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Testing the effectiveness of genetic monitoring using genetic non-invasive sampling

Cite this dataset

Schultz, Anthony et al. (2022). Testing the effectiveness of genetic monitoring using genetic non-invasive sampling [Dataset]. Dryad.


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.


This study used koala genetic samples from the Moreton Bay Rail Koala Tagging and Monitoring Program, a long-term (2013 – 2017) koala monitoring study that was part of a rail infrastructure development project in south east Queensland, Australia (-27.234°; 153.036°). During the project, the study area was extensively surveyed, and all identified koalas were captured for veterinary examination and the attachment of tracking devices. Full protocols are available in the project technical report by Hanger et al. (2017).

Genetic samples were collected during veterinary examinations and were either blood samples (stored at -20 °C) or tissue samples collected during ear tag attachment (stored in 70% ethanol). DNA was extracted using the DNeasy Blood and Tissue Kit (QIAGEN), following the manufacturer’s protocol, and DNA extracts were stored at -80 °C)

The SNP dataset used in this study is the same dataset of 8649 SNPs for 430 individuals used in Schultz et al. (2020). SNP genotyping was conducted as per Schultz et al. (2018) and Kjeldsen et al. (2018) by Diversity Arrays Technology, Canberra, using their proprietary DArTseqTM technology. DArTseqTM uses a combination of next-generation sequencing platforms and DArT complexity-reduction methods (Courtois et al., 2013; Cruz et al., 2013; Kilian et al., 2012). This process has been well documented in Melville et al. (2017), Lal et al. (2017), and Kjeldsen et al. (2018). Read depth filtering averages in DArTseq pipeline were set at three reads for reference allele, two reads for alternate.

The dataset presented here is the unfiltered SNP dataset of 8649 SNPs.

Usage notes


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.


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