Skip to main content
Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Development of nuclear microsatellite loci for Pinus albicaulis Engelm. (Pinaceae), a conifer of conservation concern

  • Marian V. Lea,

    Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft

    Affiliation Department of Integrative Biology, University of Colorado Denver, Denver, Colorado, United States of America

  • John Syring,

    Roles Conceptualization, Investigation, Methodology, Software, Visualization, Writing – review & editing

    Affiliation Department of Biology, Linfield College, McMinnville, Oregon, United States of America

  • Tara Jennings,

    Roles Formal analysis, Investigation, Methodology, Writing – review & editing

    Affiliation Pacific Northwest Research Station, United States Department of Agriculture, Forest Service, Corvallis, Oregon, United States of America

  • Richard Cronn,

    Roles Conceptualization, Funding acquisition, Validation, Writing – review & editing

    Affiliation Pacific Northwest Research Station, United States Department of Agriculture, Forest Service, Corvallis, Oregon, United States of America

  • Leo P. Bruederle,

    Roles Conceptualization, Methodology, Resources, Supervision, Validation, Writing – review & editing

    Affiliation Department of Integrative Biology, University of Colorado Denver, Denver, Colorado, United States of America

  • Jennifer Ramp Neale,

    Roles Methodology, Resources, Visualization, Writing – review & editing

    Affiliation Denver Botanic Gardens, Denver, Colorado, United States of America

  • Diana F. Tomback

    Roles Conceptualization, Funding acquisition, Project administration, Supervision, Validation, Writing – original draft

    Affiliation Department of Integrative Biology, University of Colorado Denver, Denver, Colorado, United States of America


Pinus albicaulis (whitebark pine) is a widely-distributed but rapidly declining high elevation western North American tree and a candidate for listing under the U.S. Endangered Species Act. Our objectives were to develop reliable nuclear microsatellite markers that can be used to assess within-population genetic diversity as well as seed and pollen migration dynamics, and to validate markers using two geographically proximal P. albicaulis populations. We identified 1,667 microsatellite-containing sequences from shotgun DNA libraries of P. albicaulis. Primer pairs were designed for 308 unique microsatellite-containing loci, and these were evaluated for PCR amplification success and segregation in a panel of diploid needle tissue. DNA was extracted with an SDS protocol, and primers were screened through gel electrophoresis. Microsatellites were genotyped through fluorescent primer fragment analysis. Ten novel and 13 transferred loci were found to be reproducible in analyses based on 20 foliage samples from each of two locations: Henderson Mountain, Custer Gallatin National Forest, Montana, and Mt. Washburn, Yellowstone National Park, Wyoming (USA). Transferred loci had higher numbers of alleles and expected heterozygosities than novel loci, but also revealed evidence for a higher frequency of null alleles. Eight of the 13 transferred loci deviated significantly from Hardy-Weinberg Equilibrium, and showed large positive FIS values that were likely inflated by null alleles. Mantel’s tests of transferred and novel markers showed no correlation between genetic and geographic distances within or among the two sampled populations. AMOVA suggests that 91% of genetic variability occurs within populations and 9% between the two populations. Studies assessing genetic diversity using these microsatellite loci can help guide future management and restoration activities for P. albicaulis.


North American forest trees have been exposed to a number of unprecedented health challenges that are likely to be exacerbated by warming temperatures [1]. These challenges include outbreaks of native insects and especially bark beetles [2], mortality of older age classes from drought [3], and the accidental introduction and spread of destructive exotic pests and pathogens (e.g., [4]). In fire-prone communities in the western United States, intervals between fires are projected to decline over the next century, as climate warms [5, 6], and this may lead to metapopulation extirpation and the extinction of some woody plant species [7, 8]. As populations regenerate following disturbance, the recovery of community structure and genetic diversity is extremely important [9, 10].

Pinus albicaulis Engelm. (Pinaceae, whitebark pine) is a widely distributed western North American tree species of subalpine and treeline elevations ranging from about 37° to 55° N latitude. It functions as an ecological keystone and foundation species in these high elevation communities [11, 12]. The species, however, has experienced range-wide mortality due to insect outbreaks, pathogens, and climate change; consequently, it is a candidate for listing under the U.S. Endangered Species Act [13]. Pinus albicaulis is a slow-growing species that does not produce large cone crops until at least 60 years of age [14]. It tolerates cold, dry, and windy environments, forming self-replacing stands on harsh sites. On productive sites, P. albicaulis is an early seral species in successional communities, which depend on disturbance, primarily fire, for renewal (e.g., [15]). It relies on the Clark’s nutcracker (Nucifraga columbiana Wilson) for seed dispersal, which influences its ecology and population biology [1619]. Pinus albicaulis regenerates rapidly from seed caching in burned terrain by nutcrackers following fires, with new seedlings establishing within a few years [2022]. At treeline elevations on arid sites, it is often dominant and a tree island initiator, facilitating infilling and upward expansion of the alpine treeline ecotone [2325]. Pinus albicaulis treeline communities redistribute and retain snow, regulating downstream flows in summer. In addition, the large seeds comprise an important wildlife food source for many granivorous birds besides Clark’s nutcrackers, as well as small mammals and bears [26].

Pinus albicaulis is a host for mountain pine beetles (Dendroctonus ponderosae Hopkins), which have erupted in large-scale outbreaks in the Rocky Mountains and elsewhere in the West over the past 20 years. It is also susceptible to white pine blister rust, which is caused by the introduced pathogen Cronartium ribicola J.C. Fisch. and continues to spread geographically [22, 27, 28]. Both tree mortality from mountain pine beetles and mortality and canopy damage from blister rust progressively reduce the number of cone-producing individuals, depleting genetic diversity for future generations [2]. Blister rust also infects and kills young trees, thereby reducing regeneration [29]. In P. albicaulis, some evidence suggests that heterozygotes are more tolerant of blister rust [30]. However, with declines in cone production and high mortality, heavily impacted P. albicaulis stands may be experiencing various degrees of inbreeding or pollen limitation. Given decades of decline, P. albicaulis has become a species of management and conservation concern for federal agencies in the U.S. and Canada [27, 31, 32]. Developing cost-efficient methods to identify the magnitude and apportionment of genetic diversity in P. albicaulis populations is a high priority, since this can expedite conservation actions. These actions potentially include tracking parentage to search for pest and pathogen resistance, assessment of local genetic diversity to highlight populations in need of additional protection, restoration of post-fire genetic diversity to pre-burn levels, and to inform decisions on seed collections (e.g., [33, 34]).

Nuclear microsatellites (nSSRs) have been a key tool for estimating genetic diversity in species of conservation concern. They have been used to differentiate among populations (pequi, Caryocar brasiliense Camb.) and monitor hybridization between rare and widespread taxa (Colorado hookless cactus, Sclerocactus glaucus [K. Schumann] L.D. Benson) [35, 36]. They have also been used successfully to describe genetic diversity in threatened conifers [3741]. Genetic diversity in P. albicaulis has been previously estimated using genetic markers other than nSSRs, including allozymes [42, 43], organellar simple sequence repeats [34, 44], and single nucleotide polymorphisms (SNPs) [4547]. However, nSSRs are more accurate and efficient for studies focusing on individual-level differences, including research on sibling reconstruction, linkage disequilibrium, genetic structure, parentage, and individual identification [48]. Thus, nuclear microsatellites should be a useful tool to characterize population genetic diversity and structure in P. albicaulis and to monitor spatial and temporal changes in genetic composition within stands and across populations. Despite the utility of nSSRs for surveys of genetic diversity in species of conservation concern, nuclear microsatellites have not yet been successfully developed for P. albicaulis. The species has a large, highly repetitive genome that has confounded efforts to develop single locus polymorphic microsatellites [49]. In addition, microsatellite transfer among pine species has been inconsistent [50].

To facilitate finding useful nSSR markers, many investigators look to near-relatives, such as congeneric taxa including sister species for potential transfer [40, 5153]. Pinus albicaulis is one of 21 pines in section Quinquefoliae subsection Strobus [54, 55], which in the most recent classification includes the stone pines of former subsection Cembrae [56, 57]. Divergence dates for subsection Strobus (based on nuclear molecular clock estimates; [58]) are estimated at 11–20 mya. The most recently published phylogenies for Pinus subgenus Strobus constructed from nuclear, mitochondrial, and chloroplast gene sequences variously resolve P. albicaulis with species of subgenus Strobus native to North America, Asia, and Europe [59].

Here, our first and primary objective was to develop reliable nSSR markers in P. albicaulis in order to provide an efficient, informative tool to monitor genetic diversity of populations in light of current and future threats. Our secondary objective was to determine the utility of these nSSR markers by comparing genetic diversity in two populations previously studied in the Greater Yellowstone Ecosystem [43]. To maximize the number of nSSRs, we combined screening of previously identified loci from related pine species for potential transferability with surveying P. albicaulis shotgun nuclear genomic sequences for taxon-specific SSR sequences.

Materials and methods

To develop novel microsatellite loci for P. albicaulis, we identified di- and tri-nucleotide repeats using the procedure outlined in Jennings et al. [60], a strategy that was designed to identify very short (< 80bp) paired-end sequence data from early-generation Illumina sequencers. Genomic DNA from diploid needle tissue from a single tree (Custer Gallatin National Forest, Montana; 45.443°N, -110.005°W, 2,470 m a.s.l.) was used to make a standard Illumina library containing internal ‘barcoding’ adapters (barcode 5’-CACT; [61]). This library was pooled with 15 additional barcoded libraries from other conifer species, and five pmol were sequenced on an Illumina Genome Analyzer II using 80 bp paired end reads (University of Oregon Genomics Core Facility, Eugene, OR, USA). Libraries were sorted by barcode using a custom Perl script ( For this strategy, paired microreads are concatenated and separated by 50 N’s (e.g., forward read + 50 N’s + reverse complement reverse read), and identical to nearly-identical sequences (> 95% identity) are filtered to a single representative sequence using cd-hit-454 (Niu et al. 2010). This allowed us to search for dinucleotide (>5 repeats) and trinucleotide (>4 repeats) motifs in the Read 1 or Read 2 sequences using SSR_pipeline [62], and to design flanking amplification primers in the Read 1 and Read 2 sequences using BatchPrimer3 [63] and synthesized at Integrated DNA Technologies (IDT, Coralville, IA, USA). We used default settings for BatchPrimer3, with the exception of the following parameters: product length (min = 100 bp, max = 200 bp), primer length (min = 17 bp, max = 25 bp, optimum = 19 bp), melting temperature (min = 48°C, max = 63°C, optimum = 54°C); number of primers per sequence = 1. The original sequencing reads are deposited at the NCBI Short Read Archive under accession SRR7944190. Primers for 49 nSSR loci were screened for transferability to P. albicaulis, including 14 loci from P. cembra L. (Swiss stone pine) [64, 65], three loci from P. parviflora (Japanese white pine) [66], 13 loci from P. koraiensis Siebold & Zucc. (Korean pine) [67], and 19 loci from P. strobus L. (eastern white pine) [68] (S1 Table).

DNA was extracted from silica-dried needles using an SDS-based protocol optimized for Pinus species, modified with 1μl β-mercaptoethanol per 404μl of grinding buffer [69]. DNA was eluted in 130μl TE buffer, and concentrations were determined using a NanoDrop 2000 microspectrophotometer (Thermo Scientific, Wilmington, DE, USA). Microsatellite screening used 10μl polymerase chain reaction (PCR) mixtures containing 1X GoTaq G2 Master Mix (Promega, Madison, WI, USA), BSA (1 μM), mixed forward and reverse primers (2.5 μM), and 10ng DNA. Amplifications for novel primers were performed in an Eppendorf vapo.protect Mastercycler proS using the following cycling conditions: denaturation for 5 min. at 95°C, 35 cycles of 45 s each at 95°C, 45 s at 58°C, and 15 s at 72°C, followed by a final annealing cycle of 72°C for 5 min. Transferred primers were amplified using the conditions described in the literature [6468]. PCR products were visualized on 2% agarose gel using GelRed (Biotium, Fremont, CA, USA) and a low molecular weight DNA ladder (New England BioLabs, Ipswich, MA, USA).

Fluorescent primers were designed with 5’ tags [70]. Microsatellite locus amplifications for fragment analysis were created using the Fluorescent Tag Microsatellite PCR Protocol, with minor modifications. Reactions consisted of 10.5μl mixtures containing 1X GoTaq G2 Colorless Master Mix (Promega, #M7832), BSA (1 μM), non-tagged primer (5 μM), tagged primer (0.5 μM), fluorescent tag (5 μM; VIC, PET, or FAM), and 10ng DNA using the above conditions. PCR products were mixed in multiplexes of three and sent to the DNA Laboratory at Arizona State University for fragment analysis (School of Life Sciences, Tempe, AZ, USA). Fragment analysis outputs were visualized and genotyped using GeneMarker V2.6.2 (SoftGenetics).

In association with an on-going ecological study, and to compare with past population genetic research in this region, two populations were selected from the Greater Yellowstone Ecosystem for evaluation; Henderson Mountain (Custer Gallatin National Forest, MT, USA; letter approving sample collection) and Mt. Washburn (Yellowstone National Park, WY, USA; research permit YELL-2016-SCI-6064), which are separated by 50 km (Fig 1) [11]. Overstory composition in these two subalpine stands consists of cone-bearing P. albicaulis, P. contorta Douglas, Picea engelmannii Parry ex Engelm., and Abies lasiocarpa (Hooker) Nuttall. The two stands are 0.5–5.0 km from post-fire study areas established in the Greater Yellowstone Ecosystem in 1990, so the selected stands represent putative seed sources for areas regenerating following the 1988 Yellowstone fires. Pinus albicaulis from Mt. Washburn was collected within a 13.3 ha (0.13 km2) area ranging from 2,810–3,005 m elevation in north-central Yellowstone National Park, on north and west-facing slopes, 7–9 km north of Canyon Junction. Samples from Henderson Mountain were collected within a 15.7 ha (0.16 km2) area ranging from 2,690–2,785 m elevation on south and east-facing slopes, 3 km northeast of Cooke City, MT, and 5–7 km northeast of Yellowstone Park. For both populations, 20 individuals nearest to randomly generated points were sampled, with five fascicles collected from each individual and silica-dried for extended storage. Tree diameter was measured at breast height (DBH; S2 Table).

Fig 1. Map of sample collection locations.

Top: Sample collection locations for Pinus albicaulis Engelm. (Pinaceae). Henderson Mountain, Custer Gallatin National Forest, MT and Mt. Washburn, Yellowstone National Park, WY. Bottom: Geographic location of Yellowstone National Park, Central Rocky Mountains.

Eight individuals from Henderson Mountain were screened for initial locus testing, and 20 individuals from each population were used for final population genetic assessment. Due to minor amplification failure, a total of 26 individuals from Henderson Mountain and 22 individuals from Mt. Washburn were sampled to bring the total sample size up to 20 individuals for each of the 23 loci selected from the initial screening. Standard genetic diversity measures, Hardy-Weinberg Equilibrium, and inbreeding coefficients were calculated using GenAlEx 6.5 [71, 72]. The occurrence and frequency of null alleles was estimated using ML-NullFreq with 10,000 permutations [73]. Histograms of average number of alleles per locus were created in R [74] to visualize differences between populations. Population differences in the mean number of alleles per locus and expected heterozygosity are defined as the difference between Mt. Washburn and Henderson Mountain. Differences in means were plotted in R, along with 95% confidence intervals. Partitioning of population genetic variation was also evaluated by analysis of molecular variance (AMOVA) using Nei's genetic distances and 9,999 permutations (GenAlEx 6.5). Isolation-by-distance within and between populations was tested using Mantel’s correlation with 9,999 permutations in GenAlEx 6.5 [71, 72, 75].


Of the sequences screened, we identified 1,667 (1,341 di- and 326 tri-nucleotide) microsatellite-containing sequences from shotgun DNA libraries of P. albicaulis. Primer pairs were designed for 308 unique microsatellite-containing loci, and these were evaluated for PCR amplification success and segregation in a panel of diploid needle tissue. Of these markers, ten novel P. albicaulis-derived loci amplified consistently and cleanly and were polymorphic in the two screened populations (Tables 1 and 2). The total number of alleles (NA) for these novel loci ranged from 2–9 in the combined populations, with an average of 3.3 alleles per locus (Table 2). Observed heterozygosity (HO) ranged from 0.02–0.50 ( = 0.18) and expected heterozygosity (HE) ranged from 0.02–0.40 ( = 0.18). All loci conformed with Hardy–Weinberg Equilibrium (HWE) (Table 2).

Table 1. Descriptive information for the 10 microsatellite loci isolated from Pinus albicaulis.

Table 2. Microsatellite loci, source species, and application to two populations of Pinus albicaulis in the Greater Yellowstone Ecosystem.

Although transferability of nSSR loci to the P. albicaulis genome transfer was highly variable depending on the source species, 13 of the 42 loci (27% overall) amplified successfully, yielding interpretable results (Tables 2 and 3). Sequencing revealed these nSSRs to be the expected basepair motifs, though they tended to have fewer repeats than listed for the source species. Loci developed for P. koraiensis had the highest rate of transferability, with eight of 13 (62%) tested loci amplifying in P. albicaulis, followed by P. parviflora with one of three (33%), and P. strobus with three of 19 (16%). Loci developed for P. cembra had the lowest rate of transferability with only one successful locus from a pool of 14 loci (7%). The average number of alleles per locus varied from 5–8 for the transferred microsatellites (Table 3). For transferred loci, NA ranged from 3–15 ( = 7.2), HO ranged from 0.18–0.56 ( = 0.39), and HE ranged from 0.22–0.89 ( = 0.60). Eight loci deviated from HWE (Table 2).

Table 3. Results of microsatellite locus transferability to Pinus albicaulis for primers developed in other Pinus species.

For our survey populations, the global range of alleles per locus was similar, but Henderson Mountain generally had more genetic diversity than Mt. Washburn (Fig 2, S1 Fig). Henderson Mountain had a higher average number of alleles, ranging from 2–9 ( = 4.6), with an average of 1.30 private alleles per locus. HO ranged from 0.04–0.56 ( = 0.29), and HE ranged from 0.04–0.84 ( = 0.43) (Table 2). NA for Mt. Washburn ranged from 1–12 ( = 4.1), with an average of 0.91 private alleles per locus. HO ranged from 0–0.65 ( = 0.32), and HE ranged from 0–0.90 ( = 0.41) (Table 4). Eight loci deviated from Hardy–Weinberg equilibrium in both populations (Table 2). Null allele frequency estimates for transferred loci ranged from 0–0.51 at Henderson Mountain, and 0–0.30 at Mt. Washburn (Table 2).

Fig 2. Number of alleles per locus in two Pinus albicaulis populations.

Histograms of the count frequency per locus for number of alleles for all of 23 microsatellite loci for two populations of Pinus albicaulis Engelm. (Pinaceae) A) Henderson Mountain, Custer Gallatin National Forest, MT, and B) Mount Washburn, Yellowstone National Park, WY.

Table 4. Genetic variability at microsatellite loci in two Pinus albicaulis populations.

Across all markers, the overall inbreeding coefficient (FIS) was 0.32 for Henderson Mountain, and 0.24 for Mt. Washburn. However, loci developed specifically for P. albicaulis showed a much lower inbreeding coefficient (0.007) than those transferred from other species (0.291) (Table 4); this difference is likely a consequence of the higher number of null alleles in the transferred SSR markers. An AMOVA identified 91% of variance within populations, and 9% among populations. The spatial distance between samples at Henderson Mountain was larger (range = 8–538 m, = 231) than at Mt. Washburn (range = 7–348 m, = 173) (S3 Table). Mantel’s test within populations showed no correlation between genetic and geographic distances at Henderson Mountain (rx,y = -0.120, p = 0.121), indicating that individuals are no more closely related than expected with a random spatial pattern (S2 and S3 Figs). A weakly positive correlation at Mt. Washburn (rx,y = 0.162, p = 0.051) indicated that individuals may be more closely related genetically to nearby individuals than expected under a random spatial pattern (S2 and S3 Figs). Between the two populations, there was no spatial autocorrelation (rx,y = 0.029, p = 0.149) (S3 Table).


Our primary objective—the development of reliable nSSR markers useful for assessing genetic diversity in populations of P. albicaulis—was accomplished with the identification of ten novel and 13 transferred microsatellite loci. Of the 308 novel microsatellites identified from shotgun sequencing of P. albicaulis, only ten loci were sufficiently polymorphic and reliable to be identified for marker development, corresponding to a success rate of ~3%. This is approximately one-fifth the success rate of SSRs developed in Chamaecyparis lawsoniana (A. Murray) Parl. using these same methods [60]. This conversion rate is extremely low for species of genus Pinus, which have already been characterized by low conversion rates, attributed to the complexity of their genomes (> 20Gbp) and the presence of large numbers of paralogous SSR families [76], [77], [78]. The low yield is likely due to the selection of shorter motifs, which are known to show lower mutation rates than long microsatellite repeats [79]. The success rate of SSR marker transfer from other white pines to P. albicaulis was 13 out of 49—almost 27%. The average number of alleles per locus of the transferred microsatellites (7.2) is more than twice that of the novel microsatellites (3.2), suggesting that the transferred loci may be useful in studies requiring high individual identification power, or detecting differences in spatial differentiation. For studies sensitive to the presence of null alleles and error in heterozygote identification, the markers derived from P. albicaulis are likely to perform better, albeit at the cost of variability. Notably, transferability of loci from the two east Asian species (P. koraiensis and P. parviflora) was substantially higher than for the European (P. cembra) and North American (P. strobus) species tested. This study highlights the opportunity for screening newly published novel microsatellites from closely related species in P. albicaulis, such as recently developed for P. sibirica [80]. While these were not included in the present study, they also have a high likelihood of transferability, due to the close genetic affinity between P. albicaulis and P. sibirica.

Our secondary objective was to provide a case example of the utility of these microsatellites for population genetic research by comparing genetic diversity in two populations of P. albicaulis previously studied using allozyme analysis [43]. Despite the relatively small geographic distance between these sites (50 km), the nSSR markers revealed that both Henderson Mountain and Mt. Washburn had private alleles, averaging 1.3 and 0.9 per locus, respectively. This is likely a result of the small sample sizes (20 individuals per site) used in this study, though such sample sizes should yield adequate estimates [81]. Compared to other tools for measuring genetic diversity in P. albicaulis—including allozymes [43] and SNPs [47]—the microsatellite loci described here have a higher mean number of alleles per locus, as is expected with microsatellite markers. For example, with allozyme analysis these sites showed 1.6–1.7 alleles per polymorphic locus [43]. The surveyed SSRs revealed around three times the number of alleles per polymorphic locus, with 4.1–4.6 (Table 3).

All deviations from HWE are due to an excess of homozygous genotypes (not heterozygotes) and are most likely the result of allelic dropout (null alleles). All loci that violate HWE are transferred loci, which may indicate imperfect conservation of primer binding sites in P. albicaulis, or a presence/absence polymorphism in some chromosomal lineages. In these instances, null alleles fail to amplify, leading to decreased observed heterozygosity and apparent reaction failure in null homozygous individuals [82]. The potential impact of null alleles may be revealed from our FIS values, which are large and positive for both populations, leading to potentially erroneous inferences of moderate inbreeding. Six loci—RPS119, RPS127, P5, P45, P63, and PisATG0012—were estimated to have high null allele frequencies in both populations. Loci that diverge from HWE and show evidence of null alleles should be used with caution as they inflate homozygosity and thus may not be applicable for all analyses (Table 2). Looking at only the novel loci developed for P. albicaulis, the inbreeding coefficient is very small, conforming to expectations from past research indicating random mating [43]. For studies sensitive to the presence of null alleles and error in heterozygote identification, the markers derived from P. albicaulis are less informative but may yield more accurate estimates.

Mantel's test revealed low genetic differentiation among populations and high gene flow, with no correlation between geographic distance and genetic relatedness among populations. Within populations, Mantel’s correlation shows no spatial autocorrelation at Henderson Mountain, with individuals no more or less closely related genetically to those nearby geographically than expected at random. There is weak evidence for isolation by distance in the Mt. Washburn population, with a positive Mantel correlation indicating that genetic distance between individuals increases with increasing physical distances. However, the AMOVA, which revealed that 9% of genetic differentiation occurred between populations, indicates greater population differentiation compared to SNP findings [47]. A larger sample of trees across varying distances will be required to determine the geographic distances required for isolation-by-distances and population differentiation.

Our results with P. albicaulis reveal that locus transferability is a viable option for developing microsatellites in five-needle pines, with the caveat that null alleles may show high frequencies in some interspecific comparisons. Other efforts in cross-species amplification in Pinus, however, have had mixed results, with some cases appearing successful [50, 83, 84], and others showing far lower success rates than novel marker development [67, 85]. It may be the case that the loci we tested for transfer were located at stable sites in the P. albicaulis genome (e.g., near centromeres, regions of low recombination, or in linkage with conserved genes).

These 23 nSSR markers provide a useful low-cost method to survey P. albicaulis genetic diversity, population structure, and parentage. Employing high-level PCR multiplexing [48] would further increase screening efficiency. Previous molecular studies have used isozymes and chloroplast DNA microsatellites to support the division of P. albicaulis into five “seed zones” in the Inland West [34]. Since molecular markers primarily reflect neutral processes (migration; drift), and the extent of differentiation of neutral markers is usually far lower than differentiation in adaptive traits [34], this approach by itself may be of limited value for developing seed zones that ensure seedlings are well-adapted to growing conditions at specific planting sites. Nevertheless, neutral markers like these SSRs offer a relatively rapid screen that can be used to partition genetic variation into genealogical groups, without the major expense of establishing and measuring multiple test plantations over multiple environments (e.g., [86]). These genealogical groups can be used to prioritize populations for blister rust resistance screening in P. albicaulis, and for the exploration of climate change mitigation strategies (e.g., [87, 88]). Since SSRs typically reveal neutral differentiation, these markers can also provide a baseline for identifying candidate SNPs that are associated with adaptive traits, such as cold tolerance [89], aridity traits, and wood property traits [90]. The identification of FST outliers depends upon accurate estimation of background differentiation and ancestry, and microsatellite markers are often the ideal choice for these estimates [91].

It is imperative to develop a predictive model for how much genetic diversity will remain in P. albicaulis populations as they decline in response to white pine blister rust infection and mountain pine beetle outbreaks, particularly with respect to those trees remaining as seed sources after disturbances. The time needed to recover genetic diversity following stand-replacing disturbances will be an essential research question as well, especially as time intervals between fires decrease as a result of climate change. The Greater Yellowstone Ecosystem in particular is projected to experience reduced intervals between wildfires, decreasing from the historical average of 100–500 years to fewer than 30 years by 2099 [5]. Given this trend, we will need to predict whether P. albicaulis will be able to recover genetic diversity rapidly enough to avoid bottlenecking.

Given the imperative conservation goals for P. albicaulis, SSR markers offer a complement to SNPs as a multi-allelic marker that can be used to measure the diversity—and changes in diversity—of rare genetic variation. Studies assessing genetic diversity based on these neutral SSR loci, and a more comprehensive sample of potentially-adaptive diversity (e.g., [45, 46]), can help guide future conservation efforts and restoration plans for P. albicaulis.

Supporting information

S1 Fig. Difference in number of alleles and heterozygosity between two Pinus albicaulis populations.

A) difference in mean number of alleles (-0.74) and 95% confidence interval of the difference (-2.29 to 0.81) and B) difference in mean expected heterozygosity (-0.015) and 95% confidence interval of the difference (-0.178 to 0.148) of 23 microsatellite loci for two populations of Pinus albicaulis Engelm. (Pinaceae): Henderson Mountain, Custer Gallatin National Forest, MT, and Mount Washburn, Yellowstone National Park, WY. Plotted points are the difference in means, Washburn minus Henderson, and the lines are the 95% confidence intervals around those points.


S2 Fig. Mantel's test frequency distributions in two Pinus albicaulis populations.

Frequency distribution of random Rxy versus the observed Rxy (red line) for 9999 permutations, from Mantel’s test on two Pinus albicaulis Engelm. (Pinaceae) populations (A. Henderson Mountain, Custer Gallatin National Forest, MT, and B. Mount Washburn, Yellowstone National Park, WY). Rxy is the correlation between geographic and genetic distance between individuals. A positive value indicates individuals are more closely related genetically to those nearby geographically than random, and a negative value indicating individuals are less closely related to those nearby.


S3 Fig. Correlation between geographic distance and genetic distance in two Pinus albicaulis populations.

Correlation between geographic distance and genetic distance, from Mantel’s test on two Pinus albicaulis Engelm. (Pinaceae) populations. A) Henderson Mountain, Custer Gallatin National Forest, MT showing no correlation between genetic and geographic distances (rx,y = -0.120, p = 0.121) and B) Mount Washburn, Yellowstone National Park, WY showing a positive correlation (rx,y = 0.162, p = 0.051).


S1 Table. Descriptive information for screened primers of microsatellites developed for other species of Pinus.

Locus name, source species of primer development, and allele size range for 49 primers screened for transferability to Pinus albicaulis Engelm. (Pinaceae). Bold entries successfully transferred.


S2 Table. Diameter at breast height of two sampled Pinus albicaulis populations.

Minimum, maximum, and mean diameter at breast height (DBH) in centimeters compared between Pinus albicaulis Engelm. (Pinaceae) sampled at two populations (Henderson Mountain, Custer Gallatin National Forest, MT, and Mount Washburn, Yellowstone National Park, WY).


S3 Table. Distances between sampled Pinus albicaulis within two populations in the Greater Yellowstone Ecosystem.

Minimum, maximum, and mean distance between Pinus albicaulis Engelm. (Pinaceae) sampled at two populations (Henderson Mountain, Custer Gallatin National Forest, MT, and Mount Washburn, Yellowstone National Park, WY). Rxy and P(rxy-rand > = rxy-data) from Mantel’s correlation, showing isolation by distance and spatial autocorrelation, testing for each population and for all individuals combined.



We thank Custer Gallatin National Forest and Yellowstone National Park for permission to sample whitebark pine (Yellowstone National Park research permit number YELL-2016-SCI-6064). We also thank Andrew Boddicker, Bhargavi Ramanathan, Aaron Wagner, Elizabeth Pansing, Andres Andrade, and Amy Halbach for assisting with sample collection; Dr. Mitchell McGlaughlin from the University of Northern Colorado for assisting with the fragment analysis preparation protocols; and, Scott Bingham at the Arizona State University DNA Lab for the fragment analysis results. Camille Scelfo-Dalbey, Dylan Bartholomew, Kylie Nomi, and Evan Tracy conducted the early screens of the novel loci. We are grateful to Doug Turnbull and the University of Oregon Sequencing Facility for assistance with high-throughput DNA sequencing, and to the University of Colorado Denver Office of Research Services (DFT and Dr. A.C. Mosier), Wyoming Native Plant Society (MVL), and the Pacific Northwest Region of the U.S.D.A. Forest Service for financial support for this research (RC).


  1. 1. Millar CI, Stephenson NL. Temperate forest health in an era of emerging megadisturbance. Science. 2015;349(6250): 823–826. pmid:26293954
  2. 2. Raffa KF, Aukema BH, Bentz BJ, Carroll AL, Hicke JA, Turner MG, et al. Cross-scale drivers of natural disturbances prone to anthropogenic amplification: the dynamics of bark beetle eruptions. Bioscience. 2008;58(6): 501–517.
  3. 3. Van Mantgem PJ, Stephenson NL, Byrne JC, Daniels LD, Franklin JF, Fulé PZ, et al. Widespread increase of tree mortality rates in the western United States. Science. 2009;323(5913): 521–524. pmid:19164752
  4. 4. Lovett GM, Weiss M, Liebhold AM, Holmes TP, Leung B, Lambert KF, et al. Nonnative forest insects and pathogens in the United States: impacts and policy options. Ecol Appl. 2016;26(5): 1437–1455. pmid:27755760
  5. 5. Westerling AL, Turner MG, Smithwick EAH, Romme WH, Ryan MG. Continued warming could transform Greater Yellowstone fire regimes by mid-21st century. PNAS. 2011;108(32): 13165–13170. pmid:21788495
  6. 6. Moritz MA, Parisien MA, Batllori E, Krawchuk MA, Van Dorn J, Ganz DJ, et al. Climate change and disruptions to global fire activity. Ecosphere. 2012;3(6): 49.
  7. 7. Bowman DMJS Murphy BP, Neyland DLJ Williamson GJ, Prior LD. Abrupt fire regime change may cause landscape-wide loss of mature obligate seeder forests. Global Change Biol. 2014;20: 1008–1015. pmid:24132866
  8. 8. Enright NJ, Fontaine JB, Bowman DMJS, Bradstock RA, Williams RJ. Interval squeeze: altered fire regimes and demographic responses interact to threaten woody species persistence as climate changes. Front Ecol Environ. 2015;13: 265–272.
  9. 9. Mosseler A, Major JE, Rajora OP. Old-growth red spruce forests as reservoirs of genetic diversity and reproductive fitness. Theor Appl Genet. 2003;106(5): 931–937. pmid:12647069
  10. 10. Rajora OP, Pluhar SA. Genetic diversity impacts of forest fires, forest harvesting, and alternative reforestation practices in black spruce (Picea mariana). Theor Appl Genet. 2003;106(7): 1203–1212. pmid:12748771
  11. 11. Tomback DF, Arno SF, Keane RE. The compelling case for management intervention. In: Tomback DF, Arno SF, Keane RE, editors. Whitebark pine communities: ecology and restoration. Washington, DC: Island Press; 2001. pp. 3–25.
  12. 12. Ellison AM, Bank MS, Clinton BD, Colburn EA, Elliott K, Ford CR, et al. Loss of foundation species: consequences for the structure and dynamics of forested ecosystems. Front Ecol Environ. 2005;3(9): 479–486.
  13. 13. U.S. Fish and Wildlife Service. Endangered and threatened wildlife and plants; 12-month finding on a petition to list Pinus albicaulis as Endangered or Threatened with critical habitat. Federal Register. 2011;76: 42631–42654.
  14. 14. Arno SF, Hoff RJ. Pinus albicaulis Engelm. whitebark pine. In: Burns RM, Honkala BH, editors. Silvics of North America. Washington, DC: United States Department of Agriculture; 1990. pp. 268–279.
  15. 15. Arno SF. Community types and natural disturbance processes. In: Tomback DF, Arno SF, Keane RE, editors. Whitebark pine communities: ecology and restoration. Washington, DC: Island Press; 2001. pp. 74–88.
  16. 16. Tomback DF. Dispersal of whitebark pine seeds by Clark’s nutcracker: a mutualism hypothesis. J Anim Ecol. 1982;51: 451–467.
  17. 17. Hutchins HE, Lanner RM. The central role of Clark's nutcracker in the dispersal and establishment of whitebark pine. Oecologia. 1982;55: 192–201. pmid:28311233
  18. 18. Tomback DF, Linhart YB. The evolution of bird-dispersed pines. Evol Ecol. 1990;4: 185–219.
  19. 19. Tomback DF, Schoettle AS, Chevalier KE, Jones CA. Life on the edge for limber pine: seed dispersal within a peripheral population. Ecoscience. 2005;12: 519–529.
  20. 20. Tomback DF, Hoffmann LA, Sund SK. Coevolution of whitebark pine and nutcrackers: implications for forest regeneration. In: Schmidt WC, McDonald KJ, editors. Proceedings–symposium on whitebark pine ecosystems: ecology and management of a high mountain resource, Bozeman, MT, 1989. USDA INTGTR-270. Ogden, Utah: US Department of Agriculture, Forest Service, Intermountain Research Station; 1990 pp. 118–129.
  21. 21. Tomback DF, Sund SK, Hoffmann LA. Post-fire regeneration of Pinus albicaulis: height-age relationships, age structure, and microsite characteristics. Can J For Res. 1993;23: 113–119.
  22. 22. Tomback DF, Anderies AJ, Carsey KS, Powell ML, Mellmann-Brown S. Delayed seed germination in whitebark pine and regeneration patterns following the Yellowstone fires. Ecology. 2001;82(9): 2587–2600.
  23. 23. Resler LM, Tomback DF. Blister rust prevalence in krummholz whitebark pine: implications for treeline dynamics, Northern Rocky Mountains, Montana, U.S.A. AAAR. 2008;40(1): 161–170.
  24. 24. Tomback DF, Chipman KG, Resler LM, Smith-McKenna EK, Smith CM. Relative abundance and functional role of whitebark pine at treeline in the Northern Rocky Mountains. AAAR. 2014;46(2): 407–418.
  25. 25. Tomback DF, Resler LM, Keane RE, Pansing ER, Andrade AJ, Wagner AC. Community structure, biodiversity, and ecosystem services in treeline whitebark pine communities: potential impacts from a non-native pathogen. Forests. 2016;7(1): 21.
  26. 26. Tomback DF, Kendall KC. Biodiversity losses: The downward spiral. In: Tomback DF, Arno SF, Keane RE, editors. Whitebark pine communities: ecology and restoration. Washington, DC: Island Press; 2001. pp. 243–262.
  27. 27. Tomback DF, Achuff P. Blister rust and western forest biodiversity: ecology, values and outlook for white pines. For Pathol. 2010;40: 186–225.
  28. 28. Schwandt JW, Lockman IB, Kliejunas JT, Muir JA. Current health issues and management strategies for white pines in the western United States and Canada. For Pathol. 2010;40: 226–250.
  29. 29. McDonald GI, Hoff RJ. Blister rust: an introduced plague. In: Tomback DF, Arno SF, Keane RE, editors. Whitebark pine communities: ecology and restoration. Washington, DC: Island Press; 2001. pp. 193–220.
  30. 30. Krakowski J, Aitken SN, El-Kassaby YA. Inbreeding and conservation genetics is whitebark pine. Conserv Genet. 2003;4: 581–593.
  31. 31. COSEWIC. COSEWIC assessment and status report on the Whitebark Pine Pinus albicaulis in Canada. Committee on the Status of Endangered Wildlife in Canada. 2010. Available from: Cited 10 February 2018.
  32. 32. Keane RE, Tomback DF, Aubry CA, Bower AD, Campbell EM, Cripps CL, et al. A range-wide restoration strategy for whitebark pine (Pinus albicaulis). Gen. Tech. Rep. RMRS-GTR-279. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station; 2012.
  33. 33. Leirfallom SB, Keane RE, Tomback DF, Dubrowski S. The effects of seed source health on whitebark pine (Pinus albicaulis) regeneration density after wildfire. Can J For Res. 2015;45: 1597–1606.
  34. 34. Mahalovich MF, Hipkins VD. Molecular genetic variation in whitebark pine (Pinus albicaulis) in the inland west. In: Keane RE, Tomback DF, Murray MP, Smith CM, editors. The future of high-elevation, five-needle white pines in Western North America: Proceedings of the High Five Symposium, Missoula, MT, 2010. Proceedings RMRS-P-63. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station; 2011. pp. 118–132.
  35. 35. Collevatti RG, Grattapaglia D, Hay JD. Population genetic structure of the endangered tropical tree species Caryocar brasiliense, based on variability at microsatellite loci. Mol Ecol. 2001;10(2): 349–356. pmid:11298950
  36. 36. Schwabe AL, Neale JR, McGlaughlin ME. Examining the genetic integrity of a rare endemic Colorado cactus (Sclerocactus glaucus) in the face of hybridization threats from a close and widespread congener (Sclerocactus parviflorus). Conserv Genet. 2015;16(2): 443–457.
  37. 37. Nurtjahjaningsih IL, Saito Y, Lian CL, Tsuda Y, Ide Y. Development and characteristics of microsatellite markers in Pinus merkusii. Mol Ecol Resour. 2005;5(3): 552–553.
  38. 38. Dou JJ, Zhou RC, Tang AJ, Ge XJ, Wu W. Development and characterization of nine microsatellites for an endangered tree, Pinus wangii (Pinaceae). APPS. 2013;1(2):1200134. pmid:25202511
  39. 39. Thao DV, Widyatmoko AY, Guan L, Gotoh E, Watanabe A, Shiraishi S. Isolation and characterization of tetranucleotide microsatellite markers for Pinus merkusii. Conserv Genet Res. 2013;5(2): 433–436.
  40. 40. Moraga-Suazo P, Orellana L, Quiroga P, Balocchi C, Sanfuentes E, Whetten RW, et al. Development of a genetic linkage map for Pinus radiata and detection of pitch canker disease resistance associated QTLs. Trends Ecol Evol. 2014;28(6): 1823–1835.
  41. 41. Phong DT, Lieu TT, Hien VT, Hiep NT. Genetic diversity of the endemic flat-needle pine Pinus krempfii (Pinaceae) from Vietnam revealed by SSR markers. Genet Mol Res. 2015;14(3): 7727–7739. pmid:26214454
  42. 42. Jorgensen SM, Hamrick JL. Biogeography and population genetics of whitebark pine, Pinus albicaulis. Can J For Res. 1997;27(10): 1574–1585.
  43. 43. Bruederle LP, Tomback DF, Kelly KK, Hardwick RC. Population genetic structure in a bird-dispersed pine, Pinus albicaulis (Pinaceae). Can J Bot. 1998;76(1): 83–90.
  44. 44. Richardson BA, Brunsfeld SJ, Klopfenstein NB. DNA from bird-dispersed seed and wind-disseminated pollen provides insights into postglacial colonization and population genetic structure of whitebark pine (Pinus albicaulis). Mol Ecol. 2002;11(2): 215–227. pmid:11856423
  45. 45. Liu JJ, Sniezko R, Murray M, Wang N, Chen H, Zamany A, et al. Genetic diversity and population structure of whitebark pine (Pinus albicaulis Engelm.) in western North America. PloS One. 2016;11(12): e0167986. pmid:27992468
  46. 46. Syring J, Tennessen JA, Jennings TN, Wegrzyn J, Scelfo-Dalbey C, Cronn R. Targeted capture sequencing in whitebark pine reveals range-wide demographic and adaptive patterns despite challenges of a large, repetitive genome. Front Plant Sci. 2016;7. pmid:27148310
  47. 47. Lind BM, Friedline CJ, Wegrzyn JL, Maloney PE, Vogler DR, Neale DB, et al. Water availability drives signatures of local adaptation in whitebark pine (Pinus albicaulis Engelm.) across fine spatial scales of the Lake Tahoe Basin, USA. Mol Ecol. 2017;26: 3168–3185. pmid:28316116
  48. 48. Guichoux E, Lagache L, Wagner S, Chaumeil P, Léger P, Lepais O, et al. Current trends in microsatellite genotyping. Mol Ecol Res. 2011;11(4): 591–611. pmid:21565126
  49. 49. Morse AM, Peterson DG, Islam-Faridi MN, Smith KE, Magbanua Z, Garcia SA, et al. Evolution of genome size and complexity in Pinus. PLoS ONE. 2009;4:e4332. pmid:19194510
  50. 50. González-Martínez SC, Robledo-Arnuncio JJ, Collada C, Díaz A, Williams CG, Alía R, et al. Cross-amplification and sequence variation of microsatellite loci in Eurasian hard pines. Theor Appl Genet. 2004;109: 103–11. pmid:14985972
  51. 51. Barbara T, Palma-Silva CL, Paggi GM, Bered F, Fay MF, Lexer C. Cross‐species transfer of nuclear microsatellite markers: potential and limitations. Mol Ecol. 2007;16(18): 3759–3767. pmid:17850543
  52. 52. Oreshkova NV, Sedel’nikova TS, Pimenov AV, Efremov SP. Analysis of genetic structure and differentiation of the bog and dry land populations of Pinus sibirica Du Tour based on nuclear microsatellite loci. Russ J Genet. 2014;50(9): 934–941.
  53. 53. Villalobos-Arámbula AR, Pérez de la Rosa JA, Arias A, Rajora OP. Cross-species transferability of eastern white pine (Pinus strobus) nuclear microsatellite markers to five Mexican white pines. Genet Mol Res. 2014;13(3): 7571–7576. pmid:25222257
  54. 54. Gernandt DS, López GG, García SO, Liston A. Phylogeny and classification of Pinus. Taxon. 2005;54(1): 29–42.
  55. 55. Syring J, Willyard A, Cronn R, Liston A. Evolutionary relationships among Pinus (Pinaceae) subsections inferred from multiple low-copy nuclear loci. Am J Bot. 2005;92(12): 2086–2100. pmid:21646125
  56. 56. Little EL, Critchfield WB. Subdivisions of the genus Pinus (pines). MP-1144. Washington, DC: US Department of Agriculture, Forest Service; 1969.
  57. 57. Price RA, Liston A, Strauss SH. Phylogeny and systematics of Pinus. In: Richardson DM, editor. Ecology and Biogeography of Pinus. Cambridge, MA: Cambridge University Press; 1998. pp. 49–68.
  58. 58. Willyard A, Syring J, Gernandt DS, Liston A, Cronn R. Fossil calibration of molecular divergence infers a moderate mutation rate and recent radiations for Pinus. Mol Biol Evol. 2006;24(1): 90–101. pmid:16997907
  59. 59. Hao ZZ, Liu YY, Nazaire M, Wei XX, Wang XQ. Molecular phylogenetics and evolutionary history of sect. Quinquefoliae (Pinus): Implications for Northern Hemisphere biogeography. Mol Phylogenet Evol. 2015;87: 65–79. pmid:25800283
  60. 60. Jennings TN, Knaus BJ, Kolpak S, Cronn R. Microsatellite primers for the Pacific Northwest endemic conifer Chamaecyparis lawsoniana (Cupressaceae). Am J Bot. 2011;98(11): e323–e325. pmid:22012929
  61. 61. Cronn R, Liston A, Parks M, Gernandt DS, Shen R, Mockler T. Multiplex sequencing of plant chloroplast genomes using Solexa sequencing-by-synthesis technology. Nucleic Acids Res. 2008;36(19): e122. pmid:18753151
  62. 62. Miller MP, Knaus BJ, Mullins TD, Haig SM. SSR_pipeline: A Bioinformatic Infrastructure for identifying microsatellites from paired-end Illumina high-throughput DNA sequencing data. J Hered. 2013;104(6): 881–885. pmid:24052535
  63. 63. You FM, Huo N, Gu YQ, Luo M-C, Ma Y, Hane D, et al. BatchPrimer3: a high throughput web application for PCR and sequencing primer design. BMC Bioinformatics. 2008;9: 253. pmid:18510760
  64. 64. Salzer K, Sebastiani F, Gugerli F, Buonamici A, Vendramin GG. Isolation and characterization of polymorphic nuclear microsatellite loci in Pinus cembra L. Mol Ecol Resour. 2008;9(3): 858–861. pmid:21564769
  65. 65. Lendvay B, Höhn M, Brodbeck S, Mîndrescu M, Gugerli F. Genetic structure in Pinus cembra from the Carpathian Mountains inferred from nuclear and chloroplast microsatellites confirms post-glacial range contraction and identifies introduced individuals. Tree Genet Genomes. 2014;10: 1419–1433.
  66. 66. Iwasaki T, Sase T, Takeda S, Ohsawa TA, Ozaki K, Tani N, et al. Extensive selfing in an endangered population of Pinus parviflora var. parviflora (Pinaceae) in the Boso Hills, Japan. Tree Genet Genomes. 2013;9: 693–705.
  67. 67. Yu JH, Chen CM, Tang ZH, Yuan SS, Wang CJ, Zu YG. Isolation and characterization of 13 novel polymorphic microsatellite markers for Pinus koraiensis (Pinaceae). Am J Bot. 2012; e421–e424. pmid:23028004
  68. 68. Echt CS, May-Marquardt P, Hseih M, Zahorchak R. Characterization of microsatellite markers in eastern white pine. Genome. 1996;39(6): 1102–1108. pmid:8983182
  69. 69. Chang S, Puryear J, Cairney J. A simple and efficient method for isolating RNA from pine trees. Plant Mol Biol Rep. 1993;11(2): 113–116.
  70. 70. Boutin-Ganache I, Raposo M, Raymond M, Deschepper CF. M13-tailed primers improve the readability and usability of microsatellite analyses performed with two different allele-sizing methods. Biotechniques. 2001;31(1): 24–26. pmid:11464515
  71. 71. Peakall R, Smouse PE. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes. 2006;6: 288–295.
  72. 72. Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics. 2012;28: 2537–2539. pmid:22820204
  73. 73. Kalinowski ST, Taper ML. Maximum likelihood estimation of the frequency of null alleles at microsatellite loci. Conserv Genet. 2006;7(6): 991–995.
  74. 74. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2016. URL Cited 16 October 2017.
  75. 75. Mantel N. The detection of disease clustering and a generalized regression approach. Cancer Res. 1967;27: 209–220. pmid:6018555
  76. 76. Karhu A, Dietrich J-H, Savolainen O. Rapid expansion of microsatellite sequences in pines. Mol Biol Evol. 2000;17: 259–265. pmid:10677848
  77. 77. Elsik CG, Williams CG. Low-copy microsatellite recovery from a conifer genome. Theor Appl Genet. 2001;103(8): 1189–1195.
  78. 78. Elsik CG, Williams CG. Families of clustered microsatellites in a conifer genome. Mol Gen Genet. 2001;265(3): 535–542.
  79. 79. Payseur BA, Jing P, Haasl RJ. A genomic portrait of human microsatellite variation. Mol Biol Evol. 2010;28(1): 303–12. pmid:20675409
  80. 80. Belokon MM, Politov DV, Mudrik EA, Polyakova TA, Shatokhina AV, Belokon YS, et al. Development of microsatellite genetic markers in Siberian stone pine (Pinus sibirica Du Tour) based on the de novo whole genome sequencing. Russ J Genet. 2016;52(12): 1263–1271.
  81. 81. Hale ML, Burg TM, Steeves TE. Sampling for microsatellite-based population genetic studies: 25 to 30 individuals per population is enough to accurately estimate allele frequencies. PloS one. 2012;7(9): e45170. pmid:22984627
  82. 82. DeWoody J, Nason JD, Hipkins VD. Mitigating scoring errors in microsatellite data from wild populations. Mol Ecol Resour. 2006;6(4): 951–957.
  83. 83. Chagné D, Chaumeil P, Ramboer A, Collada C, Guevara A, Cervera MT, et al. Cross-species transferability and mapping of genomic and cDNA SSRs in pines. Theor Appl Genet. 2004;109(6): 1204–1214. pmid:15448894
  84. 84. Liewlaksaneeyanawin C, Ritland CE, El-Kassaby YA, Ritland K. Single-copy, species-transferable microsatellite markers developed from loblolly pine ESTs. Theor Appl Genet. 2004;109: 361–369. pmid:15045175
  85. 85. Echt CS, May-Marquardt P. Survey of microsatellite DNA in pine. Genome 1997;40: 9–17. pmid:9061909
  86. 86. O’Neill GA, Nigh G, Wang T, Ott PK. Growth response functions improved by accounting for nonclimatic site effects. Can J For Res. 2007;37: 2724–2730.
  87. 87. Sniezko RA, Mahalovich MF, Schoettle AW, Vogler DR. Past and current investigations of the genetic resistance to Cronartium ribicola in high-elevation five-needle pines. In: Keane RE, Tomback DF, Murray MP, Smith CM, editors. The future of high-elevation, five-needle white pines in Western North America: Proceedings of the High Five Symposium, Missoula, MT, 2010. Proceedings RMRS-P-63. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station; 2011. pp. 246–264.
  88. 88. Keane RE, Holsinger LM, Mahalovich MF, Tomback DF. Restoring whitebark pine ecosystems in the face of climate change. Gen. Tech. Rep. RMRS-GTR-361. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station; 2017.
  89. 89. Eckert AJ, Bower AD, Wegrzyn JL, Pande B, Jermstad KD, Krutovsky KV, et al. Association genetics of coastal Douglas fir (Pseudotsuga menziesii var. menziesii, Pinaceae). I. Cold-Hardiness related traits. Genetics. 2009;182(4): 1289–1302. pmid:19487566
  90. 90. Eckert AJ, van Heerwaarden J, Wegrzyn JL, Nelson CD, Ross-Ibarra J, González-Martínez SC, et al. Patterns of population structure and environmental associations to aridity across the range of loblolly pine (Pinus taeda L., Pinaceae). Genetics. 2010;185(3): 969–982. pmid:20439779
  91. 91. Rosenberg NA, Pritchard JK, Weber JL, Cann HM, Kidd KK, Zhivotovsky LA, et al. Genetic structure of human populations. Science. 2002;298: 2381–2385. pmid:12493913