INTRODUCTION
Informed wildlife management requires accurate demographic data that
include recurring, reliable population estimates and an understanding of
the factors that shape population dynamics (Durner, Laidre, & York,
2018; Hamilton & Derocher, 2019; Laidre et al., 2015). Population
monitoring is especially urgent for species that are being impacted by
rapid climate change, are harvested or poached, or reside in habitats
that have been heavily altered or fragmented by human activity (Durner
et al., 2018; Laidre et al., 2015; Robinson, Morrison, & Baillie,
2014). Such monitoring can be challenging for species that have large
territories or occupy inaccessible habitat, are cryptic or elusive (e.g.
nocturnal, fossorial), or are of heightened conservation concern. For
such species, capture, direct handling, and invasive sampling may be
impractical, inappropriate, or culturally undesirable. Moreover,
traditional methods for monitoring of animal populations (e.g. aerial
censusing, mark-recapture, and radiotelemetry) can be expensive,
time-consuming, and stressful for the focal animals (Solberg, Bellemain,
Drageset, Taberlet, & Swenson, 2006; Stapleton, Atkinson, Hedman, &
Garshelis, 2014; Van Coeverden de Groot et al., 2013). When populations
have low densities, mark-recapture and aerial surveys may also be
hindered by low probabilities of capture and detection, respectively
(Garshelis & Noyce, 2006; Hayward, Miquelle, Smirnov, & Nations,
2002).
Genetic monitoring using non-invasive samples, such as scat, hair,
feathers, or skin, affords an alternative that can mitigate some of the
challenges of traditional monitoring. Non-invasive monitoring can be
deployed on a large scale and with greater frequency, potentially
enabling larger sample sizes and improved temporal monitoring. Other
benefits of non-invasive monitoring include ease of collection, reduced
disturbance to study species, potentially decreased spatial or temporal
biases, and diminished physical risk to collectors (Carroll et al.,
2018; Morin, Waits, McNitt, & Kelly, 2018; Steyer et al., 2016; Waits
& Paetkau, 2005). Further, non-invasive genetic monitoring can provide
robust and repeatable data for individual identification (including
sex), movement, and population trends (e.g. Aziz et al., 2017; Quinn,
Alden, & Sacks, 2019; Schmidt, Campbell, Govindarajulu, Larsen, &
Russello, 2020; Schultz, Cristescu, Littleford-Colquhoun, Jaccoud, &
Frère, 2018). Scat samples in particular can be useful for assessing the
health of individuals and populations, as they offer a range of other
information, including data on parasite and pathogen presence (Bergner
et al., 2019; Cristescu et al., 2019; Weese, Salgado-Bierman, Rupnik,
Smith, & van Coeverden de Groot, 2019), diet composition (e.g. via
quantitative PCR or DNA metabarcoding – Iversen et al., 2013; Nelms et
al., 2019; Ogurtsov, 2018), hormone profiles (Morden et al., 2011;
Vynne, Baker, Breuer, & Wasser, 2012), and contaminant loads (Lundin et
al., 2016; Lundin, Riffell, & Wasser, 2015).
Despite the purported advantages of non-invasive genetic monitoring, it
poses challenges that have limited its widespread implementation. A
major impediment is that the DNA from non-invasive samples may be
degraded due to environmental exposure (Bourgeois et al. 2019; Poinar,
Höss, Bada, & Pääbo, 1996; Schultz et al., 2018) and contaminated by
non-target species (Carroll et al., 2018; Taberlet, Waits, & Luikart,
1999). Often less than 5% of the total DNA that scat contains is host
DNA, with most DNA coming from pathogens, parasites, commensal bacteria,
and prey species (Han et al., 2019; Perry, Marioni, Melsted, & Gilad,
2010; Snyder-Mackler et al., 2016). Due to low quality and quantity of
host DNA, accurate quantification and genotyping of non-invasive samples
has often proved difficult when using Next Generation sequencing (NGS)
methods, which require high DNA concentration and quality (Maroso et
al., 2018; Graham et al., 2015; Taberlet et al., 1999). Such reduced
genotyping accuracy due to contaminated and degraded DNA may lead to
increased processing efforts and costs, and complicate inferences from
collected data.
Multiple genotyping methods that have been developed to improve NGS of
low-quality samples (e.g. scat, hair, or archaeological samples) often
use selectively targeted, species-specific arrays of single nucleotide
polymorphism markers or SNPs (Carroll et al., 2018). Traditional NGS
methods (e.g. double-digest restriction-associated DNA sequencing:
ddRADseq) can be used to identify large SNP panels across a focal
species genome, from which smaller, informative panels can be selected
(Andrews et al., 2018; Blåhed, Königsson, Ericsson, & Spong, 2018; Hess
et al., 2015). DNA capture, SNP genotyping assays (e.g. TaqMan), or
amplicon sequencing methods can then be used to genotype the reduced
panel with high coverage (reviewed in Meek & Larson,
2019).
Indeed, SNP genotyping has been applied to non-invasive samples from a
range of wildlife species and has yielded high genotyping success and
low genotyping error, reducing the need for systematic replicates that
increase cost and effort (Fitak, Naidu, Thompson, & Culver, 2016;
Kleinman-Ruiz et al., 2017; Kraus et al., 2015; Schultz et al., 2018;
von Thaden et al., 2017).
Genotyping-in-Thousands by sequencing (GT-seq) is among the most
promising approaches for genotyping SNP panels from non-invasively
collected DNA. It uses highly multiplexed PCR to amplify short
amplicons, followed by individual barcoding that allows rapid,
high-quality genotyping of targeted SNP panels across thousands of
individuals (Campbell, Harmon, & Narum, 2015). GT-seq library
preparation can be performed with standard lab equipment and shows
decreased genotyping error and genotyping costs relative to other
NGS-based genotyping methods, including TaqMan assays (Campbell et al.,
2015). The technique uses suites of multiplexed, species-specific
primers, which may also mitigate some of the challenges presented by
exogenous DNA and degraded host DNA in non-invasive fecal samples.
GT-seq has been successfully applied to minimally invasive cloacal swab
DNA samples collected from Western Rattlesnakes (Crotalus
oreganus ) with low rates of genotyping error and discordance relative
to RADseq (Schmidt et al., 2020). Natesh et al. (2019) also found high
genotyping success using GT-seq of non-invasive scat samples for Indian
Bengal Tigers (Panthera tigris tigris ). Thus, GT-seq has the
potential to enable efficient and economical genetic monitoring of
populations based on non-invasively collected samples, but for
implementation, requires a clear guide for development, testing, and
validation.
There is a long-expressed desire by Northern communities in Canada for
monitoring practices to use non-invasively collected samples,
particularly for polar bears (Ursus maritimus ). Current polar
bear monitoring is based primarily on surveys of 19 subpopulations (also
called management units, MUs), designated largely using mark-recapture,
radio collaring, and aerial surveys (e.g. Stapleton et al., 2014;
Taylor, Laake, McLoughlin, Cluff, & Messier, 2009). Northern
communities, including the Inuit - for whom polar bears are of key
cultural and economic importance - have voiced concern about the
invasiveness of some of these monitoring methods (e.g. mark-recapture),
potential negative impacts on polar bear health and behaviour, lack of
inclusion of Traditional Ecological Knowledge in monitoring and
management, and lack of collaboration in management activities (Wong,
Dyck, & Murphy, 2017; York, Dowsley, Cornwell, Kuc, & Taylor, 2016).
Two-thirds (13) of the 19 polar bear subpopulations are fully or
partially found in Canada (Figure 3), highlighting Canada’s need to lead
on polar bear management. However, surveys for many subpopulations are
infrequent due to logistical and economic constraints, and 11 of 19
subpopulations are data deficient (Government of Canada, 2018; Hamilton
& Derocher, 2019). As of 2019, only 6 subpopulations had population
estimates less than 10 years old (Hamilton & Derocher, 2019). These
data deficiencies preclude robust estimates of population parameters and
have limited implementation of effective management strategies. Thus, as
polar bears continue to be impacted by climate change and face
limitations in range and prey availability as rapid sea ice decline
continues (Fontúrbel, Lara, Lobos, & Little, 2018; Hamilton &
Derocher, 2019; Hunter et al., 2010; Rode et al., 2014), there is an
ever-growing, urgent need for new monitoring approaches.
Non-invasive scat surveys may enable more direct community
participation, and provide a cost-effective complement to traditional
polar bear monitoring methods. Non-invasive scat samples could be
obtained through community-level monitoring programs, in which Inuit
hunters are remunerated for field sampling efforts and surveys are
repeated regularly to better track the trajectory of polar bear
subpopulations. Non-invasive scat surveys have already been used in
Brown bears (Ursus arctos ) as an alternative source of DNA to
high quality samples, such as muscle (e.g. Giangregorio, Norman, Davoli,
& Spong, 2019). We have also established that sufficient quantities of
DNA for GT-seq protocols can be obtained and quantified from
field-collected polar bear scat (Hayward et al., 2020). Thus, polar
bears present an opportunity to demonstrate clearly GT-seq panel
development and validation, application, and usefulness for population
monitoring. Importantly, this application of GT-seq will be in
collaboration with Northern Canadian communities and will have real
socio-economic benefits for the communities involved.
In our study, we have two primary goals: 1. To test the practicality of
using GT-seq for SNP genotyping of non-invasively collected fecal
samples from polar bears and 2. To apply GT-seq to population monitoring
and expand our understanding of Canadian polar bear genetic structure by
using degraded samples that heretofore could not be genotyped with
NGS-based methods. To identify an optimized GT-seq panel, we filtered
and called SNPs from a ddRADseq dataset previously generated from
high-quality, geographically representative tissue samples (Jensen et
al., 2020). We validate the ability of our panel to differentiate
individuals and compare its ability to diagnose population structure to
a fuller suite of SNPs (i.e. all high quality SNPS identified from the
ddRADseq dataset). To validate the GT-seq assay itself, we estimate
missing data and genotyping success for multiple DNA sources, compare
hunter reports of bear sex and GT-seq-determined sex, and estimate
genotyping error for non-invasive scat samples by comparing genotypic
data from harvest-derived muscle and fecal samples collected from the
colons of the same individuals. Finally, we combine our new GT-seq data
with samples previously genotyped at our GT-seq panel loci using
ddRADseq and use this combined dataset to examine relatedness, genetic
diversity, and population structure. We demonstrate an 85.7% genotyping
success rate (individual must have >50% SNPs genotyped)
for non-invasively collected fecal samples determined to have detectable
polar bear DNA, and show that one can comprehensively characterize polar
bear population structure using an optimized, cost-efficient GT-seq
panel of 324 SNPs.