Species-specific SNP arrays for non-invasive genetic monitoring of a vulnerable bat

Abstract

Genetic tagging from scats is one of the minimally invasive sampling (MIS) monitoring approaches commonly used to guide management decisions and evaluate conservation efforts. Microsatellite markers have traditionally been used but are prone to genotyping errors. Here, we present a novel method for individual identification in the Threatened ghost bat Macroderma gigas using custom-designed Single Nucleotide Polymorphism (SNP) arrays on the MassARRAY system. We identified 611 informative SNPs from DArTseq data from which three SNP panels (44-50 SNPs per panel) were designed. We applied SNP genotyping and molecular sexing to 209 M. gigas scats collected from seven caves in the Pilbara, Western Australia, employing a two-step genotyping protocol and identifying unique genotypes using a custom-made R package, ScatMatch. Following data cleaning, the average amplification rate was 0.90 ± 0.01 and SNP genotyping errors were low (allelic dropout 0.003 ± 0.000) allowing clustering of scats based on one or fewer allelic mismatches. We identified 19 unique bats (9 confirmed/likely males and 10 confirmed/likely females) from a maternity and multiple transitory roosts, with two male bats detected using roosts, 9 km and 47 m apart. The accuracy of our SNP panels enabled a high level of confidence in the identification of individual bats. Targeted SNP genotyping is a valuable tool for monitoring and tracking of non-model species through a minimally invasive sampling approach.

Figure 1

The spatial arrangement of roosts where Macroderma gigas scats were collected. The top insert is the site location within Western Australia and the bottom inserts are movement patterns of M. gigas male number 8 (M8) and male number 10 (M10). The map is generated by an R package ‘leaflet’ version 2.2.1.

Figure 2

Macroderma gigas SNP selection for MassARRAY panels assessing for genotype variation for all 611 SNPs (a), A Pearson Principal Component Analysis of 150 SNPs (b), and Probability of Identity of 50 SNPs (c). Different colours in (a) represent 3 genotypes: blue (0) for a homozygote of the reference allele, purple (1) for a heterozygote, and red (2) for a homozygote of the alternative allele. Colours in (b) represent locations of M. gigas tissue samples collected across the Pilbara. Different P_ID colours in (c) represent the probability of identifying unrelated individuals (P_ID, green) and related individuals (P_IDsib, orange).

Figure 3

Threshold of allelic mismatch number to call Macroderma gigas scats from the same individuals with amplification rate above 90% for loci and 80% for scats. (a) Graph of different numbers of SNP mismatches allowed and numbers of group assigned to genotypes. (b) Misassignment graph (h = 1) demonstrates the frequency of pairwise allelic mismatches of raw genotypes where both samples have genotypes. The red and blue highlights represent SNP differences between faecal samples from ‘within’ and ‘between’ groups or putative individuals respectively. The red and blue dashed lines represent the upper 0.995 percentile and lower 0.005 percentile respectively. (c) Heatmap of pairwise allelic mismatches between all scats. Scats are clustered based on their similarity. A lower number of mismatches presents blue and a higher number of mismatches presents in yellow.

Figure 4

Summary of average pairwise genetic relatedness and sex of Macroderma gigas detected in each roost. Note that some bats used multiple roosts so they have been double-counted across these roosts. Likely sex is defined as a group of scats with some disagreement between markers and/or scats, but the sex selected made up the majority of the result. Bars around relatedness mean are standard error bars.

Figure 5

Pairwise genetic relatedness of Macroderma gigas calculated using Ritland. The genotype IDs are sorted by roost IDs. Genotype IDs with ? indicates the likely sex. Colour intensity indicates genetic similarity in categories of first relationship (R = 0.5; parent–offspring, full-siblings), second relationship (R = 0.25; half-sibling, uncle/aunt–nephew/niece, grandparent–grandoffspring) and third relationship (R = 0.125; full cousin, great-grandparent–great-grandoffspring, great-uncle/aunt–great-nephew/niece, half-uncle/aunt–nephew/niece). No first relationship was detected.