Dobzhansky's famous quote "Nothing in biology makes sense except in the light of

evolution" has become a cliché, but it is hard to find a statement that more

elegantly captures the essence of biological study [-@dobzhansky2013nothing]. In

a more reductionist sense, it is perhaps appropriate to state that nothing in

evolution makes sense except in the light of mutation; that is to say that

mutation in an organism's genetic code is the seed upon which evolution acts

[@lynch2016genetic]. Of course, a seed left alone will not grow; it needs the

right environment and conditions. In turn, a mutation needs a population in

order to thrive. A population can be defined as a group of interbreeding

individuals, coexisting temporally and spatially (though we will see later that

this is not always the best definition) [@milgroom1997contributions]. In a

population, if the conditions are right, a new mutation can grow in frequency

and become an established allele. The study of population genetics seeks to

understand how evolution acts upon these alleles to shape populations as they

grow, shrink, split, and adapt to their environment. Ultimately, these

populations are what give rise to species.

The classical definition of a population is reliant upon the assumption that the

individuals within the populations are panmictic, that is that they are randomly

mating so that the individuals in the next generation will have a random

assortment of alleles inherited from their parents [@rice2002evolution;

@nielsen2013introduction; @hartl1997principles]. The assumption that sex occurs

applies to most major organismal groups [@rice2002evolution;

@heitman2012evolution]. However, sex does not occur in all organisms as many

plants, fungi, protists, bacteria, animals, and viruses are known to exclusively

reproduce clonally [@rice2002evolution; @heitman2012evolution;

@arnaud2012implications; @orive1993effective; @arnaud2007standardizing]. Clonal

reproduction here means reproduction that results in offspring that are

genetically identical to the parents (note that this can include organisms

derived from the selfing of a haploid organism). Thus, the definition of a

population is perhaps best rephrased to simply indicate a group of individuals

of the same species^[This depends on your definition of a species, which is

outside of the scope of this dissertation and therefore best debated elsewhere.]

coexisting spatially and temporally [@milgroom1997contributions].

There are several costs and benefits associated with both clonal and sexual

reproductive strategies from an evolutionary perspective. For organisms whose

main mode of reproduction is sexual, there is the two-fold cost of outcrossing:

1) only half of the available genetic material is inherited by the offspring and

2) the loss of advantageous gene combinations due to recombination

[@rice2002evolution; @heitman2012evolution; @nieuwenhuis2016frequency]. These

costs are balanced by the benefits that come with recombining genetic material,

which allows these organisms to purge deleterious mutations. As clonally

reproducing organisms do not undergo regular periods of recombination, they

explicitly do not benefit from the previously mentioned advantage of

recombination [@heitman2012evolution; @nieuwenhuis2016frequency]. This leads to

a theory known as Muller's ratchet, which indicates that the overall fitness of

small populations of clonally reproducing organisms will decrease over time as

the number of deleterious mutations accumulate, eventually leading to extinction

[@felsenstein1974evolutionary; @loewe2006quantifying; @lynch1993mutational;

@lynch1990mutation]. Despite this, clonal organisms still have the advantage of

passing on 100% of their genetic material to the next generation.

It's easy to see the advantage of both of these strategies in the context of

genetic adaptation to the environment. Clonal populations that are well adapted

to their environment get to keep the combination of genes that allowed them to

be so well adapted [@heitman2012evolution]. This strategy provides an advantage

when resources are scarce and competition is high, but does not lend itself well

to adaptation against changing environments due to low genotypic diversity

[@milgroom1996recombination]. Conversely, while sexual populations lose the

competitive advantage of conserved beneficial allelic combinations, they make up

for it with high genotypic diversity, increasing the chances of surviving change

in the environment. As we shall see in the following sections, many microbial

pathogens have the ability to reproduce both sexually and asexually

[@tibayrenc1995population; @anderson1995clonality; @milgroom1996recombination].

The question of sexual reproduction and population structure in microbial

pathogens is important for the development of rational management strategies for

pathogens or beneficial microbes [@tibayrenc1990clonal;

@tibayrenc1995population; @milgroom1997contributions; @taylor1999evolutionary].

If one were developing an antimicrobial agent on a known set of strains, the

effectiveness of this agent will be diminished if these strains represent only a

fraction of the genotypic diversity due to sexual recombination

[@taylor1999evolutionary; @tibayrenc1990clonal; @smith1993how]. Since most

inference of population structure is based on neutral models that assume sexual

reproduction, it is important to identify whether or not the population in

question is reproducing sexually or clonally before one attempts to assess

population structure and infer evolutionary processes [@tibayrenc1990clonal].

Neutral molecular markers are commonly used for population genetic analyses.

Before molecular techniques became widely available for characterization of

pathogens or microbes, the variation within most pathogenic microorganisms was

described using phenotypic traits that reflected their pathogenicity (ability to

cause disease), virulence (severity of disease), or morphotype placing them into

different phenotypes [@milgroom1997contributions; @levin1999population]. When

considering markers for population genetic analysis, one of the most important

criteria is that these markers are heritable.

A wide variety of heritable molecular markers exist for population genetic

analysis [@tibayrenc1995population; @halkett2005tackling]. It is not necessary

to expound here on the variety of molecular markers available to us, but it is

useful to take note of the common features. Generally, genetic markers come in

two major flavors, selectively neutral and selected markers

[@milgroom1997contributions; @mcdonald1997population; @tibayrenc1995population].

Selectively neutral markers are used as independent variables that can reflect

the evolutionary history of descent. For selectively neutral markers, it is

important that they be independent and unlinked within the genome

[@mcdonald1997population].

Because neutral genetic markers vary independently within a population, these

are more appropriate than selected markers to detect sexual reproduction. The

theory behind this is simple; sexual reproduction breaks up associations between

markers due to recombination and independent, random assortment. This contrasts

with clonal reproduction, which transfers the entire genome in tact to the next

generation, preserving associations [@orive1993effective; @heitman2012evolution;

@nieuwenhuis2016frequency; @mcdonald1997population; @milgroom1997contributions;

@tibayrenc1995population]. This means that not only will there be significant

associations between alleles in a clonal population, but because of their

inability to purge mutations, we also expect to see excess heterozygosity

[@tibayrenc1990clonal; @tibayrenc1996towards].

By using these basic principles, core sets of statistical analyses have been

proposed to detect sexual reproduction by analyzing diversity within loci (for

diploid organisms), linkage among loci, and diversity of multi-locus genotypes

within the population [@tibayrenc1996towards; @arnaud2007standardizing;

@parks1993study; @smith1993how; @mcdonald1997population; @grunwald2003analysis].

None of these tests alone can give a definitive answer as to the nature of

reproduction in a given population [@tibayrenc1995population; @de2004clonal;

@nieuwenhuis2016frequency]. Researchers still need to use biological evidence,

such as the observation of sexual reproductive structures, to support the

presence of sex.

One of the over-arching themes in both ecology and evolution is that diversity

is important for stable population and community structure

[@magurran1988diversity; @mcdonald2002pathogen]. Agricultural systems, however,

behave very differently to natural ecosystems [@milgroom1997contributions;

@stukenbrock2008origins]. Within agricultural systems, the plants and the

landscape are homogenized, providing a steady source of food for people, but

this homogeneity combined with intensive management practices also provides

fertile ground for the evolution of plant pathogens [@stukenbrock2008origins].

McDonald identified five major factors influencing the evolution of plant

pathogens in agricultural systems: 1) mutation, 2) genetic drift, 3)

gene/genotype flow, 4) mating system, and 5) selection due to host resistance

[-@mcdonald2002pathogen].

An example of how changing one of these five factors influences plant health is

clear in the oomycete *Phytophthora infestans* (Mont.) de Bary. The causal agent

of potato late blight (and a poster child for plant pathology). *P. infestans*

has changed the very demographics of human populations across the Atlantic ocean

due to its role in the Irish potato famine [@erwin1996phytophthora]. It has a

heterothallic (no selfing) life cycle that includes both clonally and sexually

derived infectious propagules (Fig.\@ref(fig:pinflife)). The clonally derived

propagules are swimming, short-lived zoospores that can be transmitted via wind

or water, whereas the sexually derived propagules are double-walled oospores.

While *P. infestans* can survive as hyphae in infected tubers, the sexually

derived oospores can survive for years in the soil, highlighting the added risk

presented by sexual reproduction.

```{r pinflife, echo = FALSE, fig.cap = figcap2, fig.scap = figscap2}

From the time of the Great Famine to 1980, there was only one single mating

type, A1, observed in Europe, indicating that it was reproducing in a strictly

clonal fashion [@goodwin1994panglobal]. Sexual reproduction became possible when

the second mating type, A2, was introduced to Europe [@galindo1960nature].

Because of this risk, population genetic tools and methods were needed to assess

whether or not sexual reproduction had occurred. While evidence for sexual

reproduction was found [@sujkowski1994increased], it additionally appeared that

*P. infestans* was still reproducing and spreading clonally

[@goodwin1994panglobal]. @cooke2012genome analyzed isolates from 1983 to 2008

and found a rapid shift in clonal population structure, showing a dramatic

increase in the prevalence of a particularly aggressive lineage. While sexual

reproduction was not found to be prevalent, it was only via population genetic

techniques that this question could be addressed.

Computational methods are increasingly important for answering questions

crucial to plant pathology, and as more and more of our analyses are becoming

computational in nature, we need to ensure that these methods are

reproducible. Computational methods are crucial for population genetic analyses

in the 21st century [@excoffier2006computer]. There has been a call for

standards in the analysis of clonal organisms for the past twenty years, which

resulted in a plethora of computer programs [@tibayrenc1996towards;

@arnaud2007standardizing]. It was not uncommon for a researcher to use several

programs to answer a single research question, and each program would often

require its own custom input format and would not necessarily run on all

operating systems [@excoffier2006computer; @kamvar2014poppr]. The constant

reformatting of data, and the inability to automate series of analyses made it

not only challenging to communicate the methods, but also increased the chance

for error [@excoffier2006computer; @adamack2014popgenreport; @kamvar2014poppr;

@goecks2010galaxy].

Biologists often think about reproducibility in terms of wet lab or bench work.

Care is taken to ensure that all methods and data are faithfully recorded in a

lab notebook, but this is not necessarily extended to computational analyses.

The concept of reproducible research in scientific computing is attempting to

take the principles of reproducibility we use in the wet lab and extend them to

our downstream analyses *in silico*. This work attempts to introduce tools for

reproducible research in clonal population genetics in an open manner. When

researchers have open tools that can be easily shared, they can spend more time

asking questions and less time formatting data.

```{r, introduction1, echo = FALSE, fig.cap = figcap1, fig.scap = figscap1, out.width = "50%"}

The work presented here offers tools designed to answer questions related to

clonal population genetics and plant pathology (Fig. \@ref(fig:introduction1)).

In this context, the term 'tool' refers to software code used to apply

mathematical models or theory to population genetic data. The merit of this work

lies within the context of reproducible science, which ensures that the

computational environment used to create results can be replicated

[@buckheit1995wavelab]. The end goal was not simply the development of the

software, but rather, the development of software for the goal of analyzing our

own data. Serendipitously, by analyzing our own data with this software, we are

able to not only demonstrate that it can be used, but also demonstrate that an

entire analysis can be conducted in an open and reproducible manner. We

demonstrate the usefulness and flexibility of these tools by using them to show

evidence for multiple introductions in the Curry County, OR outbreak of the

sudden oak death pathogen, *Phytophthora ramorum*. We additionally assess the

power of the multilocus linkage disequilibrium measure $\bar{r}_d$ to detect

clonal reproduction.

To address the lack of tools for reproducible research in clonal population

genetics, we present the software package *poppr* in the R computing language

[@R2016; @kamvar2014poppr]. Previously, tools necessary for the analysis of

clonal populations were available in several stand-alone software programs, each

requiring different data input formats. Moreover, each program had different

levels of documentation and limited support for all computing platforms. The

novelty of *poppr* was to introduce indices of multilocus genotypic diversity,

the index of association, and a fast implementation of Bruvo's genetic distance,

and clone-correction over unlimited levels of user-specified population

hierarchies [@arnaud2007standardizing; @Agapow_2001; @bruvo2004simple]. Because

this was implemented in R these analyses can be performed in a reproducible

manner on all computing platforms.

The initial implementation of *poppr* contained basic tools for analysis of

clonal populations [@kamvar2014poppr], but lacked tools for custom definitions

of multilocus genotypes and performed poorly with genomic-scale data. Chapter 3

introduces an updated and improved *poppr* version 2.0. With high throughput

sequencing (HTS) data, the amount of missing data and genotyping error

increases, and the definition of a multilocus genotype becomes unclear.

Moreover, the calculation of the index of association scales poorly with an

increase in the number of loci. To address these limitations, we improved

*poppr* with new functionalities to define multilocus genotypes based on genetic

distance and calculate the index of association over random samples or windows

of SNP loci.

A newly-emerged disease of oak---called Sudden Oak Death---spread from

California to the Southwest corner of Oregon in 2001. Because of intense

management strategies, the epidemic was largely contained to Curry county for

the next 15 years. In 2011, an isolated patch of disease appeared in Cape

Sebastian, 12 miles from the nearest infected site. With microsatellite

genotyping performed across 2 labs and 15 years, we sought to describe the

spread of the epidemic in a population genetic context and ask the question of

whether or not there was evidence for more than one introduction event. This

work provided evidence supporting at least two introductions to Curry county

forests. All the analyses were performed in an open-source and reproducible

manner using R.

The index of association is a measure of multilocus linkage disequilibrium, that

is, a correlation coefficient across multiple loci. In sexual populations, loci

are randomly assorting due to recombination, resulting in a near-zero value of

the index of association. In clonal populations, recombination is non-existent,

meaning that loci are passed from parent to offspring in a non-independent

fashion, resulting in a significantly non-zero value of the index of

association. De Meeûs & Balloux [-@de2004clonal] demonstrated that this index

shows high variance with low levels of sexual reproduction, but due to

limitations in software, they were not able to perform power analyses. We used

*poppr* to investigate the power of the index of association to detect sexual

reproduction in simulated data sets generated with microsatellite and genomic

markers. This chapter provides novel insights on the power, sensitivity, and

scope of the index of association.