We propose to use genomics and landscape genetic analyses to determine population structures for MPB, their fungal associates, and their pine tree hosts in British Columbia in Alberta. Having recently identified patterns of spatial genetic structure at each taxonomic level, we are now determining what genetic interactions exist between the partners of this complex relationship. The information is critical for informing any regionally-specific risk assessment strategy. In addition, resource managers can use enhanced risk assessment tools for better predicting, analyzing and addressing challenges to forest feedstock supplies at risk of pest and pathogen infestation for more accurate, long-term forecasts.
Population genomics can be used to answer critical questions about MPB population structure and beetle dispersal: How related are the MPB populations that are found in various ecoregions of western Canada? Where did a regional MPB outbreak originate? Did it arise through a local expansion or from recent immigration? Was there a single immigration event into that region? These questions, in turn, raise other important concerns about the fungal associates that are vectored by MPB, who are largely responsible for the mortality of MPB-infested pines: What fungal species are associated with MPB in new outbreak populations? What is their distribution? Are there genetic interactions between fungal strains and beetles and/or pine? Answers to these questions may provide critical insights for predicting MPB spread across different regions of western Canada and potentially eastwards into Canada’s vast boreal forests.
THEME 2: Population genomics of adaptation in MPB, fungi and pine hosts along axes of MPB range expansion
2.1 Sampling along axes of MPB range expansion
(1) Conduct sampling along northern, eastern and southwestern axes of MPB range expansion.
2.2 Population genomics – pine
(1) Develop a GBS assay for lodgepole, jack pine and their hybrids to characterize genome wide
variation comprising both coding and non-coding regions.
(2) Improve the characterization of the hybrid zone between lodgepole and jack pine spanning AB and NT and estimate the spatial extent of introgression within the lodgepole – jack pine complex.
(3) Identify and characterize adaptive variation among lodgepole, jack pine and their hybrids, and determine whether they are related to host-genetic MPB spread-risk factors.
(4) Identify novel variation associated with beetle attack phenotypes using GWAS.
2.3 Population genomics – MPB
(1) Develop and apply a GBS assay for mountain pine beetle that will maximize our ability to characterize genome wide variation, including allosomes (sex chromosomes).
(2) Compare and contrast the genetic structure of expansion from historic populations to understand their evolutionary potential in a novel habitat and host.
(3) Characterize the genomic architecture of adaptation of the MPB.
(4) Conduct a genome-wide-association study of dispersal traits to identify genomic regions that contribute to observed dispersal phenotypes.
2.4 Population genomics – fungi
(1) Characterize population genomics of fungal associates across the landscape using a GBS assay.
(2) Identify genomic regions under selection.
(3) Conduct phenotypic and functional characterization of genetic variants to understand adaptive potential across environmental gradients.
2.5 Modeling – integrated landscape genomics
(1) Expand the integrated landscape genetics framework to examine the integrated landscape genomics of the MPB system.
(2) Develop statistical models to relate genomic data among taxa with spatial environmental information.
(3) Develop and apply new and data-driven models to identify loci under selection using a landscape genomics approach.
Three key hypotheses of this proposal are that (a) the genotypes of pine hosts, MPB and their fungal associates can influence interactions between these organisms, in turn modulating outbreak dynamics, (b) genotypic variation associated with outbreak-influencing traits of pine hosts, MPB and their fungal associates can be detected over landscapes, and (c) this information can be used to better assess risk. We propose to address these hypotheses using recently developed population genomics approaches to ascertain population structures and taxa distributions on the landscape. In Tria 2, we used genomic resources (Table 1) for SNP discovery and genotyping using Illumina’s Golden Gate multiplexed bead assay (max 1536 SNPs) and/or Sequenom’s mass spectroscopy assay (max 40 SNPs). These data are being used to examine genetic introgression between lodgepole and jack pine and identify loci in MPB, fungi and pines that exhibit signatures of selection by applying multiple outlier detection that include both Fst based approaches (e.g. BayeSCAN) and environmental correlation methods (e.g. MatSAM and bayenv). These results provide important first indications that these species exhibit adaptive variation over the historic and present range of MPB, and in the case of jack pine, the potential long-term range of MPB under extreme scenarios. However, the cost and format of these technologies have limited our analyses to hundreds of loci, constraining the probability of discovering the loci contributing to adaptation. In addition, pines have very large genomes limiting our genotyping to coding regions for these taxa.Discovery of adaptive variation is facilitated by extensive sampling of individuals across the landscape, genome-wide analyses of both coding and non-coding regions, and new analytical methods. With the maturation and increasing accessibility of next-generation sequencing (NGS), new approaches are emerging to characterize genome wide variation and discover loci conferring adaptive variation. One such promising technique is genotype-by-sequencing (GBS). GBS bypasses the marker assay development stage, enabling characterization of tens of thousands of SNP variants across both coding and non-coding regions of genomic DNA. The higher marker density and greater genome coverage enables genome-wide association studies (GWAS), increasing the likelihood of identifying genomic regions of adaptive significance, including regulatory elements that can be important contributors to individual variation. GBS has been used successfully to interrogate genomic variation in a number of ecologically important species. GBS is particularly suitable for non-model species, which often exhibit high degrees of diversity and large-scale structural variation. For MPB and G. clavigera, GBS is facilitated by the draft and reference quality genome assemblies, respectively. GBS can also be applied to species lacking a genome sequence, such as lodgepole and jack pine. With expansion of the beetle into novel habitats that exhibit comparatively low climatic suitability together with naïve hosts, we will test the hypothesis that selective pressures associated with these novel habitats will lead to detectable signatures of selection in MPB and fungal populations. We will further test the hypothesis that genetic differences can be detected between co-evolved and novel hosts. We propose to use GBS to test these hypotheses. Sequencing for GBS will be carried out at Institut de Biologie Intégrative et des Systèmes (IBIS) at Université Laval, with protocols optimized for each species. We will take advantage of the GBS expertise in Dr. Felix Sperling’s laboratory, who have recently established a GBS pipeline for spruce budworm (Choristoneura fumiferanae) For pines, we will determine genetic variation in novel hosts and co-evolved hosts, identify genetic differences between them, and ascertain how variation changes along environmental gradients. For MPB, we will address invasion dynamics, and assess whether there is evidence of rapid adaptation to a new host and new environment along axes of invasion. For MPB fungal associates, we will characterize phenotypic and genotypic variation across latitudinal and climatic gradients. Combining genotyping of beetle, tree and fungi with collaborative phenotyping (Themes 1 and 3) will position us to pioneer an integrated functional landscape genomics approach that examines landscape genetics, population genomics and functional analyses of the interacting organisms of the MPB system. Using these data and an emerging conceptual framework that treats genetic variation of interacting species as landscape data layers, we will model inter-relationships among these taxa in varying spatial and environmental contexts.