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

Zhian N. Kamvar, Jonah C. Brooks, and Niklaus J. Grünwald

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

Journal: **Frontiers in Genetics**

EPFL Innovation Park, Building I, CH -- 1015 Lausanne Switzerland

Published 2015-06-10. Issue: **6**,

DOI: [10.3389/fgene.2015.00208](http://dx.doi.org/10.3389/fgene.2015.00208)

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

To gain a detailed understanding of how plant microbes evolve and adapt to

hosts, pesticides, and other factors, knowledge of the population dynamics and

evolutionary history of populations is crucial. Plant pathogen populations are

often clonal or partially clonal which requires different analytical tools. With

the advent of high throughput sequencing technologies, obtaining genome-wide

population genetic data has become easier than ever before. We previously

contributed the R package *poppr* specifically addressing issues with analysis

of clonal populations. In this paper we provide several significant extensions

to *poppr* with a focus on large, genome-wide SNP data. Specifically, we provide

several new functionalities including the new function `mlg.filter` to define

clone boundaries allowing for inspection and definition of what is a clonal

lineage, minimum spanning networks with reticulation, a sliding-window analysis

of the index of association, modular bootstrapping of any genetic distance, and

analyses across any level of hierarchies.

[^1]: Supplementary data available at https://github.com/grunwaldlab/supplementary-poppr-2.0; DOI: [10.5281/zenodo.17424](http://dx.doi.org/10.5281/zenodo.17424)

To paraphrase Dobzhansky, nothing in the field of plant-microbe interactions

makes sense except in the light of population genetics [@dobzhansky2013nothing].

Genetic forces such as selection and drift act on alleles in a population. Thus,

a true understanding of how plant pathogens emerge, evolve and adapt to crops,

fungicides, or other factors, can only be elucidated in the context of

population level phenomena given the demographic history of populations

[@Mcdonald2002;

@grunwald2011evolution; @milgroom1989population]. The field of population

genetics, in the era of whole genome resequencing, provides unprecedented power

to describe the evolutionary history and population processes that drive

coevolution between pathogens and hosts. This powerful field thus critically

enables effective deployment of R genes, design of pathogen informed plant

resistance breeding programs, and implementation of fungicide rotations that

minimize emergence of resistance.

Most computational tools for population genetics are based on concepts developed

for sexual model organisms. Populations that reproduce clonally or are polyploid

are thus difficult to characterize using classical population genetic tools

because theoretical assumptions underlying the theory are violated. Yet, many

plant pathogen populations are at least partially clonal if not completely

clonal [@milgroom1996recombination; @anderson1995clonality]. Thus, development

of tools for analysis of clonal or polyploid populations is needed.

Genotyping by sequencing and whole genome resequencing provide the unprecedented

ability to identify thousands of single nucleotide polymorphisms (SNPs) in

populations [@elshire2011robust; @luikart2003power; @davey2011genome]. With

traditional marker data (e.g., SSR, AFLP) a clone was typically defined as a

unique multilocus genotype (MLG) [@grunwald2006hierarchical; @Falush01082003;

@goss2009population; @cooke2012genome; @taylor2003fungal]. Availability of large

SNP data sets provides new challenges for data analysis. These data are based on

reduced representation libraries and high throughput sequencing with moderate

sequencing depth which invariably results in substantial missing data, error in

SNP calling due to sequencing error, lack of read depth or other sources of

spurious allele calls [@mastretta2015restriction]. It is thus not clear what a

clone is in large SNP data sets and novel tools are required for definition of

clone boundaries.

The research community using the R statistical and computing language [@R] has

developed a plethora of new resources for population genetic analysis. R is

particularly appealing because all code is open source and functions can be

evaluated and modified by any user. Recently, we introduced the R package

*poppr* specifically developed for analysis of clonal populations

[@kamvar2014poppr]. *Poppr* previously introduced several novel features

including the ability to conduct a hierarchical analysis across unlimited

hierarchies, test for linkage association, graph minimum spanning networks or

provide bootstrap support for Bruvo's distance in resulting trees. *Poppr* has

been rapidly adopted and applied to a range of studies including for example

horizontal transmission in leukemia of clams [@metzger2015horizontal], study of

the vector-mediated parent-to-offspring transmission in an avian malaria-like

parasite [@chakarov2015apparent], and characterization of the emergence of the

invasive forest pathogen *Hymenoscyphus pseudoalbidus* [@gross2014population].

It has also been used to implement real-time, online R based tools for

visualizing relationships among unknown MLGs in reference databases

(http://phytophthora-id.org/) [@grunwald2011phytophthora].

Here, we introduce *poppr* 2.0, which provides a major update to *poppr*

[@kamvar2014poppr] including novel tools for analysis of clonal populations

specifically addressing large SNP data. Significant novel tools include

functions for calculating clone boundaries and collapsing individuals into

clonal groups based on a user-specified genetic distance threshold, sliding

window analyses, genotype accumulation curves, reticulations in minimum spanning

networks, and bootstrapping for any genetic distance.

As highlighted in previous work, clone correction is an important component of

population genetic analysis of organisms that are known to reproduce asexually

[@kamvar2014poppr; @milgroom1996recombination; @grunwald2003analysis]. This

method is a partial correction for bias that affects metrics that rely on allele

frequencies assuming panmixia and was initially designed for data with only a

handful of markers. With the advent of large-scale sequencing and reduced-

representation libraries, it has become easier to sequence tens of thousands of

markers from hundreds of individuals [@elshire2011robust; @davey2011genome;

@davey2010rad]. With this larger number of markers, the genetic resolution is

much greater, but the chance of genotyping error is also greatly increased and

missing data is frequent [@mastretta2015restriction]. Taking this fact and

occasional somatic mutations into account, it would be impossible to separate

true clones from independent individuals by just comparing what MLGs are

different. We introduce a new method for collapsing unique multilocus genotypes

determined by naive string comparison into multilocus lineages utilizing any

genetic distance given three different clustering algorithms: farthest neighbor,

nearest neighbor, and UPGMA (average neighbor) [@sokal1958statistical].

These clustering algorithms act on a distance matrix that is either provided by

the user or generated via a function that will calculate a distance from genetic

data such as `bruvo.dist`, which in particular applies to any level of ploidy

[@bruvo2004simple]. All algorithms have been implemented in C and utilize the

OpenMP framework for optional parallel processing [@dagum1998openmp]. Default is

the conservative farthest neighbor algorithm (Fig. \@ref(fig:Figure1)A), which

will only cluster samples together if all samples in the cluster are at a

distance less than the given threshold. By contrast, the nearest neighbor

algorithm will have a chaining effect that will cluster samples akin to adding

links on a chain where a sample can be included in a cluster if all of the

samples have at least one connection below a given threshold (Fig.

\@ref(fig:Figure1)C). The UPGMA, or average neighbor clustering algorithm is the

one most familiar to biologists as it is often used to generate ultra-metric

trees based on genetic distance (Fig. \@ref(fig:Figure1)B). This algorithm will

cluster by creating a representative sample per cluster and joining clusters if

these representative samples are closer than the given threshold.

```{r, Figure1, echo = FALSE, fig.cap = figcap1, fig.scap = figscap1, out.width = "50%"}

We utilize data from the microbe *Phytophthora infestans* to show how the

`mlg.filter` function collapses multilocus genotypes with Bruvo's distance

assuming a genome addition model [@bruvo2004simple]. *P. infestans* is the

causal agent of potato late blight originating from Mexico that spread to Europe

in the mid 19th century [@goss2014irish; @yoshida2013rise]. *P. infestans*

reproduces both clonally and sexually. The clonal lineages of *P. infestans*

have been formally defined into 18 separate clonal lineages using a combination

of various molecular methods including AFLP and microsatellite markers

[@lees2006novel; @li2013efficient]. For these data, we used `mlg.filter` to

detect all of the distance thresholds at which 18 multilocus lineages would be

resolved. We used these thresholds to define multilocus lineages and create

contingency tables and dendrograms to determine how well the multilocus lineages

were detected.

For the *P. infestans* population, the three algorithms were able to detect 18

multilocus lineages at different distance thresholds (Fig. \@ref(fig:Figure-2)).

Contingency tables between the described multilocus genotypes and the genotypes

defined by distance show that most of the 18 lineages were resolved, except for

US-8, which is polytomic (Table \@ref(tab:pinftable)).

```{r, Figure-2, echo = FALSE, message = FALSE, fig.width = 4*1.25, fig.height = 4*1.25, fig.cap = figcap2, fig.scap = figscap2}

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

We utilized simulated data to evaluate the effect of sequencing error and

missing data on MLG calling. We constructed the data using the `glSim` function

in *adegenet* [@jombart2011adegenet] to obtain a SNP data set for demonstration.

Two diploid data sets were created, each with 10k SNPs (25% structured into two

groups) and 200 samples with 10 ancestral populations of even sizes. Clones were

created in one data set by marking each sample with a unique identifier and then

randomly sampling with replacement. It is well documented that reduced-

representation sequencing can introduce several erroneous calls and missing data

[@mastretta2015restriction]. To reflect this, we mutated SNPs at a rate of 10%

and inserted an average of 10% missing data for each sample after clones were

created, ensuring that no two sequences were alike. The number of mutations and

missing data per sample were determined by sampling from a Poisson distribution

with $\lambda = 1000$. After pooling, 20% of the data set was randomly sampled

for analysis. Genetic distance was obtained with the function `bitwise.dist`,

which calculates the fraction of different sites between samples equivalent to

Provesti's distance, counting missing data as equivalent in comparison

[@prevosti1975distances].

All three filtering algorithms were run with a threshold of 1, returning a

numeric vector of length $n - 1$ where each element represented a threshold at

which two samples/clusters would join. Since each data set would have varying

distances between samples, the clonal boundary threshold was defined as the

midpoint of the largest gap between two thresholds that collapsed less than 50%

of the data.

Out of the 100 simulations run, we found that across all methods, detection of

duplicated samples had $\sim$ 98% true positive fraction and $\sim$ 0.8% false

positive fraction indicating that this method is robust to simulated

populations (supplementary materials[^1]).

In its original iteration, *poppr* introduced minimum spanning networks that

were based on the *igraph* function `minimum.spanning.tree` [@csardi2006igraph].

This algorithm produces a minimum spanning tree with no reticulations where

nodes represent individual MLGs. In other minimum spanning network programs,

reticulation is obtained by calculating the minimum spanning tree several times

and returning the set of all edges included in the trees. Due to the way

*igraph* has implemented Prim's algorithm, it is not possible to utilize this

strategy, thus we implemented an internal C function to walk the space of

minimum spanning trees based on genetic distance to connect groups of nodes with

edges of equal weight.

To demonstrate the utility of minimum spanning networks with reticulation, we

used two clonal data sets: the H3N2 flu virus data from the *adegenet* package

using years of each epidemic as the population factor, and *Phytophthora

ramorum* data from Nurseries and Oregon forests [@jombart2010discriminant;

@kamvar2014sudden]. Minimum spanning networks were created with and without

reticulation using the *poppr* functions `diss.dist` and `bruvo.msn` for the

H3N2 and *P. ramorum* data, respectively [@kamvar2014poppr; @bruvo2004simple].

To detect mlg clusters, the infoMAP community detection algorithm was applied

with 10,000 trials as implemented in the R package *igraph* version 0.7.1

utilizing genetic distance as edge weights and number of samples in each MLG as

vertex weights [@csardi2006igraph; @rosvall2008maps].

To evaluate the results, we compared the number, size, and entropy ($H$) of the

resulting communities as we expect a highly clonal organism with low genetic

diversity to result in a few, large communities. We also created contingency

tables of the community assignments with the defined populations and used those

to calculate entropy using Shannon's index with the function `diversity` from

the R package *vegan* version 2.2-1 [@oksanen2015vegan;

@shannon2001mathematical]. A low entropy indicates presence of a few large

communities whereas high entropy indicates presence of many small communities.

The infoMAP algorithm revealed 63 communities with a maximum community size of

77 and $H = 3.56$ for the reticulate network of the H3N2 data and 117

communities with a maximum community size of 26 and $H = 4.65$ for the minimum

spanning tree. The entropy across years was greatly decreased for all

populations with the reticulate network compared to the minimum spanning tree

(Fig. \@ref(fig:Figure-3)). Note that the reticulated network (Fig.

\@ref(fig:Figure-3)B) showed patterns corresponding with those resulting from a

discriminant analysis of principal components (Fig. \@ref(fig:Figure-3)D)

[@jombart2010discriminant].

```{r, Figure-3, message = FALSE, echo = FALSE, fig.cap = figcap3, fig.scap = figscap3}

Graph walking of the reticulated minimum spanning network of *P. ramorum* by the

infoMAP algorithm revealed 16 communities with a maximum community size of 13

and $H = 2.60$. The un-reticulated minimum spanning tree revealed 20 communities

with a maximum community size of 7 and $H = 2.96$. In the ability to predict

Hunter Creek as belonging to a single community, the reticulated network was

successful whereas the minimum spanning tree separated one genotype from that

community. The entropy for the reticulated network was lower for all populations

except for the coast population (supplementary materials[^1]).

Assessing population differentiation through methods such as $G_{st}$, AMOVA,

and Mantel tests relies on comparing samples within and across populations

[@nei1973analysis; @excoffier1992analysis; @mantel1967detection]. Confidence in

distance metrics is related to the confidence in the markers to accurately

represent the diversity of the data. Especially true with microsatellite

markers, a single hyper-diverse locus can make a population appear to have more

diversity based on genetic distance. Using a bootstrapping procedure of randomly

sampling loci with replacement when calculating a distance matrix provides

support for clades in hierarchical clustering.

Data in genind and genpop objects are represented as matrices with individuals

in rows and alleles in columns [@Jombart_2008]. This gives the advantage of

being able to use R's matrix algebra capabilities to efficiently calculate

genetic distance. Unfortunately, this also means that bootstrapping is a non-

trivial task as all alleles at a single locus need to be sampled together. To

remedy this, we have created an internal S4 class called "bootgen", which

extends the internal "gen" class from *adegenet*. This class can be created from

any genind, genclone, or genpop object, and allows loci to be sampled with

replacement. To further facilitate bootstrapping, a function called `aboot`,

which stands for "any boot", is introduced that will bootstrap any genclone,

genind, or genpop object with any genetic distance that can be calculated from

it.

To demonstrate calculating a dendrogram with bootstrap support, we used the

*poppr* function `aboot` on population allelic frequencies derived from the data

set `microbov` in the *adegenet* package with 1000 bootstrap replicates

[@Jombart_2008; @laloe2007consensus]. The resulting dendrogram shows bootstrap

support values $>50\%$ (Fig. \@ref(fig:microboot)) and used the following code:

```{r, echo = FALSE}

```{r, microboot, results = "hide", fig.width = 5, fig.height = 5, fig.cap = figcap4, fig.scap = figscap4}

Analysis of population genetics of clonal organisms often borrows from

ecological methods such as analysis of diversity within populations

[@milgroom1996recombination; @arnaud2007standardizing; @grunwald2003analysis].

When choosing markers for analysis, it is important to make sure that the

observed diversity in your sample will not appreciably increase if an additional

marker is added [@arnaud2007standardizing]. This concept is analogous to a

species accumulation curve, obtained by rarefaction. The genotype accumulation

curve in *poppr* is implemented in the function `genotype_curve`. The curve is

constructed by randomly sampling $x$ loci and counting the number of observed

MLGs. This repeated $r$ times for 1 locus up to $n-1$ loci, creating $n-1$

distributions of observed MLGs.

The following code example demonstrates the genotype accumulation curve for data

from @everhart2014fine showing that these data reach a small plateau and have a

greatly decreased variance with 12 markers, indicating that there are enough

markers such that adding more markers to the analysis will not create very many

new genotypes (Fig. \@ref(fig:moniliniacurve)).

```{r, echo = FALSE}

```{r, moniliniacurve, results = "hide", fig.width = 5, fig.height = 5, fig.keep = "last", message = FALSE, fig.cap = figcap5, fig.scap = figscap5}

The index of association ($I_A$) is a measure of multilocus linkage

disequilibrium that is most often used to detect clonal reproduction within

organisms that have the ability to reproduce via sexual or asexual processes

[@brown1980multilocus; @smith1993how; @milgroom1996recombination]. It was

standardized in 2001 as $\bar{r}_d$ by @Agapow_2001 to address the issue of

scaling with increasing number of loci. This metric is typically applied to

traditional dominant and co-dominant markers such as AFLPs, SNPs, or

microsatellite markers. With the advent of high throughput sequencing, SNP data

is now available in a genome-wide context and in very large matrices including

thousands of SNPs. For this reason, we devised two approaches using the index

of association for large numbers of markers typical for population genomic

studies. Both functions utilize *adegenet*'s "genlight" object class, which

efficiently stores 8 binary alleles in a single byte [@jombart2011adegenet]. As

calculation of the $\bar{r}_d$ requires distance matrices of absolute number of

differences, we utilize a function that calculates these distances directly from

the compressed data called `bitwise.dist`.

The first approach is a sliding window analysis implemented in the function

`win.ia`. It utilizes the position of markers in the genome to calculate

$\bar{r}_d$ among any number of SNPs found within a user-specified windowed

region. It is important that this calculation utilize $\bar{r}_d$ as the number

of loci will be different within each window [@Agapow_2001]. This approach would

be suited for a quick calculation of linkage disequilibrium across the genome

that can detect potential hotspots of LD that could be investigated further with

more computationally intensive methods assuming that the number of samples <<

the number of loci.

As it would necessarily focus on loci within a short section of the genome that

may or may not be recombining, a sliding window approach would not be good for

utilizing $\bar{r}_d$ as a test for clonal reproduction. A remedy for this is

implemented in the function `samp.ia`, which will randomly sample $m$ loci,

calculate $\bar{r}_d$, and repeat $r$ times, thus creating a distribution of

expected values of $\bar{r}_d$.

To demonstrate the sliding window and random sampling of $\bar{r}_d$ with

respect to clonal populations, we simulated two populations containing 1,100

neutral SNPs for 100 diploid individuals under the same initial seed. One

population had individuals randomly sampled with replacement, representing the

clonal population. After sampling, both populations had 5% random error and 1%

missing data independently propagated across all samples. On average, we

obtained a higher value of $\bar{r}_d$ for the clonal population compared to the

sexual population for both methods (Fig. \@ref(fig:Figure-6)).

```{r, Figure-6, echo = FALSE, fig.width = 5, fig.height = 3.333, fig.keep = "last", fig.cap = figcap6, fig.scap = figscap6}

Assessments of population structure through methods such as hierarchical

$F_{st}$ [@goudet2005hierfstat] and AMOVA [@michalakis1996generic] require

hierarchical sampling of populations across space or time [@linde2002population;

@everhart2014fine; @grunwald2006hierarchical]. With clonal organisms, basic

practice has been to clone-censor data to avoid downward bias in diversity due

to duplicated genotypes that may or may not represent different samples

[@milgroom1996recombination]. This correction should be performed with respect

to a population hierarchy to accurately reflect the biology of the organism.

Traditional data structures for population genetic data in most analysis tools

allow for only one level of hierarchical definition. The investigator thus had

to provide the data set for analysis at each hierarchical level.

To facilitate handling hierarchical and mutlilocus genotypic metadata, *poppr*

version 1.1 introduced a new S4 data object called "genclone", extending

*adegenet*'s "genind" object (Kamvar and Grünwald, unpublished). The genclone

object formalized the definitions of multilocus genotypes and population

hierarchies by adding two slots called "mlg" and "hierarchy" that carried a

numeric vector and a data frame, respectively. These new slots allow for

increased efficiency and ease of use by allowing these metadata to travel with

the genetic data. The hierarchy slot in particular contains a data frame where

each column represents a separate hierarchical level. This is then used to set

the population factor of the data by supplying a hierarchical formula containing

one or more column names of the data frame in the hierarchy slot.

The functionality represented by the hierarchy slot has now been migrated from

the *poppr* to the *adegenet* package version 2.0 to allow hierarchical

analysis in *adegenet*, *poppr*, and other dependent packages. The prior *poppr*

`hierarchy` slot and methods have now been renamed `strata` in *adegenet*. A

short example of the utility of these methods can be seen in the code segment

under **Bootstrapping**, above. This migration provides end users with a broader

ability to analyze data hierarchically in R across packages.

As of this writing, the *poppr* R package version 2.0 containing all of the

features described here is located at

https://github.com/grunwaldlab/poppr/tree/2.0-rc. It is necessary to install

*adegenet* 2.0 before installing *poppr*. It can be found at

https://github.com/thibautjombart/adegenet. Both of these can be installed via

the R package *devtools* [@wickham2015devtools]. More information and example

code can be found in the supplementary materials[^1].

- R version 3.0 or better

- A C compiler. For windows, it can be obtained via Rtools

(http://cran.r-project.org/bin/windows/Rtools/). On OSX, it can be obtained

via Xcode. For parallel support, gcc version 4.6 or better is needed.

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

From within R, *poppr* can be installed via:

```{r, eval = FALSE}

Several population genetics packages in R are currently going through a major

upgrade following the 2015 R hackathon on population genetics

(https://github.com/NESCent/r-popgen-hackathon) and have not yet been updated

in CRAN. We will upload *poppr* 2.0 to CRAN once all other reverse dependent

packages have been updated.

Given low cost and high throughput of current sequencing technologies we are

entering a new era of population genetics where large SNP data sets with

thousands of markers are becoming available for large populations in a genome-

wide context. This data provides new possibilities and challenges for population

genetic analyses. We provide novel tools that enable analysis of this data in R

with a particular emphasis on clonal organisms.

Particularly useful is the implementation of $\bar{r}_d$ in a genomic context

[@Agapow_2001]. Random sampling of loci across the genome can give an expected

distribution of $\bar{r}_d$, which is expected to have a mean of zero for

panmictic populations. This metric is not affected by the number of loci

sampled, is model free, and has the ability to detect population structure.

$\bar{r}_d$ is also implemented for sliding window analyses that are useful to

detect candidate regions of linkage disequilibrium for further analysis.

Clustering multilocus genotypes into multilocus lineages based on genetic

distances is a non-trivial task given large SNP data sets. Moreover, this has

not previously been implemented for genomic data for clonal populations. Clonal

assignment has previously been available in the programs \textsc{GenClone} and

\textsc{Genodive} for classical markers [@arnaud2007standardizing;

@meirmans2004genotype]. Our method with `mlg.filter` builds upon this idea and

allows the user to choose between three different approaches for clustering

MLGs. The choice of clustering algorithm has an impact on the data (Fig.

\@ref(fig:Figure1), \@ref(fig:Figure-2)), where for example a genetic distance

cutoff of 0.1 would be the difference between 14 multilocus lineages (MLLs) and

17 MLLs for nearest neighbor and UPGMA clustering, respectively (Fig.

\@ref(fig:Figure-2)). The option to choose the clustering algorithm gives the

user the ability to choose what is biologically relevant to their populations.

While there is not one optimal procedure for defining boundaries in clonal

lineages, our tool provides a means of exploring the potential MLG or MLL

boundary space.

Minimum spanning networks are a useful tool to analyze the relationships between

individuals in a population, because it reduces the complexity of a distance

matrix to the connections that are strongest. By default, these networks are

drawn without reticulations, but for clonal organisms where many of the

connections between samples are equivalent, the minimum spanning network appears

as a chain and reduces the information that can be communicated. This is

problematic because the ability to detect population structure with one instance

of a minimum spanning network is limited. Adding reticulation into the minimum

spanning network thus presents all equivalent connections and allows population

structure to be more readily detectable. As shown in Fig. \@ref(fig:Figure-3),

population structure is apparent both visually and by graph community detection

algorithms such as the infoMAP algorithm [@rosvall2008maps]. Additionally, the

current implementation in *poppr* has been successfully used in analyses such as

reconstruction of the *P. ramorum* epidemic in Oregon forests

[@kamvar2014sudden; @kamvar2015spatial].

*Poppr* 2.0 is open source and available on GitHub. Members of the community are

invited to contribute by raising issues or pull requests on our repository at

https://github.com/grunwaldlab/poppr/issues.

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

We thank Ignazio Carbone for discussions on the index of association; David

Cooke, Sanmohan Baby, and Jens Hansen for beta testing; and Thibaut Jombart for

allowing us to incorporate the `strata` slot and related methods in *adegenet*.

We also thank all the members of the 2015 R hackathon on population genetics in

Durham, NC for their advice and input

(https://github.com/NESCent/r-popgen-hackathon). This work was supported in part

by US Department of Agriculture (USDA) Agricultural Research Service Grant

5358-22000-039-00D, USDA National Institute of Food and Agriculture Grant

2011-68004-30154, USDA APHIS, the USDA-ARS Floriculture Nursery Initiative, and

the USDA-Forest Service Forest Health Monitoring Program (to NJG).