The evolution of disease resistance in plants occurs within a framework of interacting
phenotypes, balancing natural selection for life-history traits along a continuum of
fast-growing and poorly defended, or slow-growing and well-defended lifestyles. Plant
populations connected by gene flow are physiologically limited to evolving along a
single axis of the spectrum of the growth-defense trade-off, and strong local selection
can purge phenotypic variance from a population or species, making it difficult to
detect variation linked to the trade-off. Hybridization between two species that have
evolved different growth-defense trade-off optima can reveal trade-offs hidden in either
species by introducing phenotypic and genetic variance. Here, I investigated the
phenotypic and genetic basis for variation of disease resistance in a set of naturally
formed hybrid poplars.
The focal species of this dissertation were the balsam poplar (Populus balsamifera),
black balsam poplar (P. trichocarpa), narrowleaf cottonwood (P. angustifolia), and
eastern cottonwood (P. deltoides). Vegetative cuttings of samples were collected from
natural populations and clonally replicated in a common garden. Ecophysiology and
stomata traits, and the severity of poplar leaf rust disease (Melampsora medusae)
were collected. To overcome the methodological bottleneck of manually phenotyping
stomata density for thousands of cuticle micrographs, I developed a publicly available
tool to automatically identify and count stomata. To identify stomata, a deep con-
volutional neural network was trained on over 4,000 cuticle images of over 700 plant
species. The neural network had an accuracy of 94.2% when applied to new cuticle
images and phenotyped hundreds of micrographs in a matter of minutes.
To understand how disease severity, stomata, and ecophysiology traits changed
as a result of hybridization, statistical models were fit that included the expected
proportion of the genome from either parental species in a hybrid. These models in-
dicated that the ratio of stomata on the upper surface of the leaf to the total number
of stomata was strongly linked to disease, was highly heritable, and wass sensitive
to hybridization. I further investigated the genomic basis of stomata-linked disease
variation by performing an association genetic analysis that explicitly incorporated
admixture. Positive selection in genes involved in guard cell regulation, immune sys-
tem negative regulation, detoxification, lipid biosynthesis, and cell wall homeostasis
were identified.
Together, my dissertation incorporated advances in image-based phenotyping with
evolutionary theory, directed at understanding how disease frequency changes when
hybridization alters the genomes of a population.
| Identifer | oai:union.ndltd.org:uvm.edu/oai:scholarworks.uvm.edu:graddis-2162 |
| Date | 01 January 2019 |
| Creators | Fetter, Karl Christian |
| Publisher | ScholarWorks @ UVM |
| Source Sets | University of Vermont |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Graduate College Dissertations and Theses |
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