```{r, results = "asis", echo = FALSE}

Zhian N. Kamvar, Meredeth M. Larsen, Alan M. Kanaskie, Everett M.

Hansen, and Niklaus J. Grünwald

```{r, results = "asis", echo = FALSE}

Journal: **Phytopathology**

3340 Pilot Knob Rd, St Paul, MN 55121, USA

Published 2015-07, Volume 105, Issue: **7**,

DOI: [10.1094/PHYTO-12-14-0350-FI](http://dx.doi.org/10.1094/PHYTO-12-14-0350-FI)

```{r, results = "asis", echo = FALSE}

Sudden oak death caused by the oomycete *Phytophthora ramorum* was first

discovered in California toward the end of the 20th century and subsequently

emerged on tanoak forests in Oregon before its first detection in 2001 by aerial

surveys. The Oregon Department of Forestry has since monitored the epidemic and

sampled symptomatic tanoak trees from 2001 to the present. Populations sampled

over this period were genotyped using microsatellites and studied to infer the

population genetic history. To date, only the NA1 clonal lineage is established

in this region, although three lineages exist on the North American west coast.

The original introduction into the Joe Hall area eventually spread to several

regions: mostly north but also east and southwest. A new introduction into

Hunter Creek appears to correspond to a second introduction not clustering with

the early introduction. Our data are best explained by both introductions

originating from nursery populations in California or Oregon and resulting from

two distinct introduction events. Continued vigilance and eradication of nursery

populations of *P. ramorum* are important to avoid further emergence and

potential introduction of other clonal lineages.

Sudden oak death (SOD) emerged as a severe epidemic disease on coast live oak

(*Quercus agrifolia*) and tanoak (*Notholithocarpus densiflorus*) in California

in the mid 1990s and reemerged shortly thereafter on tanoak in Oregon in the

early 2000s [@hansen2008epidemiology; @rizzo2005phytophthora;

@grunwald2008phytophthora;

@everhart2014phytophthora]. SOD is caused by *Phytophthora ramorum* Werres, De

Cock & Man in't Veld, and is considered to be one of the top two oomycete

pathogens based on its scientific and economic importance [@kamoun2014top;

@werres2001phytophthora]. The Oregon epidemic was first detected during aerial

surveys in 2001 on tanoak but likely derived from initial introductions in the

late 1990s. The Oregon Department of Forestry has since monitored the epidemic

and sampled symptomatic tanoaks since 2001 [@hansen2008epidemiology]. Strains

sampled from infected sites in forest or nursery environments have been

genotyped in several labs using a range of microsatellite loci

[@grunwald2009standardizing; @ivors2006microsatellite; @prospero2004isolation;

@prospero2007population; @prospero2009migration].

*P. ramorum* has emerged repeatedly around the world as 4 distinct clonal

lineages found in North America (lineages NA1, NA2, and EU1) and Europe (EU1 and

EU2) [@grunwald2012emergence; @ivors2006microsatellite;

@vanpoucke2012discovery]. The lineages have been named by the continent on which

they first appeared, i.e. North America (= NA) or Europe (= EU) and are numbered

in order of discovery [@grunwald2009standardizing]. The NA1 clonal lineage was

first discovered in California causing SOD on tanoak and coast live oak and is

the one currently found in Curry County, Oregon, USA

[@mascheretti2008reconstruction]. The EU1 and NA2 populations were discovered

later in nursery environments and are currently only found in California,

Oregon, Washington and British Columbia while the NA1 clone has been shipped

with nursery plants from the West to the Southern and Southwestern US

[@goss2011phytophthora; @grunwald2012emergence; @goss2009population;

@ivors2006microsatellite; @prospero2009migration;

@mascheretti2008reconstruction]. The EU1 clonal lineage is the one first

discovered in Europe, but in 2007 the new EU2 lineage emerged in Northern

Ireland and since migrated to Western Scotland [@werres2001phytophthora;

@vanpoucke2012discovery]. EU1 was first introduced to Europe and eventually

migrated to the Pacific Northwest of North America [@goss2011phytophthora].

*P. ramorum* populations sampled in Oregon forests to date belong exclusively to

the NA1 clonal lineage [@hansen2008epidemiology; @prospero2007population]. Given

that NA2 and/or EU1 clones have been found in California, Oregon, Washington,

and/or British Columbia in association with nursery plant movements,

introduction of NA2 or EU1 from nursery environments to Curry County forests is

a plausible scenario [@goss2011phytophthora; @goss2009population;

@grunwald2012emergence; @prospero2009migration;

@prospero2007population]. Our present work thus monitors populations and

potential emergence of novel lineages in Oregon forests.

Our main objectives here are to describe the spatial and temporal pattern of the

populations and clonal dynamic of the SOD pathogen in Curry County in

southwestern Oregon from 2001 to the present. Specifically, we asked (1) if

novel lineages have been introduced into the forests in Curry County, (2) if

multiple introductions occurred, and (3) whether introduction might have come

from nursery populations. We sampled infected tanoaks between 2001-2014 and

characterized populations using microsatellite analysis.

The SOD infested areas are located in the Siskiyou Mountains of Curry County in

south western Oregon near the town of Brookings (42.0575$^{\circ}$ N,

124.2864$^{\circ}$ W) on the coast (Figure \@ref(fig:ramorum1))

[@prospero2007population]. The Siskiyou mountains form part of the Klamath

Mountain range (Franklin and Dyrness 1988). The vegetation in SOD infested areas

is a mosaic of different vegetation types including mixed-evergreen, redwood

(*Sequoia sempervirens*) and Douglas-fir (*Pseudotsuga menziesii*) forests with

tanoak as the dominant SOD host.

```{r, ramorum1, echo = FALSE, fig.cap = figcap1, fig.scap = figscap1}

```{r, results = "asis", echo = FALSE}

Commencing in 2001, 2-4 aerial surveys per year were conducted over the tanoak

range by the Oregon Department of Forestry and the USDA Forest Service in Curry

County. The survey detects recently killed tanoaks based on the reddish-brown

color of foliage [@hansen2008epidemiology]. All trees identified by aerial

surveys were ground checked and geographically referenced using a hand-held GPS

instrument (Garmin GPS 12XL or 60CX, Garmin International, Olathe, KS). Bark or

foliage samples were collected for determination of *P. ramorum* presence by

culturing in the field and laboratory. Host plants within the area of the

delimitation survey, generally 300 feet, were also inspected and sampled if they

were symptomatic. Maps of distribution were prepared using ArcView GIS version

3.3 and ArcMap version 10.2 (Environmental Systems Research Institute, Redlands,

CA).

```{r, results = "asis", echo = FALSE}

Isolations were made from symptomatic plant tissue onto selective CARP agar

(Difco corn meal agar, 10 ppm natamycin, 200pm NA-ampicillin, and 10 ppm

rifampicin) [@prospero2007population]. Candidate *Phytophthora* cultures were

transferred onto corn meal agar with 30 ppm $\beta$-sitosterol. *P. ramorum*

identification was confirmed by microscopic inspection for presence of

characteristic chlamydospores and deciduous sporangia [@werres2001phytophthora].

Genomic DNA was extracted using either the FastDNA SPIN kit (MP Biomedicals,

LLC; 116540600) [@goss2009population] or the cetyltrimethyl ammonium bromide

(CTAB )-chloroform-isopropanol method [@winton2001molecular]. Table

\@ref(tab:ramorum1) provides an overview of strains collected by year and region

following regions as shown in figure \@ref(fig:ramorum1).

Five microsatellite loci were utilized in this analysis: PrMS6, Pr9C3, PrMS39,

PrMS45, and PrMS43 [@grunwald2009standardizing; @prospero2004isolation;

@prospero2007population; @grunwald2008susceptibility]. Genotyping (see specific

protocols in supplementary text) of *P. ramorum* strains collected 2001-2012

occurred over several years and in several laboratories, with different

protocols and sequencers. Consequently, a concerted effort was made to create a

comprehensive dataset with identical allele calls. To detect errors, allele

calls from all five genotyped loci were generated for a subsample of 40 isolates

representing the most common multilocus genotypes from the culture collection,

and then compared to data from participating laboratories. Three of the five

loci, PrMS6, Pr9C3, and PrMS39 had identical allele calls between laboratories

for the subsampled isolates. The remaining two loci, PrMS45 and PrMS43, had

allele calls that differed by a single bp between laboratories. Data from PrMS45

and PrMS43 were therefore corrected to allow consistent comparisons of allele

calls. Given the varied nature of the genotyping data described above genotyping

of *P. ramorum* strains consists of two datasets including either 5 loci

(2001-14) or a newly developed, multiplexed method including 14 loci (samples

2013-14) (Table \@ref(tab:ramorum2)). Details on both genotyping methods can be

found in the supplementary text \@ref(text:S1) and figure \@ref(fig:ramS1).

```{r, results = 'asis', echo = FALSE}

To determine if forest populations cluster with different nursery populations

from Oregon or California, we used previously published data from our work to

determine relationships among nursery and Curry County forest populations

[@goss2011phytophthora; @goss2009population; @grunwald2009standardizing;

@prospero2009migration; @prospero2007population].

All individuals genotyped for this effort belonged to the NA1 clonal lineage

[@grunwald2009standardizing]. Thus, all analyses presented here focused on

describing the clonal dynamic using model-free approaches that avoid violation

of population genetic theory. Samples were grouped into different multilocus

genotypes (MLGs) defined by the unique combination of alleles across all

observed loci from the consensus five SSR loci genotyped across all years. For

identification purposes, unique MLGs were then assigned an arbitrary number from

1 to the total number of observed MLGs. Population genetic analysis was

conducted using the computer and statistical language R [@R2014] using various

packages as well as R functions written specifically for this project (see

github link below). Graphs and figures were created using the R packages

*ggplot2, ape, igraph, ggmap,* and *poppr* [@wickham2009ggplot2;

@csardi2006igraph; @paradis2004ape; @khale2013ggmap; @kamvar2014poppr].

Within-locus allelic diversity was analyzed across and within years and regions

using the function `locus_table()` from the R package *poppr* (Table \@ref(tab:ramtabS1))

[@kamvar2014poppr]. To address the temporal and spatial aspects of the data,

populations were analyzed both by year isolated and watershed region (Table

\@ref(tab:ramorum1); Fig. \@ref(fig:ramorum1)). Watershed regions were drawn

with ArcMap version 10.2 (Environmental Systems Research Institute, Redlands,

CA). The regions represent drainages or portions of drainages in which infected

trees were discovered as the disease progressed over time. In most cases,

ridgelines dividing drainages formed the boundary of a region. These regions

were saved as shapefiles and imported into R with *rgdal* [@bivand2014rgdal].

Genotypic diversity was analyzed within and across years and populations, with

the Shannon-Wiener index (*H*) and the Stoddard and Taylor's index (*G*),

[@shannon2001mathematical; @stoddart1988genotypic]. Both *G* and *H* measure

genotypic diversity, combining richness and evenness. If all genotypes are

equally abundant, then the value of *G* will be the number of MLGs and the value

of *H* will be the natural log of the number of MLGs. Both *G* and *H* are used

as they weigh more or less abundant MLGs more heavily, respectively

[@grunwald2003analysis]. Evenness was calculated as *E~5~*, which is an

estimator of evenness that utilizes both *H* and *G* that gives a ratio of the

number of abundant genotypes to rare genotypes [@grunwald2003analysis;

@pielou1975ecological; @ludwig1988statistical]. These were calculated with the R

packages *poppr* and *vegan* [@oksanen2013vegan; @kamvar2014poppr]. Confidence

intervals were calculated using the R package *boot* with 9,999 bootstrap

resamplings [@canty2015boot]. Richness, or the expected number of MLGs (*eMLG*),

was calculated using rarefaction from the R packages *poppr* and *vegan*

[@hurlbert1971nonconcept; @heck1975explicit]. Some statistics (AMOVA, genotypic

diversity, index of association, allelic diversity, and Nei's distance) were

also performed on clone-censored data where each MLG was represented once per

population hierarchy.

Because the analysis of genotypic diversity, richness and evenness is agnostic

to specific alleles within MLGs, assessment of genetic relatedness between MLGs

was performed using the function `bruvo.dist()` using *poppr*, which calculates

Bruvo's genetic distance, utilizing a stepwise mutation model for microsatellite

loci [@bruvo2004simple; @kamvar2014poppr]. This distance thus gives a more

fine-scale picture of relationships between individuals than band-sharing

models. These relationships were visualized with minimum spanning networks

generated using the R packages *igraph* and *poppr* [@csardi2006igraph;

@kamvar2014poppr].

If the epidemic had a single origin, a correlation between genetic and

geographic distance would be expected as populations acquire mutations over time

and clonally diverge regardless of rates of spread.

This was tested by performing Mantel tests

across all hierarchical levels in the data set utilizing the function

`mantel.randtest()` in the R package *ade4* between Bruvo's distance as

described above and Euclidean distances between geographic coordinates

[@mantel1967detection; @dray2007ade4]. *P*-values were calculated using 99,999

bootstrap replicates.

As the eradication efforts destroy the immediate habitat in an infected area,

one question that we wanted to address was whether or not genotypes were

clustering to specific regions or if they were evenly spread throughout Curry

County [@prospero2007population]. This was tested using three methods: bootstrap

analysis of Nei's genetic distance, Analysis of MOlecular VAriance (AMOVA), and

Discriminant Analysis of Principal Components (DAPC) in the R packages *poppr*,

*ade4*, and *adegenet* [@jombart2010discriminant; @excoffier1992analysis;

@kamvar2014poppr]. The bootstrap analysis utilized 10,000 bootstrap replicates

treating loci as independent units with the function `aboot()` in *poppr* and

was visualized as an unrooted neighbor-joining tree in figtree v. 1.4.2 (Figure

\@ref(fig:ramorum5)). AMOVA utilizes a distance matrix between genotypes for

which hierarchical partitions are defined and attempts to analyze the variation

within samples, between samples, between subpopulations within populations and

finally between populations. In this case, we used both the hierarchies of

samples within years within regions and samples within regions within years.

DAPC is a multivariate, model-free approach to clustering based on prior

population information [@jombart2010discriminant]. This allows us to analyze the

population structure by assessing how well samples can be reassigned into

previously defined populations. Both DAPC and AMOVA were run with and without

Hunter Creek and Pistol River South Fork due to isolated genotypes and small

sample size, respectively. For the DAPC analysis, these removed populations had

their origins predicted from the DAPC object using the function predict.dapc in

the R package *adegenet* [@jombart2010discriminant].

Since DAPC is sensitive to the number of principal components used in analysis,

the function `xvalDapc()` from the R package *adegenet* was used to select the

correct number of principal components with 1,000 replicates using a training

set of 90% of the data. The number of principal components was chosen based on

the criteria that it had to produce the highest average percent of successful

reassignment and lowest root mean squared error [@jombart2010discriminant].

Significant deviations from random population structure was tested in AMOVA

utilizing the function `randtest()` from the R package *ade4* with 9,999

bootstrap replicates [@dray2007ade4].

All data and R scripts to reproduce the analyses shown here are deposited

publicly on github

(<https://github.com/grunwaldlab/Sudden_Oak_Death_in_Oregon_Forests>) and

citable (DOI: 10.5281/zenodo.13007).

The epidemic has expanded over time from the initial focus in Joe Hall Creek NE

of Brookings, Oregon mostly north (first to N Fork Chetco High) and northwest

(Coast, Pistol River South Fork), but also east (Chetco Main and Winchuck) (Fig.

\@ref(fig:ramorum1); Table \@ref(tab:ramorum1)). To date a total of 70

multilocus genotypes have been found in forest populations (Table

\@ref(tab:ramorum1)). MLG 22 is most abundant and the only MLG detected across

the whole period (although it was not sampled in every year) (Fig.

\@ref(fig:ramorum2)). MLG 59, the second most abundant MLG, was only detected in

2011 and has a high frequency due to the sampling design applied: all 2011

strains were sampled in one concentrated area in the northwestern sampling range

geographically distant from any other location (Fig. \@ref(fig:ramorum2)). Given

that sampling strategies for some years were not comprehensive, samples from

some years have to be interpreted with caution (e.g., 2005-6, 2010-11). Samples

from 2013 and 2014 are sampled from all regions and can be considered more

representative.

```{r, ramorum2, echo = FALSE, fig.cap = figcap2, fig.scap = figscap2, out.width = "40%"}

```{r, results = "asis", echo = FALSE}

Allelic and genotype diversity within loci revealed that PrMS43 had, on average,

the highest number of alleles (n = 18). All other loci had 5 or fewer alleles

with a moderate to high amount of diversity (Table \@ref(tab:ramtabS2)).

Nevertheless, the genotype accumulation curve showed a slight plateau,

indicating that we have enough power in our data to describe a significant

number of MLGs (Fig. \@ref(fig:ramS2)). Genotypic diversity (*H* = 2.98, *G* =

8.64), evenness (*E~5~* = 0.41), and richness (*eMLG* = 7) were low as expected

for a clonal population slowly accumulating mutations over space and time (Table

\@ref(tab:ramtabS3)). A pattern of increasing diversity across years (with

number of MLGs not fewer than 10) was also observed (Table \@ref(tab:ramtabS3)).

The minimum spanning network showed that MLGs 17, 22, and 28 clustered in the

center of the network and had the highest number of connections to other

genotypes in the forest populations (Fig. \@ref(fig:ramorum3)). Most genotypes

were connected to their immediate neighbors by a genetic distance of 0.05 or the

equivalent of one mutational step across 5 diploid loci.

```{r, ramorum3, echo = FALSE, fig.cap = figcap3, fig.scap = figscap3, out.width = "75%"}

```{r, results = "asis", echo = FALSE}

A Mantel test revealed significant correlations of genetic distance and

geographic distance for most samples collected after 2003 (Table

\@ref(tab:ramorum3)). When partitioned by year, this correlation appears to

increase and become more pronounced with the progression of the epidemic. When

partitioned by region, those that are closer to the origin of the epidemic (Joe

Hall Creek and N Fork Chetco River) show significant correlation. When the

overall mantel test was run without Hunter Creek, the correlation coefficient

was reduced (0.175), but was still significant (*p* = 0.0001).

```{r, results = "asis", echo = FALSE}

Cluster analysis of populations with respect to year using Nei's genetic

distance showed no significant (&gt;70%) bootstrap support for any clades, but

does show that these tend to cluster by region as opposed to year (Fig.

\@ref(fig:ramS3)). AMOVA analysis revealed significant population structure

between regions on both clone-corrected (with respect to hierarchy) and

uncorrected data sets (Table \@ref(tab:ramtab4)). Significant structure was only

found between years within regions on the uncorrected data set. Both patterns

were observed without Hunter Creek and Pistol River South Fork isolates. DAPC

clustering showed that the first discriminant component separated Hunter Creek

from all other regions and the second discriminant component shows a gradient

from Joe Hall Creek to the coast (Fig. \@ref(fig:ramfig4)). This distinction was

reflected in the percent of correct posterior assignment of isolates to their

original populations. Over the whole data set there was an 81.5% assignment-

success rate. Hunter Creek received 100% successful reassignment. Joe Hall

Creek, Winchuck, and Coast all had &gt;85% successful reassignment whereas

Chetco Main, North Fork of the Chetco, and Pistol River South Fork all had

&lt;69% successful reassignment (Fig. \@ref(fig:ramS4)). The isolation of the

Hunter Creek isolates in the DAPC analysis was found to be mainly driven by

allele 493 at locus PrMS43 (Fig. \@ref(fig:ramS5)). The only other population to

share this allele was Joe Hall Creek where it was present in a total of 4

isolates, and only isolates found in the coastal region or North Fork Chetco

contained the allele 489, which is one mutational step away in a stepwise

mutation model of a tetranucleotide repeat locus. When DAPC was run without

Hunter Creek and Pistol River South Fork data, percent successful reassignment

for all regions did not change significantly. Prediction of sources for the

Hunter Creek data revealed that over 98% of the genotypes were assigned to the

Coast with a 99% probability.

```{r, ramotab4, echo = FALSE, results = "asis"}

```{r, ramfig4, echo = FALSE, fig.cap = figcap4, fig.scap = figscap4, fig.width = 5, fig.height = 5}

```{r, results = "asis", echo = FALSE}

We used previously published data to determine if nursery populations in

California or Oregon could have been source populations for the Oregon forest

epidemic [@goss2011phytophthora; @goss2009population;

@grunwald2009standardizing; @prospero2009migration; @prospero2007population].

Nursery data included 40 MLGs across 216 samples of NA1 clones. Of these 40, 12

MLGs matched the forest sample and 28 MLGs were unique to the nurseries. The

only region that did not contain genotypes that matched those found in nurseries

was Pistol River South Fork. When considering those 12 genotypes that were

present in both data sets, with the exception of Joe Hall Creek, all genotypes

were first isolated from nurseries before discovery in the forest. At the most

variable locus, PrMS43, both nursery populations had the allele 281 at

frequencies of 4.5% and 4.9% for CA and OR, respectively. This allele was not

observed in the forest population. Both populations contained allele 489 at

&gt;10% frequency and the CA nursery population contained allele 493 at a

frequency of 1.4%.

When nursery genotypes were added to the minimum spanning network, MLGs found at

Hunter Creek, previously isolated in the network, connected by only a single MLG

from the coast, gained more connections to nursery MLGs. Clustering with Nei's

distance revealed the Nursery isolates from CA consistently clustering closest

with Hunter Creek isolates in both clone-corrected and uncorrected data sets

(Fig. \@ref(fig:ramorum5)). DAPC clustering revealed a decrease in overall

assignment-success rate at 78%. The nursery isolates received 74% and 83%

assignment success for CA and OR nurseries, respectively.

```{r, ramorum5, echo = FALSE, fig.cap = figcap5, fig.scap = figscap5}

```{r, results = "asis", echo = FALSE}

The assignment successes for the regions before or after inclusion of nursery

data changed less than 5% for all regions except for the coast, which saw a

decrease of 17.6% when nursery populations were included (Fig.

\@ref(fig:ramS4)). Prediction of sources for nursery genotypes against the

forest data revealed that 48% of these nursery isolates were predicted to share

membership with the coast at $\geq$ 95% probability (Fig. \@ref(fig:ramS6),

\@ref(fig:ramS7)). A total of 3.2% of the isolates were predicted to share

membership with Hunter Creek at $\geq$ 99.9% membership probability.

Furthermore, 21.75% of the nursery data could not be assigned to any of the

forest populations at &gt;60% probability.

Since 3.2% of the nursery isolates had a very strong signal for Hunter Creek, we

predicted sources for Hunter Creek isolates when considering nursery isolates.

This approach determines if Hunter Creek isolates cluster more readily with

nursery or coast populations. Indeed, 92% of the Hunter Creek isolates were

predicted to share membership with California nurseries at $\geq$ 99% membership

probability (Fig. \@ref(fig:ramS8)). No Hunter Creek isolate was predicted to

share membership with a forest population at &gt;0.45% membership probability.

To date populations monitored from 2001-14 show presence of only the NA1 clonal

lineage observed previously [@prospero2007population; @prospero2009migration].

The fact that individuals belonging to the EU1 and NA2 clonal lineages have not

been found in Oregon forests, despite their presence on the west coast from

British Columbia to California is welcome news [@goss2009population;

@goss2011phytophthora; @grunwald2012emergence]. The lack of EU1 or NA2 isolates

provides evidence that monitoring for *P. ramorum* in nurseries by federal and

state agencies is helping avoid emergence of new clones in Oregon's Forests.

Our analysis provides support for a most parsimonious scenario of two

introductions into Curry county from nurseries: one initial introduction into

Curry County sometime before detection of the first infected tanoaks in 2001

from California (or less likely Oregon) nurseries followed by a second

introduction into the Hunter Creek area again from nurseries. The relative

position of the nursery populations in the minimum spanning network and DAPC

scatter plot (Fig. \@ref(fig:ramorum3), \@ref(fig:ramfig4)) suggest that the

introductions from nurseries were rare, though more even sampling and migration

models could disprove this hypothesis. Since 2001, the epidemic has spread

clonally throughout Southwestern Curry County mostly north, but also west,

towards the coast, and southeast. This clonal spread of the pathogen from the

Joe Hall area is supported partially by Mantel tests showing significant levels

of isolation by distance in years following 2002 (Table \@ref(tab:ramorum3))

along with significant AMOVA results across regions (Table \@ref(tab:ramtab4)).

The populations sampled in 2011 in Hunter Creek (Cape Sebastian) appear to have

originated from a new source and cluster into a distinct group based on DAPC

(Fig. \@ref(fig:ramfig4)). Based on the minimum spanning network, this

population would appear isolated in the epidemic if it were not for MLG 32,

which is connected with MLG 33 (found on the coast in 2010) by one mutational

step at locus PrMS43. This, in turn, is connected with the other MLGs from

Hunter Creek by one mutational step at locus PrMS39. When considering clustering

via Bruvo's distance in combination with data from nursery populations, however,

these genotypes from Hunter Creek appear to be more similar to California

nursery populations (Fig. \@ref(fig:ramorum3)) than Oregon forest populations.

Predictions based on DAPC place samples from Hunter Creek as coming from

California nurseries (Fig. \@ref(fig:ramS8)). This, in combination with the

observation that purely forest genotypes (i.e. those only found in the forest)

are connected to Hunter Creek genotypes through nursery genotypes, indicates a

possible contribution from nursery populations to the epidemic. This is

supported by the observation that all population level clustering, with and

without clone correction, places the Hunter Creek isolates adjacent to the

nursery isolates from CA (Fig. \@ref(fig:ramfig4), \@ref(fig:ramorum5)). This

appears to be driven by the high frequency of allele 246 at locus PrMS39, which

interestingly appears to segregate in an east to west fashion and is increasing

in frequency over time (Fig. \@ref(fig:ramS9), \@ref(fig:ramS10)). This, along

with the results from the DAPC clustering and subsequent prediction (Fig.

\@ref(fig:ramS6), \@ref(fig:ramS7)) provide weak support for a potential third

introduction into the coastal region from nurseries sometime after the Hunter

Creek introduction event.

An interesting aspect is the observation that there appeared to be more than one

cluster of genotypes introduced into the Joe Hall area during the early stages

of the epidemic. The two dominant clusters that appeared were the ones that

contained MLG 22 and MLG 68. The former has been found in the most recent

sampling year, whereas the latter has not been observed since 2005 or beyond the

Joe Hall area. This latter group was also the most distantly related group

overall, more distant than some nursery genotypes. While it is clear that the

eradication effort has not been entirely successful, there is some evidence that

it is having an effect as a major genotype cluster has effectively been

eradicated, although disappearance of MLGs could also be explained by being less

fit than lineages dominating now.

The Curry County epidemic is in many ways different from the epidemic in

California. When introduced into California in the mid 1990's, the causal agent

of sudden oak death was unknown and thus gave it time to clonally expand and

diversify as management strategies in natural forest systems were limited

[@rizzo2002phytophthora]. With the foresight of the epidemic in central

California, the ODF was able to implement a quarantine effort against the import

of hosts as soon as the causal agent was known (A Kanaskie, pers. comm.). This

quarantine along with aggressive eradication efforts have affected the spread of

*P. ramorum* [@mascheretti2008reconstruction]. Drawing conclusions from previous

population studies in California and applying them to the Oregon epidemic should

be undertaken with great care given the drastically differing management

scenarios [@mascheretti2008reconstruction; @mascheretti2009genetic].

Our work has some inherent drawbacks. Given the cost of aerial surveys and

subsequent ground crew work, and the fact that trees are eradicated once found,

populations are not hierarchically sampled across all years. The destructive

nature of the management approach means that it was not possible to conduct

controlled ecological experiments focusing on effects of climate and rainfall on

the spread of disease as was possible in California trials [@eyre2013poulation].

In addition, most of our work only used 5 microsatellite loci for genotyping.

Ideally, more loci should have been used as was done in other studies

[@croucher2013combining]. Although only 5 loci were used, clear patterns of

population dynamics in space and time emerged and the MLG accumulation curve

supported the fact that loci are informative. Finally, the populations genotyped

here are clonal and belong to the NA1 clonal lineage. Thus, much of the

analytical power provided by population genetic theory does not apply given that

basic assumptions would be violated [@grunwald2011evolution]. Our work uses

appropriate methods to infer patterns that are model free, yet informative such

as spatial clustering. Thus, we believe that this work provides novel and

important insights into the *P. ramorum* population biology in the Siskiyou

forest. Our data indicates that there might have been at least two introductions

into Oregon forests from nurseries. The nature of the data does not allow

inference of directional migrations given the uneven sampling strategy and

moderate number of loci used across all years. We are currently exploring

genotyping-by-sequencing (GBS) as a method that could provide further detail on

how these populations evolved over space and time [@elshire2011robust]. GBS can

provide richer detail by providing codominant SNP data across several thousand

loci sampling the whole genome.

This work was supported in part by US Department of Agriculture (USDA)

Agricultural Research Service Grant 5358-22000-039-00D, USDA APHIS, the USDA-ARS

Floriculture Nursery Initiative, the Oregon Department of Agriculture/Oregon

Association of Nurseries (ODA-OAN) and the USDA-Forest Service Forest Health

Monitoring Program.

For the years up to and including 2012, the multilocus genotype (MLG) of each

*P. ramorum* strain was determined based on microsatellite analysis of five

loci, PrMS6, Pr9C3, PrMS39, PrMS45 and PrMS43, using previously published

protocols [@grunwald2009standardizing; @prospero2004isolation;

@prospero2007population; @grunwald2008susceptibility]. Multilocus genotyping of

*P. ramorum* strains collected in 2013 and 2014 included an extra nine loci,

KI18, KI64, KI82a, KI82b, ILVOPrMS79, ILVOPrMS131, ILVOPrMS145a, ILVOPrMS145b,

ILVOPrMS145c which are amplified by an additional six primer pairs

[@ivors2006microsatellite; @vercauteren2011identification;

@vercauteren2010clonal]. The locus ILVOPrMS79, amplifies up to three alleles,

however two separate loci have yet to be described

[@vercauteren2011identification]. The addition of nine loci to the genotyping

assay coincided with the discovery by the Oregon Department of Agriculture of an

EU1 *P. ramorum* isolate in a Curry County nursery in 2012. Preceding 2012, only

NA1 isolates had been found in Curry County. Because different loci are

polymorphic for different clonal lineages, the entire panel of 14 loci was

necessary to adequately describe the *P. ramorum* population in the event that