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Article

Host Use Does Not Drive Genetic Structure of the Mountain Pine Beetle, Dendroctonus ponderosae (Coleoptera: Curculionidae: Scolytinae), in Western North America

by
Celia K. Boone
,
Kirsten M. Thompson
,
Philippe Henry
and
Brent W. Murray
*
Natural Resources and Environmental Studies Institute, University of Northern British Columbia, Prince George, BC V2N 4Z9, Canada
*
Author to whom correspondence should be addressed.
Forests 2025, 16(4), 649; https://doi.org/10.3390/f16040649
Submission received: 26 February 2025 / Revised: 21 March 2025 / Accepted: 28 March 2025 / Published: 8 April 2025
(This article belongs to the Section Forest Health)
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Abstract

:
The mountain pine beetle Dendroctonus ponderosae (Curculionidae: Scolytinae) (MPB) is one component of an intensively studied co-evolved insect–host system. We investigated the spatial genetic structure of the MPB within its historic and recent geographic range expansion as it relates to host use in western North America using 13 pre-selected microsatellite loci. Analysis of molecular variation (AMOVA) indicates that genetic structure is not correlated with the host tree species and therefore does not support the hypothesis of the formation of a host race within this species. STRUCTURE analysis delineates four main clusters in western North America: (1) northern: northern British Columbia/northern Alberta; (2) central: southern British Columbia/southern Alberta/Washington/Idaho/Montana; (3) southwestern: Oregon/California/Nevada; and (4) southeastern: Utah/Wyoming/Arizona/Colorado/South Dakota. Heterozygosity, allelic richness, and the number of private alleles are greatest in the Southwest cluster. This cluster correlates with one of the three refugia hypothesized from a recent analysis of neo-Y haplotypes and represents an important reservoir of MPBs’ genetic diversity.

1. Introduction

Insects have evolved to not only occupy virtually every terrestrial and aquatic ecosystem but also link other organisms in a multitude of symbiotic relationships. Interactions between organisms are a major determinant of the distribution and abundance of species [1], and co-evolved ecological relationships between insects and plants are ubiquitous, diverse, and highly consequential. In particular, bark beetles (Coleoptera: Curculionidae: Scolytinae) have a profound effect on conifer forest ecosystems, promoting forest succession and sustaining ecosystem services and functioning [2,3]. Scolytines appear predisposed to selection by their plant hosts and potential host specialization because they meet the criteria proposed by Thompson [4] for host use specialization, i.e., predictable co-occurrences of susceptible host tree species and abundant populations through space and time; complete their development in a single host tree, then mate and reproduce on another host tree; ability to circumvent host defenses; and utilization of host-tree volatiles to locate suitable material for colonization [5,6,7]. For example, species within Dendroctonus are generally restricted to Pinaceae as hosts and tend to specialize at the host tree genus level [8]. It is postulated that most speciation events of extant Dendroctonus could be correlated with the diversification of its primary genus of host tree from their glacial refugia during the Miocene [9]. The mountain pine beetle, D. ponderosae Hopkins (MPB), has remained conservative to the genus level of Pinus [10], with exceptions only occurring during extreme outbreak conditions [11]. Its predominant hosts are lodgepole (P. contorta Dougl.) and ponderosa (P. ponderosa Dougl. ex. Laws.) pine species, but the MPB is able to successfully reproduce within almost all pine species within its range [8,12]. The intimate association between the MPB, its symbiotic fungi, and host tree species is one of the most studied co-evolutionary relationships of bark beetles in North America.
The MPB is a facultative predatory bark beetle native to western North America with an extensive historical geographic and elevational range spanning from southern California (CA) to northern British Columbia (BC) and east to the western edge of the Great Plains in western South Dakota (SD). Cyclical MPB outbreaks have been recorded in BC since the early 1900s [13,14,15] and are part of a natural cycle facilitating the maintenance of biologically and functionally diverse forest ecosystems [16,17,18,19,20]. Its most recent geographic range expansion over the Rocky Mountains into northern Alberta (AB) during the extreme outbreak from 1999 to 2015 [21,22] was facilitated by wind and long-range beetle flights [23,24] and extended its host range into a naïve host, jack pine, Pinus banksiana Lambert [22]. Further climate-driven range expansion into sensitive higher elevation ecosystems in the western United States has negatively impacted naïve hosts such as whitebark (P. albicaulis Engelm.) and, more recently, Rocky Mountain bristlecone (P. aristata Engelm.) pine [25,26]. In western Nebraska, it is suspected that the MPB has been present for some time prior to its first recorded detection in 2011 due to the extent of ponderosa pine in western Nebraska, beetle occurrence in surrounding states, and rare encounters reported by forest entomologists [27]. The MPB is also considered rare or absent in Mexico but has recently been found in dead southwestern white pine (P. strobiformis Engelm.), south of Arizona (AZ) [28]. Concerns about further migration and establishment into vulnerable areas are substantiated by the cosmopolitan diet and potentially destructive nature of the MPB compounded by the environmental transition to increasingly favorable climate conditions. This has bolstered interest in the drivers of the genetic structure of this beetle in North America.
Influence of host species on the genetics of Dendroctonus has been investigated since the early work on the genus. Initial studies used allozymes to compare the genetic signatures of many species of bark beetles in North America. While variation was detected, many of these studies compared beetles sampled from multiple host tree species from different locations, thus conflating the effect of geography and host tree species (summarized in ref. [29]). A study that sampled declining MPB populations on sites containing two host species found that geographic location was likely a stronger influence than host tree [29]. In contrast, another study compared hosts in geographically proximal sites in Colorado and found that the effect of host was equal to that of location [30], concluding that host effects may increase genetic heterogeneity and lead to the development of pre-adapted sub-species, or host races, of MPBs. More recent studies using amplified fragment length polymorphism (AFLP) markers of the MPB from eight sites in the United States and Canada expanded host species comparisons and added a landscape genetic structure component, finding a general pattern of isolation-by-distance with little host effect [21]. However, specimens from host tree species in divergent sites were combined, which may have obscured the influence of the host. Other studies assessed the landscape genetic structure of MPBs using microsatellites [31] and single nucleotide polymorphisms (SNPs) [32,33], finding two clusters of genetic similarity divided into north and south populations. However, the host tree was excluded from these analyses, and only Batista et al. [33] included sites from the United States.
The effect of MPB host use on genetic structure can be tested by comparing geographic variation found within the MPB from its most widespread host, lodgepole pine, to those found among the other host species with more limited geographic ranges. We use an analysis of molecular variation (AMOVA) approach to assess evidence of genetic structure associated with host use. This approach places the regional genetic differences among host sites in the context of the wider geographic variation. It explores the hypothesis that host tree species influence range-wide MPB genetic structure due to preferential host selection versus geography alone driving the observed differences among sites. In the analyses presented here, we expand upon previous research into the genetic structure of the MPB in the United States and Canada using an inclusive dataset of 82 sites and then use a subset of these sites for a detailed comparison of MPB host use.

2. Materials and Methods

2.1. Site Selection and Beetle Collection

Beetles were collected between 2003 and 2012 from 153 active sites in 105 locations in three provinces in western Canada and 11 states in the western United States (US) (Figure S1). The beetles were primarily collected from lodgepole pine but also included beetles infesting six other pine species (Table 1). Sample sites were selected based on current and historical mountain pine beetle outbreak activity. These include a subset of samples from 2005 to 2008 analyzed by Samarasekera et al. [31], insects collected by B. Bentz (USDA Forest Service Region 4, Rocky Mountain Research Station, Logan, UT, USA), and previously unreported samples collected from 2010 to 2012. The 2010–2012 sample collections were conducted in targeted areas and in host tree species that were not included in previous sampling efforts (Table 1). In prior studies [31], western Canada was sampled more intensively than the US. To minimize bias during analysis of genetic structure, 82 sites were selected for a representative geographical distribution throughout western North America. In locations where multiple sites were sampled, the site with the largest sample size was selected for analysis. Beetle specimens were collected prior to emergence from individual galleries in multiple trees at each location or after emergence using traps during dispersal windows. A Global Positioning System (GPS) location was determined for each tree sampled or trapping location, and beetles were sampled, then transported alive in bark discs, then flash-frozen in liquid nitrogen or preserved in 95% ethanol. Samples were stored at or below −20 °C for DNA extraction. Site locations were visualized using ArcGIS version 10.5.

2.2. DNA Extraction and Microsatellite Amplification

Genomic DNA was isolated from whole beetles using a standard phenol/chloroform procedure [34] or an UltraClean Tissue and Cells DNA isolation kit (Mo Bio Laboratories, Carlsbad, CA, USA) following the manufacturer’s protocol. Isolated DNA was resuspended in Tris-EDTA (pH 8.0) or eluted in the manufacturer’s buffer. A preliminary assessment of DNA quantity and quality was performed using a NanoDrop® ND-1000 UV-Vis Spectrophotometer (Thermo Scientific, Waltham, MA, USA).
A total of 3858 beetles were genotyped at 13 microsatellite loci used for previous analysis within western Canada [31], using four co-amplification procedures [35]. Amplified fragments were co-loaded into two injections on an AB 3730 DNA analyzer. Band sizes were determined relative to GeneScan-500 LIZ (Applied Biosystems, Waltham, MA, USA) and scored using GeneMapper 4.0 software.

2.3. Hardy–Weinberg Equilibrium and Linkage Disequilibrium

Genotypic data from each site were examined for Hardy–Weinberg equilibrium (HWE) across loci and sites using an expansion of Fisher’s exact test. To ensure that all loci were independently assorting at all sites, linkage disequilibrium (LD) was assessed using a likelihood ratio test [36]. Statistical significance was evaluated both before and after sequential Bonferroni correction for multiple tests [37,38]. All analyses were performed using ARLEQUIN 3.5.2.2 [39].

2.4. Genetic Variation

Gene diversity and allelic richness were used to describe patterns of genetic variation across the study area. Observed (HO) and expected (HE) heterozygosity were also calculated for each sample location using the PopGenKit package 1.0 for R 2.14.2 [40]. Allelic richness was corrected for variation in sample size through rarefaction [41] executed in FSTAT 2.9.3.2 [42]. Patterns of genetic diversity were assessed for the entire study area as well as within the main clusters identified by Bayesian analysis for population structure as described below.

2.5. Population Structure

Population genetic structure was determined using a Bayesian approach with STRUCTURE 2.3.4 [43], assuming an admixture model and correlated allele frequencies, without prior sampling information. Each run was performed with 100,000 burn-in and 50,000 MCMC steps with all other parameters maintained at default values. Population structure was tested at K ranging from 1 to 10 with 20 replicates. To assess membership proportions for clusters identified by STRUCTURE, the results of the 20 replicates at the best fit K were post-processed using Clumpp 1.1.2 [44] and Structure Harvester 0.6 [45]. To capture a hierarchical level of population structure, each cluster was subsequently analyzed for nested sub-structures and evaluated as previously described. Population structuring was visualized using ArcGIS version 10.5. To gain further insight into the underlying population genetic structure and provide additional support to the results generated using STRUCTURE, data were also explored within the framework of a discriminant analysis of principle components (DAPC) [46].

2.6. Genetic Differentiation

Genetic variance was partitioned among and within clusters, and AMOVA was performed in ARLEQUIN 3.5.2.2 [39] based on pairwise FST corrected for unequal sample size using the method of Weir and Cockerham [47]. Variance components were calculated among groups (FCT), among locations within groups (FSC), among locations (FST), and within sampling locations (FIS). Each AMOVA was run with 10,000 permutations at 0.05 significance levels.
To test portioning of variation associated with host use, subsets of data were created by pairing non-lodgepole pine sites with the closest lodgepole pine site(s) within a similar geographic range (<200 km), thus ameliorating the effects of geography on the AMOVA groups, resulting in 14 non-lodgepole sites paired with 11 lodgepole sites. Non-lodgepole sites in AZ and SD were not used due to the absence of sampling in lodgepole sites within 200 km (no sites within 400 km).

3. Results

Based on our samples from 82 sites, all 13 microsatellite loci typed displayed a high amount of polymorphism with between 10 and 36 alleles/loci reported (Table 1). Reflecting the large number of alleles, average HO was also high, ranging from 0.288 to 0.806. Similar to Samarasekara et al. [31], no evidence for linkage disequilibrium among loci was found. Further, no sites displayed a large number of loci out of the Hardy–Weinberg equilibrium (HWE), with each locus displaying a significant departure from HWE (p < 0.05) between 0 and 9 times out of the 82 sites tested. Significant positive FIS values (p < 0.05) were noted for 16 populations with an overall FIS value of 0.019 (p < 0.001).
Locus-specific FST values were all significant and ranged from 0.040 to 0.219 (Table 2). This evidence of range-wide genetic structure is also noted in the overall FST value of 0.0683 (p < 0.001) among all 82 sampling locations. Pairwise population FST values range from nonsignificant to a maximum of 0.431 between sites in AZ and northern AB (Figure 1). Specimens collected from sugar pine (Pinus lambertiana Dougl.) were also noted for their pairwise divergence (FST 0.404–0.168) from all other sites. Beetles from a successful brood noted in interior hybrid spruce (Picea glauca × engelmannii) were not significantly different from adjacent lodgepole pine sites in northern BC.
To test the effects of host use on the observed genetic structure, subsets of the data were divided into two groups: lodgepole and non-lodgepole hosts (Table 3), covering a similar geographic range (Figure 2). In the first test, 14 non-lodgepole sites were paired with 11 lodgepole sites. The overall FST and FSC were similar to each other and showed values consistent with the geographic range of sites examined. In contrast, FCT values, the percent variation due to host use, are not significantly different from zero. A second test paired proximal lodgepole pine sites with non-lodgepole host sites specifically grouped for (1) ponderosa pine (five pairs), (2) white bark pine (four pairs), and (3) all others (two western white pine, two limber pine, and one sugar pine). All showed similar results.
All 82 sites were used to examine range-wide population structure. STRUCTURE analysis found two well-supported clusters at K = 2, dividing the samples into northern and southern groups (Figure 3). Among the southern cluster, additional sub-clustering was found at K = 3 and 4, supporting four clusters: (1) northern: northern BC/AB; (2) central: southern BC/AB/WA/ID/MT; (3) southwestern: OR/CA/NV; and (4) southeastern: UT/WY/AZ/CO/SD.
Patterns of genetic diversity varied among the four genetic clusters. Of the 82 sites typed, higher allelic richness and heterozygosity values were detected in sites grouped into the southwest cluster (Figure 4). Both heterozygosity and allelic richness decreased in locations further north and to the southeast. The maximum HO detected was 0.664 at Tahoe NF: Mount Rose Ski Resort, NV, and the minimum was 0.424 at Terrace, BC. The maximum allelic richness detected was 5.97 at Tahoe NF: Mount Rose Ski Resort, NV, and the minimum was 3.45 at Terrace, BC. The sites within the southwest cluster also showed the greatest number of private alleles (Table 1). The least amount of genetic variability was found in northern cluster populations compared to the remaining dataset (Figure 4).

4. Discussion

4.1. Host Use

Bark beetles exhibit behavioral plasticity in choosing host species, which allows adjustment for environmental variability, and such behaviors are likely modulated by genetic, environmental, and genetic-by-environmental drivers [48]. This study analyzes microsatellite data from a single MPB specimen per gallery from multiple host tree species and examines differences over the geographic range of MPBs. We found no evidence of genetic differentiation by host species and demonstrate the importance of geography to explain the genetic structure of MPBs, as indicated in previous studies using several different marker systems (e.g., refs. [21,31,33]). The emergence of host races requires host fidelity of beetle offspring [29], and a review by Barron [49] suggests growing evidence of Hopkins’ host-selection principle, initially proposed in 1916, which refers to the observation that many adult insects demonstrate a preference for the host species on which they themselves developed as larvae. During the most recent outbreak in BC, MPBs successfully infested non-traditional hosts, most notably Engelmann spruce. In a study comparing MPB mating and host acceptance behavior in hybrid spruce (Picea glauca (L.) H. Karst. × P. englemannii Parry ex Engelm.) and its traditional host, lodgepole pine, females reared in spruce more readily accepted spruce host material relative to pine and had higher rates of host acceptance of both pine and spruce host material than females that had developed in pine [50]. However, the opportunistic nature of MPB host use is shown by their utilization of spruce during extreme outbreak levels [50], in which the MPB was determined to be genetically identical to the surrounding lodgepole infections, and their expansion into boreal jack pine forests [22] during this recent outbreak. A preference for the host on which an insect developed could be the result of inheritance of a feeding or oviposition preference for the natal host. The basis of the heritable preference could be either genetic or an inherited environmental effect, for example, cues transmitted from the parental diet. While monoterpene composition and concentration vary with host, large variation within individual tree defensive capability is also found [51,52]. Monoterpene content of food sources of female MPBs has been shown to influence the feeding choice of offspring [53], and host selection behavior by MPBs differs between endemic and irruptive beetle populations [54], where irruptive populations select larger and better defended trees. Breeding lines derived from endemic and irruptive populations of Ips pini Say (Curculionidae: Scolytinae) exhibited differential responses to host phytochemical cues and have a heritable component, specifically in host acceptance and gallery development [55]. Furthermore, chemical similarities between historical and novel hosts can facilitate host range expansion [56,57].
Alternatively, the behavioral bias toward the rearing host could result strictly from experience within the insect’s lifetime. These mechanisms could act independently or synergistically; indeed, it is often difficult to disentangle genetic preferences from conditioned preferences. Natural successful infestation of alternate host species, including unrelated species, by irruptive MPBs may have aided in speciation of Dendroctonus [11,12]. This evidence for Hopkins’ host-selection principle in MPBs may be countered by host switching due to depleting ephemeral resources or availability of vulnerable hosts leading to no detectable effect of host use on the range-wide genetic structure. Cognato et al. [58] suggest that herbivores that utilize such hosts for only one or a few generations are less likely to become genetically isolated because any novel selection pressure imparted by a nonhost would be diminished through the use of other hosts in subsequent generations, thereby minimizing population structure caused by host selection. A possible exception to this trend was observed in beetles collected from a single site with sugar pine (P. lambertiana Douglas) in CA, which was noted as an outlier in pairwise FST comparisons (Figure 1). While the range of pairwise values falls within that observed among other sites, it becomes notable when compared with other sites in CA. Curiously, the sugar pine has a similar geographic range to Jeffrey pine (P. jeffreyi Balfour), which is infested by the Jeffrey pine beetle, Dendroctonus jeffreyi Hopkins, a host-specialist sister-species to the generalist MPB. Analysis of mitochondrial DNA variation, however, confirmed that the beetle mtDNA haplotypes collected from sugar pine were shared with or within the lineages found in MPBs [59]. However, because this result is limited to a single site, additional sites would be required to adequately investigate the possible effects of host use in this species.

4.2. Landscape-Level Genetic Structure

Knowledge of the genetic structure of populations is important for the understanding of their ecology and evolution [60]. The importance of geography to explain the genetic structure of the MPB is indicated in previous studies using several different marker systems (e.g., refs. [21,31,33]). Our expanded dataset presents strong evidence for grouping of MPB populations into two main clusters, southern and northern, consistent with previous studies [31,32,33,61]. The northern cluster is particularly genetically homogenous, showing comparative decreases in heterozygosity and allelic richness. This is consistent with the expectations of the founder effect during range expansion [62] and where mating practices are more random and traits are not exposed to the same level of environmental selection due to the number of new mutations and lineages within the expanded population [63]. While the MPB is not truly panmictic, even as an airborne insect, it is likely that environmental homogeneity of northern BC has facilitated more interbreeding than the southern populations that are separated by heterogeneous habitat and landscape barriers, such as mountains.
Our analyses also show sub-clustering in the southern cluster similar to that reported from mitochondrial [61] and SNP datasets [33,64]. Differences among the studies may be partially explained by the resolution of the different marker systems used and the distribution of sampling locations. Batista et al. [33] have a high number of sampling locations in Canada with relatively few in the US (n = 10), while Dowle et al. [63] have the majority of sampling locations in the US and few in Canada (n = 2). Our study attempted to balance the geographic distance among locations but still lacks important regions in the US sampled by Dowle et al. [64]. A general picture emerges of a central cluster extending from southern BC into the US states of ID and MT. Locations from the Cascade Mountains in WA more strongly cluster with this central cluster in our analysis, while SNP-based studies place MPBs collected in similar locations into a southwest cluster, which extends southward into OR and CA. Evidence for additional southeast clusters also exists. Our study shows a southeast cluster breaking from the central cluster at the Snake River Plain and including locations from UT/WY/CO/AZ/SD. Analysis of autosomal SNPs in this region, which includes a greater number of sampling locations in AZ and central NV, reveals the presence of an additional cluster(s)—locations from AZ, NV, and SD separate from those found in ID and CO [64]. Despite genetic evidence of gene flow among the clusters, mating studies have shown reproductive barriers among locations from the southwestern and central/southeastern clusters, which indicate possible incipient speciation [65,66]. However, MPBs are capable of extensive wind-borne transport rates above the forest canopy [23], and in the absence of reproductive barriers, should facilitate gene flow that would constrain evolution by preventing local genetic differentiation, whereas reduced dispersal is expected to lead to spatial genetic subdivisions [60,67].

4.3. Indications of Glacial Refugia

Observed heterozygosity (HO) and allelic richness are used as a proxy for expressing the genetic resilience and durability of populations [68,69]. High allelic richness is especially important as it provides a source for adaptive variation as populations face new pressures [70]. Of the four clusters generated by STRUCTURE in this study, the southwest cluster from OR/CA/NV had the highest values for HO, allelic richness, and private alleles, in agreement with previous research that also found high genetic variability within the same geographic region, with low diversity in the northern range [21,61]. This clinal reduction in variation in more northerly populations compared to southerly populations mirrors the glacial retreat from the last glacial period of the Quaternary [71,72]. It is likely that MPB populations followed the spread of pine northward during glacial retreat [73] but expanded more slowly than wind- and animal-assisted pine species, possibly due to a lack of confirmed presence of refugia in Canada and climate effects that limit population success [14]. Furthermore, invasion ecology dictates that for successful colonization to occur, the number of arriving individuals must arrive above an ‘invasion threshold’ [74], i.e., the minimum number of individuals required for colonization to occur. Increased propagule pressure has been shown to be positively related to colonization success for two bark beetle species with different host selection behaviors [75]; therefore, successful geographic and host range expansion may be constrained by lack of sustained sufficient propagule pressure exhibited during the endemic phase of the outbreak cycle.
It is important to note that, while the MPB is capable of flight, it requires external forces such as wind to assist its movement over large land barriers, such as mountain ranges [23,76]. This partitioning of habitat has led to the concept of refugia-within-refugia for the Pacific Northwest [77]. While the observed pattern of reduced diversity may be viewed as a pathway of post-glacial expansion (i.e., from the southwest glacial refugia to the central cluster, then north and southeast), an analysis of neo-Y haplotype diversity shows a more complicated pattern of post-glacial expansion. Dowle et al. [64] found three geographically separated neo-Y haplotype lineages: southwest (CA/OR/WA), southeast (AZ/NV/SD), and the Rocky Mountains (CO/UT/WY/ID/MT/BC/AB), indicating three core refugia. The Pacific Northwest of the United States has previously been identified as a glacial refugia for many different species (e.g., ref. [23]); however, the northern post-glacial expansion into Canada was primarily from a refugia associated with the rocky mountain neo-Y lineage but likely also carried an autosomal legacy from all three refugia.

4.4. Conclusions and Future Directions

Ecosystems experience considerable anthropogenic pressures, which can lead to regime shifts and changes in species distributions [78,79], and insects such as bark beetles are highly responsive to changes in environmental conditions and climate. Results from this study suggest that host tree species of MPBs have not contributed to the genetic structure of populations to a detectible level, an important finding contrary to the hypothesis of the development of cryptic host races of MPBs. However, our understanding of evolutionary processes that form the patterns of host plant use is not yet complete [80]. Different genetic and ecological conditions may result in different coevolutionary dynamics over broad geographic scales, essentially creating a coevolutionary mosaic across different spatial and temporal scales [81]. The genetic differentiation of MPBs will continue to evolve as cumulative pressures on forests from climate change, land-use change, and shifts in disturbance patterns, such as the frequency and intensity of bark beetle outbreaks, threaten forested landscape structure and function [3] and alter the distribution of hosts. If extreme epidemics become more commonplace, so may establishment into historically unfavorable landscapes and successful attacks on traditionally nonhost trees, which may be a mechanism by which host shifts and subsequent speciation events have occurred in Dendroctonus spp. [11]. Hence, detailed knowledge of host plant preferences and species-level phylogenies may provide a foundation for testing hypotheses related to the evolution of hosts and host preference [82]. The current lack of influence of host tree species provides insight into management strategies for forestry personnel, such as limiting decisions based not on species of attacked trees, but instead on stand structure and composition, ecological and environmental site conditions, and beetle population phase and brood success.
These studies can further be used to inform research on the genetic composition of the historic and current range expansion of the northern population because it also illustrates the complexity of interpreting spatial genetic patterns in terms of post-glacial expansion. Although the Pacific Northwest refugia likely represented a significant reservoir of genetic diversity, the role of this refugium in MPBs’ northern expansion into Canada remains unclear. Future studies that examine populations across the contact zone of the neo-Y haplotypes as identified by Dowle et al. [64] within WA and southern BC for both gene flow and mating success (i.e., ref. [66]) will improve understanding the genetic contribution of the refugia populations to the MPBs of WA, ID, MT, and southern BC. The wider genomic distribution of SNP datasets, including neo-Y coverage, makes these markers more suitable to address this question.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f16040649/s1, Table S1: Cooperators for mountain pine beetle sample collections in western North America, 2011–2012; Figure S1: Map of sample sites for mountain pine beetle collections for population genetic studies, 2003–2012 inclusive.

Author Contributions

B.W.M. had oversight of the entire study and manuscript preparation. Analyses were conducted by C.K.B., K.M.T., B.W.M. and P.H. Manuscript preparation was performed by C.K.B., K.M.T. and B.W.M. All authors have read and agreed to the published version of the manuscript.

Funding

Funding for various aspects of this project was provided by a Genome Canada, Genome BC, Genome Alberta, and NSERC Strategic Network Grant to the TRIA Project.

Data Availability Statement

The original contributions presented in this study are included in the article and Supplementary Materials. Further inquiries can be directed to the corresponding author.

Acknowledgments

The researchers wish to thank the many people who aided with sample collection and provided in-kind support (see Table S1). Thanks to Neil Thompson for his detailed GIS assistance. Special thanks to Sophie Dang for her excellent microsatellite typing work. We are grateful for the field and laboratory assistance of Rose Loerke, Shane Doddridge, Mike Prior, Rhiannon Montgomery, Cierra Hoerchl, Will Eisbrenner, Amanda Cookhouse and Marcelo Mora.

Conflicts of Interest

The authors declare no conflict of interest.

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Forests 16 00649 g001
Figure 1. Pairwise FST matrix of all sites sampled.
Figure 1. Pairwise FST matrix of all sites sampled.
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Forests 16 00649 g002
Figure 2. Map of study area with collection sites separated by host tree species. Closely located site markers were displaced from their geographic location on the map to allow for better visibility.
Figure 2. Map of study area with collection sites separated by host tree species. Closely located site markers were displaced from their geographic location on the map to allow for better visibility.
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Forests 16 00649 g003
Figure 3. STRUCTURE analysis of 82 sites in western North America. K = 2 is most strongly supported with weaker sub-clustering, K = 4, in the southern sampling locations. Distribution ranges for Pinus contorta and P. ponderosa are also shown. Note, to maximize contrast, different color coding in the cluster graphs was used for K = 4.
Figure 3. STRUCTURE analysis of 82 sites in western North America. K = 2 is most strongly supported with weaker sub-clustering, K = 4, in the southern sampling locations. Distribution ranges for Pinus contorta and P. ponderosa are also shown. Note, to maximize contrast, different color coding in the cluster graphs was used for K = 4.
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Forests 16 00649 g004
Figure 4. (A) Allelic richness (AR), (B) observed heterozygosity (HO), and (C) private alleles (priv) grouped by STRUCTURE-generated cluster: (1) northern: northern: BC/Alberta; (2) central:southern: BC/Alberta/Washington/Idaho/Montana; (3) southwestern: Oregon/California/Nevada and; (4) southeastern: Utah/Wyoming/Arizona/Colorado/South Dakota. The boxplots incorporate Tukey’s 5-number summary as follows: the sample minimum, defined by the horizontal line at the bottom of each plot; the lower quartile, defined by the lower limit of the box in each figure; the sample median, represented by the heavy line inside each box; the upper quartile, defining the upper limit of each box; and the sample maximum, defining the horizontal line at the top of each plot. Open circles represent potential outliers falling outside the interquartile range.
Figure 4. (A) Allelic richness (AR), (B) observed heterozygosity (HO), and (C) private alleles (priv) grouped by STRUCTURE-generated cluster: (1) northern: northern: BC/Alberta; (2) central:southern: BC/Alberta/Washington/Idaho/Montana; (3) southwestern: Oregon/California/Nevada and; (4) southeastern: Utah/Wyoming/Arizona/Colorado/South Dakota. The boxplots incorporate Tukey’s 5-number summary as follows: the sample minimum, defined by the horizontal line at the bottom of each plot; the lower quartile, defined by the lower limit of the box in each figure; the sample median, represented by the heavy line inside each box; the upper quartile, defining the upper limit of each box; and the sample maximum, defining the horizontal line at the top of each plot. Open circles represent potential outliers falling outside the interquartile range.
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Table 1. Sampling locations (82) by country and province/state for the mountain pine beetle. Variables shown include GPS coordinates, elevation, beetle host species, flight year sampled, and number of beetles genotyped (N) (bracketed number indicates number of sites per location if multiple collections were conducted). Mean observed heterozygosity (HO), mean expected heterozygosity (HE), mean number of alleles (NA), allelic richness (AR), mean number of private alleles, and inbreeding coefficient (FIS); statistical significance: * p < 0.05, ** p < 0.005.
Table 1. Sampling locations (82) by country and province/state for the mountain pine beetle. Variables shown include GPS coordinates, elevation, beetle host species, flight year sampled, and number of beetles genotyped (N) (bracketed number indicates number of sites per location if multiple collections were conducted). Mean observed heterozygosity (HO), mean expected heterozygosity (HE), mean number of alleles (NA), allelic richness (AR), mean number of private alleles, and inbreeding coefficient (FIS); statistical significance: * p < 0.05, ** p < 0.005.
LocationCodeLatitude
(N)		Longitude
(W)		Elevation
(m)		HostFlight Year
Sampled		NHOHENAARPrivate
Alleles		FIS
~Canada~
British Columbia
Fort St. JohnFJo56.57329121.40309848Pinus contorta2011330.4780.4764.463.770−0.016
Tumbler Ridge 1TR 155.53868121.98475874Pinus contorta2008690.4830.4785.463.870.077−0.002
Tumbler Ridge 2TR 255.17899121.112401047Pinus contorta2010450.4930.4994.623.7200.019
Tumbler Ridge 3TR 355.18290120.852551254Pinus contorta2011370.4920.4844.773.840−0.015
Mackenzie 1MA 154.69646122.81945737Pinus contorta2005500.5120.4985.003.900−0.018
Mackenzie 2MA 255.26532124.209671006Pinus contorta2010470.5010.4954.623.7600.001
Smithers 1SM 154.92788127.34918582Pinus contorta2010410.4350.4564.693.6300.059
Smithers 2SM 254.70141126.81831986Pinus contorta2011290.5010.4803.923.470−0.038
Fort St. James 1FJa 154.64493124.41967860Pinus contorta2005440.4670.4784.463.670.0770.035
Fort St. James 2:
John Prince
Research Forest		FJa 254.63600124.41300794Pinus contorta2003290.5010.4733.923.520−0.043
TerraceTE54.45736128.51430124Pinus contorta2010270.4420.4543.923.4500.047
Francois LakeFL54.03160124.93888781Pinus contorta2006530.4630.4564.463.470−0.005
HoustonHO53.99381126.65223829Pinus contorta2006500.4860.4765.003.680−0.012
Prince George 1PG53.90695122.80786607Pinus contorta2005480.4920.5115.924.3300.048 *
Prince George 2PG-Sx53.89944122.80250607Picea glauca × engelmannii2007260.4610.5024.463.8900.100 *
KakwaKA53.71290119.743201735Pinus contorta2007/08590.5420.5306.154.440−0.014
McBrideMB53.31225120.12705934Pinus contorta2005500.5230.5356.154.5500.033
QuesnelQU53.02741122.28135885Pinus contorta2006550.5110.5276.084.3000.040
Mount RobsonMR52.89475118.734811100Pinus contorta2005450.5830.6026.695.110.0770.043 *
ValemountVM52.80384119.26608817Pinus contorta2010133 (3)0.5500.5747.854.8200.044 **
TweedsmuirTW52.53722125.821611570Pinus albicaulis2008470.4660.4854.853.8400.049
Tatla LakeTA51.97145124.41316949Pinus contorta2006490.4870.4855.314.0600.006
Farwell CanyonFC51.78696122.64419970Pinus contorta2006560.5010.5265.774.2700.056 *
Wells GrayWG51.74090120.01217755Pinus contorta2006500.6230.6137.085.130−0.006
Lac La HacheLH51.73423121.60866996Pinus contorta2006480.5540.5616.314.6800.022
Chilcotin:
Yohetta Valley		YV51.25289123.739081417Pinus contorta2012260.5730.5344.854.270−0.061
ChasmCM51.22198121.480031081Pinus ponderosa2008300.5850.6036.315.1200.045
GoldenGO51.07443116.381601338Pinus contorta2008107 (2)0.6180.6159.005.3200.000
KootenayKO50.64360115.978331233Pinus contorta2006440.6430.6366.545.1700.000
FalklandFA50.51995119.601801147Pinus contorta2006520.6230.6137.315.190−0.006
KamloopsKA50.48598120.532251326Pinus contorta2006450.6190.6357.235.2400.036
PembertonPE50.43234122.252561145Pinus contorta2010390.5770.6006.925.2200.038
WhistlerWH50.16656122.92309699Pinus contorta2006430.5720.6145.694.4700.079 *
Purcell Wilderness: ArgentaAR50.16061116.92038547Pinus contorta2006480.5960.6187.545.270.0770.045
PeachlandPL49.79806120.055241280Pinus contorta2010460.6240.6237.005.2600.009
KimberleyKI49.69622115.960061110Pinus contorta2010960.6040.6248.385.160.0770.038 *
Cranbrook 1CR 149.40863115.646191379Pinus contorta2010480.6050.6197.385.1500.031
Cranbrook 2CR 249.43686115.568081299Pinus contorta2011400.5810.6367.545.5100.081 **
SparwoodSP49.67968114.910501373Pinus contorta2008650.6000.6147.925.140.0770.031
Merritt–SummerlandME49.63365120.129161830Pinus contorta2011340.5700.5956.084.9600.034
Crowsnest PassCP49.62933114.695601393Pinus contorta2007/08500.6430.6356.464.840−0.003
Gladstone Area:
Nancy Green		NG49.25917117.927771314Pinus contorta2006470.6600.6357.155.210−0.029
Manning ParkMP49.21708121.06856695Pinus contorta2006460.6040.6317.155.140.0770.056 *
Alberta
Peace AreaPA56.70729118.27617877Pinus contorta2010120 (3)0.4770.4775.543.700.077−0.001
FairviewFV56.12836118.54752633Pinus contorta2007/08480.4740.4835.003.8400.029
ValleyviewVV54.81928116.69167762Pinus contorta2010380.4810.4704.543.690−0.014
Grande Prairie 1GP 154.79800119.78500919Pinus contorta2007/08440.5260.5095.153.980−0.022
Grande Prairie 2GP 254.69847119.684111019Pinus contorta2010136 (3)0.4900.4875.923.770−0.004
Fox CreekFO54.31057116.63118897Pinus contorta2010670.4720.4795.153.7000.017
Lake LouiseLL51.41716116.179341562Pinus contorta2006420.6100.6216.465.0700.031
CanmoreCA51.00175115.070431472Pinus contorta2010540.6040.6116.924.890.0770.011
Saskatchewan
Cypress HillsCH49.58696110.002571316Pinus contorta2011640.6320.6316.544.980.0770.006
~United States~
Washington
Okanogan–Wenatchee NF: Bryan ButteWA-Pa48.10614120.298141971Pinus albicaulis2011240.6090.6145.544.8500.029
Okanogan–Wenatchee NF: Cooper MountainWA-Pc48.00527120.077281688Pinus contorta2011460.5800.6077.085.090.0770.054 *
Okanogan–Wenatchee NF: Gold CreekWA-Pp48.11701120.201741185Pinus ponderosa2011140.6150.5894.694.690−0.008
Okanogan–Wenatchee NF: Cottonwood CampgroundWA-Pm48.01963120.64279968Pinus monticola2011480.6320.6497.625.490.0770.038
Montana
Beaverheaed NF:
Pioneer Mountains		MT-Pc45.53437113.080112104Pinus contorta2011660.5780.6147.155.000.0770.065 **
Greenough: Lubrecht
Experimental Forest		MT-Pp46.89571113.452571279Pinus ponderosa2011440.6150.5966.695.060−0.021
Oregon
Fremont–Winema NF:
Yamsay Mountain		OR-Pc42.94653121.400152003Pinus contorta2011420.6570.6598.545.920.2310.006
Fremont–Winema NF:
Yamsay Mountain		OR-Pm42.94646121.399192042Pinus monticola2011330.6300.6328.005.810.3080.014
Fremont–Winema NF:
Yamsay Mountain		OR-Pa42.93809121.384642192Pinus albicaulis2011390.6500.6617.775.750.2310.027
Idaho
Clearwater NF:
Lolo Motorway		ID-Cl46.36302115.606941593Pinus contorta2011360.6340.6407.005.380.077−0.001
Payette NF:
Burgdorf Road		ID-Pa45.27414115.909741890Pinus contorta2011490.5980.6336.774.980.0770.064 *
Boise NF:
Bull Trout Lake		ID-Bo44.30280115.257282135Pinus contorta2011230.6250.6065.314.740.0770.012
Sawtooth NF:
Railroad Ridge		ID-RR44.14782114.555402862Pinus contorta2004250.6550.6366.155.330−0.011
Sawtooth NF:
Pettit Lake		ID-PL43.97905114.863032136Pinus contorta2003250.6370.6395.695.010.0770.024
Sawtooth NF:
South Cherry		ID-SC43.85253114.639312229Pinus contorta2003360.6100.6187.155.4100.024
Wyoming
Bridger–Teton NFWY-Pc43.13151110.865861878Pinus contorta2011480.5080.5104.463.6300.013
South Dakota
Black Hills NF:
Battle Axe Road		SD-BA43.95092103.631561718Pinus ponderosa2011/12340.5150.5136.694.890.0770.010
Black Hills NF:
Flag Mountain Road		SD-FM44.08683103.877532051Pinus ponderosa2012240.5120.5516.085.060.0770.090 *
California
Lassen NF: FR 29N17CA-Pl40.27562121.308641616Pinus lambertiana2011220.6030.6186.855.940.3080.037
Modoc NF: Lily LakeCA-Pc41.97622120.211722056Pinus contorta2011400.6120.6268.085.6000.031
Modoc NFCA-Pa41.22671120.149072362Pinus albicaulis2011420.6360.6597.625.680.1540.045 *
Nevada
Tahoe NF:
Mount Rose Ski Resort		NV-Pc39.32343119.892662587Pinus contorta2011300.6640.6747.695.970.1540.032
Utah
Ashley NF:
Uinta Mountains		UT-Pc40.87429109.779352598Pinus contorta2011410.5330.5154.543.660−0.028
Ashley NF:
Uinta Mountains		UT-Pp40.88047109.752502535Pinus ponderosa2011470.5490.5394.853.890−0.009
Logan CanyonUT-Pf41.96652111.533812149Pinus flexilis2011470.5010.4924.383.550−0.013
Colorado
Roosevelet NF:
Red Feather Lakes		CO-Pf40.74709105.606492725Pinus flexilis2011450.5460.5185.464.190.077−0.047
Roosevelet NF:
Red Feather Lakes		CO-Pp40.74801105.608122700Pinus ponderosa2011450.5050.5305.234.1000.056 *
Roosevelet NF:
Red Feather Lakes		CO-Pc40.76196105.613792657Pinus contorta2011320.5170.5415.084.2600.060
Colorado: Fraser
Experimental Forest		CO-FEF39.80280105.959903312Pinus contorta2006400.5550.5334.693.890−0.032
Arizona
Coronado NF:
Pinaleno Mountains		M033-0332.66342109.870742751Pinus strobiformis2011290.4240.4724.003.6300.108 **
Superscript in location name indicates the specific site within locations with multiple sites.
Table 2. Loci typed. Total number of alleles (NA), mean expected heterozygosity (HE), mean observed heterozygosity (HO), number of loci deviated from HWE, and fixation indices FIS and FST, and significance values *** = p < 0.0001.
Table 2. Loci typed. Total number of alleles (NA), mean expected heterozygosity (HE), mean observed heterozygosity (HO), number of loci deviated from HWE, and fixation indices FIS and FST, and significance values *** = p < 0.0001.
LocusNAHEHOHWEFixation Indices
FISpFSTp
Dpo028250.4880.46370.051ns0.067***
Dpo103250.8060.8095−0.004ns0.053***
Dpo160360.7170.7237−0.008ns0.077***
Dpo453260.6590.62470.053ns0.039***
Dpo479110.6120.60740.008ns0.110***
Dpo530100.6430.63840.008ns0.061***
Dpo566120.2880.2933−0.016ns0.040***
Dpo760220.5910.59110.000ns0.076***
Dpo780150.5640.56300.002ns0.064***
Dpo793120.5670.5702−0.006ns0.128***
MPB011150.5450.52510.037ns0.048***
MPB017230.4430.43930.009ns0.168***
MPB038180.4000.4039−0.008ns0.219***
Table 3. AMOVA of population structure due to host use. Four tests were performed, including ALL combined non-lodgepole, ponderosa pine, whitebark pine, and other pine test stands. Source of variation includes AG (FCT, among groups), APwG (FSC, among populations within groups), and WP (FST, within populations). Shown for each source of variation are degrees of freedom (d.f.), sums of squares (SS), variance component, percent of variation (% Var), and associated fixation index and p-value. The overall FST value is shown for comparison.
Table 3. AMOVA of population structure due to host use. Four tests were performed, including ALL combined non-lodgepole, ponderosa pine, whitebark pine, and other pine test stands. Source of variation includes AG (FCT, among groups), APwG (FSC, among populations within groups), and WP (FST, within populations). Shown for each source of variation are degrees of freedom (d.f.), sums of squares (SS), variance component, percent of variation (% Var), and associated fixation index and p-value. The overall FST value is shown for comparison.
TestSource of Variationd.f.SSVariance Component% VarFixation Indexp-Value
ALLAG (FCT)128.3680.0056−0.14−0.00140.440
(25)APwG (FSC)23730.9870.34118.360.0835<0.001
WP (FST)20437648.6233.743891.770.0823<0.001
Ponderosa AG (FCT)17.8760.0378−0.96−0.00960.737
(10)APwG (FSC)8173.7490.22055.620.0556<0.001
WP (FSC)8223078.3543.745095.350.0465 <0.001
WhitebarkAG (FCT)13.4930.0970−2.34 −0.02340.972
(8)APwG (FSC)6205.9270.37258.980.0878<0.001
WP (FSC)6522524.7533.872393.360.0664<0.001
Other AG (FCT)111.9510.0525−1.28−0.01280.795
(10)APwG (FSC)8251.0350.35168.550.0845<0.001
WP (FSC)7822979.7563.810492.720.0728<0.001
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Boone, C.K.; Thompson, K.M.; Henry, P.; Murray, B.W. Host Use Does Not Drive Genetic Structure of the Mountain Pine Beetle, Dendroctonus ponderosae (Coleoptera: Curculionidae: Scolytinae), in Western North America. Forests 2025, 16, 649. https://doi.org/10.3390/f16040649

AMA Style

Boone CK, Thompson KM, Henry P, Murray BW. Host Use Does Not Drive Genetic Structure of the Mountain Pine Beetle, Dendroctonus ponderosae (Coleoptera: Curculionidae: Scolytinae), in Western North America. Forests. 2025; 16(4):649. https://doi.org/10.3390/f16040649

Chicago/Turabian Style

Boone, Celia K., Kirsten M. Thompson, Philippe Henry, and Brent W. Murray. 2025. "Host Use Does Not Drive Genetic Structure of the Mountain Pine Beetle, Dendroctonus ponderosae (Coleoptera: Curculionidae: Scolytinae), in Western North America" Forests 16, no. 4: 649. https://doi.org/10.3390/f16040649

APA Style

Boone, C. K., Thompson, K. M., Henry, P., & Murray, B. W. (2025). Host Use Does Not Drive Genetic Structure of the Mountain Pine Beetle, Dendroctonus ponderosae (Coleoptera: Curculionidae: Scolytinae), in Western North America. Forests, 16(4), 649. https://doi.org/10.3390/f16040649

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