore, snow leopard
https://doi.org/10.5061/dryad.j9kd51cjp
These files are datasets and scripts to generate data in "Metabarcoding analysis insights into the link between prey and plant intake in snow leopard."
Illumina sequencing data was demultiplexed using bcl2fastq and Claident. After trimming primer sequences using Skewer, error collection, and OTU collation were conducted using scripts in miseq_.Rmd files.
Taxon assignment was conducted using Claident and results are in QCidentified files.
Metadata, OTU table, and taxonomy table were integrated into phyloseq (McMurdie & Holmes., 2013, PLOS ONE.) object by scripts in afterClaident*.Rmd files.
Data from different barcode regions were merged using mergeMarkers.Rmd file and a python3 script by da Silva et al. (2019). Subsequent statistical analysis was conducted using other.Rmd files.
Description of the data and file structure
- miseq*.Rmd are Rmarkdown files for processing Illumina sequencing data. The resulting OTU tables are OTU_table*.RData files.
- metadata*.xlsx contains metadata of each sample. The precision of the geographic coordinates was decreased to generalize the location information of threatened species. Data for unused samples were marked as NA.
- QCidentified* files are the results of taxonomy assignment by QCauto algorithm using Claident software (Tanabe & Toju., 2013, PLOS ONE.).
- OTU_table*.RData are OTU tables collated with LULU (Frøslev et al., 2017, Nature Communications.)
- track*.csv includes the number of reads recorded in each filtering process.
- afterClaident*.Rmd are Rmarkdown files creating phyloseq objects and conducting filtering and rarefaction.
- mergeMarkers.Rmd is an Rmarkdown file to prepare input for merge_markers.py (da Silva et al., 2019, Molecular Ecology Resources.).
- Dietary metric.Rmd is a Rmarkdown file receiving merged data and converting it to pyloseq. It also calculates FOO and wPOO to generate figures.
- Cooccut.Rmd is a Rmarkdown file conducting a post-hoc co-occurrence analysis (Griffith et al., 2016, Journal of Statistical Software.)
- NMDS.Rmd is a Rmarkdown file depicting NMDS plots and conducting PERMANOVA.
- CAP*.Rmd are Rmakrdown files conducting CAP with wPOO and FOO datasets, respectively.
- Randomforest*.Rmd are Rmakrdown files conducting RandomForest with wPOO and FOO datasets, respectively.
Sharing/Access information
Sequence data is available here: DDBJ BioProject (PRJDB16690)
Code/Software
bcl2fastq v2.20.422
Skewer
Claident v0.9.2022.04.28
VSEARCH v2.21.1
R version 4.2.2 (2022-10-31)
Python 3.10.10
Funding
Japan Society for the Promotion of Science, Award: 202280252, Overseas Challenge Program for Young Researcher
Japan Society for the Promotion of Science, Award: 21J23216, JSPS KAKENHI Grant-in-Aid for Scientific Research
Japan Society for the Promotion of Science, Award: 15K18471, JSPS KAKENHI Grant-in-Aid for Scientific Research
Japan Society for the Promotion of Science, Award: 20H03008, JSPS KAKENHI Grant-in-Aid for Scientific Research
Japan Society for the Promotion of Science, Award: JPJSBP120209915, JSPS Bilateral Research Program
Ministry of Education, Culture, Sports, Science and Technology, Leading Graduate Program in Primatology and Wildlife Science of Kyoto University