Tongli Wang

Associate Professor

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Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Using landscape genomics to delineate seed and breeding zones and project genetic offset for lodgepole pine (2021)

Well-defined seed and breeding zones are critical for developing adaptive forest resource management strategies. These zones are traditionally delineated based on local adaptation of phenotypic traits associated with climate variables, determination of which requires long-term field experiments. In this thesis, I applied a landscape genomics approach to delineate seed and breeding zones for lodgepole pine (Pinus contorta) in British Columbia and Alberta, Canada, based on genomic evidence of local adaptation of this widespread forest tree species across western North America. A gradient forest (GF) model was built by aggregating relationships between spatial variation in 28,954 single-nucleotide polymorphism (SNPs) and 20 climate variables across 281 lodgepole pine populations. The fitted GF model confirmed winter-related climate variables are the major climatic factors associated with genomic patterns of variation among lodgepole pine populations. I used the GF model to delineate the lodgepole pine distribution range in British Columbia and Alberta into six seed and breeding zones. Genomic-based zones delineated by the GF model are comparable to existing common garden-based zones, suggesting that this landscape genomic approach could provide an effective alternative for delineating seed and breeding zones. This approach has the potential to provide a novel and effective alternative over traditional approaches for delineating seed and breeding zone, and offers an innovative means for guiding assisted gene flow in tree species lacking data from provenance trials or common garden experiments. Additionally, using the GF model, I predicted the spatial pattern of genetic offsets associated with seed and breeding zones to identify zones that are susceptible to genotype-environment mismatches under two future climate scenarios for the 2050s.

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