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.