Testing the effectiveness of genetic monitoring using genetic non-invasive sampling
Creators
- 1. Icelandic Museum of Natural History*
- 2. Hólar University College
- 3. University of the Sunshine Coast
- 4. Endeavour Veterinary Ecology*
- 5. University of Queensland
Description
1. Effective conservation requires accurate data on population genetic diversity, inbreeding, and genetic structure. Increasingly, scientists are adopting genetic non-invasive sampling as a cost-effective population-wide genetic monitoring approach. Genetic non-invasive sampling has, however, known limitations which may impact the accuracy of downstream genetic analyses.
2. Here, using high quality SNP data from blood/tissue sampling of a free-ranging koala population (n = 430), we investigated how the reduced SNP panel size and call rate typical of genetic non-invasive samples (derived from experimental and field trials) impacts the accuracy of genetic measures, and also the effect of sampling intensity on these measures.
3. We found that genetic non-invasive sampling at small sample sizes (14% of population) can provide accurate population diversity measures, but slightly underestimated population inbreeding coefficients. Accurate measures of internal relatedness required at least 33% of the population to be sampled. Accurate geographic and genetic spatial autocorrelation analysis requires between 28% and 51% of the population to be sampled.
4. We show that genetic non-invasive sampling at low sample sizes can provide a powerful tool to aid conservation decision-making and provide recommendations for researchers looking to apply these techniques to free-ranging systems.
Notes
Non-Invasive-DNA-Testing
De-identified data and R code for simulating DNA degradation due to non-invasive genetic sampling
See README file for descriptions of the different files found in this repository and their uses.
R Script files: There are two PDF files formatted as R Markdown vignettes containing the R coding required to simulate the reduced call rates and fewer loci typically found in genotype datasets from non-invasively sampled DNA, as well as simulate spatially-explicit population subsampling. For the context of these simulations please refer to the associated pubished paper, Schultz et al (2021).
.CSV files: The four .csv files here provide de-identified data used in the our analyses. We have included this 1) for reproducibility and transparency, and 2) to provide a template for practitioners who wish to use our simulations on their own datasets.
Please read the R script documents first, all details are explained there.
Funding provided by: Department of Transport and Main Roads, Queensland Government
Crossref Funder Registry ID: http://dx.doi.org/10.13039/501100008915
Award Number:
Files
genotypes_file.csv
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Additional details
Related works
- Is derived from
- 10.5281/zenodo.5747279 (DOI)