Pumas (Puma concolor) are widespread across the Western Hemisphere and can be found in nearly every habitat type from Argentina through Canada (Whittaker 2011). Like other large carnivores, pumas feature prominently as fundamental components of wildlife conservation and management programs – yet their management needs differ drastically among jurisdictions. In the United States, legal protections for pumas range from full protection under the Endangered Species Act in Florida (P. c. coryi) to a complete absence of protection in Texas (Whittaker 2011). Across most of the western United States, pumas are managed as a game species. This variety of management purposes and regulations has created a long-standing need for reliable methods to estimate population sizes, identify individuals, assess population structure, and calculate other metrics often determined via genetic techniques (CMGWG 2005; McKinney 2011). The puma’s secretive nature, combined with inherent challenges in their capture and sampling, further highlights the need for non-invasive genotyping of this species.

Although highly informative, modern genomic approaches, such as those using next-generation sequencing, are generally impractical for the regular, and oftentimes rapid, management needs of pumas. This is due to high costs, limited access to the technological and computational requirements, and lack of availability of appropriate sample types (e.g., fresh tissue specimens). On the other hand, older methods using genetic markers such as microsatellites remain labor intensive and difficult to compare across studies (Carroll et al. 2018). In response, reduced SNP panels have risen in popularity for monitoring and assessing species of conservation or management concern (von Thaden et al. 2020).

Previously, Fitak et al. (2016) developed an assay to genotype 25 SNPs in pumas called PumaPlex (Multiplex 1). The original PumaPlex was capable of individual identification in non-invasive samples and significantly outperformed microsatellites when genotyping scat samples (Fitak et al. 2016). However, this reduced SNP set was generally unsuitable for the detection of low to moderate population subdivision. Empirical and theoretical studies have demonstrated that between two and ten times more SNPs are needed to detect similar levels of population structure when compared to microsatellites (see Gärke et al. 2012; Morin et al. 2012; Fernández et al. 2013). As such, we sought to develop an assay with enough markers to provide increased resolution for determining population structure (Table 1).

Table 1 Table describing how the multiplexes were designed and the final number of loci for each version of PumaPlex
Full size table

Here, we report the development of eight additional multiplexes testing 142 candidate SNPs previously described by Fitak et al. (2016). This research was conducted in two phases. Stage 1 included the design of Multiplexes 2 through 5, referred to as PumaPlex2 (Supp. Table 1). For Stage 2, called PumaPlex100, we designed Multiplexes 6 through 9 (Supp. Table 1) by collating verified SNPs from the first five multiplexes into a final assay containing over 100 polymorphic loci. Multiplexes were designed using Assay Design v3.1 (Sequenom, San Diego, CA, USA). We screened four multiplexes at a time on a 384-well plate with the MassARRAY system (see Gabriel et al. 2009; Goossens et al. 2016; Henriques et al. 2018) at the University of Arizona Genetics Core. Multiplex designs and primer sequences can be found in Supplemental Table 2; we followed Fitak et al. (2016) for PCR conditions and assay design criteria.

The first assay design contained 117 putative SNPs split across four multiplexes (28, 18, 35, and 36 loci per multiplex). Each multiplex was tested using DNA from 95 puma blood samples, previously extracted and genotyped using the original PumaPlex by Fitak et al., and a non-template control (NTC) sample. Of these 117 loci, 68 (58.1%) were polymorphic (Supplemental Table 3).

For PumaPlex100, we designed a set of four additional multiplexes that incorporated 29 untested candidate SNPs with 84 of the polymorphic SNPs previously described in Fitak et al. or identified in our first assay. These four multiplexes contained 36, 34, 26, and 17 loci. Each multiplex was tested using DNA extracted from 95 puma scats collected in Sonora, Mexico and one NTC sample. Scats were processed and DNA was extracted per Cassaigne et al. (2016). Of the 113 loci, 85 (75.2%) loci were polymorphic (Supplemental Table 3). Additionally, 16 loci, which were either monomorphic in this population or which failed to amplify, were previously validated as polymorphic, by Fitak et al. or on PumaPlex2, bringing the total to 101 polymorphic SNPs. The 101 SNPs are located on 87 different contigs from the puma transcriptome (Fitak et al. 2016). Users should be aware of and account for potential linkage disequilibrium among loci on the same contig (O’Leary et al. 2018).

Recently, genomic resources for pumas have expanded to include multiple draft genome assemblies (Ochoa et al. 2019; Saremi et al. 2019), mitogenomes (Ochoa et al. 2017), and large SNP datasets using ddRAD-Seq (Trumbo et al. 2019). Despite this proliferation of genomic data, there is still urgent need for genetic tools that can rapidly and efficiently inform management actions. Additionally, it remains difficult and expensive to obtain quality genomic data from non-invasive sampling, yet the genotyping of scat samples is invaluable for the management of rare, nocturnal, secretive, or fossorial species (Carroll et al. 2018). As such, we foresee that PumaPlex100, and reduced SNP panels, will continue to play a role in management, especially where non-invasive sampling is required. Finally, these SNPs and primers are not limited to use on the MassARRAY system, but can be genotyped in multiplexed, next-generation amplicon sequencing strategies (Eriksson et al. 2019; Natesh et al. 2019). Ultimately, the advantages of rapid, sensitive, and high-throughput SNP genotyping using PumaPlex100 renders it a useful tool for genetic monitoring and management of pumas.