DOI: 10.5061/dryad.1ns1rn8vq
title: Testing the effectiveness of genetic monitoring using genetic non-invasive sampling

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.
description: 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<i> et al.</i> (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 <i>et al</i>. (2020). SNP
 genotyping was conducted as per Schultz <i>et al. </i>(2018)
 and Kjeldsen <i>et al.</i> (2018) by Diversity Arrays
 Technology, Canberra, using their proprietary
 DArTseq<sup>TM</sup> technology.
 DArTseq<sup>TM</sup> 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 <i>et al.</i> (2017), Lal
 <i>et al. </i>(2017), and Kjeldsen <i>et al</i>.
 (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.
description: <strong>Non-Invasive-DNA-Testing</strong> 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.