Date of Award

2019

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Plant Biology

First Advisor

Stephen R. Keller

Abstract

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.

Language

en

Number of Pages

264 p.

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